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Microalgae are an enormous source of nutrients that can be utilized to enrich common food of inherently low nutritional value, such as gluten-free (GF) bread. Addition of the algae species: Tetraselmis chuii (Tc), Chlorella vulgaris (Cv), and Nannochloropsis gaditana (Ng) biomass led to a significant increase in proteins, lipids, minerals (Ca, Mg, K, P, S, Fe, Cu, Zn, Mn), and antioxidant activity. Although, a compromise on dough rheology and consequential sensory properties was observed. To address this, ethanol treatment of the biomass was necessary to eliminate pigments and odor compounds, which resulted in the bread receiving a similar score as the control during sensory trials. Ethanol treatment also resulted in increased dough strength depicted by creep/recovery tests. Due to the stronger dough structure, more air bubbles were trapped in the dough resulting in softer breads (23–65%) of high volume (12–27%) vs. the native algae biomass bread. Breads baked with Ng and Cv resulted in higher protein-enrichment than the Tc, while Tc enrichment led to an elevated mineral content, especially the Ca, which was six times higher than the other algae species. Overall, Ng, in combination with ethanol treatment, yielded a highly nutritious bread of improved technological and sensory properties, indicating that this species might be a candidate for functional GF bread development.

Cell wall disrupted and dried Microchloropsis gaditana (Mg), Tetraselmis chui (Tc) and Chlorella vulgaris (Cv) microalgae biomasses, with or without ethanol pre‐treatment, were added to wheat bread at a wheat flour substitution level of 12%, to enrich bread protein by 30%. Baking performance, protein quality and basic sensory properties were assessed. Compared to wheat, Mg, Tc and Cv contain higher amounts of essential amino acids and their incorporation markedly improved protein quality in the bread (DIAAS 57–66 vs 46%). The incorporation of microalgae reduced dough strength and bread volume and increased crumb firmness. This was most pronounced for Cv and Tc but could be improved by ethanol treatment. Mg gave adequate dough strength, bread volume and crumb structure without ethanol treatment. To obtain bread of acceptable smell, appearance, and colour, ethanol treatment was necessary also for Mg as it markedly reduced the unpleasant smell and intense colour of all algae breads. Ethanol treatment reduced the relative content of lysine, but no other essential amino acids. However, it also had a negative impact on in vitro protein digestibility. Our results show that Mg had the largest potential for protein fortification of bread, but further work is needed to optimize pre‐processing and assess consumer acceptance.

Cell wall disrupted and dried Microchloropsis gaditana (Mg), Tetraselmis chui (Tc) and Chlorella vulgaris (Cv) microalgae biomasses, with or without ethanol pre‐treatment, were added to wheat bread at a wheat flour substitution level of 12%, to enrich bread protein by 30%. Baking performance, protein quality and basic sensory properties were assessed. Compared to wheat, Mg, Tc and Cv contain higher amounts of essential amino acids and their incorporation markedly improved protein quality in the bread (DIAAS 57–66 vs 46%). The incorporation of microalgae reduced dough strength and bread volume and increased crumb firmness. This was most pronounced for Cv and Tc but could be improved by ethanol treatment. Mg gave adequate dough strength, bread volume and crumb structure without ethanol treatment. To obtain bread of acceptable smell, appearance, and colour, ethanol treatment was necessary also for Mg as it markedly reduced the unpleasant smell and intense colour of all algae breads. Ethanol treatment reduced the relative content of lysine, but no other essential amino acids. However, it also had a negative impact on in vitro protein digestibility. Our results show that Mg had the largest potential for protein fortification of bread, but further work is needed to optimize pre‐processing and assess consumer acceptance.

Cell wall disrupted and dried Microchloropsis gaditana (Mg), Tetraselmis chui (Tc) and Chlorella vulgaris (Cv) microalgae biomasses, with or without ethanol pre‐treatment, were added to wheat bread at a wheat flour substitution level of 12%, to enrich bread protein by 30%. Baking performance, protein quality and basic sensory properties were assessed. Compared to wheat, Mg, Tc and Cv contain higher amounts of essential amino acids and their incorporation markedly improved protein quality in the bread (DIAAS 57–66 vs 46%). The incorporation of microalgae reduced dough strength and bread volume and increased crumb firmness. This was most pronounced for Cv and Tc but could be improved by ethanol treatment. Mg gave adequate dough strength, bread volume and crumb structure without ethanol treatment. To obtain bread of acceptable smell, appearance, and colour, ethanol treatment was necessary also for Mg as it markedly reduced the unpleasant smell and intense colour of all algae breads. Ethanol treatment reduced the relative content of lysine, but no other essential amino acids. However, it also had a negative impact on in vitro protein digestibility. Our results show that Mg had the largest potential for protein fortification of bread, but further work is needed to optimize pre‐processing and assess consumer acceptance.

Cloud computing facilitates the users with on-demand services over the Internet. The services are accessible from anywhere at any time. Despite the valuable services, the paradigm is, also, prone to security issues. A Distributed Denial of Service (DDoS) attack affects the availability of cloud services and causes security threats to cloud computing. Detection of DDoS attacks is necessary for the availability of services for legitimate users. The topic has been studied by many researchers, with better accuracy for different datasets. This article presents a method for DDoS attack detection in cloud computing. The primary objective of this article is to reduce misclassification error in DDoS detection. In the proposed work, we select the most relevant features, by applying two feature selection techniques, i.e., the Mutual Information (MI) and Random Forest Feature Importance (RFFI) methods. Random Forest (RF), Gradient Boosting (GB), Weighted Voting Ensemble (WVE), K Nearest Neighbor (KNN), and Logistic Regression (LR) are applied to selected features. The experimental results show that the accuracy of RF, GB, WVE, and KNN with 19 features is 0.99. To further study these methods, misclassifications of the methods are analyzed, which lead to more accurate measurements. Extensive experiments conclude that the RF performed well in DDoS attack detection and misclassified only one attack as normal. Comparative results are presented to validate the proposed method.

Designing and modeling tunnels are a complex and labor-intensive process, exacerbated by the involvement of multiple designers and software applications to accommodate diverse geometric and subsurface conditions. This often results in reduced efficiency, productivity, and increased potential for errors. To address these challenges, the authors implemented a strategy integrating building information modeling and mathematical relationships governing tunnel geometries and rock support elements. Using Autodesk Revit, they developed a prototype that allows designers to model tunnels aligned with ground conditions without reliance on multiple software applications. This streamlined approach enhances productivity, simplifies complexities, and improves accuracy in tunnel design and review processes, including quantity take-offs. A case study comparing the prototype with conventional methods demonstrated significant improvements in efficiency, productivity, and design accuracy. This research offers practical benefits to stakeholders in tunnel projects, optimizing work quality, and outcomes in tunnel design and construction.

The characteristics of the flows of boluses with different consistencies, i.e. different rheological properties, through the pharynx have not been fully elucidated. The results obtained using a novel in vitro device, the Gothenburg Throat, which allows simultaneous bolus flow visualisation and manometry assessments in the pharynx geometry, are presented, to explain the dependence of bolus flow on bolus consistency. Four different bolus consistencies of a commercial food thickener, 0.5, 1, 1.5 and 2 Pa s (at a shear rate of 50 s−1)—corresponding to a range from low honey-thick to pudding-thick consistencies on the National Dysphagia Diet (NDD) scale—were examined in the in vitro pharynx. The bolus velocities recorded in the simulator pharynx were in the range of 0.046–0.48 m/s, which is within the range reported in clinical studies. The corresponding wall shear rates associated with these velocities ranged from 13 s−1 (pudding consistency) to 209 s−1 (honey-thick consistency). The results of the in vitro manometry tests using different consistencies and bolus volumes were rather similar to those obtained in clinical studies. The in vitro device used in this study appears to be a valuable tool for pre-clinical analyses of thickened fluids. Furthermore, the results show that it is desirable to consider a broad range of shear rates when assessing the suitability of a certain consistency for swallowing.

The Ultrasound Velocity Profiling (UVP) technique allows real-time, non-invasive flow mapping of a fluid along a 1D-measuring line. This study explores the possibility of using the UVP technique and X-ray video-fluoroscopy (XVF) to elucidate the deglutition process with the focus on bolus rheology. By positioning the UVP probe so that the pulsed ultrasonic beam passes behind the air-filled trachea, the bolus flow in the pharynx can be measured. Healthy subjects in a clinical study swallowed fluids with different rheological properties: Newtonian (constant shear viscosity and non-elastic); Boger (constant shear viscosity and elastic); and shear thinning (shear rate-dependent shear viscosity and elastic). The results from both the UVP and XVF reveal higher velocities for the shear thinning fluid, followed by the Boger and the Newtonian fluids, demonstrating that the UVP method has equivalent sensitivities for detecting the velocities of fluids with different rheological properties. The velocity of the contraction wave that clears the pharynx was measured in the UVP and found to be independent of bolus rheology. The results show that UVP not only assesses accurately the fluid velocity in a bolus flow, but it can also monitor the structural changes that take place in response to a bolus flow, with the added advantage of being a completely non-invasive technique that does not require the introduction of contrast media.

The Oriental leaf worm moth, Spodoptera litura (F.) (Lepidoptera: Noctuidae), is among the most voracious insect pests in a variety of economically important crops, particularly cotton. It has developed resistance against conventional chemical insecticides. Therefore, it is vital to evaluate an integrated application of bio- and synthetic pesticide against this pest. Nuclear polyhedrosis virus (NPV) can be a potent alternative to control this pest. The present study was conducted to evaluate the efficacy of NPV and emamectin benzoate (Proclaim® 19EC) against three geographically distinct populations of S. litura in vitro conditions. Second and fourth larval instars were treated by three different concentrations of NPV (NPV-1 2 × 10 9 , NPV-2 3 × 10 9 , and NPV-3 4 × 10 9 POB ml −1 ) and emamectin benzoate (EB 0.1 ppm) alone and in combination. The results showed that the highest mortality rate (83.28%) was recorded for NPV-3 + EB, followed by NPV-2 + EB, NPV-1 + EB, EB, NPV-3, NPV-2, and NPV-3 at all the tests. Moreover, Faisalabad (FSD) population was found more susceptible, followed by Layyah (LY) and Multan (ML) populations. Reduction in pupation, adult emergence and egg eclosion was found directly related to the pathogenicity of the applied pathogens. The results of this study revealed that biorational control of S. litura with combined application of NPV + emamectin benzoate was an effective tool.

Sensor-based environmental monitoring systems are often affected by missing, noisy, and unreliable measurements caused by sensor faults, sparse deployment, calibration drift, and communication interruptions. To address these challenges, this study proposes an intelligent virtual sensor generation framework that integrates physical-constraint-based preprocessing, statistical virtual sensor modeling, KL-divergence-based fusion, deep generative augmentation, and temporal prediction. The raw weather-station data are first refined using threshold-based filtering, physical validity constraints, and Isolation Forest-based outlier detection. To handle the circular nature of wind direction, the angle is encoded using sine and cosine components during modeling and reconstructed using the atan2 function for evaluation. Multiple statistical methods, including Inverse Distance Weighting, Kernel Density Estimation, Ridge Regression, and Copula-based modeling, are employed to generate complementary virtual sensor data. These outputs are adaptively fused using KL divergence according to their distributional similarity with real sensor data. The fused datasets are further augmented using Variational Autoencoders and Conditional Tabular Generative Adversarial Networks, and then evaluated using BiLSTM and BiGRU models with MAE, MSE, and RMSE metrics. The experimental results demonstrate that the proposed framework generates physically valid and distributionally consistent virtual sensor data. Fusion-based methods outperform standalone approaches, while VAE-based augmentation generally provides better statistical fidelity and lower prediction errors than CTGAN. Additional validation using a public NOAA weather-station dataset further supports the transferability of the proposed fusion-based virtual sensing workflow. Comparisons with TimeGAN and diffusion-based temporal generative baselines, supported by Wilcoxon signed-rank testing, confirm the statistical significance and competitive performance of the proposed framework. A quantitative computational analysis also demonstrates the practical feasibility of the framework in terms of training time, inference time, memory consumption, and scalability. Overall, the proposed framework offers a reliable and scalable solution for virtual sensing in sensor-sparse and fault-prone environmental monitoring systems.