Product Model Research Articles

ЕФЕКТИВНІ СХОВИЩА ДАНИХ ДЛЯ РІШЕНЬ МАШИННОГО НАВЧАННЯ

Awareness of the importance of using effective data warehouses to develop machine learning solutions in today's world is becoming increasingly relevant in connection with the growth of data volumes and the growing need for accurate and productive models. The article identifies major vital requirements for data storage systems to ensure effective data management in building machine learning solutions. The study underscores the practical implications of rapid data access and minimal latency, reliable storage systems with robust backup and recovery mechanisms, high-level security, flexibility in handling different data types, and cost-effectiveness. These factors are not just theoretical considerations but directly impact the efficiency and success of machine learning solutions. Together with describing the concepts of block storage and object storage, the article also compares major cloud platforms—AWS (Amazon Web Services), Microsoft Azure, and Google Cloud Platform (GCP)—each providing a comprehensive range of flexible and customizable storage, security, and data processing services. These platforms enable users to manage their information resources flexibly and efficiently, offering unique features that cater to the specific needs of machine learning applications. Understanding the nuances of each provider's offerings empowers engineers and researchers to make informed decisions that align with their unique requirements. The article provides a thorough overview of the key considerations and available options, guiding strategic decisions to support machine learning initiatives effectively. Also the importance of staying up to date with the evolution of major platforms offerings is emphasized. In conclusion, selecting the proper data storage solution is critical for enhancing the performance, security, and cost-efficiency of machine learning models.

Языковая компрессия на уровне словообразования в английском экономическом дискурсе

The purpose of the study is to identify the features of the structure and semantics of word-formation compressions in English economic discourse. Clarifying the meaning of the term "linguistic compression" gives the author the opportunity to determine the means of expressing compression at the word-formation level in the economic sphere. The research has a scientific novelty, which consists in the fact that as a result of the first comprehensive analysis of the means of compressive word formation in English economic discourse, productive word-formation models were identified and described, as well as the originality of their structure and content was determined. As a result of the study, the predominance of models of compressive word formation formed by word composition was revealed, among which the uncomplicated N + N model, the substantive conversion model V + Prep (Adv), complex phrasal words (collapsed linguistic units) and compound words. It is noted that abbreviation in English economic discourse is a fairly effective way to implement linguistic compression due to the fact that it is characterized by the highest degree of compression. The predominant are initial three-component abbreviations with a substantial basic component, truncated syllabic type abbreviations (apocopa) and graphic abbreviations, characterized by rich semantic content in combination with a compressed form of expression.

Optimisation of a multilevel logistics network for prepositioned warehouses under an omni-channel retail model

A new omni-channel retailing mode, prepositioned warehouses, which developed rapidly in China during the COVID-19 pandemic, was introduced, and a three-level logistics network optimisation model for agricultural products was presented. This study takes into account the characteristics of the omni-channel retail model, which combines the two delivery methods of vehicle delivery and consumer in-store purchase, adds the freshness penalty incurred by fresh agricultural products in the storage process of the prepositioned warehouse, and establishes a mathematical optimisation model of the three-stage logistics network of the prepositioned warehouse that meets the characteristics of omni-channel retailing on the basis of the classical EOQ inventory model. In this work, the shortest delivery time and the smallest total cost are set as the optimisation objectives, and the multi-objective problem is transformed into a single objective through normalisation and linear weighting methods to obtain a mixed-integer optimisation model. This paper solves the problems of site selection for prepositioned warehouses, merchandise replenishment strategies, and optimisation of delivery routes based on data from real cases. Additionally, through sensitivity analysis, the management inspiration of the enterprise is obtained.

Pemodelan Produk Domestik Regional Bruto (PDRB) di Indonesia Periode 2018-2021 dengan Analisis Regresi Data Panel

High and sustainable economic growth is the main condition or a must for the continuity of economic development and increased welfare. GRDP is defined as the total added value generated by all business units in an area. The analytical method used in this study is panel data regression analysis. Panel data regression is used to observe the relationship between one dependent variable and one or more independent variables. This study aims to determine the panel regression model of Gross Regional Domestic Product (GDP) in Indonesia for the period 2018 to 2021 and to find out whether the domestic investment investment variable and the cooperative business volume variable affect GRDP in Indonesia for the 2018-2021 period. The results obtained in this study are that the best panel regression model for modeling GRDP is the FEM model and the variable Domestic Investment Investment and Cooperative Business Volume are variables that have a significant effect on the GRDP variable in Indonesia for the 2018-2021 period.

IMPLEMENTATION OF THE BEST TRAINING PRODUCT SALES RECOMMENDATION SYSTEM USING COSENI SIMILARITY ALGORITHM

Introduction: Sales of training products often encounter difficulties in providing the right recommendations according to customer needs and preferences. This research aims to develop a Content-Based Filtering-based recommendation system to address this challenge, especially for PT Menara Indonesia. Method: This research focuses on the Cosine Similarity algorithm, this research designs and implements the model through Django's REST API and Streamlit's Dashboard Website. The data used consists of tabular data that includes company profiles and product models. The Content-Based Filtering method is used to improve sales efficiency and support marketing strategies. Results and Discussion: The results show that the implementation of the Cosine Similarity algorithm provides training product recommendations that are suitable and relevant to customer needs. The suitability and relevance of the recommendation results are marked by the similarity between the two items that have been combined to produce a value of 10% to 100% which has been sorted according to the recommendation results. 10% is a recommendation result that is not very similar to the input needs and 100% is the most appropriate and accurate recommendation with the input needs. Conclusion: With the use of Natural Language Processing technology, this system can produce more accurate and relevant recommendations. This research is expected to help PT Menara Indonesia in increasing sales

Analysis of Different Height Correction Models for Tropospheric Delay Grid Products over the Yunnan Mountains

Accurate tropospheric delays are of great importance for both Global Navigation Satellite System (GNSS)-based positioning and precipitable water vapor monitoring. The gridded tropospheric delay products, including zenith hydrostatic delays (ZHD) and zenith wet delays (ZWD), are the most ideal method for accessing accurate tropospheric delays. The vertical adjustment method is critical for implementing the gridded tropospheric products. In this work, we consider the different models used for grid products and assess their performance over Yunnan mountains with complex topography. We summarize the main results as follows: (1) The products can provide accurate ZHD with mean biases of −2.6 mm and mean Standard Deviation (STD) of 1.5 mm while the ZWD results from grid products show a performance with biases of −0.4 mm and STD of 1.3 cm over the Yunnan area. (2) The Tv-based model shows a better performance than the T0-based model and IGPZWD in rugged areas with large height differences. The grid products can provide hourly ZHD with biases of 3 mm and wet delay with mean biases of within 2 cm and mean STD of below 3 cm in the Yunnan mountains, which exhibit a large height difference of around 1.5 km. (3) The radiosondes results confirm that the Tv-based model has an obvious advantage in calculating ZHD height corrections for differences within 2 km while the T0-model suffers from a loss in accuracy in the case of large height differences. If the site is located more than 1 km below the reference height, the IGPZWD model can provide a better ZWD with a mean bias of 1.5 cm and a mean STD of 1.7 cm. With vertical reduction models, the grid products can provide accurate ZHD and ZWD in real time, even if in complex area.

Experimental and numerical investigation on seismic performance of novel cable-restraining composite rubber bearings for highway bridges

Introducing cable restrainers into isolation bearings is promising to enhance the seismic performance of highway bridges. However, limitations exist in cost, performance, and analysis models of current products typically composed of flat sliding bearings (FBs) and separated cables. Therefore, this study proposes a novel cable-restraining composite rubber bearing (CRCRB) comprising a composite rubber bearing (CRB) as the bearing body and continuous cables. Cyclic tests were conducted on 12 specimens, including cable-restraining FBs and CRCRBs. Then, restoring force models of the bearing bodies were developed via theoretical analysis. Those of the cables were obtained from numerical models based on derived cable profiles. Finally, incremental dynamic analysis (IDA) was performed to evaluate the effects of the CRCRBs and cable models on structural responses. The test results verify the feasibility of the new cable layout and the advantages of the CRBs as the bearing body in restoring force, damping capacity, and residual displacement. As both the radii of the curved segments and the inclination angle of the linear one in the cable profiles decreased, the cable stiffness kept rising during loading. Cable sliding at clamps enlarged the cable-free displacement and residual deformation, and was relieved by adding clamps. The proposed models fit the test data well. The IDA results illustrate the superiority of the CRCRBs in mitigating bridge damage and highlight the importance of using the precise multi-linear cable model instead of the simplified linear one to avoid overestimating responses.

A foundation model for clinical-grade computational pathology and rare cancers detection.

The analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. The success of such applications depends on the ability to model the diverse patterns observed in pathology images. To this end, we present Virchow, the largest foundation model for computational pathology to date. In addition to the evaluation of biomarker prediction and cell identification, we demonstrate that a large foundation model enables pan-cancer detection, achieving 0.95 specimen-level area under the (receiver operating characteristic) curve across nine common and seven rare cancers. Furthermore, we show that with less training data, the pan-cancer detector built on Virchow can achieve similar performance to tissue-specific clinical-grade models in production and outperform them on some rare variants of cancer. Virchow's performance gains highlight the value of a foundation model and open possibilities for many high-impact applications with limited amounts of labeled training data.

Archaeological analogues for the lifetime prediction of the canister for spent nuclear fuel in the deep geological repository: Part I. Composition of pore water and corrosion products

AbstractThis work focuses on archaeological analogues as a way of obtaining long‐term data for assessing the lifetime of a spent nuclear fuel canister. An analysis of the environment around the excavated artefacts is presented, followed by geochemical modelling of the likely corrosion products and subsequent comparison with real phases in the corrosion layer estimated by X‐ray diffraction. A total of 16 archaeological sites with fine clay soils and the potential for long‐term flooding were evaluated. Although the soil pore solution contained high levels of silicates, no such phases with iron in the corrosion products were confirmed. Carbonate corrosion products, typical of bentonite environments, were also not observed. Although oxygen access is very limited in the environments of all sites, even low concentrations shifted the equilibrium of corrosion products formed in favour of (hydroxy)oxides.

Comparative Gene Expression Analysis of Salmonella Typhimurium DT104 in Ground Chicken Extract and Brain Heart Infusion Broth.

Salmonella enterica Typhimurium DT104 (S. Typhimurium DT104) is an important foodborne pathogen that is associated with poultry and poultry products. Currently, there is very little information on the underlying molecular mechanisms that allow DT104 to survive and propagate in poultry meat and the poultry processing environment. The current study assessed the global gene expression of DT104 in ground chicken extract (GCE) compared to brain heart infusion (BHI) medium using RNA-Seq technology. DT104 was grown to the early stationary phase (ESP), inoculated into GCE or BHI, and then re-grown to the log phase before RNA was extracted and transcripts were quantified by RNA-Seq. Gene expression for DT104 grown in GCE was then compared to that of DT104 grown in BHI for samples grown to the ESP. Growth in GCE resulted in the up-regulated expression of genes related to translation, carnitine metabolism (23-283-fold change), and cobalamin (vitamin B12) biosynthesis (14-fold change). In particular, the presence of carnitine in chicken meat, and thus, in GCE, which lacks carbohydrates, may allow Salmonella to utilize this compound as a carbon and nitrogen source. This study demonstrates that RNA-Seq data can provide a comprehensive analysis of DT104 gene expression in a food model for poultry products. This study also provides additional evidence for the importance of metabolic adaptation in the ability of S. enterica to successfully adapt to and occupy niches outside of its host and provides potential targets that could be used to develop intervention strategies to control Salmonella in poultry.

A BP Neural Network Product Design Optimization Model Based on Emotional Design and Sustainable Product Design

The objective of this study is to investigate the impact of emotional design and sustainable product design on user experience and satisfaction. Additionally, the study aims to optimize the combination efficiency and material selection methods of these two design approaches, extracting optimization parameters based on emotional and sustainable product design principles. By analyzing user satisfaction through questionnaires, various product indicators were examined. The study ultimately establishes a BP neural network optimization model by analyzing the design elements of different products and the satisfaction levels of various indicators. This BP neural network design optimization model, integrating emotional and sustainable design, demonstrates an accuracy rate of over 95% in predicting user satisfaction for different design schemes. Upon examining the emotional design and sustainable design parameter tables following data standardization, and analyzing various product models, it was concluded that the rate of change in each design parameter significantly impacts the satisfaction of each product indicator, highlighting the importance of these parameters in product design optimization.

PROPER NAMES OF THE SEAS: STRUCTURE, ORIGIN, FUNCTIONING IN UKRAINIAN LANGUAGE

The main goal of our research is to collect, systematize and analyze the proper names of all seas that are in the Ukrainian language. The purpose of the research involves solving the following tasks: 1) to draw up a register of pelagonyms; 2) determine their denotative and characteristic features; 3) find out etymological and word-forming features; 4) describe the peculiarities of functioning in the Ukrainian language; 5) establish the defining features of this type of proper names. The object of analysis is 238 pelagonyms, and their denotative-characteristic, etymological-word-forming and functional features are the subject of analysis. In the process of analysis, we used genetic (to determine the origin of proper names), etymological (to find out the features of names creation), descriptive (to determine the extralingual features of pelagonyms), structural (to characterize the structure of names), functional (to describe features related to the functioning of seas names in texts of various styles) research methods and the method of quantitative calculations (to determine the productivity of different types of pelagonyms). It was established that compound constructions (the most productive models are "noun + adjective" and "noun + noun"), formed by a syntactic method, with combined motivation (in particular, locative-, memorial-, and qualitative-substantial) dominate, they are derived from onyms and appellatives. Geographical terms and anthroponyms or toponyms prevail in their composition. Most pelagonyms are ancient, hybrid, lowfrequency, emotionally neutral names that have a known and obscure etymology, are used in a scientific style, are elements of language (not speech), belong to the passive vocabulary, are unknown to the general public, and are devoid of ethnocultural meaning. In the future, we plan to pay attention to the general characteristics of other proper names: both open water bodies (for example, loughs or streams), and such categories as oikonyms, oronyms, or choronyms. Over time, this will make it possible to form a complete picture of the Ukrainian toponymy.

Investigation on NO and N2O emissions characteristics in deep peak regulation circulating fluidized bed boilers

Abstract To adapt to the increasing proportion of new energy power generation capacity, coal power must transition from its traditional role as the primary power source to serving as a fundamental backup and system regulation energy source. The circulating fluidized bed technology is known for its wide range of load regulation capabilities; however, emissions of pollutants during load regulation can exhibit significant variability. This study utilized Aspen Plus software to develop a circulating fluidized bed combustion model based on a gas-solid fluid dynamics model, an equivalent coal pyrolysis model, and a multi-phase macroscopic combustion reaction dynamics model of pyrolysis products. This model was used to predict both the temperature distribution within the furnace chamber and the distribution of pollutant concentrations. Predictions of pollutant emissions from 100 % load to 30 % load of the circulating fluidized bed were explored under the original combustion condition and 5 % proportion of recirculated flue gas. Under the primary combustion condition, the emission concentration of NO x showed a decreasing and then increasing trend with decreasing load, while the concentration of nitrous oxide, in contrast to NO x , showed an increasing and then decreasing trend. The effect of recirculated flue gas on pollutant emissions was not significant at reduced loads. This study aims to provide technical support and theoretical guidance for the management of pollutant emissions from the deep peak regulation of actual circulating fluidized beds.

Physics Constrained High-Precision Data-Driven Modeling for Multi-Path Ultrasonic Flow Meter in Natural Gas Measurement.

Ultrasonic flow meters are crucial measuring instruments in natural gas transportation pipeline scenarios. The collected flow velocity data, along with the operational conditions data, are vital for the analysis of the metering performance of ultrasonic flow meters and analysis of the flow process. In practical applications, high requirements are placed on the modeling accuracy of ultrasonic flow meters. In response, this paper proposes an ultrasonic flow meter modeling method based on a combination of data learning and industrial physics knowledge. This paper builds ultrasonic flow meter flow velocity prediction models under different working conditions, combining pipeline flow field velocity distribution knowledge for data preprocessing and loss function design. By making full use of the characteristics of the physics and data learning, the prediction results are close to the real acoustic path flow velocity distribution; thus, the model has high accuracy and interpretability. Experiments are conducted to prove that the prediction error of the proposed method can be controlled within 1%, which can meet the needs of ultrasonic flow meter modeling and subsequent performance analysis in actual production.

Thermodynamic modeling of silicon carbide formation during the Acheson process in non-stoichiometric mixtures

A brief review and critical evaluation of the literature related to the mechanism of carbothermic reduction of silicon oxide is presented. To resolve the contradictions in the literature data about the number of chemical reactions and key intermediate substances during the Acheson process, thermodynamic modeling of products of carbothermic reduction of silicon (IV) oxide at 1 bar total pressure was carried out. It was determined that CO2 and Si were absent among the intermediates at temperatures close to the silicon carbide formation temperature (from 1520 to ~2500 °С). Out of several dozen possible reactions, the two dominant reactions that result in the formation of silicon carbide in the Acheson process were identified. The effect of reagents temperature from 1000 to 3000 °С, bulk and local deviation from stoichiometry of the initial mixture on the composition of the reaction products was discovered. Obtained new data explains some empirical observations and greatly simplifies the physicochemical modeling of the Acheson process.

Promoting inclusivity in education amid the post-COVID-19 challenges: An interval-valued fuzzy model for pedagogy method selection

The advent of the COVID-19 pandemic has brought about unprecedented disruptions in the field of education, necessitating a reevaluation of pedagogical approaches in the post-COVID era. This research paper introduces a novel interval-valued fuzzy simple additive weighting (SAW), weighted product model (WPM), and weighted aggregates sum product assessment (WASPAS) multi-criteria decision-making (MCDM) framework to address the challenge of promoting inclusivity in education amidst the post-COVID era. Leveraging the uncertainty inherent in the post-pandemic educational landscape, the proposed method offers a comprehensive pedagogy selection approach incorporating interval-valued fuzzy sets to account for imprecise and ambiguous data. By integrating the principles of inclusivity and diversity, the method evaluates various pedagogical approaches and their effectiveness in fostering an inclusive learning environment for diverse student populations. The study showcases the application of pedagogy selection in real-world educational scenarios, demonstrating its potential to inform policy decisions and enable educational institutions to adapt and cater to the evolving needs of learners in the aftermath of the COVID-19 pandemic.

Determinants of behavior towards hemp-based products: an application of the theory of planned behavior

PurposeThis paper examines how U.S. consumer intentions to adopt hemp vary across product types using the theory of planned behavior (TPB).Design/methodology/approachData were collected via an online survey of U.S. residents in 2022 (n = 1,948). Two-step structural equation modeling is used to examine how TPB constructs and background factors influence intent to use five different hemp-based products: cannabidiol (CBD), clothing, food, personal care products, and pet products. Data are analyzed using R.FindingsPositive attitudes towards all categories of hemp-based products increase the probability of adoption, while subjective norm and perceived behavioral control have limited and varied significant influence across product models. Age has a consistent significant and negative influence on adoption.Research limitations/implicationsFindings highlight consumer segmentation and marketing opportunities, inform hemp stakeholder decision-making, and provide directions for future research. Given the absence of explanatory power of SN and PBC on most product models and the diversity of products and nuanced U.S. hemp policy, future research could investigate expanded iterations of TPB. Using revealed behavior could also highlight potential intention-behavior gaps and offer more robust insights for hemp stakeholders.Originality/valueFindings contribute to a limited body of information on markets and consumer demand for hemp in the U.S.

A New Hybrid Approach for Product Management in E-Commerce

Nowadays, due to the developments in technology and the effects of the pandemic, people have largely switched to e-commerce instead of traditional face-to-face commerce. In this sector, the product variety reaches tens of thousands, which has made it difficult to manage and to make quick decisions on inventory, promotion, pricing, and logistics. Therefore, it is thought that obtaining accurate and fast forecasting for the future will provide significant benefits to such companies in every respect. This study was built on the proposal of creating a cluster-based–genetic algorithm hybrid forecasting model including genetic algorithm (GA), cluster analysis, and some forecasting models as a new approach. In this study, unlike the literature, an attempt was made to create a more successful forecasting model for many products at the same time inside of single product forecasting. The proposed CBGA model success was compared separately to both the single prediction method successes and only genetic algorithm-based hybrid model successes by using real values from a popular B2C company. As a result, it has been observed that the forecasting success of the model proposed in this study is more successful than the forecasting made using single models or only the genetic algorithm.

Optimizing airflow in spiral blast freezers

Spiral blast freezing is a common unit operation used in food processing facilities for rapidly freezing a variety of foodstuffs. The purpose of a blast freezer is to generate high velocity, low temperature air flow over food products being conveyed within refrigerated enclosures to accomplish the freezing process. However, air flow patterns observed within field operating blast freezers are often suboptimal, resulting in diminished system performance. This paper applies a Monte Carlo simulation technique to a food product freezing simulation in order to identify velocity profiles that optimize the freezing process. A one-dimensional food product model is used to evaluate the interplay between the time variation in the magnitude of the air velocity over food products conveyed through the freezing system and the resulting dwell time needed to achieve a target product core temperature at the blast freezer exit. Temporal heat transfer coefficients derived from field measurements made in a newly installed spiral blast freezer serve as a basis to calibrate the one-dimensional product model.The results of the Monte Carlo analysis show freezing system performance is improved when high and stable air velocities over the product are achieved early in the freezing process dwell time. Air flow patterns within a freezing system that result in high air velocity later in the freezing process dwell time are suboptimal. Field-measured data on a newly installed spiral blast freezer showed this suboptimal air flow pattern and the use of baffling within the spiral enables improved airflow leading to an estimated 10 % increase in production throughput.

Variations of Productive Waqf Management Model in Indonesia: A Literature Study

This research examines different productive waqf management models in Indonesia. The study used a qualitative approach, focusing on literature. The findings revealed that the most commonly-implemented productive waqf management model was the productive investment one, which involved land and building rentals. In second place was the profit-sharing financing model, which included collaborations with third parties such as cooperatives, outsourcers, and other institutions. The study also identified the social infrastructure development model, by which buildings such as schools and places of worship were built. The variations in productive waqf management models in Indonesia, as found in this study, were still relatively low when compared to the strategic productive waqf land categorization compiled by the Directorate of Waqf Empowerment. This highlights the need for the Indonesian Waqf Board to develop strategies to increase the ratio of productive waqf at national and sub-national levels, which will eventually lead to the rise of the National Waqf Index.