Search Results (102)

In the era of smart agriculture, accurate identification of plant species is critical for effective crop management, biodiversity monitoring, and the sustainable use of medicinal resources. However, existing deep learning approaches often underperform when applied to fine-grained plant classification tasks due to the lack of domain-specific, high-quality datasets and the limited representational capacity of traditional architectures. This study addresses these challenges by introducing a novel, well-curated leaf image dataset consisting of 39 classes of medicinal and aromatic plants collected from the Aromatic and Medicinal Plant Research Station in Odakkali, Kerala, India. To overcome performance bottlenecks observed with a baseline Convolutional Neural Network (CNN) that achieved only 44.94% accuracy, we progressively enhanced model performance through a series of architectural innovations. These included the use of a pre-trained VGG16 network, data augmentation techniques, and fine-tuning of deeper convolutional layers, followed by the integration of Squeeze-and-Excitation (SE) attention blocks. Ultimately, we propose a hybrid deep learning architecture that combines VGG16 with Batch Normalization, Gated Recurrent Units (GRUs), Transformer modules, and Dilated Convolutions. This final model achieved a peak validation accuracy of 95.24%, significantly outperforming several baseline models, such as custom CNN (44.94%), VGG-19 (59.49%), VGG-16 before augmentation (71.52%), Xception (85.44%), Inception v3 (87.97%), VGG-16 after data augumentation (89.24%), VGG-16 after fine-tuning (90.51%), MobileNetV2 (93.67), and VGG16 with SE block (94.94%). These results demonstrate superior capability in capturing both local textures and global morphological features. The proposed solution not only advances the state of the art in plant classification but also contributes a valuable dataset to the research community. Its real-world applicability spans field-based plant identification, biodiversity conservation, and precision agriculture, offering a scalable tool for automated plant recognition in complex ecological and agricultural environments. Full article

In recent years, many chaos-based encryption algorithms have been proposed. Many of these are based on established designs and populate their S-boxes with values derived from chaotic maps, following conventional implementation strategies to enable comparison with their original non-chaotic counterparts. In contrast, this work proposes a novel approach: a Chaos-Based Substitution Box (CB-SBox) implementation, in which conventional ROM-based S-boxes are replaced by a digital circuit that directly executes a selected chaotic map. This method enables the construction of S-boxes with long word lengths through an FPGA-based programmable circuit that allows for variable S-box lengths, facilitating the analysis of S-boxes of varying sizes, and ultimately enhancing security, particularly for larger S-boxes, as demonstrated by increased resistance to linear and differential cryptanalysis. Furthermore, the proposed CB-SBox achieves reductions in both area and power consumption compared to size-comparable ROM-based S-boxes. A 19-bit chaos-based S-box consumes just 0.0238% of the area and 0.0241% of the power required by an equivalent ROM-implemented S-box while providing the same level of security. The inherent unpredictability of non-linear chaotic behavior causes the proposed chaos-based S-boxes to exhibit non-bijective characteristics, making them well suited for application in non-invertible cryptographic primitives, such as hash functions and Feistel networks. The proposed CB-SBox is implemented in a Feistel network as described in the literature, and the results are provided. Full article

Mangrove ecosystems are renowned for their rich fungal diversity, housing a plethora of multicellular fungi and yeasts. In this investigation, we examined the yeast diversity associated with various compartments (rhizospheric soil, stems, roots, leaves, barks, and flowers) of the widely distributed mangrove tree, Avicennia officinalis, from the Kumbalam and Puthuvype mangroves in central Kerala, India. Our study revealed that the yeast strains were not uniformly distributed in various compartments. The highest abundance of yeasts was found in leaves (42%), followed by sediment (21%), and the lowest in flowers (5%). Among the 45 isolates, 27% comprised red yeasts. Dominant genera included Rhodotorula (27.5%), Debaryomyces (17.6%), Kluyveromyces (5.9%), Cryptococcus (9.8%), and Candida (7.8%), while genera such as Geotrichum, Lodderomyces, Ogataea, Galactomyces, and Saitozyma were represented by single isolates. Certain yeast species, such as C. tropicalis and Rhodotorula paludegina, exhibited a cosmopolitan distribution in various plant compartments of A. officinalis. An analysis of the proximate composition of different plant compartments of A. officinalis revealed variations in C, N, S, H, Ca, K, and the C/N ratio. Interestingly, these variations were positively correlated with the yeast community composition, suggesting a potential role of the elemental composition of plants in shaping the yeast biome of A. officinalis. However, our understanding of the inter-relationships among yeast communities in different plant compartments remains limited, highlighting the need for further comprehensive investigations in this field. Full article

Food quality assessment is a critical aspect of food production and safety, ensuring that products meet both regulatory and consumer standards. Traditional methods such as sensory evaluation, chromatography, and spectrophotometry are widely used but often suffer from limitations, including subjectivity, high costs, and time-consuming procedures. In recent years, the development of electronic nose (e-nose) and electronic tongue (e-tongue) technologies has provided rapid, objective, and reliable alternatives for food quality monitoring. These bio-inspired sensing systems mimic human olfactory and gustatory functions through sensor arrays and advanced data processing techniques, including artificial intelligence and pattern recognition algorithms. The e-nose is primarily used for detecting volatile organic compounds (VOCs) in food, making it effective for freshness evaluation, spoilage detection, aroma profiling, and adulteration identification. Meanwhile, the e-tongue analyzes liquid-phase components and is widely applied in taste assessment, beverage authentication, fermentation monitoring, and contaminant detection. Both technologies are extensively used in the quality control of dairy products, meat, seafood, fruits, beverages, and processed foods. Their ability to provide real-time, non-destructive, and high-throughput analysis makes them valuable tools in the food industry. This review explores the principles, advantages, and applications of e-nose and e-tongue systems in food quality assessment. Additionally, it discusses emerging trends, including IoT-based smart sensing, advances in nanotechnology, and AI-driven data analysis, which are expected to further enhance their efficiency and accuracy. With continuous innovation, these technologies are poised to revolutionize food safety and quality control, ensuring consumer satisfaction and compliance with global standards. Full article

Non-invasive glucose monitoring has become a critical area of research for diabetes management, offering a less intrusive and more patient-friendly alternative to traditional methods such as finger-prick tests. This study presents a novel approach using a semi-solid tissue-mimicking phantom designed to replicate the dielectric properties of human skin and blood vessels. The phantom was simplified to focus solely on the skin layer, with embedded channels representing veins to achieve realistic glucose monitoring conditions. These channels were filled with D-(+)-Glucose solutions at varying concentrations (60 mg/dL to 200 mg/dL) to simulate physiological changes in blood glucose levels. A miniature patch antenna optimized to operate at 14 GHz with a penetration depth of approximately 1.5 mm was designed and fabricated. The antenna was tested in direct contact with the skin phantom, allowing for precise measurements of the changes in glucose concentration without interference from deeper tissue layers. Simulations and experiments demonstrated the antenna’s sensitivity to variations in glucose concentration, as evidenced by measurable shifts in the dielectric properties of the phantom. Importantly, the system enabled stationary measurements by injecting glucose solutions into the same blood vessels, eliminating the need to reposition the sensor while ensuring reliable and repeatable results. This work highlights the importance of shallow penetration depth in targeting close vessels for noninvasive glucose monitoring, and emphasizes the potential of microwave-based sensing systems as a practical solution for continuous glucose management. Full article

Traditional proportional–integral–derivative (PID) controllers are often utilized in industrial control applications due to their simplicity and ease of implementation. This study presents a novel control strategy that integrates the Groupers and Moray Eels Optimization (GMEO) algorithm with a Dual-Stream Multi-Dependency Graph Neural Network (DMGNN) to optimize PID controller parameters. The approach addresses key challenges such as system nonlinearity, dynamic adaptation to fluctuating conditions, and maintaining robust performance. In the proposed framework, the GMEO technique is employed to optimize the PID gain values, while the DMGNN model forecasts system behavior and enables localized adjustments to the PID parameters based on feedback. This dynamic tuning mechanism enables the controller to adapt effectively to changes in input voltage and load variations, thereby enhancing system accuracy, responsiveness, and overall performance. The proposed strategy is assessed and contrasted with existing strategies on the MATLAB platform. The proposed system achieves a significantly reduced settling time of 100 ms, ensuring rapid response and stability under varying load conditions. Additionally, it minimizes overshoot to 1.5% and reduces the steady-state error to just 0.005 V, demonstrating superior accuracy and efficiency compared to existing methods. These improvements demonstrate the system’s ability to deliver optimal performance while effectively adapting to dynamic environments, showcasing its superiority over existing techniques. Full article

Fault diagnosis in power converters is essential for keeping electrical systems stable, efficient and long-lasting. Park’s Vector Transform, discrete wavelet transform, Artificial Neural Network, Fuzzy Logic and other methods are used to diagnose faults in the power converter in both single and multiple open switch situations. These methods are implemented on the digital signal processor or controller, which needs additional hardware and consumes more processing time. This paper presents a hardware-based open switch fault diagnostic method in a 3ϕ voltage source inverter to minimize fault diagnosis time and cost. An innovative hardware-based approach that utilizes a single neuron for open switch fault diagnosis in 3ϕ voltage source inverters was successfully implemented without using a digital signal processor or controller. A gradient descent algorithm calculates the weight and bias values of a single processing neuron. Furthermore, a high-speed multiplier and adder circuit seamlessly integrate with the single processing neuron, enabling rapid real-time fault diagnosis. This method is capable of diagnosing single and multiple switch open circuit faults in switching devices under variable load conditions at different frequencies. The proposed system ensures good effectiveness and resistivity, detecting faults in less than one cycle with low implementation effort and no tuning or threshold dependence. It achieves 98% accuracy, 96% precision and 95% recall, with a 2% false positive rate. Unlike traditional methods, it eliminates DSP/controller dependency by using a single neuron-based processing circuit, reducing cost and improving real-time fault diagnosis in three-phase voltage source inverters. Full article

The current research presents a practical approach to assess energy performance and thermal comfort in households through monitoring campaigns. The campaigns are conducted in a Greek city, involving the installation of low-intrusive recording devices for hourly electricity consumption, indoor temperature, and relative humidity in different residences in winter and summer periods. The recorded indoor environmental conditions are initially compiled to the Predicted Mean Vote (PMV) index, followed by the formulation of databases of hourly electricity consumption, PMV and local outdoor climate conditions retrieved by an official source of meteorological conditions. A special algorithm for database processing was developed which takes into account the eligibility of data series, i.e., only the ones corresponding to non-zero electricity consumption are treated as eligible. First, the sequential temporal progress of energy consumption and thermal comfort is produced towards the assessment of energy-use intensity and thermal comfort patterns. Secondly, through summing of the electricity consumption within 0.5-step PMV intervals, under three outdoor temperature intervals with approximately the same number of eligible measurements, reliable interrelations of energy consumption and PMV are obtained even for residences with limited amount of measured data. It is revealed that the weekly electricity consumption ranged within 0.15–3.59 kWh/m² for the winter cases and within 0.29–1.72 kWh/m² for the summer cases. The acceptable range of −1 ≤ PMV ≤ 1 interval holds an occurrence frequency from 69.46% to 93.39% and from 37.94% to 70.31% for the winter and summer examined cases, respectively. Less resistance to discomfort conditions is observed at most of the summer examined households exhibiting the electricity peak within the 1 ≤ PMV ≤ 1.5 interval, contrary to the winter cases for which the electricity peak occurred within the −1 ≤ PMV ≤ −0.5 interval. The study provides graphical relationships of PMV and electricity consumption under various outdoor temperatures paving the way for correlating thermal comfort and energy consumption. Full article

Perioperative monitoring of the ocular heat transfer is important for increasing the safety of long-term vitreoretinal surgery. The study is aimed at studying new thermoelectric measuring devices for comprehensive perioperative monitoring of ocular temperature and heat fluxes in vitreoretinal surgery. This pilot, open-label, prospective study included 23 patients (23 eyes) with proliferative diabetic retinopathy (PDR) in both eyes. The thermoelectric devices were developed for measuring intraocular temperature in vitreoretinal surgery and for determining the ocular surface temperature (OST) and heat flux (HF) density. In all cases, OST and HF density of both eyes were recorded (before and after surgery). Intraocular temperature and temperature of the irrigation fluid were measured intraoperatively. No complications associated with the perioperative use of thermoelectric temperature and HF sensors were identified during the study. The successful application of thermoelectric temperature and HF sensors, developed specifically for ophthalmological applications, in comprehensive perioperative monitoring of ocular heat transfer in patients with PDR in vitreoretinal surgery was demonstrated for the first time. Further research is needed to confirm the benefits of perioperative temperature monitoring in vitreoretinal surgery, as well as to develop equipment for active management of temperature in surgical practice. Full article

Background/Objectives: Ovarian cancer is the deadliest gynecologic cancer, and with the majority of patients dying within the first five years of diagnosis, new therapeutic options are required. The small guanosine triphosphatase (GTPase) Ras-related nuclear protein (Ran) has been reported to be highly expressed in high-grade serous ovarian cancers (HGSOCs) and associated with poor outcomes. Blocking Ran function or preventing its expression were shown to be promising treatment strategies, however, there are currently no small molecule inhibitors available to specifically inhibit Ran function. Interestingly, a previous study suggested that the Vesicular stomatitis virus (VSV) could inhibit Ran activity. Given that VSV is an oncolytic virus (OV) and, therefore, has anti-cancer activity, we reasoned that oncolytic VSV (oVSV) might be particularly effective against ovarian cancer via Ran inhibition. Methods: We evaluated the sensitivity of patient-derived ovarian cancer cell lines to oVSV, as well as the impact of oVSV on Ran and vice versa, using overexpression systems, small interfering RNAs (siRNAs), and drug inhibition. Results: In this study, we evaluated the interplay between oVSV and Ran and found that, although oVSV does not consistently block Ran, increased Ran activation allows for better oVSV replication and tumor cell killing. Conclusions: Our study reveals a positive impact of Ran on oVSV sensitivity. Given the high expression of Ran in HGSOCs, which are particularly aggressive ovarian cancers, our data suggest that oVSV could be effective against the deadliest form of the disease. Full article

Soil salinity assessment methods based on remote sensing data are a common topic of scientific research. However, the developed methods, as a rule, estimate relatively small areas of the land surface at certain moments of the season, tied to the timing of ground surveys. Considerable variability of weather conditions and the state of the earth surface makes it difficult to assess the salinity level with the help of remote sensing data and to verify it within a year. At the same time, the assessment of salinity on the basis of multiyear data allows reducing the level of seasonal fluctuations to a considerable extent and revealing the statistically stable characteristics of cultivated areas of land surface. Such an approach allows, in our opinion, the processes of mapping the salinity of large areas of cultivated lands to be automated considerably. The authors propose an approach to assess the salinization of cultivated and non-cultivated soils of arid zones on the basis of long-term averaged values of vegetation indices and salinity indices. This approach allows revealing the consistent relationships between the characteristics of spectral indices and salinization parameters. Based on this approach, this paper presents a mapping method including the use of multiyear data and machine learning algorithms to classify soil salinity levels in one of the regions of South Kazakhstan. Verification of the method was carried out by comparing the obtained salinity assessment with the expert data and the results of laboratory tests of soil samples. The percentage of “gross” errors of the method, in other words, errors when the predicted salinity class differs by more than one position compared to the actual one, is 22–28% (accuracy is 0.78–0.72). The obtained results allow recommending the developed method for the assessment of long-term trends of secondary salinization of irrigated arable land in arid areas. Full article

The steelmaking–continuous casting process (SCCP) is a complex manufacturing process which exhibits the distinct features of process manufacturing. The SCCP involves a variety of production elements, such as multiple process routes, a wide array of smelting and auxiliary devices, and a variety of raw and auxiliary materials. The production-simulation of SCCP holds a natural advantage in being able to accurately depict the intricate production behavior involved, and this serves as a crucial tool for optimizing the production operation of the SCCP. This paper thoroughly considers the various production elements involved in the SCCP, such as the fluctuation of the converter smelting cycle, fluctuation of heat weight, and ladle operation. Based on the Plant Simulation software platform, a dynamic simulation model of the SCCP is established and detailed descriptions are provided regarding the design of an SCCP using dynamic-operation rules. Additionally, a dynamic operational control program for the SCCP is developed using the SimTalk language, one which ensures the continuous operation of the caster in the SCCP, using the discrete simulation platform. The effectiveness of the proposed dynamic simulation model is verified by the total completion time of the production plan, the transfer time of the heat among the different processes, and the frequency of ladle turnover. The simulation’s results indicate that the dynamic simulation model has a satisfactory effect in simulating the actual production process. On this basis, the application effects of different schedules are compared and analyzed. Compared with a heuristic schedule, the optimized schedule based on the “furnace–machine coordinating” mode reduces the weighted value of total completion time by 8.7 min, reduces the weighted value of transfer waiting time by 45.5 min, and the number of rescheduling times is also reduced, demonstrating a better application effect and verifying the optimizing effect of the “furnace–machine coordinating” mode on the schedule. Full article

The structure and physicochemical properties of polyvinyl alcohol (PVA) and PVA/glycerol films have been investigated by Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), thermogravimetry/differential thermal analysis (TG/DTA), and advanced scanning probe microscopy (SPM). In the pure PVA films, SPM allowed us to observe ribbon-shaped domains with a different frictional and elastic contrast, which apparently originated from a correlated growth or assembly of PVA crystalline nuclei located within individual PVA clusters. The incorporation of 22% w/w glycerol led to modification in shape of those domains from ribbon-like in pure PVA to rounded in PVA/glycerol 22% w/w films; changes in the relative intensities of the XRD peaks and a decrease in the amorphous halo in the XRD pattern were also detected, while the DTA peak corresponding to the melting point remained at almost the same temperature. For higher glycerol content, FT-IR revealed additional glycerol-characteristic peaks presumably related to the formation of glycerol aggregates, and XRD, FT-IR, and DTA all indicated a reduction in crystallinity. For more than 36% w/w glycerol, the plasticization of the films complicated the acquisition of SPM images without tip-induced surface modification. Our study contributes to the understanding of crystallinity in PVA and how it is altered by a plasticizer such as glycerol. Full article

To pave the way towards buildings’ decarbonization in the context of the European Union’s (EU) policy, the methodology of cost-optimality assessment based on regulation 244/2012/EU is a useful tool to explore and foster the application of energy technologies in buildings. Meanwhile, the fostering of concentrated solar power is included in the EU solar energy strategy. In this study, the cost-optimal methodology is employed for the techno-economic assessment of the integration of a novel solar, multi-purpose energy technology, namely a parabolic trough collector-based trigeneration system, in two building types with different characteristics, namely an office and a hospital, in Greece, thus allowing the evaluation of the cost-optimal system design and the impact of the building type on the system’s techno-economic performance. Reference buildings are defined and their energy demand is calculated through dynamic energy simulations. The trigeneration system’s performance for different design scenarios is then parametrically investigated using a simulation model. For each scenario, energy, environmental and economic indicators are calculated and the cost-optimal designs are extracted. In the cost-optimal implementation, the system covered 18.19–36.39% and 3.58–15.71% of the heating and cooling demand, respectively, while the reduction of the primary energy consumption and emissions was estimated at 10–14% and 10–16%, respectively. However, differences between the buildings related to the operation schedule and the loads led to the implementation of the system being economically more attractive in the hospital, while for the office, financial support is necessary for a viable investment. Full article