This study presents a comprehensive investigation focused on microspheres containing silver molecular clusters as a high-performance material for advanced sensor applications. The study involves modeling the behavior of these microspheres and conducting a detailed analysis of their optical characteristics.

The synthesis of microspheres with silver molecular clusters involves an ion exchange process, wherein microspheres are immersed in a molten mixture of silver nitrate (AgNO3) and sodium nitrate (NaNO3). This controlled ion exchange leads to the formation of silver molecular clusters within the glass matrix, creating a distinctive surface layer and a refractive index gradient at the microsphere boundary.

Simulation results demonstrate an extended propagation of the fundamental mode compared to conventional glass microspheres, significantly enhancing the interaction of radiation with matter. The unique optical properties of silver molecular clusters, including luminescence peaks in the visible range (400-600 nm) when excited with long-wavelength UV light (360-410 nm), are thoroughly investigated to exploit the light-matter interaction for sensory functions. Furthermore, the material's characteristics, particularly its ultraviolet and visible absorption properties, are examined to gain insights into its potential for sensor applications.

Applications for these microspheres with silver molecular clusters encompass a wide range of sensor technologies. Examples include environmental sensing for detecting pollutants or hazardous gases, biomedical applications for targeted drug delivery or bioimaging, and industrial process monitoring for precise control and optimization.

We present a smart optoelectronic system for fluid sensing by measuring the laser beam displacement induced by the difference in refractive index between liquids. In the experimental configuration, the radiation provided by a red laser diode impinges obliquely the flat surface of a plastic cuvette containing the fluid under test. After being reflected by a mirror glued onto the back side of the cuvette, thus after crossing the channel of the cuvette twice, the radiation exits the cuvette in different positions when fluids with different refractive index fill its channel, according to Snell law, and it finally reaches the active surface of a position sensitive detector (PSD). We retrieved the position of the output light beam onto the PSD as p_PSD = L/2 × (V₁ – V₂)/(V₁ + V₂), where L is the length of the active surface, V₁ and V₂ are the voltage output signals, proportional to the photocurrents I₁ and I₂ generated at the extremities of the sensitive area. The output signals provided by the PSD are visualized in real-time and acquired with an oscilloscope. Data are elaborated in MATLAB environment. We exploited the sensing platform to distinguish fluids for artificial parenteral nutrition on the basis of their refractive indices, that are determined by knowing the different concentrations of solutes such as glucose, amino acids and electrolytes. We developed a model based on ray optics in MATLAB environment: experimental results were found in good agreement with the simulations provided by the model. We successfully demonstrated the detection of artificial parenteral nutrition fluid with high sensitivity by exploiting a totally remote, non-invasive approach with the use of just a few low-cost optical elements and a biocompatible standard cuvette.

Photoacoustic spectroscopy (PAS) is a measuring principle that uses the interaction of matter and electromagnetic radiation. We developed a PAS sensor particularly suited for the investigation of solid and semi-solid samples. A quantum cascade laser (QCL) excites molecules in a pulsed way, thus generating an acoustic signal. The specific concentration can be derived from the level of the signal. Often acoustic resonances of the sample cell are exploited to improve the SNR of the PAS measurements.

In this investigation, a new cell is designed in the form of a half-open cylinder. One end is tightly sealed by the sample and the opening on the other side is intended for the entry of the laser beam. This design is particularly useful for solid and semi-solid samples, e.g. in blood glucose detection [1].

If the development of a standing wave in the cylinder is considered, the maximum of the sound pressure of the fundamental vibration will occur directly at the sample where a measurement is difficult. Therefore, the first harmonic is to be used and a small hole is placed in the wall of the resonator at the location of the pressure maximum. A sound detector can then be placed close to the hole outside the cylinder and is therefore not (or only minimally) changing the resonance conditions. A finite element simulation confirms the pressure distribution.

[1] KAYSIR, Md Rejvi, et al. Progress and Perspectives of Mid-Infrared Photoacoustic Spectroscopy for Non-Invasive Glucose Detection. Biosensors, 2023, 13. Jg., Nr. 7, S. 716.

The adoption of wireless sensor networks has brought a significant breakthrough in structural health monitoring, providing an effective alternative to the challenges associated with traditional cable-based sensors. In recent years, a growing interest in developing contactless, vision-based vibration sensors like video cameras, has led to advancements potentially alleviating the previously mentioned drawbacks. In this study, videos of a vibrating structural case study are created with a specific sampling rate, and then converted into a set of frames, so that local phase information can be extracted from all of the images. The motion matrix is then derived from the phase information; since the number of measuring points is usually greater than the number of the excited modes of the system, the problem can become over-determined. Therefore, by applying dimensionality reduction techniques, like e.g. the Non-Negative Matrix Factorization, the dimensions of the motion matrix are significantly reduced. Finally, by exploiting an output-only identification technique, modal parameters are computed. The performance of the proposed approach is assessed using numerical examples, to prove that the structural frequencies and mode shapes can be accurately identified.

Rhodamine 6G is a highly effective luminescent xanthene dye of the rhodamine family, which widely used for labeling oligonucleotides in biochemistry, cell imaging, as a sensitive layer of chemical sensors for metal ions \1\ and a classical analyte for optimizing SERS structures \2\, etc. At the same time, the features of the interaction of R6G with low molecular weight analytes present in most biochemical preparations have not been studied. This is important not only for understanding the possibility of the influence of non-target components on the analytical signal during analysis, but is also of great practical importance, since it also allows developing (bio)chemical sensors for specific applications. In this work, we studied the features of the interaction of R6G thin films with a number of low molecular weight analytes in the gas phase (to exclude cross-interaction etc.), namely, water vapor, acetic acid, ethyl alcohol, ammonia, benzene, pyridine, nitrobenzene, acetone, and acetonitrile. The kinetic features and adsorption capacity of the sensitive layer were compared with those for other sensitive layer materials (macrocyclic dibenzotetraazaanulenes, phthalocyanines, and their metal complexes).

Thin films (100 nm) of organic materials were obtained by thermal deposition in vacuum on one side of quartz crystal microbalance transducers (10 MHz), which were at a temperature of 297±2 ^oK. The average condensation rate was about 0.1 nm/min. Optical measurements of the absorption and emission spectra showed that R6G retains typical optical characteristics in a solid state, which indicates the preservation of its molecular structure during the deposition process. The adsorption characteristics were determined in the format of “electronic nose” devices \3\, i.e. simultaneously for the entire set of sensors with different sensitive layers (multi-sensor array).

An analysis of the obtained results unambiguously indicates that, among the used sensitive materials mentioned above, R6G has the maximum adsorption capacity with respect to all studied analytes in the gas phase. Despite the fact that the kinetics of the interaction of R6G with analytes significantly depends on the nature of the analyte, the interaction is controlled by the process of physical sorption and is a completely reversible process. We discuss possible mechanisms for such an “increased” adsorption capacity of R6G thin films and their potential impact on classical analytical procedures using this widely used reagent.

Finally, it is reasonable to emphasize the promise of R6G for the development of multisensor arrays for multivariate intelligent gas analysers, since the combination of R6G with various inorganic nanostructured materials (ZnO, etc., metal nanoparticles, etc.) makes it possible to purposefully change the selectivity profile of composite materials. This allows us to create efficient sensor arrays optimized for specific application, including environmental monitoring or as a potential bio-sniffer for acute toxicity assays \4\ or highly sensitive sensors of low molecular weight biological regulators of vital activity \5\.

1 Yujiao Wang, Xiaojun Wang, Wenyu Ma , Runhua Lu , Wenfeng Zhouand Haixiang Gao. Recent Developments in Rhodamine-Based Chemosensors: A Review of the Years 2018–2022, Chemosensors 2022, 10(10), 399; https://doi.org/10.3390/chemosensors10100399

2 Iryna Krishchenko, Sergii Kravchenko, Ivanna Kruglenko, Eduard Manoilov and Boris Snopok 3D Porous Plasmonic Nanoarchitectures for SERS-Based

Chemical Sensing Eng. Proc. 2022, 27(1), 41; https://doi.org/10.3390/ecsa-9-13200

3 I.V. Kruglenko, B.A. Snopok, Yu.M. Shirshov, F.J. Rowell Multisensor systems for gas analysis: optimization of arrays for classification of pharmaceutical products Semiconductor Physics, Quantum Electronics & Optoelectronics. 2004. V. 7, N 2. P. 207-216.

4 Kruglenko, I.; Kravchenko, S.; Burlachenko, J.; Kruglenko, P.; Snopok, B. Adsorbate Induced Transformations of Ovalbumin Layers in Volatile Organic Solvents: QCM Study of a Potential Bio-Sniffer for Acute Toxicity Assays. Eng. Proc. 2023, 35, 23. https://doi.org/ 10.3390/IECB2023-14574

5 Borys Snopok and Ivanna Kruglenko Analyte induced water adsorbability in gas phase biosensors: the influence of ethinylestradiol on the water binding protein capacity Analyst, 2015,140, 3225-3232

Extended object tracking is an important component of autonomous driving systems, as it enables the vehicle to accurately perceive and respond to the surrounding environment. Unlike point tracking, which treats objects as single points in space, extended object tracking takes into account the shape and size of objects, as well as their motion over time. Joint Probabilistic Data Association (JPDA) and Gaussian Mixture Probability Hypothesis Density (GM-PHD) are two popular extended object tracking methods that are being used in many different engineering applications. These two algorithms have been compared and analyzed for their performance in autonomous vehicle which uses only radar data. The limited visibility of the camera under certain conditions such as foggy, sunny, or rainy weather, and its sensitivity to obstacles such as the lens being covered with rain or snow, have played an active role in not using camera sensor. Based on the results, it is shown that both methods are good at keeping track of the multiple extended objects. However, comparison of these methods shows that GM-PHD is more advantageous than JPDA in terms of Generalized Optimal Sub-Pattern Assignment (GOSPA) metric which evaluates the performance of a tracking system by measuring the difference between the estimated and true positions of the tracked object.

Nowadays, artificial intelligence (AI) has become very prominent and impactful owing to its proficiency in accomplishing a wide variety of tasks with high levels of effectiveness and efficiency. Some of the areas where AI has demonstrated its capabilities include, but are not restricted to, visual recognition tasks like image classification, object detection, sensor data and natural language processing. Deep learning is an advanced sub-discipline of machine learning that emphasizes on refining artificial neural networks with multiple layers to apprehend intricate representations of data. It can learn useful things from raw data without manual feature engineering. In contrast, the advent of Internet of Things devices having inbuilt sensors opens up novel prospects for implementing convolutional neural networks (CNNs) directly on resource-limited devices. However, these devices have limited memory, storage, and computing power, making extensive, complex CNNs infeasible. Implementing compact CNNs with smaller models and computational needs on IoT devices enables localized AI capabilities like object recognition without relying on the cloud. This reduces latency while improving privacy and reliability. The goal of this paper is to thoroughly evaluate various compact CNN architectures for object recognition trained on a small resource-constrained platform, the NVIDIA Jetson Xavier. Rigorous experimentation identifies the best compact CNN models that balance accuracy and speed on embedded IoT devices. The key objectives are to analyze resource usage such as CPU/GPU and RAM used to train models, the performance of the CNNs, identify trade-offs, and find optimized deep learning solutions tailored for training and real-time inferencing on edge devices with tight resource constraints.

Cervical cancer remains a significant global health burden, necessitating the development of novel diagnostic tools for early detection and personalized therapeutic interventions.

This study presents the design of an innovative aptamer-based biosensor for the detection of circulating microRNAs (miRNAs) associated with cervical cancer development. The selected panel includes 20 miRNAs known to play vital roles in cervical cancer pathogenesis, regulating processes such as cellular proliferation, migration, invasion, angiogenesis, apoptosis, inflammatory responses, and metastasis.

The biosensor design relies on the unique binding properties of aptamers, single-stranded nucleic acids with high specificity for miRNA targets. All aptamers, corresponding to the miRNA panel were designed with the help of the web-based software tool NHLBI-AbDesigner. The RNA Folding Form from the Mfold Web Server was used for modeling, displaying, and analyzing the structure of designed aptamers.

The biosensor's design can be optimized to ensure high sensitivity, low limits of detection, and robust performance in clinical settings. This novel biosensor design holds great promise for facilitating non-invasive detection and personalized therapeutic approaches for cervical cancer patients.

Acknowledgments: This work has been financed by the Ministry of Research, Innovation and Digitization through Program 1 - Development of the national research and development system, Subprogram 1.2 - Institutional performance - Projects for financing excellence in R&D, Contract no. 19PFE/2021

Introduction: Low back pain (LBP) is one of the most common musculoskeletal conditions and the leading cause of disability, and 8 out of 10 people have experienced it during their lifetime. No pathological changes are found in 85 percent of all LBP cases, called non-specific LBP. Muscle stiffness and movement impairment or limitation are commonly found in people with non-specific LBP. The remaining 15% are caused by disc disease, spinal stenosis, fractures with obvious causes, structural changes that are visible on examination, and tumors. Purpose: To determine trunk kinematics in non-specific chronic LBP during activities. Methods: We used to conduct a cross-sectional study design. A total of 90 participants (45 participants with LBP and 45 without LBP), aged between 18 and 50, participated in this study. The full-body wearable Xsens system (MVN, Xsens technologies, Netherlands) was used to record the 3D movements during the trunk flexion extension and hurdle step. The back range of motion (ROM) in the sagittal, frontal, and transversal planes was calculated using a relative orientation between pelvis and thorax segments and averaged for the LBP and control group. Results: The LBP group exhibited smaller trunk ROMs than controls during the flexion extension. In contrast, trunk ROMs were higher in people with LBP during the hurdle step except for rotation transversal. Conclusion: Altered trunk kinematics during the flexion-extension and hurdle step was observed in individuals with non-specific chronic LBP.

Around the globe, agriculture has grown to a point where it is now a financially feasible way to produce more sophisticated cultivation methods. Throughout the long tradition of agriculture, this represents a pivotal moment. The widespread adoption of data and the latest technological advances of the contemporary period allowed this paradigm change. However, pests remain to blame for significant harm done to crops, which has a detrimental impact on finances, the natural world, and society. This highlights the necessity of using automated techniques to apprehend pests before they cause widespread harm. Agriculture-related issues are currently the predominant subject for research that utilizes ML. The overarching aim of this investigation is the development of an economically feasible method for pest detection in vast fields of crops that IoT enables through the utilizes pest audio sound analytics. The recommended approach incorporates numerous acoustic preparation methods from audio sound analytics. The Chebyshev filter, the Welch method, non-overlap-add method, FFT, DFT, STFT, LPC algorithm, acoustic sensors, and PID sensors were among them. 800 pest sounds were examined for features and statistical measurements before being incorporated into Multilayer Perceptron (MLP) for training, testing, and validation. The experiment's outcomes demonstrated that the proposed MLP model triumphed over the currently available DenseNet, Faster RCNN, VGG-16, ResNet-50, YOLOv5, FE-Net, DCNN, MS-ALN, and SAFFPEST approaches alongside an accuracy of 99.78%, 99.91% sensitivity, 99.64% specificity, 99.59% recall, 99.82% F1 score, and 99.85% precision. The significance of the findings rests in their potential to proactively identify pests in large agriculture fields. As a result, the cultivation of crops will improve, leading to increased economic prosperity for agricultural producers, the country, and the entire globe.