This PDF file contains the front matter associated with SPIE Proceedings Volume 12548, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

A variety of sensors utilize nanoengineered titanium oxide TiO₂ since it changes oxidation states due to high energy exposure. It has been proven to be a good sensor material for radiation sensors. It also provides changes in hydrophobicity on crystalline or glassy surfaces since it affects the contact angles when embodied matrix is treated on the surface. In addition, TiO₂ forms very thin film on most substrates and avoids surface corrosion. To increase the sensitivity of sensors one must avoid high contact angle when using materials such as TiO₂. In this paper, we propose to discuss the effect of matrix and processing on the interaction by measuring hydrophobic properties of the composites. Accordingly, polystyrene and poly methyl methacrylate filled with TiO₂ nanoparticles composites were used. The effect of water, cyclohexane, toluene, and tetrahydrofuron (THF) solvent were studied. It was observed that the mixing of copper oxide significantly alters the sensing capability since it affects the contact angle on the surface and, hence sensitivity.

CuY₂Ti₄O₁₂ (CYTO) crystalline ceramic was successfully prepared through semi-wet route. The phase formation of CYTO ceramic was confirmed by powder X-ray diffraction studies with minor secondary phases formation of Y₂O₃ and Cu₂Ti₂O₅. In the higher frequency section, the dielectric permittivity and tangent loss are temperature independent, whereas in the lower frequency section, these properties are temperature dependent. The dielectric constant of CYTO was determined as 1.2 x 10⁴ at 100 Hz and 500 K. The dielectric loss of CYTO ceramic was found 0.75 at 10 kHz and 423 K. The dielectric constant and tangent loss both reduce with rising frequency in the lower frequency regions, while these are almost constant in the higher frequency regions. Impedance properties were used to check the grain and grain boundary phenomena in this ceramic. The presence of temperature dependent Maxwell-Wagner type relaxation was established by Impedance investigation of CYTO ceramic.

We have been investigating CaCu₃Ti₄O₁₂ class of perovskites for variety of applications due to its very large dielectric constant. Several mechanisms have been proposed for the existence of the high dielectric constant based on grain boundaries and oxygen deficiencies. We will report the results of the effects of monovalent substitution to replace calcium. This will alter the size to distort the perovskite structure. In addition, we used this system as a sensor for organic agents. There were very large changes in dielectric constant and resistivity indicating this system as a very good sensor material.

TWe have observed that inhomogeneity can affect the refringence and emission significantly. We are investigating refractive index and the absorption coefficients of multi-functional sensor materials to understand the defect driven changes using birefringence interferometry. In order to simplify and faster data collection, we are exploring low-cost reflectance probe fiber optics designed in-house. In this presentation we will compare the data obtained by using this new system with available literature data. In this paper we will report results of doped ZnSe crystals grown by physical vapor transport method.

For over two years, the world has endured a coronavirus pandemic resulting in over 470 million illnesses, 6 million deaths, and substantial supply chain disruptions. Prior to effective treatments, experts estimated that in the United States alone as many as 20 – 30 million tests should be conducted weekly to safely reopen, while highly effective vaccines took nearly a year to enter general distribution. This global event has highlighted the importance of developing and deploying rapid testing and treatment options for emerging pathogens. Currently, antibodies are the gold standard for biorecognition elements used in biosensors and may also be utilized to treat infection. Unfortunately, they can be challenging to mass produce and are sensitive to biological and temperature degradation, limiting broader distribution and equitable global access. Protein Catalyzed Capture agents (PCCs) present an alluring alternative as these small peptide macrocycles are comprised of unnatural amino acids and exhibit thermal and enzymatic stability. Their chemical synthesis enables scalable and reproducible production at diminished cost, while modular functionalities allow versatile applications in sensing and therapeutics, expanding the potential for more ubiquitous access to sensing & treatment options. This work sought to derive PCC receptors with high affinity and specificity for the SARS-CoV-2 spike protein from a one bead one compound peptide library. Chemically synthesized epitope fragments of the SARS-COV-2 spike protein were screened against the library and PCC receptor leads identified through entropically favored, un-catalyzed “click” reactions. Mass spectrometry and multiplex affinity assays then enabled down-selection of promising receptors. Modular chemical modifications were made to selected receptors to enable effective integration into a graphene field effect transistor (gFET) sensor which demonstrated a practical limit of detection of 103 pfu/mL inactivated SARS-CoV- 2 virus and the ability to discriminate between SARS-COV-2 and Influenza or Rhino viruses.

Oyster mushroom has long been consumed for their delicious taste and flavour that they add to our plate. These edible herbs are also rich source of neutraceuticals that impart on it potential free radical scavenging, antioxidant, antiatherogenic, anti-tumor, immunomodulatory and anti-bacterial properties. In the present study, we deciphered the role of mushroom neutraceuticals vis a vis hallmarks of cancer via an in-silico approach. From literature survey, eleven bioactive compounds of Pleurotus ostreatus were identified e.g. Ergosterol, Rutin, β-glucan, Apigenin, Ascorbic acid, Cinnamic acid, Linoleic acid, Niacinnamide, p-coumaric acid, syringic acid and veratric acid. Molecular docking was performed taking these compounds as ligands against epidermal growth factor receptor, caspase 3 and vascular endothelial growth factor proteins, which plays important role in proliferation, apoptosis and angiogenesis of malignant cells. Our finding suggest that Rutin, Ergosterol and β-glucan has shown highest binding affinity against the three proteins and interfere in their functioning inside tumor microenvironment thereby inhibiting process of carcinogenesis and metastasis.

In this study, an advanced laser-scribing approach was optimized for fabricating miniaturized, high-density multisensors on polyimide substrates. The femtosecond 515 nm laser, with an approximately 10 μm spot size, produced significantly smaller conductive traces compared to conventional methods. A flexible integration board processed and wirelessly transmitted physiological signals to an Android device. Laser-induced graphene (LIG) electrodes and the board were integrated, detecting electrocardiogram (ECG) and temperature on human skin. The laserscribing technique improved wearable sensor performance, enabling real-time, on-the-go health monitoring possibilities.

Repeated exposure to acute occupational stressors and violence poses significant mental and physical risks to workers. However, current methods for assessing the impact of these stressors, such as subjective self-reports and single-modal wearable technologies, do not capture continuous physiological and psychological responses to workplace stressors. To address this issue, we developed a multi-modal wearable armband that can non-invasively monitor physiological signals without interfering with work requirements. The armband can continuously record electrocardiogram, photoplethysmography, activity levels, and pulse transit time (PTT) - a biomarker of cuffless blood pressure. We conducted a study with sixteen nurses working 12-hour shifts at Intensive Care Unit, where they reported incidents of violence while wearing the armband for seven consecutive days. We analyzed the ratio of low- and high-frequency components of inter-beat-intervals of heart beats (LF/HF ratio) and PTT for one hour before and after the incidents to capture physiological responses and recovery patterns. Our results showed that the LF/HF ratio increased after the incident occurrence and reached baseline within 30 minutes, while the PTT decreased and persisted for more than an hour. This finding suggests that PTT can be a reliable biomarker for stress recovery. Continuous PTT measurement can be used to optimize individual and team performances based on recovery patterns in demanding work environments. In summary, our study highlights the importance of continuous physiological monitoring in assessing the impact of workplace stressors and provides a new method for measuring stress recovery using PTT.

Flexible pressure sensors with high sensitivity have drawn a lot of attention due to their potential in a range of applications, including tactile sensing, physiological monitoring, and flexible electronics. The commercialization of these sensors is still hampered by the difficulty of getting great sensitivity while keeping cheap production costs. The low-cost method for making capacitive sensors described in this study uses a sacrificial template made of a porous PDMS polymer and a dielectric layer based on MWCNT composites. When MWCNT is added to PDMS, the composite polymer's higher dielectric constant results in the sensor's high sensitivity of 4.9 kPa^-1 below 1 kPa. Due to its extreme sensitivity, it can recognize minute changes in tactile pressure and pulse waveforms.

We propose a chipless RFID pH sensor which can be easily integrated into a bandage for wound monitoring. The sensor can detect the pH level from 4 to 7 of the wounded area through frequency shift owing to the pH sensitive dielectric parameter of chitosan hydrogel, embedded into the substrate of the sensor. The substrate is composed of fabric material which makes it a strong candidate for non-invasive wound monitoring application. The frequency shift can be wirelessly detected by RFID reader to get the status of the wounded area.

Bionic limbs have transformed the lives of individuals with missing or damaged limbs, enabling them to regain independence using electronic sensors and motors. Over the years, significant advancements have been made in prosthetic devices, with some reaching a level of sophistication that is almost indistinguishable from natural limbs. However, not all amputees have equal access to cutting-edge technology, which motivates the research and development presented in this paper. In this study, we have designed and developed a bionic arm that can be easily manufactured using additive manufacturing, paired with a wearable sensor suit that commands the actuators to execute movements. The use of gesturecontrolled wearable sensors allows for the creation of sophisticated bionic arms with applications in both civilian and military contexts. Furthermore, the team is exploring the use of advanced computer algorithms to enable fast and fluid movements, facilitating the performance of complex tasks with prosthetic limbs. This paper provides a general design overview of the bionic arm and its sensor suit, showcasing the potential of this innovative approach in revolutionizing the field of prosthetics. The use of additive manufacturing and wearable sensor technology opens up new possibilities for providing accessible and advanced prosthetic solutions for individuals with limb loss.

Artificial Neural Network (ANN) is a powerful tool to model a system using only the inputs and outputs of that system. In this paper, ANN is used to model the relation between the subject’s gender to its performance while been excited in a whole-body vibration machine (WBV). For training the ANN, 20 male and 20 female subjects were observed during an experimental setup using a WBV at different vibration frequencies in the range of 20 to 45 Hz. The apparent mass was measured for the subjects at different frequencies. The input to the ANN includes body mass index, mass, and gender of the subjects along with and the excitation frequency. The ANN shows a good performance and extract the relationship with a performance that has a root mean squared error of the relative percentage error less than 9%.

Hygiene is becoming a key issue in a world with an increasing global population and an increasing number of antibioticresistant bacteria. In the next few years, a crucial challenge will be finding better methods for disinfection. Currently, these methods are studied with traditional microbiology techniques, where bacteria are grown, exposed to a treatment and further growth is followed for several days. However, this paradigm is slow and does not offer chemical insights in the mechanism of action of disinfecting agents. Without new analytical tools, we risk slowing down the critical research into new disinfecting agents. There is a large body of literature reporting bacterial inhibition mechanisms are diverse and include cell membrane disruption, DNA/RNA damage, ribosome degradation, protein denaturation, etc. Also, it is widely known that bacteria inactivation mechanisms are complex and multiple mechanisms can be involved in a synergetic manner. Current knowledge regarding the mechanism of action for bacteria inhibition is largely based on results from analytical tools that include mass spectrometry, nuclear magnetic resonance spectroscopy and fluorescence spectroscopy in combination with a suite of biochemical assays. All these methods require significant effort and sample preparation/treatment to obtain results. In addition, chemical insights obtained from biochemical assays rely tightly to the initial hypotheses. Herein, we propose to use Raman and surface-enhanced Raman scattering (SERS) to study the mechanism of action for bacteria inhibition. Vibrational spectroscopies have numerous advantages over the current paradigm for this analysis, including low sample preparation requirements and spectra rich in chemical information. These advantages permit to monitor bacteria cultures easily and quickly, while also obtaining chemical insights regarding mechanism of action for bacteria inhibition. In this work, we will show the monitoring of treated bacteria via Raman spectroscopy and how we can use SERS to further streamline this process.

The most prevalent kind of cardiovascular illness is a heart attack, which may or may not have symptoms. The damage to the heart muscle increases with delayed treatment, which increases the risk of mortality. More than 10 million people die each year from heart attacks, and many of them may be avoided if heart attacks could be accurately predicted. To estimate the likelihood of suffering a heart attack, five different machine learning algorithms are used on the Public Health Heart Attack dataset. Several evaluation metrics, including accuracy, recall, precision, ROC curve, and F-score, were used to evaluate the models. All the models—MLP, RBF, SVM, KNN, and RF— achieved significant accuracies of more than 75%, with KNN having the greatest overall performance

The most notable advancement in the 21st Century has been in artificial intelligence (AI). Despite how far AI has progressed, how it applies to healthcare remains a significant challenge for brilliant minds all over the world. A neurological condition known as epilepsy can strike a person at any time in their life. An individual with epilepsy therefore experiences frequent to infrequent seizures, which can occasionally result in death. Electroencephalogram (EEG) signals aid in the diagnosis of this condition. However, lengthy EEG signals frequently take a day or longer to detect this disorder, even for trained neurologists, and may even cause human error. Therefore, it is essential to create a reliable and computationally efficient system. This study aims to classify seizures by creating Convolutional Neural Network (CNN) Inception ResNet V2 and short-time Fourier transform (STFT) to extract the time-frequency plane from time domain signals. This study helped to better classify health and seizures by achieving up to 100% the highest classification accuracy.

Due to high blood sugar levels, diabetic retinopathy (DR), a complication of diabetes, affects the retina in the back of the eye. It may cause blindness if undiagnosed and mistreated. The early detection and treatment of DR are made easier by retinal screening. This paper proposes using an image-based dataset to build different convolution neural network (CNN) models to detect DR in its early stages to ease the screening procedure. The accuracy achieved was 0.9615 using the VGG model and 0.9712 using the Inception-ResNet model. This study demonstrates the effectiveness of using deep learning techniques to aid in diagnosing and predicting diabetic retinopathy.

Artificial intelligence (AI) i n h ealthcare i s a constantly evolving field that must be explored. Be cause of its practicality and usefulness in estimating various ailments, focused research on AI, specifically deep l earning, is dominating. High blood pressure (BP), also known as hypertension, is a serious health condition. It causes serious issues such as heart attacks, strokes, and even death. As a result, blood pressure should be constantly monitored. The proposed study uses famous CNN models for blood pressure detection and states the results of two main CNN models. Inception-V4 and Xception achieved an accuracy of 96% and 98.8%, respectively. Other performance metrics have been calculated and discussed.This study demonstrates the effectiveness of using deep learning techniques to aid in the diagnosis and prediction of hypertension.

Epilepsy is a neurological condition caused by sudden onsets of electrical activity in the brain. This results in frequent, uncommon seizures, which can lead to severe physical consequences. In a clinical setting, data recorded using EEG (Electroencephalogram) is used to help diagnose the condition. This research focuses on the use of Short-Term Fourier transform (STFT) and feature extraction in the EEG data for the use in a majority voting model using logistic regression (LR) to detect the presence of epileptic seizures in the five EEG frequency bands ( i.e. Alpha, Beta, Gamma, Delta, and Theta). To quantify, a number of evaluation metrics have been calculated. Overall, the model was able to achieve an accuracy of up to 92%.

Sleep apnea is a disorder that has the potential to be life-threatening, that is characterized by irregular breathing patterns. In order to improve the diagnosis and prediction of sleep apnea, a study was conducted to develop a high-accuracy detection method using machine learning. This method involved the use of a convolutional neural network classifier, which was trained using public data sets of ECG signals from both apnea patients and healthy volunteers. The CNN model was able to attain a level of accuracy of 94.12% using the Xception model and 91.18% using the ResNet50 model. According to the study’s findings, using deep learning techniques can be a helpful strategy to enhance sleep apnea diagnosis and prediction.