International Segmentation of Countries Using Unsupervised Machine Learning Algorithms

As markets grow more global, global market segmentation becomes increasingly important for creating, marketing, and selling items globally. Operating a business in many nations introduces unique obstacles. International markets are more diverse than domestic markets due to differences in historical, institutional, cultural, and political environments among nations. Clustering algorithms enable multinational companies to give better-personalized products and customer services to their foreign customers. By gaining a deeper understanding of the nation’s characteristics, companies may identify the appropriate products or services to distribute, choose the best marketing channels for the target consumers, discover new and important insights, and launch new business strategies. This study used unsupervised machine learning algorithms such as Affinity Propagation, DBSCAN, and Hierarchical clustering to segment 150 countries. The segments of countries were established based on their brand awareness, gross national income, and trade liberalization, which are considered to be some of the most relevant qualities to employ when determining the macro segmentation of countries in the context of international business. This research emphasizes and recommends that various machine learning technologies be used to construct segmented and countrywide business strategies and marketing tactics in order to advance the global market expansion

The Role of Artificial Intelligence In Accelerating International Trade: Evidence From Panel Data Analysis

Technology has historically played a role in shaping international trade, but the current explosion in Artificial Intelligence has the potential to radically alter global commerce in the years ahead. In this research, we hypothesized that the AI capability of a nation has a major impact on international trade. This study discusses different ways in which technological advancements in the AI domain are improving global trade. We tested the hypothesis using the WDI, Government AI Readiness Index panel dataset of 150 countries for the years 2018-2021. Fixed effect, and Random effect panel models were applied.   The results show that the AI capability of a nation has a major positive influence on trade. The findings also show that GDP and exchange rate have significant positive impacts, and inflation and trade restrictions have negative and significant impacts on trade. The findings of this study recommend strengthening the nation’s AI capacity to increase its trade volume. AI will stimulate better economic development and open up new avenues for international trade to the extent that it fosters productivity growth. However, governments will need time to adapt and employ new AI technology, since doing so requires significant financial investments, access to skilled people, and a shift in how international companies are operated

Investigating The Relationship Between Pricing Strategies And International Customer Acquisition In The Early Stage Of SaaS: The Role Of Hybrid Pricing

Modern cloud infrastructures make it possible for SaaS businesses to provide their services to clients all over the world. As a result, it is easy for a SaaS company to operate on a worldwide scale in an early stage. Innovative SaaS services are more difficult to price than regular products. Poor pricing may lead to a misleading impression of the product, while a well-thought-out price plan can assist a business in achieving its immediate and long-term revenue objectives while also satisfying its customers. The goal of this study is to investigate which pricing strategy helps SaaS organizations attract more customers. Correlation, Random Forest Regression, and Pairwise Multiple Linear regression were applied. The correlation heatmap shows that the sales volume is highly and positively associated with hybrid pricing. This indicates that the implementation of the hybrid pricing technique is associated with more sales volume.  The majority of SaaS companies in the study sample used freemium, high-low, and hybrid. The skimming and the penetration pricing techniques were the least employed pricing tactics in SaaS.  The regression model with hybrid pricing has also shown a high explanatory performance. With an overall score of 91.89 percent, the findings of this empirical study showed a sufficient degree of accuracy. According to the random forest results, among other techniques, hybrid pricing is the most significant pricing technique in increasing sales volume in SaaS.  This study recommends that the SaaS business should employ a hybrid pricing approach in order to attract more consumers, enhance the entire experience they deliver, and therefore increase SaaS sales revenues

Condition based bridge management with SHM integration: A novel approach to remaining life estimation of bridges

Bridge deterioration and aging are important problems in the United States. According to the infrastructure report card from the American Society of Civil Engineers, as of 2016, almost one in 11 (9.1%) bridges are structurally deficient and approximately 4 out of 10 (40%) bridges are older than 50 years. Rehabilitation cost for these bridges are estimated to be about $123 billion, pointing to the need for proper bridge management plans. There are many bridge management systems in the world. All of these lack of an integrated SHM system and are subject to criticism of being subjective. Condition-Based Maintenance (CBM) coupled with the Structural Health Monitoring (SHM) data can be used to actively manage bridges while minimizing subjective effects. The current research work consisted of two primary tasks. The first task was to update the current automated CBM-SHM framework developed at the Bridge Engineering Center (BEC) at Iowa State University (ISU), by improving its current load rating calculation process. The current load rating approach underestimates the rating factor of a bridge by 20% to 40%. The load rating calculation process was improved by developing a relationship between moment of inertia and flexural strength of bridges. An extensive experimental program was conducted to validate the relationship. The proposed method may significantly improve the rating factor of a bridge. The second task was to develop a novel condition rating prediction model to predict future condition ratings of the bridges. The condition rating information in the National Bridge Inventory (NBI) database was used in this development. The research group developed two different types of future condition rating prediction models, Current Practice Model (CPM) and Deterioration Prediction Model (DPM). CPM is capable of simulating the effects of historical maintenance activities and DPM does not consider the effects of historical maintenance activities when predicting the future condition rating probabilities. Both CPMs and DPMs were quantitatively and qualitatively validated

Selecting Optimal Overseas Warehouse Location in Global Supply Chain: An Application of Binary Integer Programming

Overseas warehouses are crucial nodes in today's worldwide supply chain. They offer timely delivery of products to remote customers at a low cost and with few shipping issues. A foreign warehouse is a cross-border solution that may be used to solve cross-border B2B and B2C transactions. Strategically positioned warehouses allow organizations to effortlessly cross borders across multiple countries, regardless of where company headquarters and production or assembly units are situated. We argued that selecting an overseas warehouse site has always come down to determining where the majority of a company's foreign clients are located such that when a customer places an order, it is fulfilled by the distribution warehouse closest to that client. In this study, a warehouse site optimization framework was developed using Binary Integer Programming to help decision-makers choose the best region, or sites, to build an overseas warehouse facility to meet expected customer demands. The proposed model is focused on determining the appropriate placement of the warehouse from a set of potential locations in order to decrease the travel distance between warehouse facilities and overseas customers. The overseas customers are assumed to be served by the warehouse that is closest to them geographically. When the number of to be overseas customers served is large, they might be organized into clusters. This pre-processing is based on the assumption that the warehouse charged with servicing the overseas customers of a certain cluster would care for all of them in that cluster. We presented four distinct case situations with varying characteristics. The k-means approach is used within the context of Binary Integer Programming to divide C overseas customers into G distinct and non-overlapping subgroups. Warehouse site influences transportation costs, and when new supply routes are introduced or a current supply chain is re-engineered, a thorough warehouse placement analysis is required. The proposed model would hope to help choose the best location for warehouses as well as other immovable infrastructure in the global supply chain

Antioxidantes fenólicos en el aceite de coco: factores que afectan la cantidad y la calidad. Revisión

The total phenol content (TPC) in coconut oil varies with extraction method, variety, nature of coconut kernel components and geographical origin. Commonly reported TPCs of coconut oils extracted by dry methods and wet methods are in the range of 70-300 mg/kg and 250-650 mg/kg, respectively. Based on the commonly reported data, the TPC of coconut oil varies by up to 527 mg/kg oil, 180 mg/kg oil, and 172 mg/kg oil due to the influence of the extraction method, coconut variety and the nature of kernel components, respectively. The identity of the phenolic compounds also varies with the extraction method. Caffeic acid, catechin, p-coumaric acid, ferulic acid, and syringic acid are present in different quantities in coconut oil when extracted by all methods. However, chlorogenic acid, cinnamic acid, epigallocatechin, gallic acid, vanillic and epicatechin are present only in some coconut oils. Many free phenolic compounds present in olive oil are also present in coconut oil.El contenido total de fenoles (CTF) del aceite de coco varía según el método de extracción, la variedad, la naturaleza de los componentes del grano de coco y el origen geográfico. Los CTF comúnmente reportados de aceites de coco extraídos por métodos secos y métodos húmedos están en el rango de 70-300 mg/kg y 250-650 mg/kg respectivamente. En base a estos datos comúnmente reportados, el CTF de los aceites de coco varía hasta 527 mg/kg de aceite, 180 mg/kg de aceite y 172 mg/kg de aceite debido a la influencia del método de extracción, la variedad del coco y la naturaleza de los componentes del grano, respectivamente. La identidad de los compuestos fenólicos también varía con el método de extracción. El ácido caféico, la catequina, el ácido p-cumárico, el ácido ferúlico y el ácido siríngico están presentes en diferentes cantidades en los aceites de coco extraídos por todos los métodos. Sin embargo, el ácido clorogénico, el ácido cinámico, la epigalocatequina, el ácido gálico, la vainillina y la epicatequina están presentes solo en algunos aceites de coco. Muchos compuestos fenólicos libres que están presentes en el aceite de oliva también están presentes en el aceite de coco

Influence of osmotic suction on the shear strength of root-permeated soil

Most civil infrastructure is built on and remain under unsaturated conditions for most of its service life, so the longevity of those structures depends on the actual strength or bearing capacity of the subgrade soil. Incorporating unsaturated soil mechanics into construction practices has become challenging due to lack of understanding, especially of the mostly saline soils prevalent along the coastal belt of Australia. Omitting the benefits of salinity based osmotic suction and the influence of tree roots can lead to undue design conservatism. Previous studies have proven that the matric suction and root reinforcement influence the shear strength of natural or compacted soil, however the number of studies that focussed on the role of osmotic suction with or without the influence of tree roots are limited. The concept of green corridor or the use of native vegetation in the railway industry has become more popular over the past few decades because they are sustainable, environmentally friendly, cost-effective, long-lasting, and provide wind protection and noise barriers. Most importantly, tree roots can significantly increase the shear strength of soil because of the additional matric suction induced by root water uptake, and root reinforcement. However, the contribution that tree roots has on the shear strength of soil under coastal environmental conditions (or with osmotic suction) is yet to be investigated and discussed comprehensively

ARTICLES Representations of Queen Victoria in Sri Lankan Buddhist Monastic Art and Their Transformation

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Smart Cities and FDI

Smart cities have emerged as a worldwide trend, progressing from the implementation of sensors and technologies to enhance infrastructures and service delivery to the development of city-wide policy through the utilization of big data analysis. The goal of a "Smart City" is to improve standard of life by acquiring knowledge from information gathered from people, technologies, and networked sensors. This research argues that smart cities may attract inflows Foreign Direct Investment FDI by influencing the investment choices of global corporate players in the new age by facilitating the flow of data, technology, innovations, and best practices while offering a livable and productive environment. When deciding where to invest, foreign investors will take new criteria into account. These factors include how sociable the environment is, how stable the economic condition is, and how digitally advanced the destination is. These variables will outweigh conventional investment considerations like inexpensive labor, abundant resources, and a large population. For developing nations and rising economies where businesses need capital and knowledge to increase their worldwide sales, foreign direct investment is crucial. To maintain high growth rates the countries should attract international investors, and, most importantly, provide its citizens with a good standard of living, and therefore, should speed up its investments in sustainable smart cities. &nbsp