Traditional Recommendations Research Articles

Insomnia severity and obesity mediated by health behaviors in adolescents.

Sleep difficulties in youth have been associated with numerous negative outcomes, such as higher risk of obesity. Though the relationship between sleep and obesity is not well clarified, past research has shown that modifiable health behaviors, such as diet, physical activity, and screen time, may explain this relationship. The purpose of this study was to simultaneously examine these health behaviors as mediators of the relationship between insomnia severity and obesity among a sample of adolescents aged 12-18years. English-speaking parents/legal guardians of adolescents were invited to participate in an online survey focused on pediatric sleep patterns, health behaviors, and weight. Parents/guardians (N = 599) reported on their adolescent's diet (i.e., consumption of fruits/vegetables, fast food, and breakfast), physical activity, screen time, and BMI. A multiple mediation analysis was run to test these health behaviors as mediators of the relationship between insomnia severity and BMI, after controlling for sex, age, race, and poverty. While insomnia was related to most health behaviors, screen time was the only significant mediator of the relationship between insomnia and BMI, after controlling for the effects of the other mediators and all covariates, ab = .04, 95% CrI [.01, .09]. Screen time has implications for both adolescents' sleep health and weight. Pediatric clinicians should systematically assess for, and provide, recommendations on ways to improve sleep and screen time use, in addition to providing traditional dietary and physical activity recommendations.

Intervention for the Management of Neuropsychiatric Symptoms to Reduce Caregiver Stress: Protocol for the Mindful and Self-Compassion Care Intervention for Caregivers of Persons Living With Dementia.

Stress related to Alzheimer disease and related dementias (ADRD) is common, particularly among those who care for persons with challenging behaviors and personality or mood changes. Mindfulness and self-compassion programs are efficacious for managing stress. The skills of mindfulness and self-compassion, however, must be integrated with behavioral management skills in order to effectively improve caregiver stress. In this study, we aimed to describe the development of the Mindful and Self-Compassionate Care (MASC) program, the first program that combines mindfulness and self-compassion with behavioral management skills to decrease caregiver stress, and its evaluation in the Supporting Our Caregivers in ADRD Learning (SOCIAL) study. Using the National Institutes of Health (NIH) stage model, we describe 3 phases of work encompassing NIH Stages 1A and 1B. In phase 1, we conducted 5 focus groups (N=28) of stressed individuals caring for persons with ADRD and challenging behaviors. Rapid data analysis informed the development of a 6-week online intervention. Phase 2 (NIH stage 1A) includes an open pilot (N>10) with optional exit interviews. Phase 3 (NIH stage 1B) is a feasibility randomized controlled trial of the intervention versus the Health Education Program control. Primary outcomes focus on feasibility with secondary outcomes encompassing acceptability, credibility, fidelity, and signals of preliminary efficacy. Phase 1 follows traditional recommendations for qualitative analyses (at the point of thematic saturation) which was achieved after 5 focus groups (N=28). For the phase 2 open pilot, up to 12 participants will be recruited. For the phase 3 feasibility study, recruitment of 80 caregivers will allow the assessment of feasibility benchmarks. Data for phase 1 included 5 focus groups. In phases 2 and 3, data collection will occur through REDCap (Research Electronic Data Capture; Vanderbilt University) surveys and an optional qualitative exit interview. Analyses will include hybrid inductive-deductive analyses for qualitative data and assessment of changes in our intervention targets and outcomes using t tests and correlation analyses. In phase 1, caregivers reported interest in a brief, online stress management program. Participants held misconceptions about mindfulness and self-compassion, but after detailed explanation thoughts, these skills could be helpful when directly linked to implementation during caregiving routines. Phases 2 and 3 will be completed by the end of 2025. We describe the protocol for the Supporting Our Caregivers in ADRD Learning study, as well as the development and feasibility testing of the Mindful and Self-Compassionate Care intervention. Future work will include a fully powered efficacy-effectiveness randomized controlled trial. ClinicalTrials NCT05847153; https://clinicaltrials.gov/study/NCT05847153; and ClinicalTrials.gov NCT06276023; https://clinicaltrials.gov/study/NCT06276023. DERR1-10.2196/58356.

Pairwise Intent Graph Embedding Learning for Context-Aware Recommendation with Knowledge Graph

Different from the data sparsity that traditional recommendations suffer from, context-aware recommender systems (CARS) face specific sparsity challenges related to contextual features, i.e., feature sparsity and interaction sparsity. How knowledge graphs address these challenges remains under-discussed. To bridge this gap, in this paper, we first propose a novel pairwise intent graph (PIG) containing nodes of users, items, entities, and enhanced intents to integrate knowledge graphs into CARS efficiently. Enhanced intent nodes are generated through the specific fusion of relational sub-intent and contextual sub-intent, and they are derived from semantic information and contextual information, respectively. We develop a pairwise intent graph embedding learning (PING) framework based on it. Specifically, our PING uses a pairwise intent joint graph convolution module to obtain refined embedding of all the features, where each enhanced intent node acts as a hub to effectively propagate information among different features and between all the features and knowledge graphs. Then, a recommendation module with refined embeddings is used to replace the randomly initialized embeddings of downstream recommendation models to improve model performance. Extensive experiments on three public datasets and some real-world scenarios verify the effectiveness and compatibility of our PING.

Effect of aggregate type on strength and deformation properties of high-strength concrete during heating

Introduction. High-strength concrete is widely used in contemporary construction. Expanded introduction of high-strength concrete necessitates the need for studying its behavior at high temperatures (in case of fire) in order to ensure the required fire resistance of load-bearing reinforced concrete structures made of high-strength concrete in terms of fire safety of buildings and structures.Aim. To determine the effect of aggregate types on strength and deformation characteristics of high-strength B100 concrete when heated to temperatures from 100 °C to 800 °C with a step of 100 °C.Materials and methods. Laboratory tests of prism strength and elastic modulus of basalt and granite high-strength concrete were carried out on prism samples in a heated state according to standard methods using special heating equipment combined with laboratory pressure equipment.Results. The authors determined structure behavior factors of basalt and granite high-strength concrete during heating, specifying the decrease in compressive strength and elastic modulus. Deformation diagrams during axial compression of high-strength granite and basalt concretes under heating were drawn.Conclusions. The dynamics of reduction in strength and deformation properties is similar for granite and basalt high-strength concrete under heating and is specific for silicate aggregate concretes. The elastic moduli of basalt high-strength concrete are higher than those of granite high-strength concrete, both at 20 °C and when heated, thereby determining the dependence of high-strength concrete deformation properties on the types of aggregates. Deformation diagrams during the axial compression of high-strength granite and basalt concretes showed specific character: unilinear – when heated to temperatures of about 300–400 °C, bilinear – at higher heating temperatures, therefore differing from traditional ideas and theoretical recommendations.

Culturally-informed neuropsychological evaluations of patients with spina bifida: Application of the ECLECTIC framework to a complex pediatric medical condition

Objective: The pediatric patient populations in the United States are becoming increasingly diverse in culture. In addition to medical factors, a variety of sociocultural factors (including educational systems, language, immigration status, etc.) can impact a child’s cognitive development, performance on traditional neuropsychological measures, provider interactions, differential diagnosis, and recommendations. A culturally-informed neuropsychological evaluation aims to understand the impact of sociocultural factors and integrate them into the assessment approach. Fujii’s ECLECTIC framework (acronym for Education & Literacy, Culture/Acculturation, Language, Economic Issues, Communication Style, Testing Situation, Intelligence Conceptualization, and Context of Immigration) can be used to guide culturally-informed neuropsychological evaluations of children with complex medical conditions. Method: Using the ECLECTIC framework, this paper describes clinical considerations and offers practical solutions for providing culturally-informed pediatric neuropsychology evaluations. Four diverse patients with spina bifida are reviewed. Results: The case presentations highlight the application of the ECLECTIC framework within a medically complex pediatric patient population. Conclusions: Spina bifida is one example of a pediatric medical population in which both medical and sociocultural factors can impact cognitive development and the evaluation process. The ECLECTIC framework defines a variety of sociocultural factors that can influence cognitive development and multiple aspects of a pediatric neuropsychological evaluation. Pediatric neuropsychologists should use the ECLECTIC framework to provide culturally-informed evaluations for our increasingly diverse patient populations.

Protein Requirements for Maximal Muscle Mass and Athletic Performance Are Achieved with Completely Plant-Based Diets Scaled to Meet Energy Needs: A Modeling Study in Professional American Football Players.

American football players consume large quantities of animal-sourced protein in adherence with traditional recommendations to maximize muscle development and athletic performance. This contrasts with dietary guidelines, which recommend reducing meat intake and increasing consumption of plant-based foods to promote health and reduce the risk of chronic disease. The capacity of completely plant-based diets to meet the nutritional needs of American football players has not been studied. This modeling study scaled dietary data from a large cohort following completely plant-based diets to meet the energy requirements of professional American football players to determine whether protein, leucine, and micronutrient needs for physical performance and health were met. The Cunningham equation was used to estimate calorie requirements. Nutrient intakes from the Adventist Health Study 2 were then scaled to this calorie level. Protein values ranged from 1.6-2.2 g/kg/day and leucine values ranged from 3.8-4.1 g/meal at each of four daily meals, therefore meeting and exceeding levels theorized to maximize muscle mass, muscle strength, and muscle protein synthesis, respectively. Plant-based diets scaled to meet the energy needs of professional American football players satisfied protein, leucine, and micronutrient requirements for muscle development and athletic performance. These findings suggest that completely plant-based diets could bridge the gap between dietary recommendations for chronic disease prevention and athletic performance in American football players.

Bibliometric Analysis on Equine-Assisted Interventions.

Equine Assisted Interventions (EAIs) integrate the active participation of horses in therapeutic or educational interventions. A bibliometric analysis was carried out on this topic, using traditional bibliometric laws and recommendations. For this purpose, a search on the Web of Science (WoS) Core Collection database was carried out, obtaining 333 documents. Annual publications followed an exponentially increasing trend (R2 = 86%), pointing out that this topic is a growing interest among researchers, publishers, and journals. The USA was the most productive country worldwide and Jeong-yi Kwon and Ji Lee were the prolific co-authors. The WoS category with the highest number of papers was Rehabilitation (84 papers). The Journal of Alternative and Complementary Medicine, and Pediatric Physical Therapy were the journals with the highest number of publications. The most cited paper was "State of the Evidence Traffic Lights 2019: Systematic Review of Interventions for Preventing and Treating Children with Cerebral Palsy". The most used author keywords were rehabilitation, balance, and those related to specific populations such as Cerebral Palsy and Autism Spectrum Disorder. These results suggest that EAIs is a topic of increasing interest for researchers, editors, and professionals.

Practice challenge recommendations in online judge using implicit rating extraction and utility sequence patterns

PurposeThe existing traditional recommendations based on content-based filtering (CBF), collaborative filtering (CF) and hybrid approaches are inadequate for recommending practice challenges in programming online judge (POJ). These systems only consider the preferences of the target users or similar users to recommend items. In the learning environment, recommender systems should consider the learning path, knowledge level and ability of the learner. Another major problem in POJ is the learners don't give ratings to practice challenges like e-commerce and video streaming portals. This purpose of the proposed approach is to overcome the abovementioned shortcomings.Design/methodology/approachTo achieve the context-aware practice challenge recommendation, the data preparation techniques including implicit rating extraction, data preprocessing to remove outliers, sequence-based learner clustering and utility sequence pattern mining approaches are used in the proposed approach. The approach ensures that the recommender system considers the knowledge level, learning path and learning goals of the learner to recommend practice challenges.FindingsExperiments on practice challenge recommendations conducted using real-world POJ dataset show that the proposed system outperforms other traditional approaches. The experiment also demonstrates that the proposed system is recommending challenges based on the learner's current context. The implicit rating extracted using the proposed approach works accurately in the recommender system.Originality/valueThe proposed system contains the following novel approaches to address the lack of rating and context-aware recommendations. The mathematical model was used to extract ratings from learner submissions. The statistical approach was used in data preprocessing. The sequence similarity-based learner clustering was used in transition matrix. Utilizing the rating as a utility in the USPAN algorithm provides useful insights into learner–challenge relationships.

Application of recommendation system based on fuzzy logic in music culture communication and music teaching management

Music has been utilized throughout history as a medium for cultural communication and artistic expression, embodying various nations’ and societies’ ideologies and experiences. Music culture communication is crucial for encouraging cultural diversity and understanding and developing social cohesion and community building among people. Music teaching management is the process of setting up, arranging, and executing music education programs in a manner that successfully teaches students the essential skills and information necessary for becoming proficient musicians. Users’ exact preferences for various areas of attraction cannot be determined, nor are users’ choices for traditional music recommendations sufficiently accurate. A recommender system estimates or anticipates people’s preferences and offers appropriate recommendations. First, the sparsity problem emerges when insufficient data is accessible for the recommendation, and the coverage is one of the key drawbacks of social labeling. Cold start issues might be difficult since new music learners might not have given sufficient details about their musical tastes. Hence, the Hybridized Fuzzy logic-based Content and Collaborative Music Recommendation (HFC2MR) system is proposed to create personalized music teaching plans that are effective and engaging for each student based on their music preferences and learning outcomes. Enhanced Fuzzy C-Means clustering is used in collaborative recommendations to group users based on their shared musical tastes and to provide each user with more individualized, accurate music recommendations based on other users’ listening habits and preferences in the same cluster. Subsequently, an assessment of the recommender system using parameters like accuracy, precision, f1-score, and recall ratio is shown with optimal cluster selection. The coverage ratio is used to compare experimental data based on skill capacity covered through the assessment of music teaching. RMSE metric is used to evaluate the accuracy of students’ performance based on music attributes related to teaching goals.

Personalized multi-attribute recommendation method based on partial order set

With the rapid development of big data and continuous optimization of online shopping platforms, personalized recommendation has become a standard feature of recommendation methods. In order to effectively provide personalized recommendations to customers, improve recommendation accuracy, and customer satisfaction, it is necessary to consider customers’ preferences for multiple product attributes when making product recommendations. However, existing recommendation methods require precise calculation of product attribute weights, which is computationally expensive, complex, and often results in unstable weight values. This paper proposes a multi-attribute recommendation method based on consumer decision preference information that overcomes the need for weights and reflects personalized customer preferences. Based on the acquisition of customer product attribute preference sequences, a partial order relation for recommended products is constructed using partial order set theory. Finally, the recommended products are determined through the partial order Hasse diagram, where the top layer elements of the Hasse diagram represent the recommended product set. This method addresses challenges that traditional content-based recommendations cannot overcome. The experiment in this paper uses a dataset of 30,000 records from Beeradvocate beer reviews. The experimental results show that, compared to traditional multi-attribute recommendation methods, this method only requires decision-maker preference information to complete product recommendations, requiring less information and having lower computational costs, resulting in more robust results.

Effect of Loratadine for Pegfilgrastim-Induced Bone Pain

AimsBreast cancer patients on chemotherapy who receive pegfilgrastim to prevent neutropenia may experience severe bone pain as a side effect. Traditional treatment recommendations include nonsteroidal anti-inflammatory drugs (NSAIDs), acetaminophen, opioids, and/or antihistamine use. However, little research was found comparing these interventions. The study aim was to address the gaps in literature and to explore the use of and perceived effectiveness of loratadine versus acetaminophen or NSAIDs in women with breast cancer treated with pegfilgrastim. This study also sought to understand how patients became aware of loratadine or other treatments for management of bone pain. Design/MethodsThis cross-sectional study used survey methods to collect data from 66 adult female breast cancer patients receiving chemotherapy with pegfilgrastim. ResultsThe incidence of bone pain was 45% (n = 30) in our sample, but more than half (n = 45; 69%) of the women took either acetaminophen, NSAIDs, or loratadine alone or in combination to prevent bone pain. All medication were rated as effective by patients, with acetaminophen slightly more effective than loratadine, and loratadine more effective than NSAIDs. ConclusionsAcetaminophen, NSAIDs, and loratadine are easily available and inexpensive. However, unlike acetaminophen and NSAIDs, loratadine is dosed once a day and well tolerated with minimal adverse effects. Clinical ImplicationsRandomized controlled trials are needed to adequately assess the effectiveness of all three medication options. Because little is known about optimal use of any of these medications for pegfilgrastim-induced bone pain, it is also important to identify the optimal time to initiate treatment and ideal treatment duration.

Негативное влияние гистерезисных явлений на процесс разработки месторождений нефти и газа

It is known that irreversibilities of hysteresis nature of global or local scale, lead to the necessity to revise the methods of solving the main problems of hydrodynamic modelling and analysis of dynamic systems, as they can radically affect the traditional conclusions and practical recommendations. It is assumed that in the steady-state mode of motion or filtration, phase displacements make all sorts of oscillations in the vicinity of the unstable position, due primarily to irreversible hysteresis phenomena and jumps arising from the instability of the front of oil displacement by water, the manifestation of capillary effects, hysteresis of phase transitions, irreversibility of deformations under excessive load at large pressure drops, including hydraulic fracturing. With this purpose in the theory of catastrophes the dynamic models of growth are investigated and control parameters are selected with the calculation and the possibility to predict the expected local irreversibilities and instabilities before the onset of bifurcation. Hysteresis behaviour in the filtration process is generated by lithological differences of geofluidodynamic medium and its geological and physical characteristics and properties of saturating fluids. And as a rule, it is provoked by not always adequate, excessive external load on the tested medium. The proposed solutions are part of a new concept of unsteady waterflooding, which provides for early prediction and taking measures in case of instability of the displacement front and, as a consequence, water breakthrough into production wells. It is of great interest to prevent the consequences of these negative effects on the quantitative components of technological and economic indicators, in the case of oil field development we are talking about the final recovery factor of oil, gas and condensate. System optimisation of oil field development is designed for a certain technological and economic effect, including additional oil production, effective mobilisation and saving of injected and withdrawn water and gas.

Подходы к диспансерному наблюдению детей дошкольного возраста c разными типами психосоматической конституции, страдающих хроническим аденоидитом

The article presents a program of medical and social support for preschool children with different types of psychosomatic constitution suffering from complicated chronic adenoiditis. The study involved 82 children aged 4 to 7 years, who were referred after inpatient treatment under the supervision of a paediatrician and an otorhinolaryngologist of a polyclinic. Children in the main group (n = 82; of these, 23 (47.9%) were classified as introverted and 25 (52.1%) as extroverted types of psychosomatic constitution) were given recommendations differentiated according to the identified constitutional features of the course of chronic adenoiditis. Children of the control group (n = 34) underwent traditional rehabilitation. After 6 months, all children in the main group showed persistent remission and regression of clinical manifestations characteristic of their type, and their quality of life improved. In children of the control group, who received traditional recommendations, the positive dynamics was weaker. Thus, the program of support for preschool children with chronic adenoiditis, taking into account the constitutional features of the course of the disease, can be used in the organization of the outpatient stage of rehabilitation. Key words: introverts, rehabilitation program, psychosomatic constitution, chronic adenoiditis, extroverts

Trusted Knowledge Infusion Model-based Recommender System for IoT based B2B applications

The exponential growth and usage of Internet, social sites, and e-commerce results extensive amount of information everywhere. It becomes very difficult for the people to search something, so they go for the offered suggestions which are pertinent for them rather than searching from a potentially overwhelming number of options. Recommender Systems are the solutions for such difficulties. There are many powerful Recommender Systems available for e-commerce, websites, books, tourism, and documents, but recommendations for IoT-based applications need of new discoveries. Traditional recommendations methods are not sufficient for big data based scalable and heterogeneous IoT environment. In this paper, we propose a knowledge infusion model-based hybrid recommendation model for IoT-based B2B applications. The proposed model is analyzed with a real dataset, and the evaluation represents that model performs well in terms execution time, RMSE, precision and F1-score as compared with the existing models.

Analysis of the development course of traditional Chinese medicine standardization and recommendations on future work

This paper systematically reviews the development course of traditional Chinese medicine (TCM) standardization. Based on the characteristics of TCM standardization at different stages, the development course can be divided into 3 stages, namely the “initial development stage” (from the 1980s to the “10th Five-Year Plan” period), the “rapid development stage” (from the “11th Five-Year Plan” period to the “13th Five-Year Plan” period), and the “high-quality development stage” (from the “14th Five-Year Plan” period to the present). The “initial development stage” is characterized by the emergence of awareness regarding TCM standardization, the initial exploration of TCM standardization, and a gradual but slow progress in development. During the “rapid development stage,” relevant government departments began integrating TCM standardization into development plans, resulting in the extensive development of the TCM standard system and its supporting structures. Since the commencement of the “high-quality development stage,” TCM standardization has progressively shifted from a quantity-oriented model to 1 emphasizing quality and efficiency. Overall, TCM standardization has made remarkable progress after more than 40 years of development. However, it still faces various challenges, including unbalanced development in the standard system, the need for improvement in the development quality of group standards, and insufficient levels of promotion and application. It is recommended to further enhance the formulation of national and industry TCM standards, implement relevant management measures, promote the innovative implementation and application of TCM standards, establish an integrated, comprehensive service platform for TCM standards, and strengthen the talent training for TCM standardization.

Institutional violence from police militarization and drug cartel wars as a 'Big Event' and its influence on drug use harms and HIV risk in people who inject drugs on the U.S.-Mexico border.

Globally, the US-Mexico Border is one of the largest drug trafficking regions, with Ciudad Juarez (CJ) and El Paso (EP) making up the second-largest border crossing in the world. Border communities are places where the risk of drug use harm and infectious diseases such as HIV are augmented due to the confluence of factors operating across the physical, social, economic and policy environment. Although the two cities are economically, culturally, and socially intertwined, each has distinct criminal justice systems and policy practices aimed at curtailing substance use. Between 2008 and 2011, the CJ/EP region experienced an unprecedented level of violence that stemmed from the intersection of police militarization and drug cartel wars, which profoundly shaped every aspect of life. Little research has documented the impact of drug cartel wars on the drug use and health harms of people who inject drugs (PWID) living in CJ and EP. The purpose of the study is to understand the effect that the drug cartel war had on the drug use harms and HIV risk of PWID. We conducted 40 in-depth interviews with people who inject drugs who resided in CJ or EP and had used heroin or crack cocaine in the last 30 days, and asked how police militarization and drug cartel war affected their daily lives. The risk environment framework informed the analysis and interpretation of findings. Findings indicated that the risk environment was profoundly altered as PWID residing in CJ experienced profound changes in their daily lives that promoted engagement in behaviors that increased drug use and health harms including HIV risk, exacerbated trauma, and prevented use of substance use treatment and harm reduction services. The risk environment was also altered in EP, where PWID experienced drug supply shortages, violent policing practices, and reduced availability of harm reduction services. Findings underscore the permeability of risk environments across geographical borders. The intersection of law enforcement militarization and drug cartel wars can be conceptualized as a 'big event' because it disrupts the drug market economy, leads to drug shortages, promotes entrance into the drug market economy by people who use drugs, reshapes drug use sites, and constrains the provision of harm reduction services. The stability of the harm reduction system in CJ was negatively impacted and limited the ability of individuals to reduce harm. Our findings show that drug cartel wars render the CJ/EP region extremely susceptible to drug use and health harms, while also creating vulnerability by severely restricting its ability to respond. Traditional recommendations to intervene to limit the impact of risk environments on the drug use harms of PWID need to be reconsidered in the context of drug cartel wars.

The Little Bid More, the Merrier? Quantifying the Effects of Filler-Item Recommendations in Contingent Free Shipping

As the contingent free shipping strategy (CFS) becomes the standard practice among e-commerce sites, customers may abandon it due to the difficulty in reaching the free-shipping threshold or may take advantage of it by adding “filler items” to reach the threshold before the checkout (called fill-in purchases). This paper looks into the filler-item recommendation strategy under which the e-tailer proactively recommends filler items to encourage fill-in purchases. Different from most traditional product recommendations, the nature of filler-item recommendation renders its effectiveness in reshaping customer behaviors and economic outcomes unpredictable. The empirical analysis demonstrates how the availability of filler-item recommendations affected customers' purchase behavior. Specifically, the filler-item recommendation system resulted in 5.4% more fill-in purchase behavior and, on average, 0.35 more filler items in each shopping basket, or approximately 3% in transaction volume. However, the findings also suggest a downside of the filler-item recommendation: 80% of the incremental transactions were fill-in purchases, and the benefits for e-tailers were marginal or even harmful. This paper is among the first to contribute to the literature by quantifying the value of the filler-item recommendation system, which also offers guidance for e-commerce practitioners.

Applied Molecular-Based Quality Control of Biobanked Samples for Multi-Omics Approach.

Biobanks are vital for high-throughput translational research, but the rapid development of novel molecular techniques, especially in omics assays, poses challenges to traditional practices and recommendations. In our study, we used biospecimens from oncological patients in Polish clinics and collaborated with the Indivumed Group. For serum/plasma samples, we monitored hemolysis, controlled RNA extraction, assessed cDNA library quality and quantity, and verified NGS raw data. Tissue samples underwent pathologic evaluation to confirm histology and determine tumor content. Molecular quality control measures included evaluating the RNA integrity number, assessing cDNA library quality and quantity, and analyzing NGS raw data. Our study yielded the creation of distinct workflows for conducting preanalytical quality control of serum/plasma and fresh-frozen tissue samples. These workflows offer customization options to suit the capabilities of different biobanking entities. In order to ensure the appropriateness of biospecimens for advanced research applications, we introduced molecular-based quality control methods that align with the demands of high-throughput assays. The novelty of proposed workflows, rooted in innovative molecular techniques, lies in the integration of these QC methods into a comprehensive schema specifically designed for high-throughput research applications.

AN ONTOLOGY BASED PRODUCT RECOMMENDATION SYSTEM FOR NEXT GENERATION E-RETAIL

ABSTRACT The number of e-commerce resources has increased considerably. Thus, it has become important for sellers to be able to quickly recommend products to potential buyers. Some product recommendation systems developed for this purpose. However, due to the lack of semantics, the systems’ success in recommending accurate products according to user preferences is low. In this study carried out within the scope of a state-funded R&D project, an ontology-based personalized product recommendation system named E-Prod was developed. E-Prod tracks various e-commerce systems in real time and transfers the product information to the ontology model. E-Prod uses a novel recommendation approach that combines machine learning and semantic matching to provide personalized recommendations. The system learns user’s preferences based on semantic relationships between products by monitoring their behaviors. In this way, accurate recommendations are made by semantic matching between products and user preferences. E-Prod has been tested with over 250 registered users and compared to traditional collaborative recommendations in terms of accuracy, precision, and recall. As a result, E-Prod outperformed traditional methods by 92.79% accuracy, 92.93% precision, and 90.58% recall. Within the scope of this study, E-Prod covers the clothing, shoes, and bag retail sectors. However, it provides a generic infrastructure for new generation e-commerce systems. Its reusable modules can be adapted to any domain.

Time of insemination in relation to pregnancy rates in beef cattle after oestrus detection with automated activity monitoring system

BackgroundThe timing of artificial insemination is critical to achieve acceptable results in cattle production systems. Over the past 60 years the length and expression of oestrus in dairy cattle has altered. Recent studies have indicated the optimal timing for insemination after the commencement of oestrus may now be earlier than traditional recommendations in beef cattle, as is the case in dairy cattle. The aim of the current study was to evaluate the effect of time from onset of oestrus [as determined by an automated activity monitoring system (AAMS)] to artificial insemination (AI) on pregnancy outcome in Norwegian beef cattle. Five commercial beef suckler herds participated in a cohort study by providing data on the time of AAMS alarm and time of AI. Blood sampling on the day of AI was performed and serum progesterone concentration measured. Pregnancy detection was performed by transrectal ultrasonography and aging of the fetus performed when necessary. A mixed logistic regression model was fitted to study the effect of time from AAMS alarm to AI on pregnancy outcome. Time categories used in the model were < 12 h, 12–24 h, and > 24 h.ResultsAI periods (n = 229) with serum progesterone concentration < 1 ng/mL were available for analysis. Overall pregnancy risk per AI for the whole study period was 65.5%, with an inter-herd variation from 10 to 91%. Median time elapsed from AAMS alarm to AI was 17.75 h. Herd affected pregnancy outcome (P = 0.001), while breed and parity status (heifer/cow) did not. The time category closer to AAMS alarm 0–12 h showed a numerically lower pregnancy risk as compared to the baseline group which had AI 12–24 h after onset of oestrus.ConclusionThis study found no evidence to support a change in the recommended timing of AI in beef suckler cows.