Description Of Production Research Articles

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.

Online Auction App

The online auction app described represents a sophisticated yet accessible platform, leveraging the robust capabilities of Firebase Real time Database and Firebase Storage. Its user- centric design fosters seamless interaction, enabling effortless login and signup processes for users eager to explore a treasure trove of valuable and rare products. Through a meticulously organized interface, users can not only peruse listings but also contribute their own prized possessions to the auction ecosystem, creating a dynamic marketplace of coveted items. The product description pages offer comprehensive insights accompanied by vivid imagery, empowering users to make informed decisions with confidence. Real-time bidding capabilities ensure an exhilarating experience, with automatic increments through proxy bidding ensuring fairness and efficiency. Moreover, the incorporation of secure payment gateways instills trust and reliability, enhancing the overall transactional experience. With a commitment to user satisfaction evident in its ad-free and free-of-charge model, this app stands as a testament to the seamless convergence of cutting- edge technology and user-centric design principles in the realm of online auctions. Keywords: Online auction app, Firebase Real time Database, Firebase Storage, User-friendly, Valuable and rare products, Login and signup, Product description, Bidding, Real-time updates, Proxy bidding, Secure payment gateways, User satisfaction, No ads, Free-of-charge, Seamless customization, Detailed information, Images, Categorization, Tags, Trustworthy transactional experience

Implementation of a prehospital whole blood program: Lessons learned.

Early blood administration by Emergency Medical Services (EMS) to patients suffering from hemorrhagic shock improves outcomes. Prehospital blood programs represent an invaluable resuscitation capability that directly addresses hemorrhagic shock and mitigates subsequent multiple organ dysfunction syndrome. Prehospital blood programs must be thoughtfully planned, have multiple safeguards, ensure adequate training and credentialing processes, and be responsible stewards of blood resources. According to the 2022 best practices model by Yazer etal, the four key pillars of a successful prehospital program include the following: (1) the rationale for the use and a description of blood products that can be transfused in the prehospital setting, (2) storage of blood products outside the hospital blood bank and how to move them to the patient in the prehospital setting, (3) prehospital transfusion criteria and administration personnel, and (4) documentation of prehospital transfusion and handover to the hospital team. This concepts paper describes our operational experience using these four pillars to make Maryland's inaugural prehospital ground-based low-titer O-positive whole blood program successful. These lessons learned may inform other EMS systems as they establish prehospital blood programs to help improve outcomes and enhance mass casualty response.

Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm

Abstract. The processes responsible for methane (CH4) emissions from boreal wetlands are complex; hence, their model representation is complicated by a large number of parameters and parameter uncertainties. The arctic-enabled dynamic global vegetation model LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator) is one such model that allows quantification and understanding of the natural wetland CH4 fluxes at various scales, ranging from local to regional and global, but with several uncertainties. The model contains detailed descriptions of the CH4 production, oxidation, and transport controlled by several process parameters. Complexities in the underlying environmental processes, warming-driven alternative paths of meteorological phenomena, and changes in hydrological and vegetation conditions highlight the need for a calibrated and optimised version of LPJ-GUESS. In this study, we formulated the parameter calibration as a Bayesian problem, using knowledge of reasonable parameters values as priors. We then used an adaptive Metropolis–Hastings (MH)-based Markov chain Monte Carlo (MCMC) algorithm to improve predictions of CH4 emission by LPJ-GUESS and to quantify uncertainties. Application of this method on uncertain parameters allows for a greater search of their posterior distribution, leading to a more complete characterisation of the posterior distribution with a reduced risk of the sample impoverishment that can occur when using other optimisation methods. For assimilation, the analysis used flux measurement data gathered during the period from 2005 to 2014 from the Siikaneva wetlands in Southern Finland with an estimation of measurement uncertainties. The data are used to constrain the processes behind the CH4 dynamics, and the posterior covariance structures are used to explain how the parameters and the processes are related. To further support the conclusions, the CH4 flux and the other component fluxes associated with the flux are examined. The results demonstrate the robustness of MCMC methods to quantitatively assess the interrelationship between objective function choices, parameter identifiability, and data support. The experiment using real observations from Siikaneva resulted in a reduction in the root-mean-square error (RMSE), from 0.044 to 0.023 gC m−2 d−1, and a 93.89 % reduction in the cost function value. As a part of this work, knowledge about how CH4 data can constrain the parameters and processes is derived. Although the optimisation is performed based on a single site's flux data from Siikaneva, the algorithm is useful for larger-scale multi-site studies for a more robust calibration of LPJ-GUESS and similar models, and the results can highlight where model improvements are needed.

A comprehensive phenomenological model for unburned fuel emission simulation applied to natural gas engines

The development of lean-burn concepts for internal combustion engines, utilizing a homogeneous air/fuel charge, aims to improve multiple aspects simultaneously, including thermal efficiency, fuel consumption, nitric oxide, and carbon monoxide emissions. However, the adoption of this technology may result in a notable increase in the emission of unburned hydrocarbons (uHC) compared to stoichiometric engines. The sources of uHC are diverse and their relative significance depends on fuel characteristics, engine operating conditions, and geometric features of the combustion chamber. This concern becomes more pronounced when considering engines fueled with natural gas, as methane has a high global warming potential compared to other pollutant emissions. This research proposes an enhancement to an existing simulation model that describes the primary mechanisms involved in uHC formation, which are filling/emptying of the piston top-land crevice volume and the flame wall quenching, with subsequent uHC post-oxidation. The improvements include the calculation of flame penetration into crevice volumes, the description of uHC production from non-piston crevices, and the introduction of a novel scavenging model. The uHC model is integrated into commercial software (GT-Power) as a “user routine”. Validation of the model is conducted with reference to three large bore engines (W31DF, W31SG, and W46SG) with bore sizes of 31 cm and 46 cm, fueled with natural gas, and operated under lean mixtures (λ > 2), employing different ignition methods (dual fuel mode or pre-chamber device). The uHC model is validated against engine-out measurements, exhibiting an average error of 10.0 % for W31DF, 19.4 % for W31SG, and 14.0 % for W46SG engines, respectively. The reliability of the uHC model is demonstrated by the absence of case-specific adjustments to its tuning constants, even when there are significant variations in engine load and geometric design of the crevice volumes.

Modeling and simulation for n-decane cracking flow based on detailed reaction kinetics and complete real gas thermodynamics under supercritical pressure

The real gas thermodynamics and the detailed reaction kinetics are two crucial components in studying the flow of hydrocarbon fuel with cracking reactions under supercritical pressure. However, these two aspects have not been effectively integrated to provide stable and accurate predictions of experimental results. This study combined a detailed cracking mechanism with a real gas thermodynamic model based on the Peng-Robinson equation of state, and introduced real gas chemical potential. By comparing with experimental measurement results, the impacts of the thermodynamic and reaction models on simulation accuracy were carefully studied. The results demonstrate that, within a wide range of conditions (pressure 3–10 MPa, flow rate 1–5 g/s, temperature 300–1004 K), the calculation model based on real gas thermodynamics predicts outlet temperatures and conversion rates with higher accuracy compared to ideal gas. The non-ideal gas effect of chemical potential at 3 MPa has a slight impact on the n-decane cracking reaction system. But this effect increases with increasing pressure. Under 10 MPa pressure, the real gas effects of chemical potential may result in changes reaching 40% in the rate of progress variables (ROPs). Furthermore, when using the same complete real gas thermodynamics, the Detailed mechanism has higher accuracy in predicting fuel temperature, conversion rate and product distribution, than PPD or Lumped mechanisms. The differences in the simulation results from different reaction models mainly due to the different descriptions of reaction rate rules and intermediate products. This work provides a more accurate method for simulating the supercritical pressure cracking process of fuel in cooling channels.

Public food procurement as a tool of sustainable food and nutrition policy—fat products

IntroductionEnsuring a sustainable and responsible diet, particularly in the procurement of fatty products, holds paramount importance for early childhood development. Dietary fats significantly influence children’s growth and well-being, both in the short and long term. Schools and kindergartens play a pivotal role in shaping children’s dietary habits. This study aims to quantitatively and qualitatively analyze public procurement orders conducted by educational institutions.Materials and methodsOut of 1,126 public procurement orders, 197 met inclusion criteria, leading to the identification of 1,248 products categorized as sources of fats in children’s diets. The study conducted both quantitative and qualitative analyses on the identified products.ResultsCriteria commonly employed by purchasers were derived from product descriptions. While product composition, especially fat content and the absence of certain additives, received due attention, organoleptic characteristics criteria were frequently overlooked. Sustainable procurement criteria were given the least consideration. The study highlights a notable reliance on vegetable oils, predominantly rapeseed oil. However, it reveals a worrisome prevalence of animal-derived fats, including butter, mayonnaise, pork belly, and lard. Although plant-based fats constitute around 52.77% of total orders, the substantial presence of animal fats poses challenges to maintaining a balanced and healthy diet for children.ConclusionThe study underscores the necessity of establishing specific criteria for evaluating the quality of delivered products, especially fatty items, in educational settings. Standardized guidelines are crucial to promote healthier food choices, encourage sustainable diets, and ultimately enhance the overall health and well-being of children.

Metadata Integration for Spam Reviews Detection on Vietnamese E-Commerce Websites

The problem of detecting spam reviews (opinions) has received significant attention in recent years, especially with the rapid development of e-commerce. Spam reviews are often classified based on comment content, but in some cases, it is insufficient for models to accurately determine the review label. In this work, we introduce the ViSpamReviews v2 dataset, which includes metadata of reviews with the objective of integrating supplementary attributes for spam review classification. We propose a novel approach to simultaneously integrate both textual and categorical attributes into the classification model. In our experiments, the product category proved effective when combined with deep neural network (DNN) models, while text features performed well on both DNN models and the model achieved state-of-the-art performance in the problem of detecting spam reviews on Vietnamese e-commerce websites, namely PhoBERT. Specifically, the PhoBERT model achieves the highest accuracy when combined with product description features generated from the SPhoBert model, which is the combination of PhoBERT and SentenceBERT. Using the macro-averaged F1 score, the task of classifying spam reviews achieved 87.22% (an increase of 1.64% compared to the baseline), while the task of identifying the type of spam reviews achieved an accuracy of 73.49% (an increase of 1.93% compared to the baseline).

Study on the Promotion Effect of “Live Broadcasting with Shouting Style” in Live Rooms Under the Elaboration Likelihood Model

A new phenomenon of “shouting-style sales” has emerged in live e-commerce, but the research on its effects is currently insufficient. Exploring the short-term and long-term effects of “shouting-style sales” is beneficial for exploring how live e-commerce can develop better and longer-lasting, providing experiences and insights for live e-commerce rooms, especially brand-owned ones. By conducting a questionnaire survey of consumers who have experience with live e-commerce shopping, we can understand how consumers perceive “shouting-style sales” and what factors influence consumers’ ordering and repurchasing. The survey shows that “shouting-style sales” has no short-term effect on promoting orders, and it cannot promote repurchase in the long term either. Consumers may use both central and peripheral routes when shopping in live e-commerce, but they tend to use central route thinking when repurchasing. The emotional appeal and the degree of discounts in live rooms are the main factors influencing consumer purchases, while product quality and the level of product description are the main factors influencing consumer repurchases.

The Impact of Sentiment Scores Extracted from Product Descriptions on Customer Purchase Intention

This study investigates whether and how the textual content of product descriptions, especially the sentiment element, influences buyers’ purchase intentions. Using year-round digital transaction data from Mercari, a leading e-Commerce platform in Japan, we examine the interplay of hard and soft information signals exchanged between sellers and buyers. The study addresses two crucial questions: (1) Do the descriptions that sellers provide on product sales pages impact the buyer’s intent to purchase? and (2) In what way does the description influence the buyer’s purchase intention? Quantitative analysis is used to understand the relationship between product descriptions, sentiment elements, and purchase intentions. The results show that sentiment factors in product descriptions can serve as high-quality “signals” that can help buyers make informed purchasing decisions and reduce information asymmetry between buyers and sellers. This research contributes to understanding decision-making in online markets, particularly the role of soft information and sentiment analysis.

Multi-Identity Recognition of Darknet Vendors Based on Metric Learning

Dark web vendor identification can be seen as an authorship aliasing problem, aiming to determine whether different accounts on different markets belong to the same real-world vendor, in order to locate cybercriminals involved in dark web market transactions. Existing open-source datasets for dark web marketplaces are outdated and cannot simulate real-world situations, while data labeling methods are difficult and suffer from issues such as inaccurate labeling and limited cross-market research. The problem of identifying vendors’ multiple identities on the dark web involves a large number of categories and a limited number of samples, making it difficult to use traditional multiclass classification models. To address these issues, this paper proposes a metric learning-based method for dark web vendor identification, collecting product data from 21 currently active English dark web marketplaces and using a multi-dimensional feature extraction method based on product titles, descriptions, and images. Using pseudo-labeling technology combined with manual labeling improves data labeling accuracy compared to previous labeling methods. The proposed method uses a Siamese neural network with metric learning to learn the similarity between vendors and achieve the recognition of vendors’ multiple identities. This method achieved better performance with an average F1-score of 0.889 and an accuracy rate of 97.535% on the constructed dataset. The contributions of this paper lie in the proposed method for collecting and labeling data for dark web marketplaces and overcoming the limitations of traditional multiclass classifiers to achieve effective recognition of vendors’ multiple identities.

Analyzing the Influence of Product Descriptions, Ratings, and Reviews on Online Consumer Purchasing Decisions

This project aims to find out how the product description, rating, and review affect the purchasing decision of consumers purchasing in online. This project is also meant to identify the awareness of giving review and rating is important and how the product description plays a important role in the purchase decision. A total sample of 169 is taken from the customers. The data have been collected using Structured Questionnaire and google forms from the customers. The customers have been enquired about their demographic profile, purchase patten, about their level of consumption, experience towards the brand, positive and negative impact involved while purchasing through online. For the analysis purpose, statistical tools and techniques like Chi Square, Regression. Keywords: Online Purchasing, Negative Impact, Postive Impact.

Online Shopping System

The main objective of this system is to make global shopping possible, were the customers buy things from anywhere in India. The customer will be displayed with the available products of the company, and the customer will be asked to select the items based on his choice and all information about the product like price, product id model will be given to the customer based on his requirements they make an order of the product. The modules are: Product Master, Customer Module, Purchase Module, Order Module, Billing Module. Daily, special and emergency deliveries are available. At any time during off-hours, 24 hours a day, seven days a week, we can provide your record. As days goes offline shopping has reduced and online shopping is increased. This Software will help you know the various products, product description, price range, discounts so that the customers can easily identify which product will suit best for them. The Web model that was used in the implementation of the On-Line Shopping Database Application is very simple each request from the webpage invokes a request, which then communicates with the Objectivity/DB to retrieve the requested data. The resulting data is passed by a request object to. The client-side HTML that displays the result to the user.

E-Auction-A Web Based Electronic Auctioning System

Abstract: E-auction is a web-based electronic auctioning system that enables users to participate in auctions online. The system provides a plat- form for sellers to post their products for auction and for buyers tobid on them. The e-auction system is designed to be user-friendly and easily accessible, with features that allow for secure transactions and real-time bidding .The e-auction system provides several features for both buyers and sellers. For sellers, the system allows them to create auction listings with product descriptions, images, and starting bids. They can also set reserve prices, which are the minimum prices at which they are willing to sell their products. Sellers can monitor the progress of their auctions and accept or reject bids as they come in. The e-auction system also includes security features to protect against fraud and unauthorized access. The sys- tem uses SSL encryption to secure all communications between theclient and server, and user authentication is required for access to the system. The system also logs all transactions and provides audittrails for monitoring and investigation purposes. In conclusion, the e-auction system is a powerful tool for buyers and sellers alike, providing a secure and user-friendly platform for online auctions. Its features and capabilities make it an ideal solution for businesses andindividuals looking to buy or sell products online.