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Firms like Uber, Amazon, and TripAdvisor have popularized the rating of people, goods, and services. These entities receive scores (e.g., through online reviews) in a variety of presentation formats: incremental (a raw score per episode; e.g., 5–5–2), cumulative (updated average scores; e.g., 5–5–4), or a combination thereof. This article focuses on prevalent situations in which a score deviates from prior scores and examines how the presentation format of the scores impacts decision makers' (e.g., consumers, managers) evaluations of the entity scored. Across a wide variety of settings, nine experiments document that when a generally well-performing (poorly performing) entity suddenly receives a negative (positive) score, overall performance will be perceived as less negative (positive) when shown in a cumulative format compared with an incremental or combined format. This effect appears to be stronger when the deviating episode is more representative (e.g., due to higher recency or internal attribution). The authors also find evidence for their proposed explanation: a cumulative format distorts individuals' perceptions of the underlying raw score of the deviating episode. These findings imply that presenting scores in alternative formats may affect marketing outcomes (e.g., customer churn, product choice, technology adoption, new product success, and user engagement on peer-to-peer platforms). [ABSTRACT FROM AUTHOR]

As customers increasingly rely on online reviews for making consumption decisions, the dangers arising from misinformation and fakery have become an acute source of concern for consumers, firms, and society at large. Many online review platforms claim a role as guardians of trust in the information exchange process. Yet, little is known about the practices firms can utilize to design platforms that build and safeguard consumer trust. The authors draw on governance and identity disclosure literature streams to propose five practices that mitigate fakery and build trust in the platform: monitoring, exposure, community building, status endowment, and identity disclosure. Five studies (1) establish the individual and joint effects these practices have on platform trust, (2) identify the mediating processes by which the practices build trust, (3) verify the ecological value of the conceptual framework, (4) provide support of the salience of the practices, and (5) show how the practices build trust above and beyond review content–level characteristics. Interaction effects suggest that exposure, community building, and status endowment obviate the need for identity disclosure in building platform trust, a finding that mitigates related privacy concerns. Collectively, the findings can improve platform design, enabling review platforms to fulfill their promise of mitigating fakery and increasing trust. [ABSTRACT FROM AUTHOR]

Prior research has indicated that consumers' decisions are significantly influenced by online reviews. However, existing research has focused mainly on attributes (e.g., average ratings) that are not fully controlled by firms; only limited research has investigated how controllable attributes (e.g., review display formats) affect consumers. Drawing on visual perception research, the authors examine the effectiveness of two prominent graphical display formats used by major e-commerce platforms: one that displays rating distributions in a proportional format (e.g., Amazon) and one that does so in a simple format (e.g., Google). The results indicate that due to the changes in graphs' reference points caused by the shrunken x-axis in simple bar graphs, consumers respond more positively to an item when its rating distribution is displayed in a graphically simple (vs. proportional) format. This effect is moderated by the distribution's peak value (i.e., the share of the most frequent rating) and imbalance score (i.e., the difference between the share of positive and negative ratings). Furthermore, even an item's future ratings are influenced by the graphical format in which its prior ratings are displayed. The contributions to the marketing literature are discussed, and insights that can aid managers in making more informed decisions are provided. [ABSTRACT FROM AUTHOR]

Prior research has shown that user-generated content (UGC) on social media is an important source for consumers seeking information about mental health treatment. The purpose of this study is to explore UGC about prescription drugs for depression as a type of digital 'pharma' folklore. Using qualitative inquiry, the authors analyse 73 user-generated product reviews on YouTube. Findings yield a compelling portrait of people's struggle with mental illness and their treatment journey. Applying folklore as a framework provides a novel strategy for analysing and theorising the cultural worldview of a consumer group. It allows marketers to decipher the knowledge being passed on to others from product review videos and gain a deeper understanding of stories that resonate with consumers seeking treatment with depression drugs. [ABSTRACT FROM AUTHOR]

Language is critical to the effectiveness of marketing messages. Achieving a desired outcome requires arranging words to formulate a message (i.e., syntax), but this task is not trivial. The authors study the role of syntax in marketing communications by focusing on syntactic surprise (i.e., how unexpected the syntax of a message is). They introduce a measure that captures syntactic surprise, establishes its internal and external validity, and tests its effectiveness for marketing messages. In a series of studies that include field data and randomized field experiments from contexts such as donations, advertising, and product reviews, the authors show that a message's syntactic surprise is related to its effectiveness. This relationship follows an inverted U-shape, such that medium-syntactic-surprise messages are the most effective. The authors then conduct experiments on Facebook and Instagram to demonstrate how these findings can be used to write effective marketing messages. In collaboration with two independent companies, they show that ads for products, services, or jobs that are written with medium syntactic surprise result in higher click-through rates than ads written with low or high syntactic surprise. [ABSTRACT FROM AUTHOR]

Alkali-activated materials (AAMs) are regarded as a substitute for Portland cement. They have high chloride resistance and a low carbon dioxide footprint. The aim of this review is to provide a multi-scale perspective to understand material–product–microstructure–property relationships in terms of the chloride binding behaviour of AAMs. The physical and chemical chloride stability of different reaction products is summarised from nanostructure, to microstructure to macro properties. An analysis of studies in the literature gives an overview of recent progress in chloride transport in AAMs influenced by different reaction products. Results show that a higher calcium/silicon, aluminium/silicon molar ratios and alkali content increase the formation of amorphous phases, leading to a denser microstructure and lower chloride penetration in AAMs. Higher magnesium oxide and aluminium oxide contents result in increased formation of hydrotalcite. The enhanced physical and chemical absorption of chloride by hydrotalcite leads to higher resistance of chloride penetration in AAMs. Investigation of increasing chloride resistance could potentially focus on increasing gel and hydrotalcite formation. [ABSTRACT FROM AUTHOR]

Conventional radiography (CR) is primarily utilized for fracture diagnosis. Artificial intelligence (AI) for CR is a rapidly growing field aimed at enhancing efficiency and increasing diagnostic accuracy. However, the diagnostic performance of commercially available AI fracture detection solutions (CAAI-FDS) for CR in various anatomical regions, their synergy with human assessment, as well as the influence of industry funding on reported accuracy are unknown. Peer-reviewed diagnostic test accuracy (DTA) studies were identified through a systematic review on Pubmed and Embase. Diagnostic performance measures were extracted especially for different subgroups such as product, type of rater (stand-alone AI, human unaided, human aided), funding, and anatomical region. Pooled measures were obtained with a bivariate random effects model. The impact of rater was evaluated with comparative meta-analysis. Seventeen DTA studies of seven CAAI-FDS analyzing 38,978 x-rays with 8,150 fractures were included. Stand-alone AI studies (n = 15) evaluated five CAAI-FDS; four with good sensitivities (> 90%) and moderate specificities (80–90%) and one with very poor sensitivity (< 60%) and excellent specificity (> 95%). Pooled sensitivities were good to excellent, and specificities were moderate to good in all anatomical regions (n = 7) apart from ribs (n = 4; poor sensitivity / moderate specificity) and spine (n = 4; excellent sensitivity / poor specificity). Funded studies (n = 4) had higher sensitivity (+ 5%) and lower specificity (-4%) than non-funded studies (n = 11). Sensitivity did not differ significantly between stand-alone AI and human AI aided ratings (p = 0.316) but specificity was significantly higher the latter group (p < 0.001). Sensitivity was significant lower in human unaided compared to human AI aided respectively stand-alone AI ratings (both p ≤ 0.001); specificity was higher in human unaided ratings compared to stand-alone AI (p < 0.001) and showed no significant differences AI aided ratings (p = 0.316). The study demonstrates good diagnostic accuracy across most CAAI-FDS and anatomical regions, with the highest performance achieved when used in conjunction with human assessment. Diagnostic accuracy appears lower for spine and rib fractures. The impact of industry funding on reported performance is small. [ABSTRACT FROM AUTHOR]

BACKGROUND A successfully developed topical botulinum toxin product would overcome the disadvantages of injectable administration commonly seen with all available botulinum products, including the potential pain, bleeding, and bruising associated with injection. OBJECTIVE Identify the disadvantages of injectable administration of botulinum products and advantages of topical administration. Identify previous attempts to develop topical products and review the only topical product candidate (ET-01, abbreviated EMX) under clinical development. MATERIALS AND METHODS A literature review regarding injectable botulinum products and topical botulinum product candidates and an analysis of data regarding EMX development were performed. RESULTS Multiple disadvantages of injectable administration and advantages of topical administration of botulinumtoxin were identified. If available, topical botulinum products are expected to expand the clinical use of botulinum toxin. Several previous attempts to develop topical products were identified. EMX has been studied for lateral canthal line treatment in multiple controlled phase 2 clinical trials including over 700 subjects. EMX has demonstrated statistically significant positive results. CONCLUSION The development of a topical botulinum toxin is appealing because it overcomes multiple disadvantages observed with injectable administration and would likely result in increased clinical use of botulinum toxin. The development of EMX shows promise and merits further study. [ABSTRACT FROM AUTHOR]

Studies have found that competitive products' online review ratings (ORRs) have a spillover effect on the focal product's sales. However, the spillover effect of online review sentiment (ORS) as an essential component of online review analysis has yet to be studied. In this study, we analyze online review content from JD.com using the latent Dirichlet allocation to identify the product attribute topics that consumers are most concerned about. We then construct a baseline regression model of ORS and ORRs to explore the effects of online competitive product reviews on focal product sales. Moreover, we examine how the interaction between ORS and critical factors of online reviews affect sales. Our results indicate that the ORS of competitive products has a negative effect on focal product sales, and the effect is greater than the ORS and ORRs of focal products, respectively. In addition, the ORS of competitive products inhibits the sale of focal products as evaluations of product attributes become more positive or online review usefulness increases. We also find that the effect of ORRs of competitive products is not significant, which may be because clothing, as an experiential product, requires consumers to gain more information about specific usage scenarios before making a decision. This study provides a more accurate basis for consumer decision-making and offers retailers a novel approach to developing marketing strategies. [ABSTRACT FROM AUTHOR]

Summary: The baking industry is greatly interested in incorporating banana peel and mango peel into a broader spectrum of baked goods. This review delves into the innovative utilisation of banana and mango peels in various baked products, specifically focusing on biscuits, cookies, cakes, and cake doughnuts. Incorporating banana peel in baked goods has gained attention for its potential to enhance flavour, texture, and nutritional value. Similarly, mango peel has been explored for its unique properties and potential contributions to baked items. This review emphasises the health benefits of banana and mango peels, including antioxidants, fibre, and other nutritional advantages that help prevent diseases and promote overall well‐being. Limited scientific studies may explore using banana peel and mango peel specifically as functional ingredients in baked products. This review highlights the utilisation of banana peel and mango peel in baked products, focusing on their influence on product quality, sensory characteristics, and potential health benefits. It presents a sustainable and innovative approach to creating healthier, more flavourful baked products. [ABSTRACT FROM AUTHOR]

Summary: Meat is widely regarded as the optimal source of protein for human consumption. However, meat is susceptible to spoilage and microbial contamination throughout processing, transportation and storage stages. Traditional thermal processing techniques are commonly employed in the global meat industry. While these methods ensure microbiological safety, they may also impact the sensory and functional attributes of meat products. To address these limitations, non‐thermal processing technologies are progressively replacing conventional thermal approaches. This review will primarily focus on the potential application of non‐thermal processing technologies as alternatives to traditional methods in meat and meat product processing. Various technologies exhibiting promising performance in this field will be presented. Additionally, the mechanisms, advantages and limitations of these non‐thermal technologies in recent years will be summarised, along with a discussion on the challenges and prospects they present. This study provides an overview of non‐thermal processing technology specifically applied to meat products, emphasising the underlying mechanisms associated with different approaches. By employing diverse technical combinations, it is possible to address existing processing limitations and expand their applicability range. Non‐thermal processing technology demonstrates significant potential for enhancing meat processing practices; however, concerns regarding safety aspects and limited clinical verification about to environmental pollution have impeded its widespread adoption within the food industry. [ABSTRACT FROM AUTHOR]

Many online platforms are now offering free samples to seasoned reviewers, hoping to get feedback. While these reviewers are given free samples to review, they also buy and review products themselves. The regular ratings for the purchased products are the majority. This brings up the question: Does receiving free products make them rate their personal purchases more positively? And if so, why? We explored two possibilities. First, uncertainty reduction mechanism: The idea that trying free samples makes buyers more confident in their purchases, leading to greater satisfaction and higher ratings for the purchased products; Second, reciprocity mechanism: The idea that reviewers might feel obliged to give better ratings as a "thank you" for the free samples or with the expectations of getting more free samples, which could introduce bias. Our research indicates that giving free samples mainly helps in reducing purchase uncertainty, making customers genuinely happier with their subsequent purchases. So, online platforms can benefit from this strategy, as it seems to uplift genuine positive reviews rather than create biased ones. However, it is still essential to monitor for any undue bias to maintain trustworthiness in reviews. Many e-commerce platforms have launched reviewer incentive programs, in which the platforms send free samples to experienced reviewers in exchange for feedback on the samples. This study focuses on an unexplored aspect of this practice: the impact of free samples on reviewers' ratings of subsequently purchased products (i.e., regular ratings). We theorize that free samples can increase regular ratings through two mechanisms: uncertainty reduction and reciprocity. We collect observational data from Amazon.com and find that after beginning to receive free samples from a platform-initiated reviewer incentive program (i.e., Vine), reviewers' regular ratings increased by 2.25% on average (i.e., 0.093 more stars on the five-star scale). Our follow-up analyses at a granular product-category level reveal that the increase in regular ratings occurs primarily for purchased products that share common attributes with free samples that reviewer has received previously. To further delve into the mechanisms at work, we conduct a randomized controlled experiment in which participants are asked to purchase one cookie among three flavor options. Before the cookie purchase, participants either receive nothing in advance (i.e., control group) or receive one of four free samples (i.e., four treatment groups). The results indicate that uncertainty reduction (via the common attribute between the free sample and the options for purchase) significantly mediates the impact of the free sample on the ratings of the purchased cookie. We conclude that platform-initiated free samples can help consumers understand products with similar attributes, thereby reducing prepurchase uncertainty and leading to higher regular ratings. We discuss the managerial and scholarly implications of the findings. History: Ravi Bapna, Senior Editor; Zhenhui (Jack) Jiang, Associate Editor. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72121001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2019.0176. [ABSTRACT FROM AUTHOR]

The significance of product attributes, as reflected in online reviews during the initial purchase, may undergo changes following positive or negative product usage experiences. The purpose of this research was to investigate whether positive or negative product consumption experiences influence shifts in the perceived importance of product attributes and subsequent product choices, based on online quality ratings by attributes. A series of experiments demonstrated that the importance associated with an attribute's expectation in the pre‐purchase process shifted after a negative consumption experience with that attribute. The results showed that a change in both attribute importance and product choice following a negative attribute experience. A less important attribute that malfunctioned during the consumption experience became more crucial in the subsequent purchase (Study 1). However, this shift in importance may not be universal for all attributes; it was observed only when the attribute was moderately important (Study 2). This study contributes valuable empirical findings to the existing literature on online reviews by highlighting dynamic shifts in the importance weight of product attributes. E‐commerce practitioners are encouraged to allow formal purchasers to leave product reviews without time constraints, providing a platform for reflecting users' lifelong consumption experiences. [ABSTRACT FROM AUTHOR]

Marketers are concerned about how consumers' readings of product reviews and product rating statistics affect product evaluations. Previous studies generally regard product review content and rating statistics as affecting product evaluations independently. This may not be true. Typically, consumers enter a review section with pre‐existing positive expectations about a product. When they see a negative average rating in this section, their expectancies are disconfirmed. Their surprise stemming from the disconfirmation makes them read the reviews more carefully. Thus, review reading affects product evaluation more strongly when viewing a negative average rating. Conversely, a positive average rating results in expectancy confirmation, which weakens the effect of review reading. An eye‐tracking experiment using a mock‐resort review section supported these predictions. These findings have implications for marketers in behavioral advertising, responding to reviews, and consumers writing reviews. [ABSTRACT FROM AUTHOR]

Detecting suggestions in online review requires contextual understanding of review text, which is an important real-world application of natural language processing. Given the disparate text domains found in product reviews, a common strategy involves fine-tuning bidirectional encoder representations from transformers (BERT) models using reviews from various domains. However, there hasn't been an empirical examination of how BERT models behave across different domains in tasks related to detecting suggestion sentences from online reviews. In this study, we explore BERT models for suggestion classification that have been fine-tuned using single-domain and cross-domain Amazon review datasets. Our results indicate that while single-domain models achieved slightly better performance within their respective domains compared to cross-domain models, the latter outperformed single-domain models when evaluated on cross-domain data. This was also observed for single-domain data not used for fine-tuning the single-domain model and on average across all tests. Although fine-tuning single-domain models can lead to minor accuracy improvements, employing multi-domain models that perform well across domains can help in cold start problems and reduce annotation costs. [ABSTRACT FROM AUTHOR]

Acquiring a single sentiment score dependent on all the reviews will benefit the buyers and sellers in making the decision more accurately. The raw format of user-generated content lacks a legitimate language structure. It, therefore, acts as an obstacle for applying the Sentiment analysis task, which aims to predict the true emotion of a sentence by providing a score and its nature. This paper concentrates on obtaining a single sentiment score using a hybrid Long Short-Term Memory encoder–decoder model. This research uses the text normalization process to transform the sentences consisting of noise, appearing as incorrect grammar, abbreviations, freestyle, and typographical errors, into their canonical structure. The experimental outcomes confirm that the proposed hybrid model performs well in standardizing the raw E-commerce website review, enriched with hidden information and provided a single sentiment score influenced by all the review scores for the product. [ABSTRACT FROM AUTHOR]

This article presents validation and conformity testing of the Sentinel-3 Ocean Land Colour Instrument (OLCI) green instantaneous fraction of absorbed photosynthetically active radiation (FAPAR) and OLCI terrestrial chlorophyll index (OTCI) canopy chlorophyll content (CCC) products with fiducial reference measurements (FRM) collected in 2018 and 2021 over two sites (Las Tiesas—Barrax, Spain, and Wytham Woods, UK) in the context of the European Space Agency (ESA) Fiducial Reference Measurement for Vegetation (FRM4Veg) initiative. Following metrological principles, an end-to-end uncertainty evaluation framework developed in the project is used to account for the uncertainty of reference data based on a two-stage validation approach. The process involves quantifying uncertainties at the elementary sampling unit (ESU) level and incorporating these uncertainties in the upscaling procedures using orthogonal distance regression (ODR) between FRM and vegetation indices derived from Sentinel-2 data. Uncertainties in the Sentinel-2 data are also accounted for. FRM-based high spatial resolution reference maps and their uncertainties were aggregated to OLCI's native spatial resolution using its apparent point spread function (PSF). The Sentinel-3 mission requirements, which give an uncertainty of 5% (goal) and 10% (threshold), were considered for conformity testing. GIFAPAR validation results revealed correlations > 0.95, RMSD ~0.1, and a slight negative bias (~−0.06) for both sites. This bias could be partly explained by the differences in the FAPAR definitions between the satellite product and the FRM-based reference. For the OTCI-based CCC, leave-one-out cross-validation demonstrated correlations > 0.8 and RMSDcv ~0.28 g·m−2. Despite the encouraging validation results, conclusive conformity with the strict mission requirements was low, with most cases providing inconclusive results (driven by large uncertainties in the satellite products as well as by the uncertainties in the upscaling approach). It is recommended that mission requirements for bio-geophysical products are reviewed, at least at the threshold level. It is also suggested that the large uncertainties associated with the two-stage validation approach may be avoided by directly comparing with spatially representative FRM. [ABSTRACT FROM AUTHOR]

This study examines how unexpected exogenous events, labelled as suprises , affect the utility of experience goods reported in online rating systems. Using over 300,000 reviews of accommodation services listed on Booking.com, the research investigates whether online ratings capture the impact of surprises related to meteorological conditions and whether they create additional biases in service evaluation. The study finds that sudden changes in weather conditions have a significant impact on experienced utility, with the effect varying based on the direction of the surprise. Additionally, in line with the hedonic adaption theory, we find that the duration of consumption moderates the surprise effect, reducing its impact on reported utility. • Online reviews signal product/service quality. • External factors may influence consumers' quality assessments. • We perform an empirical analysis of unexpected externalities on experience goods evaluations. • Positive/negative surprises have asymmetric impacts on reported utility. • Surprise effects on utility are moderated by the consumption span. [ABSTRACT FROM AUTHOR]

Food waste production reaches actually about 1.3 billion tonnes per year, corresponding to the emission of 3.3 billion tonnes equivalent of CO2, thus calling for improved recycling. Here we review food waste conversion into energy and products such as biohydrogen, biogas, biofuel, biodiesel, biochar, bioplastics, fertilizers, animal feed, organic acids, enzymes, and proteins. Food waste can be treated by incineration, pyrolysis, composting, anaerobic digestion, hydrothermal carbonization, and landfilling. Properties of food waste influence the efficiency of conversion. [ABSTRACT FROM AUTHOR]

The abundance of publicly available data on the internet within the e-marketing domain is consistently expanding. A significant portion of this data revolve around consumers' perceptions and opinions regarding the goods or services of organizations, making it valuable for market intelligence collectors in marketing, customer relationship management, and customer retention. Sentiment analysis serves as a tool for examining customer sentiment, marketing initiatives, and product appraisals. This valuable information can inform decisions related to future product and service development, marketing campaigns, and customer service enhancements. In social media, predicting ratings is commonly employed to anticipate product ratings based on user reviews. Our study provides an extensive benchmark comparison of different deep learning models, including convolutional neural networks (CNN), recurrent neural networks (RNN), and bi-directional long short-term memory (Bi-LSTM). These models are evaluated using various word embedding techniques, such as bi-directional encoder representations from transformers (BERT) and its derivatives, FastText, and Word2Vec. The evaluation encompasses two setups: 5-class versus 3-class. This paper focuses on sentiment analysis using neural network-based models for consumer sentiment prediction by evaluating and contrasting their performance indicators on a dataset of reviews of different products from customers of an online women's clothes retailer. [ABSTRACT FROM AUTHOR]

As the online market grows rapidly, people are relying more on product review when they purchase the product. Hence, many companies and researchers are interested in analyzing product review which essentially a text data. In the current literature, it is common to use only text analysis tools to analyze text dataset. But in our work, we propose a method that utilizes both text analysis method such as topic modeling and statistical network model to build network among individuals and find interesting communities. We introduce a promising framework that incorporates topic modeling technique to define the edges among the individuals and form a network and uses stochastic blockmodels (SBM) to find the communities. The power of our proposed method is demonstrated in real-world application to Amazon product review dataset. [ABSTRACT FROM AUTHOR]

Chinese bayberry (Myrica rubra Sieb. et Zucc.) fruit (CBF) is widely growing in eastern Asia, mainly in China. CBFs contain a high number of polyphenols, especially anthocyanins. However, CBF can be easily damaged and cannot be stored for more than 3 days at 20–22 °C and 9–12 days at 0–2 °C. Thus, this review explores the potential options for the long-term preservation and consumption of CBFs, as well as the importance of CBF by-products. The review also addressed the biological activity of CBFs and their food products. The consumption period of CBF can be increased by employing a variety of processing techniques, including rapid freezing, drying, juicing, making wine and vinegar, and adding it to meals. Additionally, oil and bioactive substances can be extracted from the CBF by-products, such as kernel and pomace. The greatest options to extend the duration of CBF ingestion are thought to be CBF powder, juice, and wine. Furthermore, CBFs and their derivatives exhibit many biological activities such as antibacterial, anticancer, antidiabetic, and antioxidant properties. The two biological activities that were most potent were antidiabetic and antioxidant. However, more biological research, particularly clinical-based research, is advised to investigate the potential health benefits of CBF and its products. [ABSTRACT FROM AUTHOR]

Social connections are frequently leveraged as supplementary data to enhance recommendation systems (RS). In real-world scenarios, the relationships between multiple users within social networks exhibit considerable complexity and diversity. However, prevalent recommendation approaches based on graphs tend to assume pairwise interactions to uncover user preferences. This approach overlooks the influence of intricate and multifaceted social relationships on user preferences, representing a neglect of the higher-order complexities inherent in user relations. It is also observed that most approaches neglect the fact that similar items have similar shared interests when grouped with users. This shows that there is an active connection among the attributes of items and users. Hence, this study works on the drawback of current Social RS: 1. Relations among the items are not explored well, 2. Social relations and homogeneity are not diverse. To address these issues, this study introduces a novel methodology based on clustering and heterogenous hypergraph, termed as HyperSegRec. HyperSegRec (Hypergraph Segmentation based RS) encompasses three parallel processes: User segmentation through Clustering, Item Similarity through extended graph using the cosine similarity matrix method, and a Weighted aggregation process, these processes models the user-item (where user-user as well as user-item relations is mined through clustering and hypergraph representation) and the item-item relations are mined through an extended graph matrix which is constructed with the cosine similarity method and hypergraph representation. These process in the proposed approach are trained individually over BPR (Bayesian Personalized Ranking) based loss function and evaluated against state-of-the-art models based on Recall and NDCG for top-K (K = 10, 20, 50) Recommendations. The proposed model is evaluated on two datasets: Movieslens 100 k and Amazon beauty products review. Compared to other state-of-the-art models, HyperSegRec achieves a 9.92% improvement on NDGC@50. Hence, HyperSegRec demonstrates superior performance in capturing user preferences and providing precise recommendations compared to existing state-of-the-art models, showcasing its potential to enhance recommendation systems for diverse datasets. [ABSTRACT FROM AUTHOR]

We investigate the strategic role of a recommender who cares about accuracy and whose recommendations influence product quality. In the presence of such feedback effects, recommendations have a self-fulling property: the recommendation agent can select any firm that will end up being the firm with the best quality. This produces important inefficiencies that include (a) a lack of incentive to acquire valuable information, (b) a status quo bias, and (c) the avoidance of risky innovations. Monetary payments from firms may work in mitigating these inefficiencies, while competition between recommenders and monetary payments from consumers are ineffective. [ABSTRACT FROM AUTHOR]

In this work, we evaluated the efficacy of Google’s Pathways Language Model (GooglePaLM) in analyzing sentiments expressed in product reviews. Although conventional Natural Language Processing (NLP) techniques such as the rule-based Valence Aware Dictionary for Sentiment Reasoning (VADER) and the long sequence Bidirectional Encoder Representations from Transformers (BERT) model are effective, they frequently encounter difficulties when dealing with intricate linguistic features like sarcasm and contextual nuances commonly found in customer feedback. We performed a sentiment analysis on Amazon’s fashion review datasets using the VADER, BERT, and GooglePaLM models, respectively, and compared the results based on evaluation metrics such as precision, recall, accuracy correct positive prediction, and correct negative prediction. We used the default values of the VADER and BERT models and slightly finetuned GooglePaLM with a Temperature of 0.0 and an N-value of 1. We observed that GooglePaLM performed better with correct positive and negative prediction values of 0.91 and 0.93, respectively, followed by BERT and VADER. We concluded that large language models surpass traditional rule-based systems for natural language processing tasks.

AbstractThe presence of impurities in active pharmaceutical ingredients (APIs) and drug products represents a risk to patients’ health. Such substances are related to diverse side effects and may have mutagenic potential. That’s why it is necessary to establish acceptable limits for these by-products, to minimize the risk associated with medicinal therapy. This work focused on presenting a critical review of relevant points related to the presence of impurities in pharmaceuticals. The main legislation and guidelines from the FDA, EMA, ICH, and Pharmacopeias about the subject were evaluated, and recent articles related to the topic were searched in Scopus, ScienceDirect, PubMed, and Web of Science from 2013 to 2023. Additionally, the analytical techniques used for quantifying impurities were discussed, along with relevant tests for assessing the toxicological and mutagenic risks of these by-products. Recent legislation, including ICH Q3A (R2), ICH Q3B (R2), ICH M7 (R2), ICH Q3D (R2), ICH Q3C (R9), ICH Q3E, ICH Q6A, ICH M3 (R2), as well as FDA and EMA guidelines, highlights a comprehensive and effective framework for controlling impurities in pharmaceuticals. Despite this, there remains a lack of harmonization and standardized procedures across different regions. From the review of scientific literature, we observed that advancements in analytical techniques have significantly improved the sensitivity and selectivity in detecting impurities and degradation products. This underscores the ongoing commitment of health agencies and the pharmaceutical industry to ensure the safety and efficacy of medicinal products. [ABSTRACT FROM AUTHOR]

Nowadays, online shopping has become a typical option for customers to make purchases due to the rapid technological development of the Internet. Sentiment analysis (SA) of a huge count of user reviews on e-commerce sites can successfully increase the fulfillment of user, but it is difficult so far to predict the precise sentiment polarizations of the user evaluations owing to the variations on textual arrangement, sequence length, and complex logic. In this manuscript, Inception Recurrent Convolutional Neural Network with ResNet Transfer Learning by Red Fox Optimization Algorithm using sentiment analysis on product review (IRCNN-ResNet-RFOA-SA-PR) is proposed. Here, the input data are collected through Amazon Product Reviews database and then preprocessing the input data by using whitespace tokenization, Snowball stemming, and Gensim lemmatization. The preprocessing data are supplied to the ternary pattern and discrete wavelet transform for feature extraction. Then the optimal features are selected by Fire Hawk Optimization Algorithm. After that, IRCNN-ResNet recommends the product based on SA. Finally, IRCNN-ResNet parameter is optimized by RFOA. The proposed method attains 22.37%, 31.08%, and 21.90% greater accuracy and 19.37%, 21.08%, and 25.40% greater precision when compared with existing models, such as machine learning-based SA-PR including new term weighting with feature selection mode, weighted word embedding with deep neural network-based SA-PR, and SA for e-commerce PRs in Chinese utilizing sentiment lexicon with deep learning, respectively. [ABSTRACT FROM AUTHOR]

Online product reviews (OPR) are a commonly used medium for consumers to communicate their experiences with products during online shopping. Previous studies have investigated the helpfulness of OPRs using frequency-based, linguistic, meta-data, readability, and reviewer attributes. In this study, we explored the impact of robust contextual word embeddings, topic, and language models in predicting the helpfulness of OPRs. In addition, the wrapper-based feature selection technique is employed to select effective subsets from each type of features. Five feature generation techniques including word2vec, FastText, Global Vectors for Word Representation (GloVe), Latent Dirichlet Allocation (LDA), and Embeddings from Language Models (ELMo), were employed. The proposed framework is evaluated on two Amazon datasets (Video games and Health & personal care). The results showed that the ELMo model outperformed the six standard baselines, including the fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. In addition, ELMo achieved Mean Square Error (MSE) of 0.0887 and 0.0786 respectively on two datasets and MSE of 0.0791 and 0.0708 with the wrapper method. This results in the reduction of 1.43% and 1.63% in MSE as compared to the fine-tuned BERT model on respective datasets. However, the LDA model has a comparable performance with the fine-tuned BERT model but outperforms the other five baselines. The proposed framework demonstrated good generalization abilities by uncovering important factors of product reviews and can be evaluated on other voting platforms. [ABSTRACT FROM AUTHOR]

Nematophagous fungi have been widely evaluated in the biological control of parasitic helminths in animals, both through their direct use and the use of their derived products. Fungal bioproducts can include extracellular enzymes, silver nanoparticles (AgNPs), as well as secondary metabolites. The aim of this study was to conduct a systematic review covering the evaluation of products derived from nematophagous fungi in the biological control of parasitic helminths in animals. In total, 33 studies met the inclusion criteria and were included in this review. The majority of the studies were conducted in Brazil (72.7%, 24/33), and bioproducts derived from the fungus Duddingtonia flagrans were the most commonly evaluated (36.3%, 12/33). The studies involved the production of extracellular enzymes (48.4%, 16/33), followed by crude enzymatic extract (27.2%, 9/33), secondary metabolites (15.1%, 5/33) and biosynthesis of AgNPs (9.1%, 3/33). The most researched extracellular enzymes were serine proteases (37.5%, 6/16), with efficacies ranging from 23.9 to 85%; proteases (31.2%, 5/16), with efficacies from 41.4 to 95.4%; proteases + chitinases (18.7%, 3/16), with efficacies from 20.5 to 43.4%; and chitinases (12.5%, 2/16), with efficacies ranging from 12 to 100%. In conclusion, extracellular enzymes are the most investigated derivatives of nematophagous fungi, with proteases being promising strategies in the biological control of animal helminths. Further studies under in vivo and field conditions are needed to explore the applicability of these bioproducts as tools for biological control. [ABSTRACT FROM AUTHOR]

Purpose: This research analyzes online consumer reviews and ratings to assess e-retail order fulfillment performance. The study aims to (1) identify consumer journey touchpoints in the order fulfillment process and (2) determine their relative importance for the consumer experience. Design/methodology/approach: Text mining and analytics were employed to examine over 100 m online purchase orders, along with associated consumer reviews and ratings from Amazon US. Using natural language processing techniques, the corpus of reviews was structured to pinpoint touchpoints related to order fulfillment. Reviews were then classified according to their stance (either positive or negative) toward these touchpoints. Finally, the classes were correlated with consumer rating, measured by the number of stars, to determine the relative importance of each touchpoint. Findings: The study reveals 12 touchpoints within the order fulfillment process, which are split into three groups: delivery, packaging and returns. These touchpoints significantly influence star ratings: positive experiences elevate them, while negative ones reduce them. The findings provide a quantifiable measure of these effects, articulated in terms of star ratings, which directly reflect the influence of experiences on consumer evaluations. Research limitations/implications: The dataset utilized in this study is from the US market, which limits the generalizability of the findings to other markets. Moreover, the novel methodology used to map and quantify customer journey touchpoints requires further refinement. Practical implications: In e-retail and logistics, comprehending touchpoints in the order fulfillment process is pivotal. This understanding helps improve consumer interactions and enhance satisfaction. Such insights not only drive higher conversion rates but also guide informed managerial decisions, particularly in service development. Originality/value: Drawing upon consumer-generated data, this research identifies a cohesive set of touchpoints within the order fulfillment process and quantitatively evaluates their influence on consumer experience using star ratings as a metric. [ABSTRACT FROM AUTHOR]

Purpose: This research aims to examine the factors that influence customers' product return intentions and proposes that YouTube product reviews can mitigate customers' desire to return a product. Design/methodology/approach: The proposed theoretical research model and hypothesized relationship were investigated using a quantitative process. This study used 302 data from Indonesian young adult respondents to examine the structural model, which was analyzed using the SmartPLS 3.2 software package. Findings: The results show that YouTube product reviews, product fit uncertainty and customer satisfaction are the key determinants of customers' product return intention. Furthermore, the results show that the credibility of YouTube product reviews has a major impact on customers' familiarity with a product, satisfaction and the likelihood of returning goods to sellers. Practical implications: In the e-commerce industry, increasing the use of YouTube product reviews will help businesses eliminate unnecessary product returns. Sellers are also encouraged to collaborate with YouTube producers to review specific products, which can benefit companies by raising brand awareness and gaining customer feedback. Furthermore, YouTube online product reviews can help consumers avoid having an unpleasant shopping experience that causes emotional reactions and lowers satisfaction. Originality/value: Most research has not considered antecedents in observing the product return phenomenon; this study observes a prerequisite of consumer product returns (i.e. information asymmetry and product familiarity) and investigates the relationships between YouTube product reviews, customer satisfaction and product return intention. [ABSTRACT FROM AUTHOR]

Fermented camel milk, named shubat in Central Asia, is historically and culturally important because it is mainly consumed by Kazakh people who live not only in Kazakhstan but also in close neighboring countries. However, despite its cultural and dietetic significance for this local population, research on its composition and processing technology and the richness of its microflora is relatively scarce. The present review of this product, which is an important beverage in the Kazakh culture, provides up-to-date information regarding its main components and their variability according to different factors, surveys recent changes in the processing technologies for making it using modern techniques, and explores the biodiversity of its microflora. It was reported that the protein, vitamin C, and calcium contents in shubat vary between 1.19 and 5.63%, 28 and 417 mg L−1, and 1.03 and 1.88 g L−1. The lactose content totally disappears. Shubat contains a complex microbial consortium that contributes to its strong reputation for health benefits, but a scientific demonstration of these claims has only been partially achieved. [ABSTRACT FROM AUTHOR]

In order to contribute to an assessment of the role of food in the risks of transmission of methicillin-resistant Staphylococcus aureus (MRSA), a review was undertaken of research on this microorganism in milk and dairy products published from January 2001 to February 2024. A total of 186 publications were selected, 125 for dairy products and 61 for bulk-tank milk (BTM). MRSA was detected in 68.8% of the research into dairy products and 73.8% of investigations relating to BTM, although in most studies the prevalence was less than 5%. Of the set of S. aureus strains isolated, approximately 30% corresponded to MRSA. The foods most extensively contaminated with this microorganism were raw milk and some types of soft cheese. Determination of the mecA gene on its own is known not to suffice for the detection of all MRSA strains. The great diversity of techniques used to study MRSA in milk and dairy products made it difficult to draw comparisons between studies. It would thus be advisable to develop a standardized protocol for the study of this microorganism in foods. [ABSTRACT FROM AUTHOR]

Aquatic products are easily spoiled during storage due to oxidation, endogenous enzymes, and bacteria. At the same time, compared with synthetic antioxidants, based on the antibacterial and antioxidant mechanism of biological agents, the development of natural, nontoxic, low-temperature, better-effect green biological preservatives is more acceptable to consumers. The type and molecular structure of polyphenols affect their antioxidant and antibacterial effectiveness. This review will describe how they achieve their antioxidant and antibacterial effects. And the recent literature on the mechanism and application of polyphenols in the preservation of aquatic products was updated and summarized. The conclusion is that in aquatic products, polyphenols alleviate lipid oxidation, protein degradation and inhibit the growth and reproduction of microorganisms, so as to achieve the effect of storage quality control. And put forward suggestions on the application of the research results in aquatic products. We hope to provide theoretical support for better exploration of the application of polyphenols and aquatic product storage. [ABSTRACT FROM AUTHOR]

AbstractConsumers are increasingly interested in additive-free products with a fresh taste, leading to a growing trend in high pressure processing (HPP) as an alternative to thermal processing. This review explores the impact of HPP on the properties of juices, smoothies, and purees, as well as its practical applications in the food industry. Research findings have explained that HPP is a most promising technology in comparison to thermal processing, in two ways i.e., for ensuring microbial safety and maximum retention of micro and macro nutrients and functional components. HPP preserves natural color and eliminates the need for artificial coloring. The review also emphasizes its potential for enhancing flavor in the beverage industry. The review also discusses how HPP indirectly affects plant enzymes that cause off-flavors and suggests potential hurdle approaches for enzyme inactivation based on research investigations. Scientific studies regarding the improved quality insights on commercially operated high pressure mechanisms concerning nutrient retention have paved the way for upscaling and boosted the market demand for HPP equipment. In future research, the clear focus should be on scientific parameters and sensory attributes related to consumer acceptability and perception for better clarity of the HPP effect on juice and smoothies/purees. [ABSTRACT FROM AUTHOR]

Ozone treatment is a non-thermal method for disinfection; ozone is a powerful oxidizing agent that has been shown to be effective in reducing microbial load, extending the shelf life of meat products. This mini-review covers the analysis of the antimicrobial ozone activity in different meats (beef, poultry, pork, seafood, etc.), emphasizing the ozone application methods (liquid or gaseous phase), the applied concentrations and contact times and the effects of ozone treatment onmeat quality, safety, and sensory properties. It has been demonstrated that ozone is effective against a broad range of microorganisms, including both Gram-positive and Gram-negative bacteria, spores, and vegetative cells. The efficacy of ozone depends on various factors, such as concentration, type of treatment, temperature, and presence of organic material. Ozone treatment, known for its rapid decomposition and lack of residue, provides an environmentally friendly alternative to traditional chemical sanitizers. Ozone treatments exhibit promising results in enhancing the safety and extending the shelf life of meat products. According to the findings, the application of ozone is an effective technology for prolonging the shelf life of different types of meats and meat products, requiring careful establishment of conditions on a case-by-case basis. [ABSTRACT FROM AUTHOR]

The Room-Effect originated in environmental psychology, where the room a person was in influences how they were judged by people. Subsequent studies extended this to the Room-Product-Effect indicating its application beyond the original domain of rooms or environments to designed products. It provides a new research method for investigating the effect a product has on the person associated with it. However, no review exists summarizing the research into the Room-Product-Effect from its inception to the present. Therefore, this paper provides a systematic review that elucidates the origin, and evolution of the Room-Product-Effect, while also describing the research methods employed in its investigation. To accomplish this, six databases (Scopus, Web of Science, ScienceDirect, JStor, ProQuest, China National Knowledge Infrastructure) were used to search and locate relevant studies (the search was conducted from the time of database construction to 1 June 2023). A total of 7080 articles were retrieved. Eighteen articles that met the inclusion criteria were identified and included. This review introduces the concept, its origins, the development of the Room-Effect and the subsequent Room-Product-Effect. Additionally, ongoing research into the Room-Product-Effect are identified. [ABSTRACT FROM AUTHOR]

In recent times, online shopping has become commonly used method for consumers to make purchases and engage in consumption with the rapid advancement of Internet technology. Enhancing user satisfaction is achievable through sentiment analysis (SA) of the enormous user evaluations found on e-commerce platforms. However, accurately predicting the sentiment orientations of these user reviews remains a challenge due to varying sequence lengths, text arrangements, and intricate logic. Nowadays, sentiment analysis is widely employed to assess customer feedback, which holds great significance in determining a product's success. In the past, people relied on word-of-mouth reviews to judge a product's quality. This practice of sentiment analysis is extensively applied in social media. Natural language processing (NLP) plays a crucial role in deciphering sentiment, also referred to as opinion mining or emotion AI, as it encompasses the collective perception of customers. In this manuscript, a Hamiltonian Deep Neural Networks-based Sentiment Analysis on Product Recommendation System (HDNN-SCOA-SA-PR) is proposed. First, the data are gathered from Amazon Product Reviews dataset. Then the data are pre-processed utilizing adaptive self-guided filtering for space tokenization, Gensim lemmatization, and Snowball stemming. By using Structured Optimal Graph-Based Sparse Feature Extraction, the features are extracted. Extracted features are selected using Single Candidate Optimization Algorithm. Finally, the classification process is done using Hamiltonian deep neural network and classified sentiment analysis as positive, negative, neutral. The proposed HDNN-SCOA-SA-PR method is activated in Python, and the efficiency of the proposed method is analyzed with different metrics, such as accuracy, sensitivity, RoC, precision, error rate, F1-score,computation time. ROC is evaluated and compared to the existing methods, such as sentiment analysis based upon machine learning of online product reviews with term weighting including feature selection (SAPR-FS-ENN), sentiment analysis of product reviews depend upon weighted word embeddings along deep neural networks (SAPR-WWE-DNN), improving sentiment analysis for social media applications utilizing an ensemble deep learning language (ISA-SMA-ECN-PR), respectively. [ABSTRACT FROM AUTHOR]

Food fraud is a major threat to the integrity of the nut supply chain. Strategies using a wide range of analytical techniques have been developed over the past few years to detect fraud and to assure the quality, safety, and authenticity of nut products. However, most of these techniques present the limitations of being slow and destructive and entailing a high cost per analysis. Nevertheless, near-infrared (NIR) spectroscopy and NIR imaging techniques represent a suitable non-destructive alternative to prevent fraud in the nut industry with the advantages of a high throughput and low cost per analysis. This review collects and includes all major findings of all of the published studies focused on the application of NIR spectroscopy and NIR imaging technologies to detect fraud in the nut supply chain from 2018 onwards. The results suggest that NIR spectroscopy and NIR imaging are suitable technologies to detect the main types of fraud in nuts. [ABSTRACT FROM AUTHOR]

In this work, we evaluated the efficacy of Google's Pathways Language Model (GooglePaLM) in analyzing sentiments expressed in product reviews. Although conventional Natural Language Processing (NLP) techniques such as the rule-based Valence Aware Dictionary for Sentiment Reasoning (VADER) and the long sequence Bidirectional Encoder Representations from Transformers (BERT) model are effective, they frequently encounter difficulties when dealing with intricate linguistic features like sarcasm and contextual nuances commonly found in customer feedback. We performed a sentiment analysis on Amazon's fashion review datasets using the VADER, BERT, and GooglePaLM models, respectively, and compared the results based on evaluation metrics such as precision, recall, accuracy correct positive prediction, and correct negative prediction. We used the default values of the VADER and BERT models and slightly finetuned GooglePaLM with a Temperature of 0.0 and an N-value of 1. We observed that GooglePaLM performed better with correct positive and negative prediction values of 0.91 and 0.93, respectively, followed by BERT and VADER. We concluded that large language models surpass traditional rule-based systems for natural language processing tasks. [ABSTRACT FROM AUTHOR]

The rapid development of online products paves the way to share customers' opinions on amazon products. Unstructured text reviews and customer feedback are popular resources for customers when making decisions. However, reading through all the evaluations is tiresome, but the volume of customer feedback is enormous. The ability to forecast the precise sentiment polarities of user textual feedback evaluations for a particular entity is still difficult because of phrase length restrictions, textual order variations, and logical complexities. Therefore, an aspect level of analysis is needed, which support the retailers in understanding customer expectation and then modifying the product accordingly. However, many existing machine learning algorithms are available for sentiment detection but fail in accuracy rate. This paper proposes a novel sigTan-Beta Activation Function for Convolution Neural Networks (CNN) to attain remarkable and effective results. First, the sample dataset is pre-processed, and text strings are converted into the vector using Word2Vec, which computes the distance between words and groups them based on similarity. Afterwards, CNN extracts the sensitive features from the data and classifies the product reviews. The proposed model uses the sigTan-Beta Activation Function, which tunes the weight of the neurons to gain accurate performance. The proposed classified as positive or negative classes using the amazon review dataset. The proposed sigTan-Beta Activation Function for Convolution Neural Network (CNN) experiment performs better than existing methods in terms of accuracy, precision and F1-score. Our proposed sigTan-Beta Activation Function for Convolution Neural Network (CNN) achieves 94.5% accuracy to the existing ABO-RF algorithm (89.9%). [ABSTRACT FROM AUTHOR]

The placing of plant protection products (PPPs) on the market in the European Union is governed by numerous regulations. These regulations are among the most stringent in the world, however they have been the subject of criticisms especially because of the decline in biodiversity. The objectives of this work were to review (1) the functioning and actors involved in the PPP framework processes, (2) the construction of the environmental risk assessment focused on biodiversity, and (3) the suggested ways to respond to the identified limits. Both literature from social sciences and ecotoxicology were examined. Despite the protective nature of the European regulation on PPPs, the very imperfect consideration of biodiversity in the evaluation process was underlined. The main limits are the multiplicity of applicable rules, the routinization of the evaluation procedures, the lack of consideration of social data, and the lack of independence of the evaluation. Strengths of the regulation are the decision to integrate a systemic approach in the evaluation of PPPs, the development of modeling tools, and the phytopharmacovigilance systems. The avenues for improvement concern the realism of the risk assessment (species used, cocktail effects...), a greater transparency and independence in the conduct of evaluations, and the opening of the evaluation and decision-making processes to actors such as beekeepers or NGOs. Truly interdisciplinary reflections crossing the functioning of the living world, its alteration by PPPs, and how these elements question the users of PPPs would allow to specify social actions, public policies, and their regulation to better protect biodiversity. [ABSTRACT FROM AUTHOR]

Natural products play an important part in synthetic chemistry since they have many pharmacological properties and are used as active drug compounds in pharmaceutical chemistry. However, synthesis of these complex molecules is difficult due to the requirement of various synthetic steps, which include highly regio- and stereoselectivity. Therefore, oxidative radical cyclization assisted by manganese(III) acetate serves as an important step in obtaining spiro-, tricyclic, tetracyclic, and polycyclic derivatives of these compounds. Manganese(III)-based reactions offer a single-step regio- and stereoselective cyclizations and α-acetoxidations, reducing the number of synthetic steps. Also, the manganese(III)-mediated oxidative free radical cyclization method has been successfully applied for the synthesis of cyclic structures found in many natural products. This article presents a broad overview of manganese(III)-based radical reactions of natural products as a key step in overall synthesis. The authors have classified natural product synthesis processes assisted by manganese(III) acetate as intermolecular, intramolecular, oxidation, acetoxidation, aromatization, and polymerization reactions, respectively. [ABSTRACT FROM AUTHOR]

This review article addresses the antioxidant properties of different natural products, including ascorbic acid, gallic acid, oxalic acid, L-glutathione (GSH), bacteriorhodopsin, green tea polyphenols, glucose, hydroxycinnamic acid, ethanoic acid, betanin, and L-glutathione, in the reduction of graphene oxide (rGO). rGO can cause damage to cells, including oxidative stress and inflammation, limiting its application in different sectors that use graphene, such as technologies used in medicine and dentistry. The natural substances reviewed have properties that help reduce this damage, neutralizing free radicals and maintaining cellular integrity. This survey demonstrates that the combination of these antioxidant compounds can be an effective strategy to minimize the harmful effects of rGO and promote cellular health. [ABSTRACT FROM AUTHOR]

In this milieu, ozone technology has emerged as an avant-garde non-thermal mode of disinfection with potential applications in the food industry. This eco-friendly technology has a comprehendible adeptness in replacing alternative chemical sanitizers and is recognized as a generally safe disinfectant for fruits and vegetables. Several researchers have been focusing on the biochemical impacts of ozone on different quantitative and qualitative aspects of fruits and vegetables. A collection of those works is presented in this review highlighting the effect of ozone on the functional, antioxidant, and rheological properties of food. This can be a benevolent tool for discovering the processing states of ozone applications and ensuing influence on safety and quality attributes of previously studied foods and opening further research areas. It extends shelf life and never leaves any harmful residues on the product since it decomposes to form oxygen. It was seen that the impact on a specific property of food was dependent on the ozone concentration and treatment time, and the adverse effects of ozone exposure can be alleviated once the processing conditions are optimized. The present review can be used as a baseline for designing different food processing operations involving ozone. [ABSTRACT FROM AUTHOR]

Most of the review research papers previously published were mostly focused on solar dryer design, development, performance evaluation, modification, technologies of solar dryers, etc. There were no works of literature reviews that specifically concerned how much solar dryers can carry. So, the review gives some clues about the carrying capacity of solar dryers. Measuring or knowing solar capacity has critical importance in the drying industry. It helps to produce quality dried products, design efficient solar dryers, and provide valuable insights for researchers, engineers and policymakers involved in solar drying technologies. The current review systematically examines the relevant scientific literature published between 2000 and 2023. The exclusion and inclusion criteria were used to identify the documents. A total of 1230 studies were selected for analysis, encompassing a wide range of geographical regions, crop types, and solar drying technologies. Based on the review conducted; solar dryers (direct, indirect, mixed, and hybrid) can vary between 1 and 250 kg in capacity applied for agricultural products drying purposes. According to the reviewed articles, the minimum loading capacities designed and recorded in the first, second, third, and fourth quinquennial periods were 1 kg, 1 kg, 4.75 kg.m−2, and 5.4 kg.m−2, respectively. In the same order as the minimum, the maximum loading capacities observed in the stated quinquennial periods were 250 kg/per day, 250 kg, 70 kg, and 45 kg, respectively. [ABSTRACT FROM AUTHOR]