Digital Twins in Agriculture: Orchestration and Applications.

Digital Twins have emerged as an outstanding opportunity for precision farming, digitally replicating in real-time the functionalities of objects and plants. A virtual replica of the crop, including key agronomic development aspects such as irrigation, optimal fertilization strategies, and pest management, can support decision-making and a step change in farm management, increasing overall sustainability and direct water, fertilizer, and pesticide savings. In this review, Digital Twin technology is critically reviewed and framed in the context of recent advances in precision agriculture and Agriculture 4.0. The review is organized for each step of agricultural lifecycle, edaphic, phytotechnologic, postharvest, and farm infrastructure, with supporting case studies demonstrating direct benefits for agriculture production and supply chain considering both benefits and limitations of such an approach. Challenges and limitations are disclosed regarding the complexity of managing such an amount of data and a multitude of (often) simultaneous operations and supports.

Classifying marine mammals signal using cubic splines interpolation combining with triple loss variational auto-encoder.

In practical applications of passive sonar principles for extracting characteristic frequencies of acoustic signals, scientists typically employ traditional time-frequency domain transformation methods such as Mel-frequency, Short time Fourier transform (STFT), and Wavelet transform (WT). However, these solutions still face limitations in resolution and information loss when transforming data collected over extended periods. In this paper, we present a study using a two-stage approach that combines pre-processing by Cubic-splines interpolation (CSI) with a probability distribution in the hidden space with Siamese triple loss network model for classifying marine mammal (MM) communication signals. The Cubic-splines interpolation technique is tested with the STFT transformation to generate STFT-CSI spectrograms, which enforce stronger relationships between characteristic frequencies, enhancing the connectivity of spectrograms and highlighting frequency-based features. Additionally, stacking spectrograms generated by three consecutive methods, Mel, STFT-CSI, and Wavelet, into a feature spectrogram optimizes the advantages of each method across different frequency bands, resulting in a more effective classification process. The proposed solution using an Siamese Neural Network-Variational Auto Encoder (SNN-VAE) model also overcomes the drawbacks of the Auto-Encoder (AE) structure, including loss of discontinuity and loss of completeness during decoding. The classification accuracy of marine mammal signals using the SNN-VAE model increases by 11% and 20% compared to using the AE model (2013), and by 6% compared to using the Resnet model (2022) on the same actual dataset NOAA from the National Oceanic and Atmospheric Administration - United State of America.

Estimation of the Acoustic Transducer Beam Aperture by Using the Geometric Backscattering Model for Side-Scan Sonar Systems.

In this paper, we propose an algorithm for estimating the beam aperture of the acoustic transducers by using the geometric backscattering model for side-scan sonar systems. The geometric backscattering model is developed to describe the propagation paths of the signal transmitted from the transducers towards the seabed and backscatters to the hydrophones. To evaluate our proposed algorithm, we have developed a side-scan sonar system. The side-scan sonar system uses two transducers, operating on two different frequencies and focusing on two different wave beams, to scan the images of the seabed. The proposed algorithm provides the estimated beam apertures of each transducer. Our obtained results agree quite well with the parameters provided by the manufacturers. Moreover, these results are used to calibrate the scanned images. We provide the scanned sonar 3D images of the Dong Do lakebed, Vietnam, to justify our proposal.

Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems.

Channel estimation plays a critical role in the system performance of wireless networks. In addition, deep learning has demonstrated significant improvements in enhancing the communication reliability and reducing the computational complexity of 5G-and-beyond networks. Even though least squares (LS) estimation is popularly used to obtain channel estimates due to its low cost without any prior statistical information regarding the channel, this method has relatively high estimation error. This paper proposes a new channel estimation architecture with the assistance of deep learning in order to improve the channel estimation obtained by the LS approach. Our goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile for simulations in 5G-and-beyond networks under the level of mobility expressed by the Doppler effects. The system model is constructed for an arbitrary number of transceiver antennas, while the machine learning module is generalized in the sense that an arbitrary neural network architecture can be exploited. Numerical results demonstrate the superiority of the proposed deep learning-based channel estimation framework over the other traditional channel estimation methods popularly used in previous works. In addition, bidirectional long short-term memory offers the best channel estimation quality and the lowest bit error ratio among the considered artificial neural network architectures.

Classification of Surface Vehicle Propeller Cavitation Noise Using Spectrogram Processing in Combination with Convolution Neural Network.

This paper proposes a method to enhance the quality of detecting and classifying surface vehicle propeller cavitation noise (VPCN) in shallow water by using the improved Detection Envelope Modulation On Noise (DEMON) algorithm in combination with the modified Convolution Neural Network (CNN). To improve the quality of the VPCN spectrogram signal, we apply the DEMON algorithm while analyzing the amplitude variation (AV) to detect the fundamental frequencies of the VPCN signal. To enhance the performance of the traditional CNN, we adapt the size of the sliding window in accordance with the properties of the VPCN spectrogram data, and also reconstruct the CNN layer structure. As for the results, the fundamental frequencies contented in the VPCN spectrogram data can be detected. The analytical results based on the measured data show that the accuracy of the VPCN classification obtained by the proposed method is above 90%, which is higher than those obtained by traditional methods.

Serum hs-CRP measured prior transplantation predicts of new-onset diabetes after transplantation in renal transplant recipients.

BACKGROUND: To assess the incidence of new-onset diabetes after transplantation (NODAT) for the first year post-transplantation and the predictive value of high-sensitivity C-reactive Protein (hs-CRP) before transplantation for NODAT prediction in kidney transplantation patients. MATERIAL AND METHODS: A study of 251 consecutive adult end-stage kidney disease patients transplanted kidneys from living donors, follow-up during the first year to find NODAT. We diagnosed NODAT based on blood glucose or HbA1c following to the criteria of the American Diabetes Association. RESULTS: The ratio of NODAT was 12.4%. The mean age, mean BMI, the proportion of arteriosclerosis, and the median hs-CRP level in NODAT group were significantly higher than those of non-NODAT group with p < 0.05. Age, BMI and serum hs-CRP had a predictive value for NODAT (Age: AUC = 0.62, p < 0.05, BMI: AUC = 0.626, hs-CRP: AUC = 0.748, p < 0.001). CONCLUSION: Serum hs-CRP level measured prior transplantation is a good predictor for NODAT in renal transplant recipients.

Techno-economic assessment of hybrid extraction and distillation processes for furfural production from lignocellulosic biomass.

BACKGROUND: Lignocellulosic biomass is one of the most promising alternatives for replacing mineral resources to overcome global warming, which has become the most important environmental issue in recent years. Furfural was listed by the National Renewable Energy Laboratory as one of the top 30 potential chemicals arising from biomass. However, the current production of furfural is energy intensive and uses inefficient technology. Thus, a hybrid purification process that combines extraction and distillation to produce furfural from lignocellulosic biomass was considered and investigated in detail to improve the process efficiency. This effective hybrid process depends on the extracting solvent, which was selected based on a comprehensive procedure that ranged from solvent screening to complete process design. RESULTS: Various solvents were first evaluated in terms of their extraction ability. Then, the most promising solvents were selected to study the separation feasibility. Eventually, processes that used the three best solvents (toluene, benzene, and butyl chloride) were designed and optimized in detail using Aspen Plus. Sustainability analysis was performed to evaluate these processes in terms of their energy requirements, total annual costs (TAC), and carbon dioxide (CO2) emissions. The results showed that butyl chloride was the most suitable solvent for the hybrid furfural process because it could save 44.7% of the TAC while reducing the CO2 emissions by 45.5% compared to the toluene process. In comparison with the traditional purification process using distillation, this suggested hybrid extraction/distillation process can save up to 19.2% of the TAC and reduce 58.3% total annual CO2 emissions. Furthermore, a sensitivity analysis of the feed composition and its effect on the performance of the proposed hybrid system was conducted. CONCLUSIONS: Butyl chloride was found to be the most suitable solvent for the hybrid extraction/distillation process of furfural production. The proposed hybrid sequence was more favorable than the traditional distillation process when the methanol fraction of the feed stream was <3% and more benefit could be obtained when that fraction decreased.

Multi-step control over self-assembled hydrogels of peptide-derived building blocks and a polymeric cross-linker.

We present a detailed study of self-assembled hydrogels of bundled and cross-linked networks consisting of positively charged amyloid-like nanofibers and a triblock copolymer with negatively charged end blocks as a cross-linker. In a first step small oligopeptides self-assemble into macrocycles which are held together by reversible disulfide bonds. Interactions between the peptides cause the macrocycles to assemble into nanofibers, which form a reversible hydrogel. The physical properties of the hydrogel are tuned using various methods such as control over the fibre length, addition of a cross-linking copolymer, and addition of salt. We establish a relationship between the bulk mechanical properties, the properties of the individual fibers and the hydrogel morphology using characterization techniques operating at different length scales such as rheology, atomic force microscopy (AFM) and cryo transmission electron microscopy (Cryo-TEM). This allows for a precise control of the elastic behaviour of these networks.

Controlling the Structure and Length of Self-Synthesizing Supramolecular Polymers through Nucleated Growth and Disassembly.

Directing self-assembly processes out-of-equilibrium to yield kinetically trapped materials with well-defined dimensions remains a considerable challenge. Kinetically controlled assembly of self-synthesizing peptide-functionalized macrocycles through a nucleation-growth mechanism is reported. Spontaneous fiber formation in this system is effectively shut down as most of the material is diverted into metastable non-assembling trimeric and tetrameric macrocycles. However, upon adding seeds to this mixture, well-defined fibers with controllable lengths and narrow polydispersities are obtained. This seeded growth strategy also allows access to supramolecular triblock copolymers. The resulting noncovalent assemblies can be further stabilized through covalent capture. Taken together, these results show that self-synthesizing materials, through their interplay between dynamic covalent bonds and noncovalent interactions, are uniquely suited for out-of-equilibrium self-assembly.

Temperature-sensitive colloidal phase behavior induced by critical Casimir forces.

We report Monte Carlo simulations of phase behavior of colloidal suspensions with near-critical binary solvents using effective pair potentials from experiments. At off-critical solvent composition, the calculated phase diagram agrees well with measurements of the experimental system, indicating that many-body effects are limited. Close to the critical composition, however, agreement between experiment and simulation becomes poorer, signaling the increased importance of many-body effects. Both at and off the critical solvent concentration, the colloidal phase diagram is qualitatively similar to those of molecular systems and obeys the principle of corresponding states with one striking difference: it occurs in a narrow temperature interval of <1 °C below the solvent phase separation temperature.

Controlling colloidal phase transitions with critical Casimir forces.

The critical Casimir force provides a thermodynamic analogue of the quantum mechanical Casimir force that arises from the confinement of electromagnetic field fluctuations. In its thermodynamic analogue, two surfaces immersed in a critical solvent mixture attract each other due to confinement of solvent concentration fluctuations. Here, we demonstrate the active assembly control of colloidal equilibrium phases using critical Casimir forces. We guide colloidal particles into analogues of molecular liquid and solid phases via exquisite control over their interactions. By measuring the critical Casimir pair potential directly from density fluctuations in the colloidal gas, we obtain insight into liquefaction at small scales. We apply the van der Waals model of molecular liquefaction and show that the colloidal gas-liquid condensation is accurately described by the van der Waals theory, even on the scale of a few particles. These results open up new possibilities in the active assembly control of micro and nanostructures.

Single crystal growth and anisotropic crystal-fluid interfacial free energy in soft colloidal systems.

We measure the anisotropy of the crystal-fluid interfacial free energy in soft colloidal systems. A temperature gradient is used to direct crystal nucleation and control the growth of large single crystals in order to achieve well-equilibrated crystal-fluid interfaces. Confocal microscopy is used to follow both the growth process and the equilibrium crystal-fluid interface at the particle scale: heterogeneous crystal nucleation, the advancing interface, and the stationary equilibrium interface. We use the measured growth velocity to determine the chemical potential difference between crystal and fluid phases. Well-equilibrated, large crystal-fluid interfaces are then used to determine the interfacial free energy and its anisotropy directly from thermally excited interface fluctuations. We find that while the measured average interfacial free energy is in good agreement with values found in simulations, the anisotropy is significantly larger than simulation values. Finally, we investigate the effect of impurities on the advancing interface. We determine the critical force needed to overcome impurity particles from the local interface curvature.

Separation of D-psicose and D-fructose using simulated moving bed chromatography.

Simulated moving bed (SMB) processes have been widely used in the sugar industries with ion-exchange resin as a stationary phase. D-psicose, a rare monosaccharide known as a valuable pharmaceutical substrate, was synthesized by the enzymatic conversion from D-fructose. The SMB process was adopted to separate D-psicose from D-fructose. Before the SMB experiment, the reaction mixture including D-psicose and D-fructose was treated by a deashing process to remove contaminants, such as buffers, proteins, and other organic materials. Four columns packed with Dowex 50WX4-Ca2+ (200-400 mesh) ion-exchange resins were used in the four-zone SMB. Single-step frontal analysis was performed to estimate the isotherm parameters of each monosaccharide. The operating conditions of the SMB process were determined based on the Equilibrium Theory. According to the simulation of the SMB process, the purity and yield of extract product (D-psicose) achieved were 99.04 and 97.46%, respectively and those of raffinate product (D-fructose) were 99.06 and 99.53%, respectively. Under the optimized operating condition, complete separation (extract purity = 99.36%, raffinate purity = 99.67%) was achieved experimentally.

Nematodes (order Oxyurida Skrjabin, 1923) in human and primates in Viet Nam

A total of 40 species of nematodes belong to Oxyurida order that parasitized in vertebrates, were discovered and listed in Vietnam up to now. Among them, 3 species of nematode parasite in human and primates, including: Enterobius vermincularis, E. bipapillosus and E. nyctecebi. Enterobius vermincularis commonly parasitize in human and primates. E. bipapillosus and E. nyctecebi are popular and parasitize in some species of primates but not found in human