Appl. Sci., Volume 14, Issue 10 (May-2 2024) – 348 articles

The article presents an advanced two-dimensional (2D) computational fluid dynamics (CFD) model of an external gear pump which considers relief grooves. Relief grooves are limiting design features for the flow process of this type of pump, and their influence in existing studies is considered by a three-dimensional (3D) model only. The structural modification proposed by the authors is beyond the possibilities of real implementation, but it gives the possibility to precisely model the pump’s design features. In contrast to the existing studies (using 3D CFD), the proposed advanced 2D model requires significantly fewer computing resources. Numerical experiments were carried out using the 2D model at different pump operating modes depending on the rotation frequency (950–1450 min⁻¹) and pressure load (5–150 bar). The numerical results were validated by a real-world experiment for the same pump operating modes using an existing laboratory experimental setup. An analysis of the CFD model and real experiment results was carried out by determining a quantitative index of match (FIT), which varies in the range of 97.93–99.82%. This proves the performance of the proposed CFD model, which can be further used as a part of more complex hydraulic systems models. Full article

Microgreens are a new, rapidly growing group of foodstuffs. The decorative function of these is often accompanied by their use in traditional dishes. As microgreens are eaten at very early stages, when the development of the epidermis is at its minimum, the bioavailability of minerals will be found to be higher in microgreens then in mature vegetables. So, microgreens can be an excellent functional food, especially for mineral-deficient populations, although they can also be a source of contaminants such as heavy metals or nitrates and nitrites. The purpose of this study was to measure the levels of selected heavy metals (i.e., cadmium, arsenic, lead, chromium, aluminium, zinc, copper, cobalt, molybdenum, manganese, vanadium, boron, antimony, thallium, titanium and strontium), as well as nitrates and nitrites, in microgreens at various stage of vegetation, using uncommon oilseed plants like nigella—Nigella sativa L., safflower—Carthamus tinctorius L., and camelina—Camelina sativa L. The examined microgreens of rare oilseed plants may be a source of contaminants and nitrates. The mineral profile of these plants is mainly determined by their genotype. Microgreens’ cultivation involves compliance with safety standards and replicable conditions to guarantee that the highest nutritional value is reached at the lowest possible contaminant level. Full article

Integrated energy systems (IESs) can easily accommodate renewable energy resources (RESs) and improve the utilization efficiency of fossil energy by integrating various energy production, conversion, and storage technologies. However, the coupled multi-energy flows and the uncertainty of RESs bring challenges regarding optimal scheduling. Therefore, this study proposes an energy bus-based matrix-modeling method and a coordinated scheduling strategy for the IES. The matrix-modeling method can be used to formulate the steady- and transient-state balances of the multi-energy flows, and the transient model can clearly express the multi-time-scale characteristics of the different energy flows. The model parameters are fitted with data from experiments and the literature. To address the inherent randomness of the RESs and loads, a coordinated scheduling strategy is designed that contains two components: day-ahead optimization and rolling optimization. Day-ahead optimization uses the system steady-state model and multiple scenarios from the RES and load forecast data to minimize the operation cost while rolling optimization is based on the system’s transient-state model and aims to achieve the optimal real-time scheduling of the energy flows. Finally, a case study is conducted to verify the advantages and effectiveness of the proposed model and optimization method. The results show that stochastic optimization reduces the total daily cost by 1.48% compared to deterministic optimization when considering the prediction errors associated with the RESs and loads, highlighting the stronger adaptability of stochastic optimization to prediction errors. Moreover, rolling optimization based on the system’s transient-state model can reduce the errors between day-ahead scheduling and rolling correction. Full article

In the development of intelligent mines, unmanned driving transportation has emerged as a key technology to reduce human involvement and enable unmanned operations. The operation of unmanned vehicles in mining environments relies on remote operation, which necessitates the low-latency transmission of high-definition video data across multiple channels for comprehensive monitoring and precise remote control. To address the challenges associated with unmanned driving in mines, we propose a comprehensive scheme that leverages the capabilities of 5G super uplink, edge collaborative computing, and advanced video transmission strategies. This approach utilizes dual-frequency bands, specifically 3.5 GHz and 2.1 GHz, within the 5G super uplink framework to establish an infrastructure designed for high-bandwidth and low-latency information transmission, crucial for real-time autonomous operations. To overcome limitations due to computational resources at terminal devices, our scheme incorporates task offloading and edge computing methodologies to effectively reduce latency and enhance decision-making speed for real-time autonomous activities. Additionally, to consolidate the benefits of low latency, we implement several video transmission strategies, such as optimized network usage, service-specific wireless channel identification, and dynamic frame allocation. An experimental evaluation demonstrates that our approach achieves an uplink peak rate of 418.5 Mbps with an average latency of 18.3 ms during the parallel transmission of seven channels of 4K video, meeting the stringent requirements for remote control of unmanned mining vehicles. Full article

Since 2021, China’s promotion of common prosperity has captured global attention and sparked considerable debate. Yet, scholarly examination of the Chinese public’s attitudes toward this policy, which is crucial for guiding China’s strategic directions, remains limited. To address this gap, this paper collects 256,233 Sina Weibo posts from 2021 to 2023 and utilizes text mining methods such as temporal and trend analysis, keyword analysis, topic analysis, and sentiment analysis to investigate the attitudes and emotions of the Chinese people towards common prosperity. The posts holding negative sentiments are also analyzed, so as to uncover the underlying reasons for the dissatisfaction among Chinese citizens regarding common prosperity. Our analysis reveals that China’s strategy for promoting common prosperity is primarily focused on economic development rather than wealth redistribution. Emphasis is placed on enhancing education, achieving regional balance, implementing market-oriented reforms, and improving livelihoods. Notably, there is increasing public dissatisfaction, particularly with issues such as irregularities in financial and real estate markets, growing wealth inequality, exploitation by capital, generation of illicit income, and regional development imbalances. These challenges necessitate urgent and effective policy interventions. Full article

A gearbox compound fault intelligent diagnosis method based on the period group sparse model is proposed for the problem that the fault features are coupled with each other and the fault components are superimposed on each other and difficult to be separated in the gearbox compound fault signal. Firstly, a binary sequence is constructed to embed the fault pulse period as a priori knowledge into the group sparse model to decouple and separate the composite fault signal while maintaining the amplitude and sparsity of the extracted features. Secondly, the wavelet packet energy features of the decoupled signals are extracted to improve the data quality while enhancing the characterization ability of the dictionary in the classification model. Finally, the wavelet packet energy features are imported into the sparse dictionary classification model, and the fault diagnosis is completed by outputting the fault categories using the self-driven characteristics of the data. The results show that the fault identification accuracy using the proposed method is 97%. In addition, the experimental validation under different states and working conditions with different rotational speeds allows the superiority and effectiveness of the algorithm in this paper to be tested and has the feasibility of a practical application in engineering. Full article

This study presents a novel microfluidic mixer designed, fabricated, and characterized using additive manufacturing technology — stereolithography (SLA) — and harnessing capillarity principles achieved through microstructure patterning. Micromixers are integral components in optimizing mixing and reaction processes within microfluidic systems. The proposed microdevice employs a tank mixing method capable of blending two fluids. With a channel length of up to 6 mm, the process time is remarkably swift at 3 s, and the compact device measures 35 × 40 × 5 mm. The capillarity-driven working flow rates range from 1 $\mathrm{L}$/$\mathrm{s}$ to 37 $\mathrm{L}$/$\mathrm{s}$, facilitated by channel dimensions varying between 400 $\mathrm{m}$ and 850 $\mathrm{m}$. The total liquid volume within the device channels is 1652 $\mathrm{m}\mathrm{L}$ (6176 $\mathrm{L}$ including the supply tanks). The mix index, representing the homogeneity of the two fluids, is approximately 0.55 along the main channel. The manufacturing process, encompassing printing, isopropyl cleaning, and UV (ultraviolet) curing, is completed within 90 min. This microfluidic mixer showcases efficient mixing capabilities, rapid processing, and a compact design, marking it as a promising advancement in microfluidic technology. The new microfluidic mixer is a major step forward in microfluidic technology, providing a cost-effective and flexible solution for various uses. Its compatibility with SLA additive manufacturing allows for quick prototyping and design improvements, making it valuable for research and practical applications in chemistry, biology, and diagnostics. This study highlights the importance of combining advanced manufacturing techniques with basic fluid dynamics to create effective and easy-to-use microfluidic solutions. Full article

This study presents the predicted hydrodynamic characteristics of different rudder plane configurations on the stern of a full-scale submarine in deep water, which are obtained using the Reynolds-Averaged Navier–Stokes method in Ansys Fluent Solver. First, the results obtained for the X-rudder plane configuration are verified according to previous numerical and experimental results in order to assess the accuracy of the simulation procedure. The X-rudder plane, Y-rudder plane, and Cross-rudder plane configurations in deep water with deflection angles ranging from −21 degrees to +21 degrees are then simulated. Next, the hydrodynamic forces and moments of the Cross-plane, X-plane, and Y-plane rudder configurations obtained through simulation are analyzed using Taylor’s expansion to estimate the hydrodynamic coefficients. The obtained results demonstrate that the X-force of the X-plane rudder configuration is larger than the corresponding forces acting on the Cross-plane rudder and Y-plane rudder configurations. Meanwhile, the Y-force and Z-force of the X-plane rudder configuration are significantly greater than the corresponding forces of the left configurations. The same tendency can be seen in the moment of the X-plane rudder about the y- and z-axes. However, the roll moment induced by the Y-plane and Cross-plane rudder configurations is significantly larger than that under the X-plane rudder configuration. Full article

Banana bunchy top virus (BBTV) was recently detected in Mozambique and appears to be limited to the provinces of Gaza, Maputo and Zambezia, but it has great potential to spread to other provinces. Despite its importance, nothing is known about the BBTV isolates that occur in Mozambique. In this study, the sequences of the S and R genes of forty isolates chosen as representatives of samples collected previously from eleven farms of the four administrative posts of the district of Chóckwè, province of Gaza, were sequenced and analyzed. The S-DNA nucleotide sequences of the analyzed isolates were highly conserved, with identity ranging from 97% to 100%. The same was observed for the R-DNA sequences, with most identities ranging between 98% and 100% among the isolates from Chókwè and above 90% when compared to the isolates from GenBank. The phylogenetic analysis showed that the Mozambican BBTV isolates belong to the Pacific–Indian Oceans (PIO) group, showing greater proximity to the isolate JQ820453 from Malawi than to the isolates from sub-Saharan countries, which were grouped in a distinct subclade. This is the first study conducted to determine the molecular characteristics of BBTV isolates present in Mozambique. Full article

This study aims to generate visually useful imagery by preventing cropping while maintaining resolution and minimizing the degradation of stability and distortion to enhance the stability of a video for Augmented Reality applications. The focus is placed on conducting research that balances maintaining execution speed with performance improvements. By processing Inertial Measurement Unit (IMU) sensor data using the Versatile Quaternion-based Filter algorithm and optical flow, our research first applies motion compensation to frames of input video. To address cropping, PCA-flow-based video stabilization is then performed. Furthermore, to mitigate distortion occurring during the full-frame video creation process, neural rendering is applied, resulting in the output of stabilized frames. The anticipated effect of using an IMU sensor is the production of full-frame videos that maintain visual quality while increasing the stability of a video. Our technique contributes to correcting video shakes and has the advantage of generating visually useful imagery at low cost. Thus, we propose a novel hybrid full-frame video stabilization algorithm that produces full-frame videos after motion compensation with an IMU sensor. Evaluating our method against three metrics, the Stability score, Distortion value, and Cropping ratio, results indicated that stabilization was more effectively achieved with robustness to flow inaccuracy when effectively using an IMU sensor. In particular, among the evaluation outcomes, within the “Turn” category, our method exhibited an 18% enhancement in the Stability score and a 3% improvement in the Distortion value compared to the average results of previously proposed full-frame video stabilization-based methods, including PCA flow, neural rendering, and DIFRINT. Full article

To discover the spatial–temporal patterns of sea surface temperature (SST) in the South China Sea (SCS), this paper proposes a spatial–temporal co-clustering algorithm optimized by information divergence. This method allows for the clustering of SST data simultaneously across temporal and spatial dimensions and is adaptable to large volumes of data and anomalous data situations. First, the SST data are initially clustered using the co-clustering algorithm. Second, we use information divergence as the loss function to refine the clustering results iteratively. During the iterative optimization of spatial clustering results, we treat the temporal dimension as a constraint; similarly, during the iterative optimization of temporal clustering, we treat the spatial dimension as a constraint. This is to ensure better robustness of the algorithm. Finally, this paper conducts experiments in the SCS to verify our algorithm. According to the analysis of the experimental results, we have drawn the following conclusions. First, the use of the spatial–temporal co-clustering algorithm reveals that the SST in the SCS exhibits strong seasonal patterns in the temporal clustering results. The spatial distribution of SST varies significantly in different seasons. There is a slight difference in SST between the northern and southern regions of the SCS in winter, but the largest difference is in summer. Second, during ocean anomalies, our proposed algorithm can identify the corresponding abnormal patterns. When ENSO occurs, the seasonal distribution pattern of SST in the SCS is destroyed and replaced by an abnormal temporal pattern. The results indicate that during ENSO events, the SST in specific months in the SCS exhibits a correlation with the SST observed 4–5 months afterward. Full article

Aluminium smelter waste (ASW) is a big contributor to landfills, and its recycling has been of great interest. This study investigates the tribological properties of aluminium matrix syntactic foams manufactured using an Al 6082 alloy and ASW. Ball-on-disc tests were conducted under both dry and lubricated conditions. Under dry sliding conditions, the coefficient of friction (COF) had an initial sharp increase, followed by a gradual decrease and finally a steady state as the sliding distance increased. The wear surfaces showed the presence of adhesive, abrasive and oxidative wear, with some presence of delamination. Syntactic foams containing small ASW particles led to a decrease in surface roughness, decrease in the average COF and decrease in specific wear. Heating large ASW particles before manufacturing the syntactic foams enhanced overall wear properties because the particles are hardened due to a compositional change. The T6 treatment of the syntactic foams enhanced the wear properties due to the hardening of the Al matrix. The average COF of the ASW syntactic foams was higher than that of the E-sphere syntactic foam, which was predominantly abrasive wear. The specific wear of the ASW syntactic foams can be higher or lower than the E-sphere syntactic foam, depending on the ASW particle size. Under lubricated sliding test conditions, the wear was reduced significantly, and the type changed from predominantly adhesive to predominantly abrasive. The porous ASW particles acted as lubricant reservoirs and provided a constant supply of lubricant, further improving the lubrication effect. Full article

The group of innovative ingredients in cosmetic preparations includes bio-ferments (Bs), which are characterized by high bioactivity and biocompatibility, and one of the plants rich in bioactive compounds that has a beneficial effect on the skin and the body is Silybum marianum. Bio-ferments obtained from this plant are becoming increasingly useful as active ingredients in cosmetics. In the present study, four different bio-ferments were obtained by fermentation of pomace (B-P), extract (B-E), oil (B-O), and seeds (B-S) of milk thistle. Their biodegradability (%B), total polyphenols content (Folin–Ciocalteu method), and antimicrobial, antioxidant (DPPH, ABTS, and FRAP methods), chelating (Fe²⁺ ions), and reduction (Cu²⁺ and Fe³⁺ ions) properties, as well as the acidity, were evaluated. The contact angle using the sessile drop method was assessed to investigate bio-ferments’ impact on skin wettability. Finally, the content of selected phenolic acids in the Bs was evaluated using the HPLC method, while the lactic acid (LA) content was assessed using the GC-MS method. All bio-ferments were characterized by high polyphenols content (13.56 ± 0.10–15.28 ± 0.12 mmol GA/L B), chelating (0.08 ± 0.01–0.17 ± 0.01 mmol Fe²⁺/L B) and antioxidant activity (DPPH method, 2.41 ± 0.01–3.53 ± 0.01 mmol Tx/L B), and reducing Cu²⁺ and Fe³⁺ ions. Gallic acid, protocatechuic acid, caffeic acid, neochlorogenic acid, coumaric acid, and LA were identified in Bs. The most increased antibacterial activity for B-P was observed for a strain of Staphylococcus aureus (MIC = 250 μL/mL) and Pseudomonas aeruginosa (MIC = 250 μL/mL). Simultaneously, B-S demonstrated the highest inhibitory effects against Escherichia coli (MIC = 125 μL/mL), emphasizing the varied antimicrobial profiles of these bio-ferments against different bacterial strains. Research on aerobic biodegradation demonstrated a high level of degradation (%B= 60 ± 1–65 ± 3), and all Bs were categorized as readily degradable according to the OECD classification. Full article

In this study, the ultrasonic extraction (UAE) of free phenolic compounds and relative biological activities of the Bulgarian peach variety “Filina” was optimized using chemometric techniques (response surface methodology). A Box–Behnken design was used to reveal the variation in the hydro module, temperature, duration, and extractant on the total phenolic content, total flavonoid content, antioxidant potential, and inhibitory activity on each yield. The results revealed that the optimal conditions included a hydro module of 20, a duration of 39.33 min, a temperature of 70 °C, and an extractant of 96.64% to retrieve the highest level of bioactive compounds. The calculated parameters were discovered to be following the projected values. Full article

Strong winds can lead to more complex ice shedding oscillation processes for overhead conductors, inducing flashovers, strand breakages and other accidents. This study analyzes the aerodynamic parameters of several typical icing features and establishes a numerical model for ice shedding on overhead conductors under strong wind conditions. The results show that for the same amount of icing, the resistance and lift force on the conductor changes with ice shape, wind attack angle and wind speed, which has a significant effect on the ice shedding jumping process. When the wind attack angle approaches 180°, the airflow resistance of the fan-shaped and D-shaped icing conductors significantly increases. And in the process of ice shedding response of transmission lines, the lateral amplitude may exceed 20 m, which increase the discharge risk of horizontally arranged conductors. Moreover, for the significant lateral oscillation of conductors by ice shedding under strong wind, the maximum horizontal displacement is approximately 1.6 times the difference in lateral position before and after ice shedding. Full article

The classical bistable stochastic resonance algorithm has an inherent output saturation defect that restricts the amplitude of the output signal. This paper examines the causes of this phenomenon and its negative impact on the detection of weak signals. Proposing the Unsaturated Bistable Stochastic Resonance (UBSR) detection algorithm involves constructing a segmented potential function using a linear function to eliminate the effect of higher-order terms in the classical stochastic resonance algorithm. A new type of segmented potential function has been created by combining exponential and linear functions. This new function helps to eliminate the impact of higher-order terms in classical algorithms while also improving the noise immunity of the stochastic resonance system. This results in the development of the accelerated stochastic resonance (ASR) detection algorithm. In this paper, the Kramers escape rate and output signal-to-noise ratio of two improved stochastic resonance algorithms are theoretically derived and compared with the classical bistable stochastic resonance algorithms, and the proposed algorithms are able to effectively avoid the output saturation phenomenon and have more excellent detection performance under strong background noise. Full article

Biochar (BC), which can be produced from several feedstocks, has been widely studied. However, the BC derived from highly pyrolytic shrubs, such as broom and gorse, has been less frequently used and only partially characterized. These wastes, when used for the preparation of biochar, can fix carbon and contribute to environmental conservation, helping to achieve sustainable development objectives. Eight biochars from broom and gorse were produced and fully analyzed, providing a more complete and novel description, with new insights for assessing their utilization. The aims of this study were to elucidate the effects of feedstock, pyrolysis temperature, and residence time on biochar properties and to assess the adequacy of these biochars as fuel. Elemental and proximate analyses and estimations of the lower and higher heating values were performed, and physical and chemical properties, as well as several other related energy indices, were determined. The experimental results showed that the temperature was a key factor in the properties of the biochars, while residence time was less important. The BCs obtained from the two feedstocks did not show important effects on the properties, which is consistent with the fact that they are woody legumes. These biochars had a high carbon content and were thermally stable. The BCs also had a high calorific value and suitable energetic properties. Additionally, their PAH contents were low, indicating that the use of these biochars would be safe. In conclusion, broom- and gorse-derived biochars can be considered as renewable fuels for green energy production. Full article

Passive radar detection emerges as a pivotal method for environmental perception and target detection within radar applications. Through leveraging its advantages, including minimal electromagnetic pollution and efficient spectrum utilization, passive radar methodologies have garnered increasing interest. In recent years, there has been an increasing selection of passive radar signal sources, and the emerging 5G has the characteristics of a high-frequency band, high bandwidth, and a large number of base stations, which give it significant advantages for use in passive radar. Therefore, in this paper, we introduce a passive radar target detection method based on 5G signals and design a rotating target speed measurement experiment. In the experiment, this paper validated the method of detecting rotating targets using 5G signals and evaluated the measurement accuracy, providing a research foundation for passive radar target detection using 5G signals and detecting rotating targets such as drone rotors. Full article

This study is concerned with the nonlinear free vibration of a cracked functionally graded porous cylindrical panel reinforced with graphene platelets by introducing a phase-field crack model. Conventional crack modeling by separating the grid nodes lying on the crack line is not only painstaking but also suffers from numerical instability. To overcome this problem, the internal crack is modeled by adopting the phase-field formulation and a virtual geometry rotation. The nonlinear numerical method is developed based on the first-order shear deformation theory incorporated with the von Kármán geometry nonlinearity in the framework of the 2-D extended natural element method, a recently introduced mesh-free method. The crack-induced singular field is represented by adopting the crack-tip singular functions, and the troublesome numerical locking is restrained by combining the MITC3+ shell concept and the shear stabilization factor. The curved shell surface is mapped to a 2-D rectangular NEM grid to avoid difficulty in defining the interpolation functions. The developed numerical method is verified through a comparison with the reference solutions, and the large-amplitude free vibration of porous cracked functionally graded grapheme platelet-reinforced cylindrical panels is profoundly examined by changing the major parameters. Full article

This study delves into the fusion of ground-based and drone-based ground-penetrating radar (GPR) technologies in archaeological exploration. Set against the backdrop of the Hulata solar panel construction site in Israel, the research confronts daunting obstacles such as clayey soil, accurate detection of small objects, and the imperative of timely reporting crucial for construction management. The drone-based GPR, a testament to technological innovation, showcases remarkable adaptability to challenging terrains, dispelling doubts about electromagnetic wave decay in clayey soil. Methodologically, the study employs detailed orthophoto mapping and grid-type surveys. The correlation of the results significantly bolsters the reliability of archaeological discoveries, uncovering scattered artifacts buried approximately 1–1.5 m below the surface. Meticulous excavations validate the geophysical surveys, affirming the presence of structures constructed from boulders. The application at the Hulata site validates the adaptability of drone-based GPR in challenging terrains. It provides a swift, cost-effective, and minimally invasive alternative to traditional excavation techniques, thereby transforming the field of archaeology. Full article

Accurate bird species recognition is crucial for ecological conservation, wildlife monitoring, and biological research, yet it poses significant challenges due to the high variability within species and the subtle similarities between different species. This paper introduces an automatic bird species recognition method from images that leverages feature enhancement and contrast learning to address these challenges. Our method incorporates a multi-scale feature fusion module to comprehensively capture information from bird images across diverse scales and perspectives. Additionally, an attention feature enhancement module is integrated to address noise and occlusion within images, thus enhancing the model’s robustness. Furthermore, employing a siamese network architecture allows effective learning of common features within instances of the same class and distinctions between different bird species. Evaluated on the CUB200-2011 dataset, our proposed method achieves state-of-the-art performance, surpassing existing methods with an accuracy of 91.3% and F1 score of 90.6%. Moreover, our approach showcases a notable advantage in scenarios with limited training data. When utilizing only 5% of the training data, our model still achieves a recognition accuracy of 65.2%, which is significantly higher than existing methods under similar data constraints. Notably, our model exhibits faster execution times compared to existing methods, rendering it suitable for real-time applications. Full article

Bacterial infections are the second-leading cause of death, globally. The prevalence of antibacterial resistance has kept the demand strong for the development of new and potent drug candidates. It has been demonstrated that Src protein tyrosine kinases (TKs) play an important role in the regulation of inflammatory responses to tissue injury, which can trigger the onset of several severe diseases. We carried out a search for novel Src protein TK inhibitors, commencing from reported highly potent anti-bacterial compounds obtained using the Mannich reaction, using a combination of e-pharmacophore modeling, virtual screening, ensemble docking, and core hopping. The top-scoring compounds from ligand-based virtual screening were modified using protein structure-based design approaches, and their binding to the Src homology-2 domain of p56^lck TK was predicted using ensemble molecular docking. We have prepared a database of 202 small molecules and have identified six novel top hits that can be subjected to further investigation. We have also performed in silico ADMET property prediction for the hit compounds. This combined computer-aided drug design approach can serve as a starting point for identifying novel TK inhibitors that could be further subjected to in vitro studies and validation of antimicrobial activity. Full article

The objective of this study was to prepare zein–gum Arabic nanocapsules with rosehip oil (NC-RH), apply them to pork tenderloin, and analyze the changes in collagen structure under different conditions (pH 6.5 and 4.0) and temperatures (25 °C and 4 °C). NC-RHs were prepared using the nanoprecipitation method. Nanocapsules had a particle size of 423 ± 4.1 nm, a polydispersity index of 0.125 ± 3.1, a zeta potential value of −20.1 ± 0.41 mV, an encapsulation efficiency of 75.84 ± 3.1%, and backscattering (ΔBS = 10%); the antioxidant capacity of DPPH was 1052 ± 4.2 µM Eq Trolox and the radical scavenging capacity was 84 ± 0.4%. The dispersions exhibited Newtonian behavior at 25 °C and 4 °C. Incorporating NC-RH into acid marination benefited the tenderness, water-holding capacity, and collagen swelling, and favored changes in myofibrillar proteins corroborated with histological tests. The conditions with the best changes in pork tenderloin were a pH of 4.0 at 4 °C with an NC-RH-administered 11.47 ± 2.2% collagen area. Incorporating rosehip nanocapsules modifies collagen fibers and can be applied in pork marinades to increase the shelf life of a functional product. Full article

Despite cardiac magnetic resonance (CMR) with late gadolinium enhancement (LGE) being the current gold standard for non-invasive myocardial characterization and fibrosis quantification, its accessibility is limited, particularly in acute settings and in certain patient populations with contraindications to magnetic resonance imaging. Late iodine enhancement (LIE) in computed tomography (CT) imaging has emerged as a potential alternative, capitalizing on the similarities in the contrast kinetics between gadolinium and iodinated contrast agents. Studies have investigated LIE-CT’s effectiveness in myocardial infarction (MI) detection, revealing promising outcomes alongside some disparities compared to LGE-CMR. LIE-CT also proves beneficial in diagnosing non-ischemic heart diseases such as myocarditis, hypertrophic cardiomyopathy, and sarcoidosis. While LIE-CT demonstrates good accuracy in detecting certain myocardial pathologies, including acute MI and chronic fibrotic changes, it has limitations, such as the inability to detect diffuse myocardial enhancement. Nonetheless, thanks to the availability of optimized protocols with minimal radiation doses and contrast medium administration, integrating LIE-CT into cardiac CT protocols could enhance its clinical utility, particularly in acute settings, providing valuable prognostic and management insights across a spectrum of cardiac ischemic and non-ischemic conditions. Full article

The textile industry is one of the most important industries in the European Union. The main environmental problems of the textile industry are the high water consumption, the generated pollution, the variety of chemicals used and the high energy demand. Recently, adsorbents with a large specific surface area and low weight, such as aerogels, have attracted great interest as promising materials for removing dyes from polluted water. Cellulose aerogels are inexpensive and non-toxic. Langmuir and Freundlich isotherms were chosen as the best method to describe the performance of the adsorbent. In this study, the adsorption efficiency of Congo red, Naphthol green B, Rhodamine B and Methylene blue were determined by using an adsorbent synthesized from paper and cardboard waste. The total organic carbon concentration was chosen as an indicator of the concentration of the dyes in the solutions. The aerogel capsules had 5% cellulose content. It was found that the adsorption capacity of the aerogel in the solutions of Congo red varied from 0.028 mg/g to 14.483 mg/g; in the solutions of Naphthol green B, from 0.013 mg/g to 7.698 mg/g; in the solutions of Rhodamine B, from 0.020 mg/g to 8.768 mg/g; and in the solutions of Methylene blue, from 0.024 mg/g to 13.538 mg/g. Full article

In this study, we quantified relevant biophysical characteristics of two elite rowers across a wide range of intensities. Two <40-year-old male and female Olympic and World Championship finalists performed a 7 × 3 min protocol plus 1 min maximal effort on a rowing ergometer. The intensity increase resulted in maximum values of 79.4 ± 2.4 and 69.7 ± 1.5 mL/min/kg for oxygen uptake, 179.3 ± 5.7 and 152.5 ± 2.9 L/min for ventilation, 170 ± 1 and 173 ± 0 bpm for heart rate, 10.6 and 15.8 mmol/L for blood lactate concentration, and 38.1 ± 0.03 and 38.8 ± 0.03 °C for core temperature for the male and female rowers. The percentage of power corresponding to a previously conducted maximum 2000 m rowing ergometer test and the work at each step increased from 49 to 127 and 42 to 103% and from 226.8 to 398.9 J and 174.0 to 250.0 J, from low to extreme intensities, for the male and female. Concurrently, there was a decrease in cycle length and propulsive time, followed by an increase in maximal handle drive velocity, with the rise in rowing intensity. These world-class rowers seem capable of maintaining physiological and technical profiles (and a remarkable capacity to generate substantial power) at this phase of their careers possibly due to long-term engagement in elite-level training. Biophysical data provide valuable referential information for guiding rowers to improve their performance. Full article

Video classification is a challenging task in computer vision that requires analyzing the content of a video to assign it to one or more predefined categories. However, due to the vast amount of visual data contained in videos, the classification process is often computationally expensive and requires a significant amount of annotated data. Because of these reasons, the low-data-based video classification area, which consists of few-shot and zero-shot tasks, is proposed as a potential solution to overcome traditional video classification-oriented challenges. However, existing low-data area datasets, which are either not diverse or have no additional modality context, which is a mandatory requirement for the zero-shot task, do not fulfill the requirements for few-shot and zero-shot tasks completely. To address this gap, in this paper, we propose a large-scale, general-purpose dataset for the problem of multi-modal low-data-based video classification. The dataset contains pairs of videos and attributes that capture multiple facets of the video content. Thus, the new proposed dataset will both enable the study of low-data-based video classification tasks and provide consistency in terms of comparing the evaluations of future studies in this field. Furthermore, to evaluate and provide a baseline for future works on our new proposed dataset, we present a variational autoencoder-based model that leverages the inherent correlation among different modalities to learn more informative representations. In addition, we introduce a regularization technique to improve the baseline model’s generalization performance in low-data scenarios. Our experimental results reveal that our proposed baseline model, with the aid of this regularization technique, achieves over 12% improvement in classification accuracy compared to the pure baseline model with only a single labeled sample. Full article

To address the challenges in industrial precision component detection posed by existing point cloud datasets, this research endeavors to amass and construct a point cloud dataset comprising 1101 models of miniature gears. The data collection and processing procedures are elaborated upon in detail. In response to the segmentation issues encountered in point clouds of small industrial components, a novel Point Transformer network incorporating a multiscale feature fusion strategy is proposed. This network extends the original Point Transformer architecture by integrating multiple global feature extraction modules and employing an upsampling module for contextual information fusion, thereby enhancing its modeling capabilities for intricate point cloud structures. The network is trained and tested on the self-constructed gear dataset, yielding promising results. Comparative analysis with the baseline Point Transformer network indicates a notable improvement of 1.1% in mean Intersection over Union (mIoU), substantiating the efficacy of the proposed approach. To further assess the method’s effectiveness, several ablation experiments are designed, demonstrating that the introduced modules contribute to varying degrees of segmentation accuracy enhancement. Additionally, a comparative evaluation is conducted against various state-of-the-art point cloud segmentation networks, revealing the superior performance of the proposed methodology. This research not only aids in quality control, structural detection, and optimization of precision industrial components but also provides a scalable network architecture design paradigm for related point cloud processing tasks. Full article