Description Of Production Research Articles

Analysis of the Insurance Applicant's Obligation of Truthful Disclosure

Since China implemented the reform and opening-up policy in 1978, the insurance industry has gradually embarked on a path of sustained growth and development. In 2001, China joined the World Trade Organization, ushering in an unprecedented era of expansion and diversification in the insurance industry. Against the backdrop of increasing public risk awareness, insurance has become an important tool for individuals seeking to mitigate potential losses in their risk efforts. However, this trend has also raised concerns about the ethical and legal responsibilities of insurance companies. A common dissatisfaction expressed by policyholders is that there is a difference between the optimistic description of insurance products during the sales process and the refusal of claims for false reasons after risks occur. This phenomenon has raised doubts about the true disclosure obligations of insurance companies under the Insurance Law and the necessity of improving transparency and accountability in insurance transactions. This study aims to delve deeper into this complex issue by examining the legal framework of policyholders' true disclosure obligations under the context of insurance contracts. By doing so, it seeks to contribute to the ongoing legal reform discussions within the insurance industry, with the ultimate goal of promoting a more fair, transparent, and harmonious insurance ecosystem that benefits all stakeholders.

Multization: Multi-Modal Summarization Enhanced by Multi-Contextually Relevant and Irrelevant Attention Alignment

This article focuses on the task of Multi-Modal Summarization with Multi-Modal Output for China JD.COM e-commerce product description containing both source text and source images. In the context learning of multi-modal (text and image) input, there exists a semantic gap between text and image, especially in the cross-modal semantics of text and image. As a result, capturing shared cross-modal semantics earlier becomes crucial for multi-modal summarization. However, when generating the multi-modal summarization, based on the different contributions of input text and images, the relevance and irrelevance of multi-modal contexts to the target summary should be considered, so as to optimize the process of learning cross-modal context to guide the summary generation process and to emphasize the significant semantics within each modality. To address the aforementioned challenges, Multization has been proposed to enhance multi-modal semantic information by multi-contextually relevant and irrelevant attention alignment. Specifically, a Semantic Alignment Enhancement mechanism is employed to capture shared semantics between different modalities (text and image), so as to enhance the importance of crucial multi-modal information in the encoding stage. Additionally, the IR-Relevant Multi-Context Learning mechanism is utilized to observe the summary generation process from both relevant and irrelevant perspectives, so as to form a multi-modal context that incorporates both text and image semantic information. The experimental results in the China JD.COM e-commerce dataset demonstrate that the proposed Multization method effectively captures the shared semantics between the input source text and source images, and highlights essential semantics. It also successfully generates the multi-modal summary (including image and text) that comprehensively considers the semantics information of both text and image.

INTERPRETABLE AND GENERALIZABLE HTS CODE CLASSIFICATION FRAMEWORK

The automation of Harmonized Tariff Schedule (HTS) code classification - a crucial task in international trade that entails assigning standardized codes to goods for tariff and regulatory purposes - is examined in this paper using GPT-3, a cutting-edge Large Language Model (LLM). The focus on creative prompt-engineering techniques and the usage a frozen model approach eliminates the need for further model fine-tuning. The suggested approach makes use of GPT-3’s built-in capabilities to decipher intricate product descriptions and reliably assign them to the appropriate HTS categories. We show through an array of experiments that strategic prompt adjustments including few-shot prompting allowed the model's initial top-1 accuracy of 23 percent to be significantly increased to an average of 73 percent. The main strengths of the approach are the explainability for the chosen category as well as the fully generalizable nature of the methodology.

Complications of collagen biostimulators in Brazil: Description of products, treatments, and evolution of 55 cases.

Complications of temporary and permanent fillers have been extensively studied. However, there is a lack of comparative data regarding poly-L-lactic acid (PLLA), calcium hydroxyapatite (CaHA), and polycaprolactone (PCL) known as collagen biostimulators. This study addressed the complications of collagen biostimulators concerning their diagnosis, type of product, treatment, and monitoring. An electronic questionnaire was sent to Brazilian dermatologic ultrasound experts to identify complications related to biostimulators. The type of biostimulator, location of application, number of vials injected, application plan, time between injection treatment and complication, injector profile, treatment, and prognosis were assessed. Fifty-five cases were identified, of which 49.1% were caused by PLLA-Elleva®, 23.6% by CaHA (alone or combined with hyaluronic acid), 20.0% by PLLA-Sculptra®, and 7.3% by PCL. The most affected area was the face (72.7%), with nodules being the most common clinical form (89.1%), generally occurring late (60.0%) (>1 month). Only one case was injected at an incorrect depth (musculoaponeurotic system-SMAS). Despite several treatments, including saline (45.5%), hyaluronidase (25.5%), diluted corticosteroids (23.6%), and energy-based devices (10.9%), only five cases showed complete resolution. Hyaluronidase was beneficial in complications related to fillers when there was an association of calcium hydroxyapatite with hyaluronic acid (p < 0.01). Complications from collagen biostimulators were more common on the face, typically manifesting about 1 month after treatment. These issues seemed to be related more to the properties of the products rather than inadequate technique. Furthermore, hyaluronidase demonstrated efficacy only in cases where there was an association with HA.

Sensory, emotional, and appropriateness of plant- and meat-based burgers.

The plant-based meat alternative market is experiencing rapid growth. However, whether this growth extends to mainstream consumers will depend on the sensory profile, emotional profile, and situational appropriateness of these products. This study provides a sensory comparison between two plant-based burgers, one hybrid burger, and a conventional 100% ground beef burger. The sensory evaluation was carried out under blind and informed conditions using a between-subject design. Participants (n=177) were asked to rate the appearance, flavor, odor, and texture of each product and indicate their overall liking. In addition, 26 sensory terms were evaluated using the rate-all-that-apply technique. The study also measured the emotional profile and the situational appropriateness elicited by each product using the check-all-that-apply technique. The results showed that (a) in the blind condition, there were no significant differences observed in overall liking across the four burgers; (b) the plant-based burger made with pea protein received the lowest overall liking score, and its evaluation was not positively influenced by product information disclosure; (c) providing product information influenced the perceived intensity of the attributes associated with meat; (d) discriminatory ability for emotions was higher in the informed condition; and (e) for the situational appropriateness, when prioritizing healthy eating, participants considered plant-based burgers more suitable than the groundbeef burger. PRACTICAL APPLICATION: Our results demonstrate that product descriptions could have an impact on consumer acceptance of different meat burger alternatives. In order to launch successfull meat alternatives, product developers and communication marketing specialists should consider the extent to which these alternatives resemble regular meat products in terms of their sensory and emotional profiles and context of use.

Consumer Protection in Online Shopping

Consumer Protection in Online Shopping

A STUDY ON DIGITAL MARKETING OF FLIPKART

Efficient digital marketing tactics are crucial for companies to succeed in the ever-changing world of e-commerce. Flipkart is one of the most prominent online marketplaces in India, and this study examines their digital marketing strategies. Examining Flipkart's strategy across different digital channels and how they affect the company's brand visibility, consumer engagement, and sales is the main objective of this research. In order to better understand how Flipkart draws in and keeps customers, as well as how to increase engagement and conversion rates, this study analyzes the company's website design and user experience. In addition, it delves into Flipkart's SEO tactics, namely its attempts to rise in the SERPs and target certain keywords. Content marketing campaigns run by Flipkart, such as blog articles, product descriptions, videos, and social media postings, are examined in the research. It delves into the ways Flipkart uses content to captivate its audience and boost sales. The research also examines Flipkart's approach to social media marketing, looking closely at the company's content and interaction tactics across sites like Facebook, Instagram, and Twitter. This study delves into Flipkart's email marketing initiatives, specifically looking at their subscriber segmentation tactics and the content of the emails they send out. Also covered is an examination of Flipkart's paid advertising campaigns, including display, search engine, and social media ads, as well as an analysis of the company's channel allocation strategies. In this case study, we take a look at how Flipkart uses mobile marketing and influencer marketing to boost engagement and sales through its app and its partnerships with influential users. It also checks Flipkart's analytics, which include how well the company uses data to monitor the efficacy of its digital advertising activities. Keywords: Flipkart, digital marketing, e-commerce, online marketplace, SEO, content marketing, social media marketing, email marketing, paid advertising, influencer marketing, mobile marketing, analytics, user experience, customer engagement, sales optimization, India.

Constructing a Supplier-Buyer Network: Imported Goods Flow Tracking in the Tax Transaction Records in Armenia

Taxpayer networks are among main topics of research given the evolving applicability of modern technological solutions and raising importance for tax authorities to reveal hidden relationships or patterns in the taxpayer behavior. In this study, taxpayer networks are visualized and analyzed with the application of network analysis techniques and a generalizable methodology for product traceability over the supply chain is suggested. In essence, taxpayer networks depict the web of connections among taxpayers, highlighting the flow of goods, services, and financial transactions between them. Previous studies address the issue of product classification from transactional tax documents. Modern NLP techniques were applied for product categorization given only product description in Armenian language and with limited information. The developed algorithm was used as a starting point to identify which taxpayers were involved in the trade of a specific good and thus for network construction. A set of products was chosen for construction of the taxpayer network forming the supply chain from importers to final consumers. Adjusted by a markup, the amount of rice imported to Armenia was found and identified in the last nodes of the supply chain sold to final consumer and documented in tax receipts. Factors like local production are also considered during the analysis and solutions are suggested including price filters on the products subject to trade to filter out local production.

Recommendation System Building based on CNN and TF-IDF Approaches

Online shopping has becoming increasingly popular. Customers remain engaged with shopping sites due to effective product recommendations. By analyzing users’ past actions and preferences, shopping platforms effectively identify customer interests and desires, enhancing their overall shopping experience. This paper presents a recommendation system that displays 5 items relevant to the product currently being viewed by the customer. The system processes images with an adapted ResNet-50, a deep learning image classification model in convolutional neural network (CNN) and use the acquired embedded vector to determine similarities with cosine functions. Additionally, the system employs term frequency-inverse document frequency (TF-IDF) method for text analysis in product descriptions, generating word embedding that assists recommendations. This blend of visual and textual analysis ensures that the suggestions closely match the item’s category. The system achieves 94.72% accuracy in subcategories’ classification, a 4% increase compared to using the CNN method alone, confirming its effectiveness in recommending relevant items.

Advances in the Homogeneously Catalyzed Hydrogen Production from Biomass Derived Feedstocks: a Review.

Biomass‐derived feedstocks for hydrogen production are crucial as an alternative to fossil fuel especially in those areas where green electricity and clean water are scarce. In this framework the transformation of simple (formic acid, alcohols) and more complex (polyalcohols, sugars and cellulose) bio‐derivatives in pure hydrogen is recognized as a promising approach. Parallel to great effort in heterogeneous catalysis, milder molecular systems represent a more selective eye for alternative solutions and mechanistic insights. In the present review the introduction summarizes the challenges in the catalytic utilization of biomass‐derived feedstocks, followed by the advances in homogeneously catalyzed hydrogen production from different substrates which will cover formic acid, with oustanding efficiency with noble metals and promising results with earth abundant ones and alcohols and polyalcohols, with particular emphasis to the development of heterogenized systems, ligand assisted catalysts and bi‐catalytic synergistic solutions which allow to avoid base and to promote catalyst stability and recyclability. In the last part, description of hydrogen production from more complex substrates, such as sugars and cellulose, will show the role of molecular complexes in main and side reactions. Critical comments on the reported advances are provided along the whole discussion.

Manufacturing-induced stochastic constitutive behaviors of additive manufactured specimens: testing, data-driven modeling, and optimization

PurposeFused deposition modeling (FDM) is an extensively used additive manufacturing method with the capacity to build complex functional components. Due to the machinery and environmental factors during manufacturing, the FDM parts inevitably demonstrated uncertainty in properties and performance. This study aims to identify the stochastic constitutive behaviors of FDM-fabricated polylactic acid (PLA) tensile specimens induced by the manufacturing process.Design/methodology/approachBy conducting the tensile test, the effects of the printing machine selection and three major manufacturing parameters (i.e., printing speed S, nozzle temperature T and layer thickness t) on the stochastic constitutive behaviors were investigated. The influence of the loading rate was also explained. In addition, the data-driven models were established to quantify and optimize the uncertain mechanical behaviors of FDM-based tensile specimens under various printing parameters.FindingsAs indicated by the results, the uncertain behaviors of the stiffness and strength of the PLA tensile specimens were dominated by the printing speed and nozzle temperature, respectively. The manufacturing-induced stochastic constitutive behaviors could be accurately captured by the developed data-driven model with the R2 over 0.98 on the testing dataset. The optimal parameters obtained from the data-driven framework were T = 231.3595 °C, S = 40.3179 mm/min and t = 0.2343 mm, which were in good agreement with the experiments.Practical implicationsThe developed data-driven models can also be integrated into the design and characterization of parts fabricated by extrusion and other additive manufacturing technologies.Originality/valueStochastic behaviors of additively manufactured products were revealed by considering extensive manufacturing factors. The data-driven models were proposed to facilitate the description and optimization of the FDM products and control their quality.

Product information recognition in the retail domain as an MRC problem

This paper presents the task of recognizing product information (PI) (i.e., product names, prices, materials, etc.) mentioned in customer statements. This is one of the key components in developing artificial intelligence products to enable businesses to listen to their customers, adapt to market dynamics, continuously improve their products and services, and improve customer engagement by enhancing effectiveness of a chatbot. To this end, natural language processing (NLP) tools are commonly used to formulate the task as a traditional sequence labeling problem. However, in this paper, we bring the power of machine reading comprehension (MRC) tasks to propose another, alternative approach. In this setting, determining product information types is the same as asking “Which PI types are referenced in the statement?” For example, extracting product names (which corresponds to the label PRO_NAME) is cast as retrieving answer spans to the question “Which instances of product names are mentioned here?” We perform extensive experiments on a Vietnamese public dataset. The experimental results show the robustness of the proposed alternative method. It boosts the performance of the recognition model over the two robust baselines, giving a significant improvement. We achieved 92.87% in the F1 score on recognizing product descriptions at Level 1. At Level 2, the model yielded 93.34% in the F1 score on recognizing each product information type.

COVID-19 and Comorbidities: What Has Been Unveiled by Metabolomics?

The COVID-19 pandemic has brought about diverse impacts on the global population. Individuals with comorbidities were more susceptible to the severe symptoms caused by the virus. Within the crisis scenario, metabolomics represents a potential area of science capable of providing relevant information for understanding the metabolic pathways associated with the intricate interaction between the viral disease and previous comorbidities. This work aims to provide a comprehensive description of the scientific production pertaining to metabolomics within the specific context of COVID-19 and comorbidities, while highlighting promising areas for exploration by those interested in the subject. In this review, we highlighted the studies of metabolomics that indicated a variety of metabolites associated with comorbidities and COVID-19. Furthermore, we observed that the understanding of the metabolic processes involved between comorbidities and COVID-19 is limited due to the urgent need to report disease outcomes in individuals with comorbidities. The overlap of two or more comorbidities associated with the severity of COVID-19 hinders the comprehension of the significance of each condition. Most identified studies are observational, with a restricted number of patients, due to challenges in sample collection amidst the emergent situation.

Kajian hazard analysis critical control point pada proses produksi air minum dalam kemasan

Bottled drinking water is raw water that has been processed and packaged properly so that it is safe for direct consumption. One of the concepts and strategies for ensuring food safety and quality that is considered more effective and has been recognized for its reliability internationally is the Hazard Analysis Critical Control Point (HACCP) food safety management system. This research aims to determine how to implement Hazard Analysis Critical Control Point (HACCP) in the demineralized water production process at CV. Tirta Guna Mandiri. The method used in the research is the descriptive method which describes the objects in the research. The data collection technique used is by conducting library studies, interviews, Focus Group Discussions (FGD), and documentation. The research results show that CV. Tirta Guna Mandiri has implemented 12 steps to implement HACCP which include establishing a HACCP team, product description, intended use, flow diagram verification, hazard analysis, determining Critical Control Points (CCP), determining critical limits, establishing a monitoring system, determining corrective actions, verification procedures, and establishing documentation

First description of adenosine production by Gnomoniopsis smithogilvyi, causal agent of chestnut brown rot

Gnomoniopsis smithogilvyi (Gnomoniaceae, Diaporthales) is the main causal agent of chestnut brown rot on sweet chestnut worldwide. The rotting of nuts leads to alterations in the organoleptic qualities and decreased fruit production, resulting in significant economic losses. In 2021, there was an important outbreak of chestnut rot in southern Galicia (Spanish northwest). The profile of secondary metabolites from G. smithogilvyi was studied, especially to determine its capability for producing mycotoxins, as happens with other rotting fungi, due to the possible consequences on the safety of chestnut consumption. Secondary metabolites produced by isolates of G. smithogilvyi growing in potato dextrose agar (PDA) medium were identified using liquid chromatography coupled with high-resolution mass spectrometry. Three metabolites with interesting pharmacological and phyto-toxicological properties were identified based on their exact mass and fragmentation patterns, namely adenosine, oxasetin, and phytosphingosine. The capacity of G. smithogilvyi to produce adenosine in PDA cultures was assessed, finding concentrations ranging from 176 to 834 µg/kg. Similarly, the production of mycotoxins was ruled out, indicating that the consumption of chestnuts with necrotic lesions does not pose a health risk to the consumer in terms of mycotoxins.

Pelatihan E-Commerce Pada Pelaku Usaha Mikro Kecil Menengah Di Kebon Kosong Jakarta Pusat

There are still many UMKM who do not know the existence of e-commerce, and there are still many UMKM who make transactions manually. Currently the emergence of e-commerce has a very positive impact on UMKM and is very helpful in supporting effective and efficient promotion and sales activities. This research aims to find out the influence of e-commerce on the increase in sales of Usaha Mikro Kecil dan Menengah (UMKM). The object of this research is the Zona Warkop in Kebon Kosong, Central Jakarta City. The purpose of this service activity is to introduce and practice e-commerce in the implementation of sales activities, and is expected to develop its creative ideas to balance the lifestyle of the people today. The data collection techniques used include observation, interview, and documentation. The advantage of selling through e-commerce is that it is easily accessible to UMKM through their mobile phones and it is very easy to share photos and videos and provide interesting product descriptions so that consumers are interested.

Campaign-Induced Interpersonal Communication: Identifying Turn Types and Exploring Their Interdependencies

ABSTRACT Media campaigns produce effects when audience members discuss the campaign messages. Fundamental to the understanding of how talk bridges exposure and effect is a knowledge of its content. However, prior attempts to measure talk content have largely been limited to gross characteristics of the interactions. We argue that advances in this line of research require representing post-exposure talk as campaign-induced interpersonal communication (CIC), with a focus on how individuals produce and exchange messages in conversations. Interaction data were gathered in two experiments in which pairs of participants co-viewed, then discussed health-related public service announcements. Analysis of the data produced (a) an empirically-derived typology of talk turns that provided a comprehensive, content-based description of message production and (b) evidence that turn pairs are associated in theoretically meaningful ways in exchanges that displayed properties of deliberative interactions. These findings lay a foundation for future research on the antecedents and outcomes of CIC.

Predicting Real-World Penny Auction Durations by Integrating Game Theory and Machine Learning

Game theory and machine learning are two widely used techniques for predicting the outcomes of strategic interactions among humans. However, the game theory-based approach often relies on strong rationality and informational assumptions, while the machine learning-based approach typically requires the testing data to come from the same distribution as the training data. Our work studies how to integrate the two techniques to address these weaknesses. We focus on the interactions among real bidders in penny auctions, and develop a three-stage framework to predict the distributions of auction durations, which indicate the numbers of bids and auctioneer revenues. Specifically, we first leverage a pre-trained neural network to encode the descriptions of products in auctions into embeddings. Second, we apply game theory models to make preliminary predictions of auction durations. In particular, we tackle the challenge of accurately inferring parameters in game theory models. Third, we develop a Multi-Branch Mixture Density Network to learn the mapping from product embeddings and game-theoretic predictions to the distributions of actual auction durations. Experiments on real-world penny auction data demonstrate that our framework outperforms both game theory-based and machine learning-based prediction approaches.

Visual Language – Let the Product Say What You Want

Visual Language is a multitasking on-line system focusing on e-commerce, which involves in generating accurate product descriptions for sellers and providing convenient product retrieval service for customers. To achieve this goal, the system adopts image description technology and multi-modal retrieval technology. By utilizing cross-modal generation technique, we could help sellers on rapid uploading products and customers on rapid retrieval, which could improve the experience of both sellers and customers.

Comparison of open comments and check-all-that-apply to collect reasons for liking and disliking chocolates in preference mapping

Collecting Open Comments (OC) is a common way to learn about the product features that consumers like and dislike in internal preference mapping studies. OC has the advantage to provide a direct answer to the question “What do you like/dislike about this product?” without consumers. However, because OC does not suggest any sensory terms, consumers sometimes struggle to properly verbalize their perceptions leading to data and information that are difficult to interpret and translate into product improvement recommendations. In addition, the encoding of OC is a cumbersome, time-consuming, and possibly expensive task depending on the context of study. In this context, we explored the use of the Check-All-That-Apply (CATA) method as an alternative to OC to collect reasons for like and dislike reported by consumers. We conducted two parallel studies in which respondents evaluated 6 out of 8 milk chocolates using an incomplete block design. The only noticeable difference between the two studies was that respondents reported reasons for liking and disliking using either OC (n = 204) or CATA (n = 157). Results did not show any striking difference between mean overall liking scores and taste clusters in both studies. The drivers of liking and disliking associated with each product were also largely consistent, although some differences in citation rates and attitudes were noted. For instance, respondents tended to disregard appearance and texture in favor of flavor with OC as compared to CATA. In addition, the drivers of disliking provided by OC were less diverse and less frequently cited than in CATA. Overall, our results suggest that CATA is a promising alternative to OC for liking-oriented product description provided that special care is taken to build a relevant list of CATA terms.