Search Results (98)

Robots are increasingly emerging as effective platforms to overcome a wide range of challenges in agriculture. Beyond functioning as standalone systems, agricultural robots are proving valuable as collaborative platforms, capable of supporting and integrating with humans and other technologies and agricultural activities. In this study, we designed and implemented an automated system embedded in a ground-based robotic platform to support spraying drone operations. The system consists of a robotic platform that carries the spraying drone along with all necessary support devices, including a water tank, chemical reservoirs, a mixer, generators for drone battery charging, and a top landing pad. The system is controlled with a mobile app that calculates the total amount of water and chemicals required and sends commands to the platform to prepare the application mixture. The input information in the app includes the field area, application rate, and up to three chemical dosages simultaneously. Additionally, the platform allows the drone to take off from and land on it, enhancing both safety and operability. A set of pumps was used to deliver water and chemicals as specified in the mobile app. To automate pump control, we used Arduino technology, including both the microcontroller and a programming environment for coding and designing the mobile app. To validate the system’s effectiveness, we individually measured the amount of water and chemical delivered to the mixer tank and compared it with conventional manual methods for calculating chemical quantities and preparation time. The system demonstrated consistent results, achieving high precision and accuracy in delivering the correct amount. This study advances the field of agricultural robotics by highlighting the role of collaborative platforms. Particularly, the system presents a valuable and low-cost solution for small farms and experimental research. Full article

This study investigates the combined use of electron beam irradiation (EBI) and nanotechnology to develop improved food packaging films. EBI, commonly applied for sterilization, can alter polymer microstructure, while irradiated cellulose nanocrystals (CNCs) offer enhanced functionality when incorporated into biopolymer matrices. Here, CNCs were irradiated with doses up to 50 kGy, leading to the formation of carboxyl and aldehyde groups, confirmed by FTIR analysis, as a consequence of the initial formation of free radicals and peroxides that may subsist in that original form or be converted into various carbonyl groups. Flexible films were obtained by incorporating pristine and EB-irradiated CNCs in an internal mixer, using minute amounts of poly(ethylene oxide) (PEO) to facilitate the dispersion of the filler within the polymer matrix. The resulting PLA/PEO/CNC films were evaluated for their mechanical, thermal, barrier, and antioxidant properties. The results showed that structural modifications of CNCs led to significant enhancements in the performance of the composite films, including a 30% improvement in water barrier properties and a 50% increase in antioxidant activity. These findings underscore the potential of irradiated CNCs as effective additives in biopolymer-based active packaging, offering a sustainable approach to reduce dependence on synthetic preservatives and potentially extend the shelf life of food products. Full article

Early detection of tuberculosis plays a critical role in effective treatment management. Like active tuberculosis, early identification of inactive forms such as latent or healed tuberculosis is essential to prevent future reactivation. In this study, we developed a deep-learning-based binary classification model to distinguish between active and inactive tuberculosis cases. Our model architecture incorporated an EfficientNet backbone with an MLP-Mixer classification head and was fine-tuned on a dataset annotated by Cheonan Soonchunhyang Hospital. To enhance predictive performance, we applied transfer learning using weights pre-trained on the JFT-300M dataset via the Noisy Student training method. Unlike conventional models, our approach achieved competitive results, with an accuracy of 96.3%, a sensitivity of 95.9%, and a specificity of 96.6% on the test set. These promising outcomes suggest that our model could serve as a valuable asset to support clinical decision-making and streamline early screening workflows for latent tuberculosis. Full article

Surface electromyography (sEMG) non-invasively captures the electrical activity generated by muscle contractions, offering valuable insights into motion intentions. While sEMG has been widely applied to general gesture recognition in rehabilitation, there has been limited exploration of specific, intricate daily tasks, such as the pouring action. Pouring is a common yet complex movement requiring precise muscle coordination and control, making it an ideal focus for rehabilitation studies. This research proposes a granular computing-based deep learning approach utilizing ConvMixer architecture enhanced with feature fusion and granular computing to improve gesture recognition accuracy. Our findings indicate that the addition of hand-crafted features significantly improves model performance; specifically, the ConvMixer model’s accuracy improved from 0.9512 to 0.9929. These results highlight the potential of our approach in rehabilitation technologies and assistive systems for restoring motor functions in daily activities. Full article

The increasing demand for critical metals essential for renewable energy technologies necessitates efficient and environmentally sustainable extraction methods. Ilmenite (FeTiO₃) and similar ore deposits serve as abundant sources of primary elements while also incorporating a suite of strategically significant trace elements, including REEs and Hf, among others. Mixer–settler units are extensively utilized in metal purification processes. It is important to develop approaches for tracking the metal’s extraction process online and optimizing flow dynamics. One widely adopted technique for evaluating the flow dynamics of the various components is the residence time distribution (RTD) measurement, which provides insights into the hydrodynamic behavior of process reactors. This study explored the application of radiotracer techniques for online monitoring of solvent extraction processes in hydrometallurgy, focusing on Hf recovery. A mixer–settler system was employed using di(2-ethylhexyl) phosphoric acid (D2EHPA) as the extractant and the 1M HNO₃ aqueous phase of Ti ore. The radiotracer ¹⁸¹Hf was synthesized via neutron activation and introduced into the system to track phase distribution and RTD. Real-time monitoring revealed over 95% extraction efficiency within 133 min (8000 s). The RTD studies validated system performance using perfect mixers in series and axial dispersion models. The calculated mean residence time of 100 min (6000 s) closely aligned with the theoretical 104 min (6240 s), confirming the model accuracy. The findings demonstrate the viability of radiotracers in monitoring solvent extraction, offering real-time insights into flow dynamics and extraction efficiency. Full article

The continuous synthesis of nanoparticles (NPs) has been actively studied due to its great potential to produce NPs with reproducible and controllable physicochemical properties. Here, we achieved the high throughput production of nanostructured lipid carriers (NLCs) using a coaxial turbulent jet mixer with an added heating system. This device, designed for the crossflow of precursor solution and non-solvent, combined with the heating system, efficiently dissolves solid lipids and surfactants. We reported the flow regime according to the Reynolds number (Re). Also, we confirmed the size controllability of NLCs as dependent on both Re and lipid concentration. The optimized synthesis yields NLCs around 80 nm, ideal for targeted drug delivery by enhanced permeability and retention (EPR) effect. The coaxial turbulent jet mixer enables effective mixing, producing uniform size distribution of NLCs. The NLCs prepared using the coaxial turbulent jet mixer were smaller, more uniform, and had higher drug loading compared to the NLCs synthesized by a bulk nanoprecipitation method, showcasing its potential for advancing nanomedicine. Full article

The development of deep learning has led to the proposal of various models for human activity recognition (HAR). Convolutional neural networks (CNNs), initially proposed for computer vision tasks, are examples of models applied to sensor data. Recently, high-performing models based on Transformers and multi-layer perceptrons (MLPs) have also been proposed. When applying these methods to sensor data, we often initialize hyperparameters with values optimized for image processing tasks as a starting point. We suggest that comparable accuracy could be achieved with fewer parameters for sensor data, which typically have lower dimensionality than image data. Reducing the number of parameters would decrease memory requirements and computational complexity by reducing the model size. We evaluated the performance of two MLP-based models, MLP-Mixer and gMLP, by reducing the values of hyperparameters in their MLP layers from those proposed in the respective original papers. The results of this study suggest that the performance of MLP-based models is positively correlated with the number of parameters. Furthermore, these MLP-based models demonstrate improved computational efficiency for specific HAR tasks compared to representative CNNs. Full article

Constructing agricultural microbial gasification plants near livestock farms is essential for technical, economic, and environmental reasons. Utilising substrates from these farms allows for producing electricity, heat, and environmentally friendly manure. However, biogas plants often face technical challenges. This study evaluates the power quality of an agricultural biogas plant on a dairy farm. It was found that the plant was connected via a cable with an insufficient conductor cross-section, leading to significant voltage overshoots exceeding 14.6%, which prevented the activation of the second generator. Both generators could operate after replacing the feed-in cable, but considerable fluctuations in the feed-in voltage persisted. Further measurements indicated the need for changes in the digester design. Specifically, replacing the current two mixers with more lower-powered mixers operating alternately was proposed. Sharing these solutions more broadly can help prevent similar issues in future microbial gas plant constructions and optimise electricity production. Full article

During the preparation of high dilutions, repeated external vibration (shaking) is used. We hypothesized that it was the vibration treatment, and not the negligible content of the initial substance, that underlies the activity of highly diluted preparations. In order to test this, the vibration was separated from the dilution process. After vibrating two tubes together on a vortex mixer (one containing water and the other the initial substance) the electrical conductivity and radio frequency radiation intensity of water differed from the unvibrated control, and the ability to exert a modifying effect on the target solution appeared, as assessed using ELISA and terahertz spectroscopy, appeared. Thus, the properties of the neutral carrier (water) changed after non-contact exposure to the initial substance. We have named this process ‘crossing’ and its products ‘aqueous iterations of the initial substance’. Several aqueous iterations with different physical properties were obtained, some of which have a modifying effect and others cause various chemical (catalytic) and biological (antiviral) effects similar to those of the initial substance. This indicates that during crossing, substances enter into post-vibration supramolecular interactions. At the nanoscale level, aqueous iterations and the initial substance are structurally symmetrical, which allows us to assume that the preservation of the symmetry of substances subjected to vibration treatment is the basis of the post-vibration interaction phenomenon. Full article

Geopolymers are cementitious materials with exceptional mechanical and physical properties, making them suitable for aerospace applications. Considering their excellent performance, the present investigation aims to develop geopolymers with designed physical properties to address some issues in the aerospace industry. In this sense, the influence of the alkaline activator on the final properties was evaluated. For the development of the geopolymers, sodium hydroxide and sodium metasilicate solutions were preparedto obtain the alkaline activator. The synthesis process also consisted of a mixing stage using a mixer to obtain a homogenous paste. After mixing, the curing process consisted of a first thermal treatment at 60 °C for 4 h to evaporate the excess water, avoid excessive contraction, and promote strength at early ages. Subsequently, the geopolymers were left at rest for 28 days until the final properties were achieved. The influence of the solid-to-liquidratio (S/L) on the microstructure of the geopolymers was evaluated. For this purpose, X-ray fluorescence spectrometry, X-ray diffraction, and infrared spectrometry analyses were performed. The results show that the content of the alkaline activator promotes variations inthe presence of different crystalline phases, which is more noticeable as the S/L ratio increases. Likewise, the infrared spectra display peaks at different wavelengths regarding the variations in elemental composition, which are more evident with the changes in the S/L ratio. In addition, physical studies, such as thermal conductivity and resistance to gamma radiation were conducted for different geopolymer compositions. The results indicate that changes in properties are not too sensitive to compositional variations, although slight modifications exist. Finally, these studies are significant as aerospace-focused materials are directly exposed to this kind of phenomena. The designed geopolymers have to be able to resist and maintain their properties through exposure to any energy. Full article

A 30~60 GHz broadband down-conversion mixer driven by low local oscillator (LO) power is presented. The down-conversion mixer utilizes an input signal coupling technique based on the Marchand balun to achieve broadband operation and achieves low LO power drive and low DC power consumption through the use of a weak inversion bias with Gilbert switching devices. The broadband conversion of single-ended to differential signals is achieved using the Marchand balun with compensation lines, and an equivalent circuit analysis is performed. For the intermediate frequency (IF) output, a self-biased IF trans-impedance amplifier with current reusing and an active IF balun structure are used to achieve signal amplification and single-ended signal output. Test results show that the proposed mixer achieves a conversion gain of −1.2 to 6.4 dB in an IF output bandwidth of 0.1 to 5 GHz at radio frequency (RF) input frequencies of 30 to 60 GHz and LO driving power of −10 dBm. The DC power consumption of the core mixing unit of the proposed mixer is 4.8 mW, and the DC power consumption including the IF amplifier is 28.3 mW. The proposed mixer uses a 65 nm CMOS technology with a chip area of 0.26 mm². Full article

A classical emulsion formulation based on petrolatum and mineral oil as the internal phase with emulsifier wax as a typical topical emulsion cream was investigated for the effect of process parameters on drug product quality and performance attributes. The Initial Design of Experiment (DoE) suggested that an oil phase above 15%, coupled with less than 10% emulsifying wax, resulted in less stable emulsions. Different processing parameters such as homogenization speed, duration, cooling rate, and final temperature showed minimal influence on properties and failed to improve stability. The final DoE suggested that the optimal emulsion stability was achieved by introducing a holding period midway through the cooling stage after solvent addition. Within the studied holding temperature range (25–35 °C), a higher holding temperature correlated with increased emulsion stability. However, the application of shear during the holding period, using a paddle mixer, adversely affected stability by disrupting the emulsion microstructure. IVRT studies revealed that the release of lidocaine was higher in the most stable emulsion produced at a holding temperature of 35 °C compared to the least stable emulsion produced at a holding temperature of 25 °C. This suggests that a holding temperature of 35 °C improves both the stability and active release performance. It appears that a slightly higher holding temperature, 35 °C, allows a more flexible and stable emulsifying agent film around the droplets facilitating stabilization of the emulsion. This study offers valuable insights into the relationship between process parameters at various stages of manufacture, microstructure, and various quality attributes of emulsion cream systems. The knowledge gained will facilitate improved design and optimization of robust manufacturing processes, ensuring the production of the formulations with the desired critical quality attributes. Full article

In the cement concrete manufacturing industry, mixers are critical pieces of equipment that play an essential role. Mixers ensure, by mechanically mixing the materials that make up the concrete, the homogeneity of the mixture. Since the active elements of the mixer in the concrete industry—the mixing blades—come into permanent contact with the mineral aggregates in the mixture formed by water and cement, they are permanently subjected to a strong abrasive–erosive wear process. The authors of this article were concerned with the establishment of tribological models for studying the wear of mixing blades, in order to identify the influence of their constructive parameters on the wear intensity. A complex model (Kraghelsky–Nepomnyashchi model) was adopted for the study. The modeling results revealed that the wear intensity decreases with an increasing blade angle of attack and increases linearly with increasing speed, as well as with an increasing friction coefficient. The modeling results confirm that the wear intensity is lowest when the mixing blade is inclined at a 60° angle, while the highest value is recorded for 30°. By identifying the angle at which the greatest wear of blades occurs, interventions can be made in the design of a more durable mixer (with the optimal installation angle of the mixer blades), thus requiring fewer corrective maintenance interventions. Based on these findings, we conclude that the complex model used in the experiment can provide a convenient and efficient tool for the study of erosive–abrasive phenomena. Full article

This paper presents a fourth-order discrete-time direct RF-to-digital Delta-Sigma receiver architecture for flexible receivers with a wide frequency range. The use of a current-driven passive mixer with RF feedback enables high-Q bandpass filtering and relaxes the linearity requirement of the RF amplifier. In addition, the reconfigurable passive/active loop filter offers a good compromise between power consumption, linearity, and dynamic range. The other important feature of the proposed architecture is the use of a sampling frequency that is a divisor of the LO frequency. This solves several problems such as the upmixing of quantization noise, the need to reconfigure the Delta-Sigma loop when changing the LO frequency, and the use of two independent clocks for the LO and the sampling frequency. The circuit was implemented using 65 nm CMOS technology. The I/Q Direct Delta-Sigma receiver has an RF bandwidth of 20 MHz and a sampling frequency of 400 MHz. Measurement results show a very high dynamic range of up to 80 dB with a peak SNDR of 46 dB for a power consumption of 46 mW at 800 MHz. Full article

Identifying violent activities is important for ensuring the safety of society. Although the Transformer model contributes significantly to the field of behavior recognition, it often requires a substantial volume of data to perform well. Since existing datasets on violent behavior are currently lacking, it will be a challenge for Transformers to identify violent behavior with insufficient datasets. Additionally, Transformers are known to be computationally heavy and can sometimes overlook temporal features. To overcome these issues, an architecture named MLP-Mixer can be used to achieve comparable results with a smaller dataset. In this research, a special type of dataset to be fed into the MLP-Mixer called a sequential image collage (SIC) is proposed. This dataset is created by aggregating frames of video clips into image collages sequentially for the model to better understand the temporal features of violent behavior in videos. Three different public datasets, namely, the dataset of National Hockey League hockey fights, the dataset of smart-city CCTV violence detection, and the dataset of real-life violence situations were used to train the model. The results of the experiments proved that the model trained using the proposed SIC is capable of achieving high performance in violent behavior recognition with fewer parameters and FLOPs needed compared to other state-of-the-art models. Full article