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A cymbal transducer has a simple structure consisting of a piezoceramic disk and metallic caps and has broadband characteristics when built as an array. The finite element method (FEM) is generally used to analyze the characteristics of acoustic transducers. However, the FEM requires a longer analysis time as the model becomes larger, which makes it limited and less efficient for analyzing the cymbal array. In this study, a new equivalent circuit with higher efficiency and accuracy, comparable to that of the FEM, was proposed to analyze the performance of cymbal arrays. The equivalent circuit for the array was constructed by connecting the equivalent circuits of individual cymbal transducers in parallel with a radiation impedance matrix that included both the self- and mutual radiation characteristics of the array. The validity of the new equivalent circuit was verified by measuring the transmitting voltage response of a cymbal array specimen and comparing it with that calculated using the circuit. The comparison confirmed the efficiency of the equivalent circuit in analyzing the characteristics of the cymbal array. The proposed equivalent circuit can facilitate the design of a large array of cymbal transducers.

A vector hydrophone is an underwater acoustic sensor that can detect the direction of a sound source. Wide-band characteristics and high sensitivity enhance the performance of underwater surveillance systems in complex environments. A vector hydrophone comprising a triaxial piezoelectric accelerometer and spherical hydrophone was fabricated and tested in the air and underwater. The vector hydrophone was designed to exceed the quantitative figures of merit (i.e., receiving voltage sensitivity and bandwidth) of commercially available hydrophones. Accelerometer performance was enhanced by placing a pair of piezoelectric single crystals on each axis and modifying the seismic mass material. The receiving voltage sensitivity of the omnidirectional hydrophone was approximately −160 dB relative to 1 V/μPa with the amplifier in water; the sensitivity of the accelerometer exceeded 300 mV/g in air and −215 dB relative to 1 V/μPa underwater over the frequency range of interest. The receiving directivity of the vector hydrophone was validated underwater, which confirmed that it could detect the direction of a sound source.

Cymbal transducers are frequently used as an array rather than a single element because of their high quality factor and low energy conversion efficiency. When used as an array, cymbal transducers are likely to have a big change in their frequency characteristics due to the interaction with neighboring elements. In this study, we designed an array pattern of cymbal transducers to achieve a wide frequency bandwidth using this property. First, cymbal transducers with specific center frequencies were designed. Next, a 2 × 2 planar array was constructed with the designed transducers, where dielectric polarity directions of the transducers were divided into two cases (i.e., same and different). For the array, the effect of the difference in the center frequencies and the spacing between the transducers on the acoustic characteristics of the entire array was analyzed. Based on the results, the structural pattern of the array was optimized to have the maximum fractional bandwidth while maintaining the transmitting voltage response over a given requirement. The design validity was verified by making cymbal array prototypes, followed by measuring their performances and comparing them with that of the design.

Photodynamic therapy (PDT) combined with upconverting nanoparticles (UCNPs) are viewed together as an effective method of ablating tumors. After absorbing highly tissue-penetrating near-infrared (NIR) light, UCNPs emit a shorter wavelength light (~660 nm) suitable for PDT. In this study, we designed and prepared highly red fluorescence-emitting silica-coated core-shell upconverting nanoparticles modified with polyethylene glycol (PEG5k)-folic acid and tetrakis(4-carboxyphenyl)porphyrin (TCPP) (UCNPs@SiO2-NH2@FA/PEG/TCPP) as an efficient photodynamic agent for killing tumor cells. The UCNPs consisted of two simple lanthanides, erbium and lutetium, as the core and shell, respectively. The unique core-shell combination enabled the UCNPs to emit red light without green light. TCPP, folic acid, and PEG were conjugated to the outer silica layer of UCNPs as a photosensitizing agent, a ligand for tumor attachment, and a dispersing stabilizer, respectively. The prepared UCNPs of ~50 nm diameter and −34.5 mV surface potential absorbed 808 nm light and emitted ~660 nm red light. Most notably, these UCNPs were physically well dispersed and stable in the aqueous phase due to PEG attachment and were able to generate singlet oxygen (1O2) with a high efficacy. The HeLa cells were treated with each UCNP sample (0, 1, 5, 10, 20, 30 μg/mL as a free TCPP). The results showed that the combination of UCNPs@SiO2-NH2@FA/PEG/TCPP and the 808 nm laser was significantly cytotoxic to HeLa cells, almost to the same degree as naïve TCPP plus the 660 nm laser based on MTT and Live/Dead assays. Furthermore, the UCNPs@SiO2-NH2@FA/PEG/TCPP was well internalized into HeLa cells and three-dimensional HeLa spheroids, presumably due to the surface folic acid and small size in conjunction with endocytosis and the nonspecific uptake. We believe that our UCNPs@SiO2-NH2@FA/PEG/TCPP will serve as a new platform for highly efficient and deep-penetrating photodynamic agents suitable for various tumor treatments.

In recent years, Non-Orthogonal Multiple Access (NOMA) system has emerged as a promising candidate for multiple access frameworks due to the evolution of deep machine learning, trying to incorporate deep machine learning into the NOMA system. The main motivation for such active studies is the growing need to optimize the utilization of network resources as the expansion of the internet of things (IoT) caused a scarcity of network resources. The NOMA addresses this need by power multiplexing, allowing multiple users to access the network simultaneously. Nevertheless, the NOMA system has few limitations. Several works have proposed to mitigate this, including the optimization of power allocation known as joint resource allocation(JRA) method, and integration of the JRA method and deep reinforcement learning (JRA-DRL). Despite this, the channel assignment problem remains unclear and requires further investigation. In this paper, we propose a deep reinforcement learning framework incorporating replay memory with an on-policy algorithm, allocating network resources in a NOMA system to generalize the learning. Also, we provide extensive simulations to evaluate the effects of varying the learning rate, batch size, type of model, and the number of features in the state.