Search Results (409)

Spirulina (Arthospira platensis) is known for its rich content of natural compounds like phycocyanin, chlorophylls, carotenoids, and high protein levels, making it a nutrient-dense food. Over the past decade, research has aimed to optimize the extraction, separation, and purification of these valuable metabolites, focusing on technologies such as high-pressure processing, ultrasound-assisted extraction, and microwave-assisted extraction as well as enzymatic treatments, chromatographic precipitation, and membrane separation. In this study, various extraction methods (conventional vs. ultrasound-assisted), solvents (water vs. phosphate buffer), solvent-to-biomass ratios (1:5 vs. 1:10), and ultrafiltration (PES membrane of MWCO 3 kDa, 2 bar) were evaluated. The quantities of total protein, phycocyanin (PC), chlorophyll a (Cla), and total carotenoids (TCC) were measured. The results showed that ultrasound-assisted extraction (UAE) with phosphate buffer at a 1:10 ratio yielded a metabolite-rich retentate (MRR) with 37.0 ± 1.9 mg/g of PC, 617 ± 15 mg/g of protein, 0.4 ± 0.2 mg/g of Cla, and 0.15 ± 0.14 mg/g of TCC. Water extraction in the concentration process achieved the highest concentrations in MRR, with approximately 76% PC, 92% total protein, 62% Cla, and 41% TCC. These findings highlight the effective extraction and concentration processes to obtain a metabolite-rich retentate from Spirulina biomass, reducing the volume tenfold and showing potential as a functional ingredient for the food, cosmetic, and pharmaceutical industries. Full article

The purpose of this research was to create a predictive model for a medium-speed wire electrical discharge machine (WEDM) utilizing an artificial neural network (ANN). Medium-speed WEDM experiments were developed based on the I-optimal mixture design for machining, the Inconel 718 superalloy. During the experiment, the input parameters were the spark ontime, spark offtime, wire feed, and current, with the material removal rate (MRR) and surface roughness (Ra) selected as performance indicators. The ANN model was trained on experimental data and built using a feed-forward backpropagation neural network with a (4-8-2) structure and the Bayesian regularization (BR) learning approach. The model correctly predicted the relationship between the medium-speed WEDM’s primary process parameters and machining performance. An integrated ANN model and the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) were used to determine the ideal parameters for the MRR and Ra, resulting in a set of Pareto-optimal solutions. The confirmation experiment revealed that the mean prediction error between the experimental and ideal solutions had a maximum error percentage of 1% for the MRR and 2% for the Ra, which are within acceptable ranges. This showed that the best process–parameter combinations were better for the MRR and Ra. Full article

This study examines the dry milling process of a rare-earth-based magnesium alloy, emphasizing the optimization of the milling parameters and their impact on the surface quality, cutting forces, and the rate of material removal. The objective is to improve our comprehension of the milling behavior of the Mg-Gd-Y-Er alloy. The Taguchi technique is adopted to formulate the experimental design. This study methodically investigates the influence of heat treatment (T4 and T6) on milling performance, and the effects of speed, feed rate, and depth of cut. The output variables considered for this investigation are the surface roughness (Ra, Rz, Sa, and Sz), material removal rate (MRR), and cutting force. To optimize the milling parameters and achieve superior outcomes, the multi-objective optimization technique TOPSIS is used. At a feed rate of 150 mm/min, a spindle speed of 1500 rpm, and a depth of cut of 1 mm, the T4-treated sample exhibits a minimum surface roughness value of 0.0305 µm. The highest resultant force values of 96.4416 N and 176.1070 N for 200 °C and 225 °C T6-treated alloys are obtained by combining process parameters such as a spindle speed of 1500 rpm, a feed rate of 50 mm/min, and a depth of cut of 1.5 mm. Furthermore, the maximum closeness coefficient value is achieved by combining a spindle speed of 1000 to 1500 rpm, a feed rate of 150 mm/min, and a depth of cut of 0.5 mm to 1 mm. The closeness coefficient value is significantly influenced by the most significant process parameters, as indicated by the ANOVA results. Full article

Remembering software library components and mastering their application programming interfaces (APIs) is a daunting task for programmers, due to the sheer volume of available libraries. API completion tools, which predict subsequent APIs based on code context, are essential for improving development efficiency. Existing API completion techniques, however, face specific weaknesses that limit their performance. Pattern-based code completion methods that rely on statistical information excel in extracting common usage patterns of API sequences. However, they often struggle to capture the semantics of the surrounding code. In contrast, deep-learning-based approaches excel in understanding the semantics of the code but may miss certain common usages that can be easily identified by pattern-based methods. Our insight into overcoming these challenges is based on the complementarity between these two types of approaches. This paper proposes a combinatorial method of API completion that aims to exploit the strengths of both pattern-based and deep-learning-based approaches. The basic idea is to utilize a confidence-based selector to determine which type of approach should be utilized to generate predictions. Pattern-based approaches will only be applied if the frequency of a particular pattern exceeds a pre-defined threshold, while in other cases, deep learning models will be utilized to generate the API completion results. The results showed that our approach dramatically improved the accuracy and mean reciprocal rank (MRR) in large-scale experiments, highlighting its utility. Full article

Aedes albopictus is considered one of the major invasive species in the world and can transmit viruses such as dengue, Zika, or chikungunya. The Sterile Insect Technique (SIT) can be used to suppress the native populations of Ae. albopictus. Mark–release–recapture (MRR) studies are crucial to support the development of the release strategy during the SIT application. Meanwhile, weather conditions can affect the MRR trial’s results and it is critical to understand the influence of climatic factors on the results. In October 2022, 84,000 irradiated sterile males were released for three consecutive weeks in Faro, Southern Portugal. Mosquitoes were recaptured by human landing collection (HLC) one, two, four, and six days after release. Generalized linear models with a negative binomial family and log function were used to estimate the factors associated with the number of recaptured mosquitoes, prevalence ratios, and the 95% confidence intervals (CIs). A total of 84,000 sterile male mosquitoes were released, with 528 recaptured (0.8%) by HLC. The prevalence of recaptured mosquitoes was 23% lower when the wind intensity was moderate. Marked sterile males had an average median distance travelled of 88.7 m. The median probability of daily survival and the average life expectancy were 61.6% and 2.1 days, respectively. The wild male population estimate was 443.33 males/ha. Despite no statistically significant association being found with humidity, temperature, and precipitation, it is important to consider weather conditions during MRR trial analyses to obtain the best determinant estimation and a more efficient application of the SIT in an integrated vector management program. Full article

Beat-by-beat monitoring of hemodynamic parameters in the left ventricle contributes to the early diagnosis and treatment of heart failure, valvular heart disease, and other cardiovascular diseases. Current accurate measurement methods for ventricular hemodynamic parameters are inconvenient for monitoring hemodynamic indexes in daily life. The objective of this study is to propose a method for estimating intraventricular hemodynamic parameters in a beat-to-beat style based on non-invasive PCG (phonocardiogram) and PPG (photoplethysmography) signals. Three beagle dogs were used as subjects. PCG, PPG, electrocardiogram (ECG), and invasive blood pressure signals in the left ventricle were synchronously collected while epinephrine medicine was injected into the veins to produce hemodynamic variations. Various doses of epinephrine were used to produce hemodynamic variations. A total of 40 records (over 12,000 cardiac cycles) were obtained. A deep neural network was built to simultaneously estimate four hemodynamic parameters of one cardiac cycle by inputting the PCGs and PPGs of the cardiac cycle. The outputs of the network were four hemodynamic parameters: left ventricular systolic blood pressure (SBP), left ventricular diastolic blood pressure (DBP), maximum rate of left ventricular pressure rise (MRR), and maximum rate of left ventricular pressure decline (MRD). The model built in this study consisted of a residual convolutional module and a bidirectional recurrent neural network module which learnt the local features and context relations, respectively. The training mode of the network followed a regression model, and the loss function was set as mean square error. When the network was trained and tested on one subject using a five-fold validation scheme, the performances were very good. The average correlation coefficients (CCs) between the estimated values and measured values were generally greater than 0.90 for SBP, DBP, MRR, and MRD. However, when the network was trained with one subject’s data and tested with another subject’s data, the performance degraded somewhat. The average CCs reduced from over 0.9 to 0.7 for SBP, DBP, and MRD; however, MRR had higher consistency, with the average CC reducing from over 0.9 to about 0.85 only. The generalizability across subjects could be improved if individual differences were considered. The performance indicates the possibility that hemodynamic parameters could be estimated by PCG and PPG signals collected on the body surface. With the rapid development of wearable devices, it has up-and-coming applications for self-monitoring in home healthcare environments. Full article

Ti6Al4V is a widely used alloy due to its excellent mechanical qualities and resistance to corrosion, which make it fit for automotive, aerospace, defense, and biomedical sectors. Due to its high strength and limited heat conductivity, it is difficult to machine. Both the workpiece’s and the electrode’s conductivity are important factors that impact the electro-discharge machining (EDM) process. In this case, the machining efficiency is also influenced by the electrode selection. As a result, choosing the right electrode and machining parameters is essential to improving EDM performance on the Ti6Al4V alloy. Research on EDM for Ti6Al4V is limited, with little focus on electrode material selection and shape. The impact of EDM settings on MRR, TWR, and surface roughness is complex, and a comprehensive optimization strategy is needed. Copper electrodes are widely used, but further investigation is needed on EDM’s effects on Ti6Al4V’s surface properties and surface integrity. Addressing these research gaps will improve the understanding and application of EDM for Ti6Al4V, focusing on parameter optimization, surface integrity, and thermal and mechanical effects. By employing copper tools to optimize four crucial EDM process parameters—peak current, duty cycle, discharge current, and pulse-on time—this research aims to increase surface integrity and machining performance. A comprehensive Taguchi experimental design is used to systematically alter the EDM settings. By optimizing parameters using tolerance intervals and response modelling, the recently developed RAMS-RATMI approach improves the dependability of the EDM process and increases machining efficiency. With the optimized EDM settings, there were notable gains in depth of cut enhancement, surface roughness minimization, tool wear rate (TWR) reduction, and material removal rate (MRR). The results of the surface integrity examination showed fewer heat-affected zones, fewer microcracks, and a thinner recast layer. Analysis of variance was used to verify the impact and resilience of the optimized parameters. Superior machining performance, higher surface quality, and increased operational dependability were attained with the Ti6Al4V-optimized EDM process, providing industry practitioners with insightful information and useful recommendations. Full article

To address the limitations in capturing complex semantic features between entities and the incomplete acquisition of entity and relationship information by existing patent knowledge graph reasoning algorithms, we propose a reasoning method that integrates semantic and structural features for patent knowledge graphs, denoted as SS-DSA. Initially, to facilitate the model representation of patent information, a directed graph representation model based on the patent knowledge graph is designed. Subsequently, structural information within the knowledge graph is mined using inductive learning, which is combined with the learning of graph structural features. Finally, an attention mechanism is employed to integrate the scoring results, enhancing the accuracy of reasoning outcomes such as patent classification, latent inter-entity relationships, and new knowledge inference. Experimental results demonstrate that the improved algorithm achieves an up to approximately 30% increase in the MRR index compared to the ComplEx model in the public Dataset 1; in Dataset 2, the MRR and Hits@n indexes, respectively, saw maximal improvements of nearly 30% and 112% when compared with MLMLM and ComplEx models; in Dataset 3, the MRR and Hits@n indexes realized maximal enhancements of nearly 200% and 40% in comparison with the MLMLM model. This effectively proves the efficacy of the refined model in the reasoning process. Compared to recently widely applied reasoning algorithms, it offers a superior capability in addressing complex structures within the datasets and accomplishing the completion of existing patent knowledge graphs. Full article

Double-sided planetary grinding (DSPG) with a fixed abrasive is widely used in sapphire substrate processing. Compared with conventional free abrasive grinding, it has the advantages of high precision, high efficiency, and environmental protection. In this study, we propose a material removal rate (MRR) model specific to the fixed-abrasive DSPG process for sapphire substrates, grounded in the trajectory length of abrasive particles. In this paper, the material removal rate model is obtained after focusing on the theoretical analysis of the effective number of abrasive grains, the indentation depth of a single abrasive grain, the length of the abrasive grain trajectory, and the groove repetition rate. To validate this model, experiments were conducted on sapphire substrates using a DSPG machine. Theoretical predictions of the material removal rate were then juxtaposed with experimental outcomes across varying grinding pressures and rotational speeds. The trends between theoretical and experimental values showed remarkable consistency, with deviations ranging between 0.2% and 39.2%, thereby substantiating the model’s validity. Moreover, leveraging the insights from this model, we optimized the disparity in the material removal rate between two surfaces of the substrate, thereby enhancing the uniformity of the machining process across both surfaces. Full article

As a migratory invasive pest, Spodoptera frugiperda (fall armyworm, FAW) has recently posed a serious threat to food security in newly invaded areas (especially in Africa and Asia). Understanding its migration (or dispersal) patterns in newly invaded areas is crucial for regional forecasting and management efforts. By screening an appropriate marking technique to conduct mark–release–recapture (MRR) experiments, the migration patterns of the FAW can be effectively studied. In this study, we added different concentrations of Calco Oil Red N-1700 (an oil-soluble marker) to a self-made artificial diet and assessed the rearing and marking efficacy. The results indicated that a concentration of 0.2% of Calco Oil Red N-1700 in the diet was optimal for marking adult FAWs. The biological indicators (e.g., developmental duration, reproductive parameters, and flight ability) of FAWs fed this diet were basically consistent with those of FAWs fed a normal diet, with a larval stage of 15.46 days, a pupal stage of 9.81 days, a pupal mass of 278.18 mg, an adult longevity of 15.41 days, and an egg deposition count of 1503.51. Meanwhile, the flight distance, duration, and velocity were 24.91 km, 7.16 h, and 3.40 km/h, respectively (12 h tethered-flight tests), without difference with the control. Females and males exhibited distinctive marking colors (red or pink) that persisted for at least 5 and 9 days, respectively. This study developed an economically effective internal marking method for the adult FAW, laying the foundation for conducting MRR experiments. This will help clarify the migration behavior and routes of the FAW, providing a scientific basis for formulating effective pest management strategies. Full article

Session-based recommendation uses past clicks and interaction sequences from anonymous users to predict the next item most likely to be clicked. Predicting the user’s subsequent behavior in online transactions becomes a problem mainly due to the lack of user information and limited behavioral information. Existing methods, such as recurrent neural network (RNN)-based models that model user’s past behavior sequences and graph neural network (GNN)-based models that capture potential relationships between items, miss different time intervals in the past behavior sequence and can only capture certain types of user interest patterns due to the characteristics of neural networks. Graphic models created to improve the current session reduce the model’s success due to the addition of irrelevant items. Moreover, attention mechanisms in recent approaches have been insufficient due to weak representations of users and products. In this study, we propose a model based on the combination of skip-gram and transformer (SkipGT) to solve the above-mentioned drawbacks in session-based recommendation systems. In the proposed method, skip-gram both captures chained user interest in the session thread through item-specific subreddits and learns complex interaction information between items. The proposed method captures short-term and long-term preference representations to predict the next click with the help of a transformer. The transformer in our proposed model overcomes many limitations in turn-based models and models longer contextual connections between items more effectively. In our proposed model, by giving the transformer trained item embeddings from the skip-gram model as input, the transformer has better performance because it does not learn item representations from scratch. By conducting extensive experiments with three real-world datasets, we confirm that SkipGT significantly outperforms state-of-the-art solutions with an average MRR score of 5.58%. Full article

Mycobacterium tuberculosis (Mtb), as a typical intracellular pathogen, possesses several putative restriction–modification (R-M) systems, which restrict exogenous DNA’s entry, such as bacterial phage infection. Here, we investigate Rv2528c, a putative Mrr-like type IV restriction endonuclease (REase) from Mtb H37Rv, which is predicted to degrade methylated DNA that contains m6A, m5C, etc. Rv2528c shows significant cytotoxicity after being expressed in Escherichia coli BL21(DE3)pLysS strain. The Terminal deoxynucleotidyl transferase dUTP Nick-End Labeling (TUNEL) assay indicates that Rv2528c cleaves genomic DNA in vivo. The plasmid transformation efficiency of BL21(DE3)pLysS strain harboring Rv2528c gene was obviously decreased after plasmids were in vitro methylated by commercial DNA methyltransferases such as M.EcoGII, M.HhaI, etc. These results are consistent with the characteristics of type IV REases. The in vitro DNA cleavage condition and the consensus cleavage/recognition site of Rv2528c still remain unclear, similar to that of most Mrr-family proteins. The possible reasons mentioned above and the potential role of Rv2528c for Mtb were discussed. Full article

Link prediction is a key technique for connecting entities and relationships in a graph reasoning field. It leverages known information about the graph structure data to predict missing factual information. Previous studies have either focused on the semantic representation of a single triplet or on the graph structure data built on triples. The former ignores the association between different triples, and the latter ignores the true meaning of the node itself. Furthermore, common graph-structured datasets inherently face challenges, such as missing information and incompleteness. In light of this challenge, we present a novel model called Multi-source Information Graph Embedding with Ensemble Learning for Link Prediction (EMGE), which can effectively improve the reasoning of link prediction. Ensemble learning is systematically applied throughout the model training process. At the data level, this approach enhances entity embeddings by integrating structured graph information and unstructured textual data as multi-source information inputs. The fusion of these inputs is effectively addressed by introducing an attention mechanism. During the training phase, the principle of ensemble learning is employed to extract semantic features from multiple neural network models, facilitating the interaction of enriched information. To ensure effective model learning, a novel loss function based on contrastive learning is devised, effectively minimizing the discrepancy between predicted values and the ground truth. Moreover, to enhance the semantic representation of graph nodes in link prediction, two rules are introduced during the aggregation of graph structure information. These rules incorporate the concept of spreading activation, enabling a more comprehensive understanding of the relationships between nodes and edges in the graph. During the testing phase, the EMGE model is validated on three datasets, including WN18RR, FB15k-237, and a private Chinese financial dataset. The experimental results demonstrate a reduction in the mean rank (MR) by 0.2 times, an improvement in the mean reciprocal rank (MRR) by 5.9%, and an increase in the Hit@1 by 12.9% compared to the baseline model. Full article

Acoustic emission (AE) technology has been widely utilized to monitor the SiC wafer lapping process. The root-mean-square (RMS) of the time–domain eigenvalues of the AE signal has a linear relationship with the material removal rate (MRR). However, the existence of background noise severely reduces signal monitoring accuracy. Noise interference often leads to increased RMS deviation and signal distortion. In the study presented in this manuscript, a frequency threshold noise reduction approach was developed by combining and improving wavelet packet noise reduction and spectral subtraction noise reduction techniques. Three groups of SiC lapping experiments were conducted on a fixed abrasive pad, and the lapping acoustic signals were processed using three different noise reduction approaches: frequency threshold, wavelet packet, and spectral subtraction. The results show that the noise reduction method using the frequency threshold is the most effective, with the best coefficient of determination (R²) for the linear fit of the RMS to the MRR. 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