A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks.

In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model.

Advanced Sensing System for Sleep Bruxism across Multiple Postures via EMG and Machine Learning.

Diagnosis of bruxism is challenging because not all contractions of the masticatory muscles can be classified as bruxism. Conventional methods for sleep bruxism detection vary in effectiveness. Some provide objective data through EMG, ECG, or EEG; others, such as dental implants, are less accessible for daily practice. These methods have targeted the masseter as the key muscle for bruxism detection. However, it is important to consider that the temporalis muscle is also active during bruxism among masticatory muscles. Moreover, studies have predominantly examined sleep bruxism in the supine position, but other anatomical positions are also associated with sleep. In this research, we have collected EMG data to detect the maximum voluntary contraction of the temporalis and masseter muscles in three primary anatomical positions associated with sleep, i.e., supine and left and right lateral recumbent positions. A total of 10 time domain features were extracted, and six machine learning classifiers were compared, with random forest outperforming others. The models achieved better accuracies in the detection of sleep bruxism with the temporalis muscle. An accuracy of 93.33% was specifically found for the left lateral recumbent position among the specified anatomical positions. These results indicate a promising direction of machine learning in clinical applications, facilitating enhanced diagnosis and management of sleep bruxism.

Preparation and Performance Analysis of 3D Thermoformed Fluidic Polymer Temperature Sensors for Aquatic and Terrestrial Applications.

Employing a combination of Polyethylene terephthalate (PET) thermoforming and 3D-printed cylindrical patterns, we carefully engineer a linear resistive temperature sensor. This intricate process involves initial PET thermoforming, yielding a hollow cylindrical chamber. This chamber is then precisely infused with a composite fluid of graphite and water glue. Ensuring electrical connectivity, both ends are affixed with metal wires and securely sealed using a hot gun. This cost-effective, versatile sensor adeptly gauges temperature shifts by assessing composite fluid resistance alterations. Its PET outer surface grants immunity to water and solubility concerns, enabling application in aquatic and aerial settings without extra encapsulation. Rigorous testing reveals the sensor's linearity and stability within a 10 °C to 60 °C range, whether submerged or airborne. Beyond 65 °C, plastic deformation arises. To mitigate hysteresis, a 58 °C operational limit is recommended. Examining fluidic composite width and length effects, we ascertain a 12 Ω/°C sensitivity for these linear sensors, a hallmark of their precision. Impressive response and recovery times of 4 and 8 s, respectively, highlight their efficiency. These findings endorse thermoforming's potential for fabricating advanced temperature sensors. This cost-effective approach's adaptability underscores its viability for diverse applications.

Small intestinal model for electrically propelled capsule endoscopy.

The aim of this research is to propose a small intestine model for electrically propelled capsule endoscopy. The electrical stimulus can cause contraction of the small intestine and propel the capsule along the lumen. The proposed model considered the drag and friction from the small intestine using a thin walled model and Stokes' drag equation. Further, contraction force from the small intestine was modeled by using regression analysis. From the proposed model, the acceleration and velocity of various exterior shapes of capsule were calculated, and two exterior shapes of capsules were proposed based on the internal volume of the capsules. The proposed capsules were fabricated and animal experiments were conducted. One of the proposed capsules showed an average (SD) velocity in forward direction of 2.91 ± 0.99 mm/s and 2.23 ± 0.78 mm/s in the backward direction, which was 5.2 times faster than that obtained in previous research. The proposed model can predict locomotion of the capsule based on various exterior shapes of the capsule.

Tuning the optoelectronic properties of hematite with rhodium doping for photoelectrochemical water splitting using density functional theory approach.

Hematite (Fe2O3) is one of the best candidates for photoelectrochemical water splitting due to its abundance and suitable bandgap. However, its efficiency is mostly impeded due to the intrinsically low conductivity and poor light absorption. In this study, we targeted this intrinsic behavior to investigate the thermodynamic stability, photoconductivity and optical properties of rhodium doped hematite using density functional theory. The calculated formation energy of pristine and rhodium doped hematite was - 4.47 eV and - 5.34 eV respectively, suggesting that the doped material is thermodynamically more stable. The DFT results established that the bandgap of doped hematite narrowed down to the lower edge (1.61 eV) in the visible region which enhanced the optical absorption and photoconductivity of the material. Moreover, doped hematite has the ability to absorb a broad spectrum (250-800) nm. The enhanced optical absorption boosted the photocurrent and incident photon to current efficiency. The calculated results also showed that the incorporation of rhodium in hematite induced a redshift in optical properties.

Duodenum identification mechanism for capsule endoscopy.

The aim of this study is to implement a duodenum identification mechanism for capsule endoscopes because commercially available capsule endoscopes sometimes present a false negative diagnosis of the duodenum. One reason for the false negative diagnosis is that the duodenum is the fastest moving part within the gastrointestinal tract and the current frame rate of the capsule is not fast enough. When the capsule can automatically identify that it is in the duodenum, the frame rate of the capsule can be temporarily increased to reduce the possibility of a false negative diagnosis. This study proposes a mechanism to identify the duodenum using capacitive proximity sensors that can distinguish the surrounding tissue and transmit data using RF communication. The implemented capsule (D11 mm × L22 mm) was smaller than the commercially available capsule endoscopes, and power consumption was as low as 0.642 mW. Preexperiments were conducted to select an appropriate electrode width in order to increase the signal-to-noise ratio (SNR), and in vitro experiments were conducted to verify whether the implemented capsule could identify the duodenum within 3 s. The experiment showed that the identification rate of duodenum was 93% when the velocity of the capsule was less than 1 cm/s.

Optoelectronic stimulation of the brain using carbon nanotubes.

This paper presents the simulation results of a novel technique to stimulate the brain using a carbon nanotubes (CNT) based optically activated stimulator. This technique could be a promising alternative solution to overcome the limitations occurring in the conventional electrical stimulation of the brain and the newly developed opto-genetic stimulation. In this technique, the CNT stimulator, which generated an electrical current when exposed to light, was implanted in the brain. This current stimulated the nearby neurons to generate an action potential. The simulation results illustrated that a single-wall carbon nanotube of 50 nm² size could stimulate a 40 µm² area of the brain, whereas a multiwall carbon nanotube could cover a 12 µm² area of the brain. Additionally, simulations were also performed to determine the optimal shape and appropriate coating material for commercial optical stimulators to maximize the stimulation efficacy in the brain.