Search Results (473)

This work discusses the applicability of lightweight aggregate-encapsulated n-octadecane with 1.0 wt.% of Cu nanoparticles, for enhanced thermal comfort in buildings by providing thermal energy storage functionality to no-fines concrete. A straightforward two-step procedure (impregnation and occlusion) for the encapsulation of the nano-additivated phase change material in lightweight aggregates is presented. Encapsulation efficiencies of 30–40% are achieved. Phase change behavior is consistent across cycles. Cu nanoparticles provide nucleation points for phase change and increase the rate of progression of phase change fronts due to the enhancement in the effective thermal conductivity of n-octadecane. The effective thermal conductivity of the composites remains like that of regular lightweight aggregates and can still fulfil thermal insulation requirements. The thermal response of no-fines concrete blocks prepared with these new aggregates is also studied. Under artificial sunlight, with a standard 1000 W·m⁻² irradiance and AM1.5G filter, concrete samples with the epoxy-coated aggregate-encapsulated n-octadecane-based dispersion of Cu nanoparticles (with a phase change material content below 8% of the total concrete mass) can effectively maintain a significant 5 °C difference between irradiated and non-irradiated sides of the block for ca. 30 min. Full article

As artificial intelligence (AI) becomes increasingly prevalent, it has become a topic of interest in education. The use of AI in education poses complex issues, not only in terms of its impact on teaching and learning outcomes but also in terms of the ethical considerations regarding personal data and the individual needs of each student. Our study systematically analyzed empirical research on the use of AI in primary education, specifically for children aged 4–12 years old. We reviewed 35 articles indexed in SCOPUS, filtered them according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzed them, and categorized the findings. The research focused on the studies’ objectives, learning content, learning outcomes, learning activities, and the pedagogy of activities or the AI tools. Our categorization resulted in three main categories of research objectives regarding the creation, implementation, and evaluation of AI tools and five categories for learning content: AI and ML (machine learning) concepts in STEM and STEAM, language learning, mathematics, arts, and various other subjects. The learning activities were split into four categories: apply, engage, interact, use; project-based learning with multiple activities; experience and practice; and students as tutors. The learning outcomes were split into three levels: cognitive, affective, and psychomotor. The pedagogy of AI tools falls into four categories: constructivism, experiential learning, AI-assisted learning, and project-based learning. The implications for teacher professional development are discussed. Full article

Personalized news recommendation can alleviate the information overload problem, and accurate modeling of user interests is the core of personalized news recommendation. Existing news recommendation methods integrate the titles and contents of news articles that users have historically browsed to construct user interest models. However, this method ignores the phenomenon of “title–content mismatching” in news articles, which leads to the lack of precision in user interest modeling. Therefore, a hybrid news recommendation method based on title–content matching is proposed in this paper: (1) An interactive attention network is employed to model the correlation between title and content contexts, thereby enhancing the feature representation of both; (2) The degree of title–content matching is computed using a Siamese neural network, constructing a user interest model based on title–content matching; and (3) neural collaborative filtering (NCF) based on factorization machines (FM) is integrated, taking into account the perspective of the potential relationships between users for recommendation, leveraging the insensitivity of neural collaboration to news content to alleviate the impact of title–content mismatching on user feature modeling. The proposed model was evaluated on a real-world dataset, achieving an nDCG of 83.03%, MRR of 81.88%, AUC of 85.22%, and F1 Score of 35.10%. Compared to state-of-the-art news recommendation methods, our model demonstrated an average improvement of 0.65% in nDCG and 3% in MRR. These experimental results indicate that our approach effectively enhances the performance of news recommendation systems. Full article

Hyperspectral detection of the change rate of organic matter content in agricultural remote sensing requires a high signal-to-noise ratio (SNR). However, due to the large number and efficiency limitation of the components, it is difficult to improve the SNR. This study uses high-efficiency convex grating with a diffraction efficiency exceeding 50% across the 360–850 nm range, a back-illuminated Complementary Metal Oxide Semiconductor (CMOS) detector with a 95% efficiency in peak wavelength, and silver-coated mirrors to develop an imaging spectrometer for detecting soil organic matter (SOM). The designed system meets the spectral resolution of 10 nm in the 360–850 nm range and achieves a swath of 100 km and a spatial resolution of 100 m at an orbital height of 648.2 km. This study also uses the basic structure of Offner with fewer components in the design and sets the mirrors of the Offner structure to have the same sphere, which can achieve the rapid adjustment of the co-standard. This study performs a theoretical analysis of the developed Offner imaging spectrometer based on the classical Rowland circular structure, with a 21.8 mm slit length; simulates its capacity for suppressing the +2nd-order diffraction stray light with the filter; and analyzes the imaging quality after meeting the tolerance requirements, which is combined with the surface shape characteristics of the high-efficiency grating. After this test, the grating has a diffraction efficiency above 50%, and the silver-coated mirrors have a reflection value above 95% on average. Finally, the laboratory tests show that the SNR over the waveband exceeds 300 and reaches 800 at 550 nm, which is higher than some current instruments in orbit for soil observation. The proposed imaging spectrometer has a spectral resolution of 10 nm, and its modulation transfer function (MTF) is greater than 0.23 at the Nyquist frequency, making it suitable for remote sensing observation of SOM change rate. The manufacture of such a high-efficiency broadband grating and the development of the proposed instrument with high energy transmission efficiency can provide a feasible technical solution for observing faint targets with a high SNR. Full article

Recommender Systems (RSs) have recently emerged as a practical solution to the information overload problem users face when searching for digital content. In general, RSs provide their respective users with specialized advice and guidance in order to make informed decisions on the selection of suitable digital content. This paper is a systematic literature review of recent (2011–2023) publications on RSs designed and developed in the context of education to support teachers in particular—one of the target groups least frequently addressed by existing RSs. A body of 61 journal papers is selected and analyzed to answer research questions focusing on experimental studies that include RS evaluation and report evaluation results. This review is expected to help teachers in better exploiting RS technology as well as new researchers/developers in this field in better designing and developing RSs for the benefit of teachers. An interesting result obtained through this study is that the recent employment of machine learning algorithms for the generation of recommendations has brought about significant RS quality and performance improvements in terms of recommendation accuracy, personalization and timeliness. Full article

In the modern digital age, users are exposed to a vast amount of content and information, and the importance of recommendation systems is increasing accordingly. Traditional recommendation systems mainly use matrix factorization and collaborative filtering methods, but problems with scalability due to an increase in the amount of data and slow learning and inference speeds occur due to an increase in the amount of computation. To overcome these problems, this study focused on optimizing LightGCN, the basic structure of the graph-convolution-network-based recommendation system. To improve this, techniques and structures were proposed. We propose an embedding enhancement method to strengthen the robustness of embedding and a non-combination structure to overcome LightGCN’s weight sum structure through this method. To verify the proposed method, we have demonstrated its effectiveness through experiments using the SELFRec library on various datasets, such as Yelp2018, MovieLens-1M, FilmTrust, and Douban-book. Mainly, significant performance improvements were observed in key indicators, such as Precision, Recall, NDCG, and Hit Ratio in Yelp2018 and Douban-book datasets. These results suggest that the proposed methods effectively improved the recommendation performance and learning efficiency of the LightGCN model, and the improvement of LightGCN, which is most widely used as a backbone network, makes an important contribution to the entire field of GCN-based recommendation systems. Therefore, in this study, we improved the learning method of the existing LightGCN and changed the weight sum structure to surpass the existing accuracy. Full article

Livestock blood, typically considered a waste byproduct of the slaughter industry, has the potential to be a valuable resource in the environmental and agricultural industries owing to its high protein content. This study reports the mechanisms involved in developing a continuous process capable of processing 5 tons/day of livestock blood into high purity amino acid liquid fertilizer and dried protein feedstuff simultaneously. Large-scale processing units were fabricated for the ultrasonic pretreatment and solubilization of proteins, enzymatic degradation of dissolved proteins for amino acid conversion, solid-liquid separation using a membrane filter press to produce high purity amino acid liquid fertilizer, and microwave drying of the solid component to produce dry protein feedstuff. The main processing units were integrated into a continuous, efficient system. The final amino acid liquid fertilizer and dry protein feedstuff contained >20% amino acids and approximately 78% protein, respectively. An economic feasibility analysis of the integrated system based on an annual processing capacity of 3000 tons of livestock blood yielded a total annual profit of 17.4 million euros (5812 euros/ton). This study presents an efficient and profitable approach to repurposing the waste generated by slaughterhouses toward agriculture and feed production. Full article

Ozone (O₃) pollution is a critical issue for human health, crop yield, vegetation growth biodiversity, and food safety. Several protection strategies from O₃-induced injuries have been proposed for crops. Here, we investigated if the foliar application of wood distillate (WD), a plant-based biostimulant applied once a week (0.2%, v/v) for four consecutive weeks, could have a protective effect against the damage caused by chronic O₃ concentrations (80 ppb O₃, 5 h day⁻¹ for 28 days) in basil plants (chosen as model horticultural plant). The results revealed that plants exposed to O₃ showed severe chlorotic spots localized in the interveinal adaxial surface, chlorophyll loss (−25% compared to controls maintained in filtered air), and membrane impairment as indicated by the significant increase in malondialdehyde content (+62%). Conversely, plants exposed to O₃ and treated with WD exhibited a reduction in visible injuries, preservation of membrane integrity, and production of antioxidant compounds such as abscisic and salicylic acids (+21 and +62%, respectively), suggesting a protective effect of WD. This research highlights new results regarding the efficacy of WD in mitigating the negative effects of O₃-induced oxidative pressure in basil plants. Full article

As modern technologies, particularly home assistant devices and sensors, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information at 15-min or even 5-min intervals, including weather forecasts, outputs from renewable energy source (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the local energy market (LEM). The goal for these prosumers is to reduce costs while ensuring their home’s comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by large language models (LLMs), Scikit-llm and zero-shot classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy. A comparison with a content-based filtering system is provided considering the performance metrics that are relevant for prosumers. Full article

With the growth of digital media and social networks, sharing visual content has become common in people’s daily lives. In the food industry, visually appealing food images can attract attention, drive engagement, and influence consumer behavior. Therefore, it is crucial for businesses to understand what constitutes attractive food images. Assessing the attractiveness of food images poses significant challenges due to the lack of large labeled datasets that align with diverse public preferences. Additionally, it is challenging for computer assessments to approach human judgment in evaluating aesthetic quality. This paper presents a novel framework that circumvents the need for explicit human annotation by leveraging user engagement data that are readily available on social media platforms. We propose procedures to collect, filter, and automatically label the attractiveness classes of food images based on their user engagement levels. The data gathered from social media are used to create predictive models for category-specific attractiveness assessments. Our experiments across five food categories demonstrate the efficiency of our approach. The experimental results show that our proposed user-engagement-based attractiveness class labeling achieves a high consistency of 97.2% compared to human judgments obtained through A/B testing. Separate attractiveness assessment models were created for each food category using convolutional neural networks (CNNs). When analyzing unseen food images, our models achieve a consistency of 76.0% compared to human judgments. The experimental results suggest that the food image dataset collected from social networks, using the proposed framework, can be successfully utilized for learning food attractiveness assessment models. Full article

Anaerobic wastewater treatment is, in many cases, a justified alternative to typical activated sludge processes, from a technological, economic, and ecological point of view. The optimisation of fermentation reactors is primarily concerned with increasing the biodegradation of organic compounds and biogas production, as well as improving efficiency in the removal of nitrogen and phosphorus compounds. The aim of the research was to determine the impact of using low-cost recycled filling on the efficiency of treating real confectionery wastewater in a vertical anaerobic labyrinth flow bioreactor. The experiments focused on selecting the organic loading rate that would allow for the effective biodegradation and removal of pollutants, as well as the efficient production of biomethane. It was found that the tested reactor can operate efficiently at a maximum organic loading rate (OLR) of 7.0–8.0 g of chemical oxygen demand (COD)/L·d. In this OLR range, high efficiency was guaranteed for both wastewater treatment and biogas production. However, increasing the OLR value to 8.0 g COD/L·d had a significant negative effect on the methane (CH₄) content in the biogas. The most efficient variants achieved a biodegradation efficiency of around 90% of the organic compounds, a CH4 content of over 70% in the biogas, and a biogas yield of over 400 L/kg of COD removed. A significant influence of the applied OLR on the ratio of free organic acids (FOS) to total alkaline capacity (TAC) and pH was observed, as well as a strong correlation of these indicators with the specific biogas yield and CH4 content. The application of a solution based on the use of a hybrid system of anaerobic granulated sludge and an anaerobic filter resulted in an efficient treatment process and an almost complete elimination of suspensions from the wastewater. Full article

This work and manuscript focus on how 3D scanning methodologies and post-processing analyses may help us to gain a deeper investigation of cuneiform tablets beyond the written content. The dataset proposed herein is a key part of the archaeological collection preserved in the Musei Reali of Turin in Italy; these archaeological artefacts enclose further important semantic information extractable through detailed 3D documentation and 3D model filtering. In fact, this scanning process is a fundamental tool for better reading of sealing impressions beneath the cuneiform text, as well as for understanding micrometric evidence of the fingerprints of scribes. Most of the seal impressions were made before the writing (like a watermark), and thus, they are not detectable to the naked eye due to cuneiform signs above them as well as the state of preservation. In this regard, 3D scanning and post-processing analysis could help in the analysis of these nearly invisible features impressed on tablets. For this reason, this work is also based on how 3D analyses may support the identification of the unperceived and almost invisible features concealed in clay tablets. Analysis of fingerprints and the depths of the signs can tell us about the worker’s strategies and the people beyond the artefacts. Three-dimensional models generated inside the Artec 3D ecosystem via Space Spider scanner and Artec Studio software were further investigated by applying specific filters and shaders. Digital light manipulation can reveal, through the dynamic displacement of light and shadows, particular details that can be deeply analysed with specific post-processing operations: for example, the MSII (multi-scale integral invariant) filter is a powerful tool exploited for revealing hidden and unperceived features such as fingerprints and sealing impressions (stratigraphically below cuneiform signs). Finally, the collected data will be handled twofold: in an open-access repository and through a common data environment (CDE) to aid in the data exchange process for project collaborators and common users. Full article

Saliva has shown considerable promise as a diagnostic medium for point-of-care (POC) and over-the-counter (OTC) diagnostic devices due to the non-invasive nature of its collection. However, a significant limitation of saliva-based detection is undesirable interference in a sensor’s readout caused by interfering components in saliva. In this study, we develop standardized sample treatment procedures to eliminate bubbles and interfering molecules while preserving the sample’s target molecules such as spike (S) protein and glucose. We then test the compatibility of the pretreatment system with our previously designed SARS-CoV-2 and glucose diagnostic biosensing systems for detecting S protein and glucose in subject saliva. Ultimately, the effectiveness of each filter in enhancing biomarker sensitivity is assessed. The results show that a 20 mg nylon wool (NW) filter shows an 80% change in viscosity reduction with only a 6% reduction in protein content, making it an appropriate filter for the salivary S protein diagnostic system. Meanwhile, a 30 mg cotton wool (CW) filter is identified as the optimal choice for salivary glucose detection, achieving a 90% change in viscosity reduction and a 60.7% reduction in protein content with a minimal 4.3% reduction in glucose content. The NW pretreatment filtration significantly improves the limit of detection (LOD) for salivary S protein detection by five times (from 0.5 nM to 0.1 nM) and it reduces the relative standard deviation (RSD) two times compared to unfiltered saliva. Conversely, the CW filter used for salivary glucose detection demonstrated improved linearity with an R² of 0.99 and a sensitivity of 36.6 μA/mM·cm², over twice as high as unfiltered saliva. This unique filtration process can be extended to any POC diagnostic system and optimized for any biomarker detection, making electrochemical POC diagnostics more viable in the current market. Full article

Liquor has a complex system with high dimensional components. The trace components in liquor are varied and have low content and complex coordination relationships. This study aimed to solve the problem of reliance on smell and taste. Based on the characteristics of near-infrared spectrum response to hydrogen-containing groups, qualitative analysis was carried out in combination with machine learning technology. Firstly, an iterative adaptive weighted penalized least squares algorithm with spectral peak discrimination was used for baseline correction to effectively retain useful information in the feature absorption peaks. Then, the convolution smoothing algorithm was used to filter the noise, and the spectral curve smoothness was adjusted using the convolution window width. The near-infrared spectrum has a high dimension. Monte Carlo random sampling combined with an improved competitive adaptive reweighting method was used to evaluate the importance of spectral sampling points. According to the importance coefficient, the dimension of the spectral data set was optimized by using an exponential attenuation function through an iterative operation, and the data set with the smallest root-mean-square error was taken as the characteristic spectrum. The nonlinear separability of characteristic spectra was further improved by kernel principal component analysis. Finally, a liquor quality recognition model based on principal component analysis was established by using the hierarchical multiclass support vector machine method. Our key findings revealed that the prediction accuracy of the model reached 96.87% when the number of principal components was 5–12, with more than 95% of the characteristic information retained. These results demonstrated that this rapid nondestructive testing method resolved the challenge posed by relying on subjective sensory evaluation for liquor analysis. The findings provide a reliable analytical approach for studying substances with high-dimensional component characteristics. Full article

The objective of this study was to propose a teacher training program based on the flipped classroom model with MOOCs to strengthen access to information and information literacy among primary education teachers in the Lambayeque region of Peru. The non-experimental design was assumed with a quantitative approach and a propositional, descriptive type. A diagnosis was made using a questionnaire given to 917 primary school teachers. It was discovered that nearly all of the items in the questionnaire revealed a deficiency in the ability to navigate, search, and filter information, data, and digital content; the highest percentages were at the Basic level, with the exception of the item expressing information needs in an organized manner, which was at the Advanced C2 level. The lowest percentage was at the Advanced C1 level, and the majority of the lower percentages were at the Advanced level. In addition, there are competency deficiencies in the evaluation of information, data, and digital content of nearly all the items: the highest percentages were at the Basic level, with the exception of the item involving the processing of information, data, and digital content, where 26.4% were at the Intermediate B1 level and just 2.8% managed to be at the highest level, which is Advanced C2. Furthermore, when it came to storage and retrieval of information, data, and digital content competency, all the high percentages were at the Basic level, and all the low percentages were at the highest level, that is, Advanced. These findings helped us to understand that teachers have only a basic knowledge of information literacy and information competency. As a result, it is necessary to advocate for a teacher training program based on the flipped classroom model with MOOCs. This idea was supported by the opinions of five experts, who stated that its implementation would enable primary teachers of Regular Basic Education in the region of Lambayeque (Peru) to develop their access to information and information literacy competency area. Full article