Cone-beam CT landmark detection for measuring basal bone width: a retrospective validation study.

BACKGROUND: Accurate assessment of basal bone width is essential for distinguishing individuals with normal occlusion from patients with maxillary transverse deficiency who may require maxillary expansion. Herein, we evaluated the effectiveness of a deep learning (DL) model in measuring landmarks of basal bone width and assessed the consistency of automated measurements compared to manual measurements. METHODS: Based on the U-Net algorithm, a coarse-to-fine DL model was developed and trained using 80 cone-beam computed tomography (CBCT) images. The model's prediction capabilities were validated on 10 CBCT scans and tested on an additional 34. To evaluate the performance of the DL model, its measurements were compared with those taken manually by one junior orthodontist using the concordance correlation coefficient (CCC). RESULTS: It took approximately 1.5 s for the DL model to perform the measurement task in only CBCT images. This framework showed a mean radial error of 1.22 ± 1.93 mm and achieved successful detection rates of 71.34%, 81.37%, 86.77%, and 91.18% in the 2.0-, 2.5-, 3.0-, and 4.0-mm ranges, respectively. The CCCs (95% confidence interval) of the maxillary basal bone width and mandibular basal bone width distance between the DL model and manual measurement for the 34 cases were 0.96 (0.94-0.97) and 0.98 (0.97-0.99), respectively. CONCLUSION: The novel DL framework developed in this study improved the diagnostic accuracy of the individual assessment of maxillary width. These results emphasize the potential applicability of this framework as a computer-aided diagnostic tool in orthodontic practice.

Phenotypic Characterization and Comparative Genomic Analyses of Mycobacteriophage WIVsmall as A New Member Assigned to F1 Subcluster.

In this study, we conducted the morphological observation, biological and genomic characterization, evolutionary analysis, comparative genomics description, and proteome identification of a recently isolated mycobacteriophage, WIVsmall. Morphologically, WIVsmall is classified as a member of the Siphoviridae family, characterized by a flexible tail, measuring approximately 212 nm in length. The double-stranded phage genome DNA of WIVsmall spans 53,359 base pairs, and exhibits a G + C content of 61.01%. The genome of WIVsmall comprises 103 protein-coding genes, while no tRNA genes were detected. The genome annotation unveiled the presence of functional gene clusters responsible for mycobacteriophage assembly and maturation, replication, cell lysis, and functional protein synthesis. Based on the analysis of the phylogenetic tree, the genome of WIVsmall was classified as belonging to subgroup F1. A comparative genomics analysis indicated that the WIVsmall genome exhibited the highest similarity to the phage SG4, with a percentage of 64%. The single-step growth curve analysis of WIVsmall revealed a latent period of 120 min, and an outbreak period of 200 min.

Zero-Biased Bionic Fingertip E-Skin with Multimodal Tactile Perception and Artificial Intelligence for Augmented Touch Awareness.

Electronic skins (E-Skins) are crucial for future robotics and wearable devices to interact with and perceive the real world. Prior research faces challenges in achieving comprehensive tactile perception and versatile functionality while keeping system simplicity for lack of multimodal sensing capability in a single sensor. Two kinds of tactile sensors, transient voltage artificial neuron (TVAN) and sustained potential artificial neuron (SPAN), featuring self-generated zero-biased signals are developed to realize synergistic sensing of multimodal information (vibration, material, texture, pressure, and temperature) in a single device instead of complex sensor arrays. Simultaneously, machine learning with feature fusion is applied to fully decode their output information and compensate for the inevitable instability of applied force, speed, etc, in real applications. Integrating TVAN and SPAN, the formed E-Skin achieves holistic touch awareness in only a single unit. It can thoroughly perceive an object through a simple touch without strictly controlled testing conditions, realize the capability to discern surface roughness from 0.8 to 1600 µm, hardness from 6HA to 85HD, and correctly distinguish 16 objects with temperature variance from 0 to 80 °C. The E-skin also features a simple and scalable fabrication process, which can be integrated into various devices for broad applications.

Building (001) oriented FAPbI3 films for high-performing perovskite solar cells.

The solution processed FAPbI3 perovskite usually suffers from chaotic orientations. Herein, a template structure of oriented 2D perovskite is used to obtain a high-quality FAPbI3 film with (001) preferred orientation by cation exchange. The highly oriented BA2PbI4 serves as a growth template and promotes the (001) orientation of the 3D perovskite. The dominantly (001) orientated FAPbI3 perovskite exhibits uniform surface morphology and suppressed film defects.

Genetic Engineering and Biosynthesis Technology: Keys to Unlocking the Chains of Phage Therapy.

Phages possess the ability to selectively eliminate pathogenic bacteria by recognizing bacterial surface receptors. Since their discovery, phages have been recognized for their potent bactericidal properties, making them a promising alternative to antibiotics in the context of rising antibiotic resistance. However, the rapid emergence of phage-resistant strains (generally involving temperature phage) and the limited host range of most phage strains have hindered their antibacterial efficacy, impeding their full potential. In recent years, advancements in genetic engineering and biosynthesis technology have facilitated the precise engineering of phages, thereby unleashing their potential as a novel source of antibacterial agents. In this review, we present a comprehensive overview of the diverse strategies employed for phage genetic engineering, as well as discuss their benefits and drawbacks in terms of bactericidal effect.

Intelligent Cubic-Designed Piezoelectric Node (iCUPE) with Simultaneous Sensing and Energy Harvesting Ability toward Self-Sustained Artificial Intelligence of Things (AIoT).

The evolution of artificial intelligence of things (AIoT) drastically facilitates the development of a smart city via comprehensive perception and seamless communication. As a foundation, various AIoT nodes are experiencing low integration and poor sustainability issues. Herein, a cubic-designed intelligent piezoelectric AIoT node iCUPE is presented, which integrates a high-performance energy harvesting and self-powered sensing module via a micromachined lead zirconate titanate (PZT) thick-film-based high-frequency (HF)-piezoelectric generator (PEG) and poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) nanofiber thin-film-based low-frequency (LF)-PEGs, respectively. The LF-PEG and HF-PEG with specific frequency up-conversion (FUC) mechanism ensures continuous power supply over a wide range of 10-46 Hz, with a record high power density of 17 mW/cm3 at 1 g acceleration. The cubic design allows for orthogonal placement of the three FUC-PEGs to ensure a wide range of response to vibrational energy sources from different directions. The self-powered triaxial piezoelectric sensor (TPS) combined with machine learning (ML) assisted three orthogonal piezoelectric sensing units by using three LF-PEGs to achieve high-precision multifunctional vibration recognition with resolutions of 0.01 g, 0.01 Hz, and 2° for acceleration, frequency, and tilting angle, respectively, providing a high recognition accuracy of 98%-100%. This work proves the feasibility of developing a ML-based intelligent sensor for accelerometer and gyroscope functions at resonant frequencies. The proposed sustainable iCUPE is highly scalable to explore multifunctional sensing and energy harvesting capabilities under diverse environments, which is essential for AIoT implementation.

Recent Progress in the Energy Harvesting Technology-From Self-Powered Sensors to Self-Sustained IoT, and New Applications.

With the fast development of energy harvesting technology, micro-nano or scale-up energy harvesters have been proposed to allow sensors or internet of things (IoT) applications with self-powered or self-sustained capabilities. Facilitation within smart homes, manipulators in industries and monitoring systems in natural settings are all moving toward intellectually adaptable and energy-saving advances by converting distributed energies across diverse situations. The updated developments of major applications powered by improved energy harvesters are highlighted in this review. To begin, we study the evolution of energy harvesting technologies from fundamentals to various materials. Secondly, self-powered sensors and self-sustained IoT applications are discussed regarding current strategies for energy harvesting and sensing. Third, subdivided classifications investigate typical and new applications for smart homes, gas sensing, human monitoring, robotics, transportation, blue energy, aircraft, and aerospace. Lastly, the prospects of smart cities in the 5G era are discussed and summarized, along with research and application directions that have emerged.

Flourishing energy harvesters for future body sensor network: from single to multiple energy sources.

Body sensor network (bodyNET) offers possibilities for future disease diagnosis, preventive health care, rehabilitation, and treatment. However, the eventual realization demands reliable and sustainable power sources. The flourishing energy harvesters (EHs) have provided prominent techniques for practically addressing the concurrent energy issue. Targeting for a specific energy source, wearable EHs with a sole conversion mechanism are well investigated. Hybrid EHs integrating different effects for a single source or multi-sources are attaining growing attention, for they provide another degree of freedom concerning a higher-level energy utility. Merging EHs with other functional electronics, diversified functional self-sustainable systems are developed, paving the way for the accomplishment of bodyNET. This review introduces the evolution of wearable EHs from a single effect to hybridized mechanisms for multiple energy sources and wearable to implantable self-sustainable systems. Last, we provide our perspectives on the future development of hybrid EHs to be more competitive with conventional batteries.

Artificial Intelligence-Enabled Caregiving Walking Stick Powered by Ultra-Low-Frequency Human Motion.

The increasing population of the elderly and motion-impaired people brings a huge challenge to our social system. However, the walking stick as their essential tool has rarely been investigated into its potential capabilities beyond basic physical support, such as activity monitoring, tracing, and accident alert. Here, we report a walking stick powered by ultra-low-frequency human motion and equipped with deep-learning-enabled advanced sensing features to provide a healthcare-monitoring platform for motion-impaired users. A linear-to-rotary structure is designed to achieve highly efficient energy harvesting from the linear motion of a walking stick with ultralow frequency. Besides, two kinds of self-powered triboelectric sensors are proposed and integrated to extract the motion features of the walking stick. Augmented sensing functionalities with high accuracies have been enabled by deep-learning-based data analysis, including identity recognition, disability evaluation, and motion status distinguishing. Furthermore, a self-sustainable Internet of Things (IoT) system with global positioning system tracing and environmental temperature and humidity amenity sensing functions is obtained. Combined with the aforementioned functionalities, this walking stick is demonstrated in various usage scenarios as a caregiver for real-time well-being status and activity monitoring. The caregiving walking stick shows the potential of being an intelligent aid for motion-impaired users to help them live life with adequate autonomy and safety.

Development Trends and Perspectives of Future Sensors and MEMS/NEMS.

With the fast development of the fifth-generation cellular network technology (5G), the future sensors and microelectromechanical systems (MEMS)/nanoelectromechanical systems (NEMS) are presenting a more and more critical role to provide information in our daily life. This review paper introduces the development trends and perspectives of the future sensors and MEMS/NEMS. Starting from the issues of the MEMS fabrication, we introduced typical MEMS sensors for their applications in the Internet of Things (IoTs), such as MEMS physical sensor, MEMS acoustic sensor, and MEMS gas sensor. Toward the trends in intelligence and less power consumption, MEMS components including MEMS/NEMS switch, piezoelectric micromachined ultrasonic transducer (PMUT), and MEMS energy harvesting were investigated to assist the future sensors, such as event-based or almost zero-power. Furthermore, MEMS rigid substrate toward NEMS flexible-based for flexibility and interface was discussed as another important development trend for next-generation wearable or multi-functional sensors. Around the issues about the big data and human-machine realization for human beings' manipulation, artificial intelligence (AI) and virtual reality (VR) technologies were finally realized using sensor nodes and its wave identification as future trends for various scenarios.

Effects of Cr Substitution on Negative Thermal Expansion and Magnetic Properties of Antiperovskite Ga1-x Cr x N0.83Mn3 Compounds.

Negative thermal expansion (NTE) and magnetic properties were investigated for antiperovskite Ga1-x Cr x N0.83Mn3 compounds. As x increases, the temperature span (ΔT) of NTE related with Γ5g antiferromagnetic (AFM) order is expanded and shifted to lower temperatures. At x = 0.1, NTE happens between 256 and 318 K (ΔT = 62 K) with an average linear coefficient of thermal expansion, α L = -46 ppm/K. The ΔT is expanded to 81 K (151-232 K) in x = 0.2 with α L = -22.6 ppm/K. Finally, NTE is no longer visible for x ≥ 0.3. Ferromagnetic order is introduced by Cr doping and continuously strengthened with increasing x, which may impede the AFM ordering and thus account for the broadening of NTE temperature window. Moreover, our specific heat measurement suggests the electronic density of states at the Fermi level is enhanced upon Cr doping, which favors the FM order rather than the AFM one.

A Simple Extraction Method of Young's Modulus for Multilayer Films in MEMS Applications.

Based on the first resonance frequency measurement of multilayer beams, a simple extraction method has been developed to extract the Young's modulus of individual layers. To verify this method, the double-layer cantilever, as a typical example, is analyzed to simplify the situation and finite element modeling (FEM) is used in consideration of the buckling and unbuckling situation of cantilevers. The first resonance frequencies, which are obtained by ANSYS (15.0, ANSYS Inc., Pittsburgh, PA, USA) with a group of thirteen setting values of Young's modulus in the polysilicon layer are brought into the theoretical formulas to obtain a new group of Young's modulus in the polysilicon layer. The reliability and feasibility of the theoretical method are confirmed, according to the slight differences between the setting values and the results of the theoretical model. In the experiment, a series of polysilicon-metal double-layer cantilevers were fabricated. Digital holographic microscopy (DHM) (Lyncée Tech, Lausanne, Switzerland) is used to distinguish the buckled from the unbuckled. A scanning laser Doppler vibrometer (LDV) (Polytech GmbH, Berlin, Germany) system is used to measure the first resonance frequencies of them. After applying the measurement results into the theoretical modulus, the average values of Young's modulus in the polysilicon and gold layers are 151.78 GPa and 75.72 GPa, respectively. The extracted parameters are all within the rational ranges, compared with the available results.