A dental intraoral image dataset of gingivitis for image captioning.

One of the most striking topics in Artificial Intelligence (AI) is Image captioning that aims to integrate computer vision and natural language processing to create descriptions for each image. In this paper, we propose a new dataset designed specifically for image captioning in gingivitis diagnosis using deep learning. It includes 1,096 high-resolution intraoral images of 12 anterior teeth and surrounding gingival tissue that were collected under controlled conditions with professional-grade photography equipment.

Comparing the accuracy of two machine learning models in detection and classification of periapical lesions using periapical radiographs.

Background: Previous deep learning-based studies were mainly conducted on detecting periapical lesions; limited information in classification, such as the periapical index (PAI) scoring system, is available. The study aimed to apply two deep learning models, Faster R-CNN and YOLOv4, in detecting and classifying periapical lesions using the PAI score from periapical radiographs (PR) in three different regions of the dental arch: anterior teeth, premolars, and molars.

Methods: Out of 2658 PR selected for the study, 2122 PR were used for training, 268 PR were used for validation and 268 PR were used for testing.

A Dataset of apical periodontitis lesions in panoramic radiographs for deep-learning-based classification and detection.

Deep learning has been studied in recent years to identify periapical lesions- a significant indicator of periapical periodontitis in radiographs. An accurate dataset is essential for constructing an efficient learning model for detecting periapical lesions. In order to achieve this goal, we gathered and created a database of panoramic radiographs containing periapical lesions from the High-quality Dental Treatment Centre, School of Dentistry, Hanoi Medical University, between January 2016 and March 2021.

Event-triggered impulsive quasi-synchronization for BAM neural networks with reliable redundant channel.

This work addresses the quasi-synchronization of delay master-slave BAM neural networks. To improve the utilization of channel bandwidth, a dynamic event-triggered impulsive mechanism is employed, in which data is transmitted only when a preset event-triggered mechanism or a forced impulse interval is satisfied. In addition, to guarantee the reliability of information transmission, a reliable redundant channel for BAM neural networks is adopted, whose transmission scheduling strategy is designed on the basis of the packet dropouts rate of the main communication channels.

Double-Layer Detection Model of Malicious PDF Documents Based on Entropy Method with Multiple Features.

Traditional PDF document detection technology usually builds a rule or feature library for specific vulnerabilities and therefore is only fit for single detection targets and lacks anti-detection ability. To address these shortcomings, we build a double-layer detection model for malicious PDF documents based on an entropy method with multiple features. First, we address the single detection target problem with the fusion of 222 multiple features, including 130 basic features (such as objects, structure, content stream, metadata, etc.

A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis.

Biomedical data analysis is essential in current diagnosis, treatment, and patient condition monitoring. The large volumes of data that characterize this area require simple but accurate and fast methods of intellectual analysis to improve the level of medical services. Existing machine learning (ML) methods require many resources (time, memory, energy) when processing large datasets.

Einstein exponential operation laws of spherical fuzzy sets and aggregation operators in decision making.

The spherical fuzzy set (SFS) model is one of the newly developed extensions of fuzzy sets (FS) for the purpose of dealing with uncertainty or vagueness in decision making. The aim of this paper is to define new exponential and Einstein exponential operational laws for spherical fuzzy sets and their corresponding aggregation operators. We introduce the operational laws for exponential and Einstein exponential SFSs in which the base values are crisp numbers and the exponents (weights) are spherical fuzzy numbers.

Leveraging Knowledge Graphs and Natural Language Processing for Automated Web Resource Labeling and Knowledge Mobilization in Neurodevelopmental Disorders: Development and Usability Study.

Background: Patients and families need to be provided with trusted information more than ever with the abundance of online information. Several organizations aim to build databases that can be searched based on the needs of target groups. One such group is individuals with neurodevelopmental disorders (NDDs) and their families.

Mobility of LoRaWAN Gateways for Efficient Environmental Monitoring in Pristine Sites.

Environmental monitoring of delicate ecosystems or pristine sites is critical to their preservation. The communication infrastructure for such monitoring should have as little impact on the natural ecosystem as possible. Because of their wide range capabilities and independence from heavy infrastructure, low-power wide area network protocols have recently been used in remote monitoring.

Identification and Characterization of Short-Term Motor Patterns in Rest Tremor of Individuals with Parkinson's Disease.

(1) Background: The dynamics of hand tremors involve nonrandom and short-term motor patterns (STMPs). This study aimed to (i) identify STMPs in Parkinson’s disease (PD) and physiological resting tremor and (ii) characterize STMPs by amplitude, persistence, and regularity. (2) Methods: This study included healthy (N = 12, 60.

Control of perovskite film crystallization and growth direction to target homogeneous monolithic structures.

Getting performant organo-metal halide perovskite films for various application remains challenging. Here, we show the behavior of solvent and perovskite elements for four different perovskites families and nine different initial precursor solution systems in the case of the most popular preparation process which includes an anti-solvent dripping-assisted spin coating of a precursor solution and a subsequent thermal annealing. We show how the initial solution composition affects, first, the film formed by spin coating and anti-solvent dripping and, second, the processes occurring upon thermal annealing, including crystal domain evolution and the grain growth mechanism.

Plasmonic-Thermoelectric Nanotweezers for Immersive SERS Mapping.

Surface-enhanced Raman spectroscopy (SERS) technology usually uses metallic nanoparticles to enhance Raman scattering signals, thereby significantly adding to molecule-level recognition and detection. However, realization of nanometer-scaled SERS imaging in liquid environments is extremely difficult due to the requirements of both precise scanning of single metallic nanoparticle and high enhancement field and thus has never been achieved before. To overcome this obstacle, we demonstrate an immersive nanometer-scaled SERS mapping technology, based on dynamic scanning of a single metallic nanoparticle with a plasmonic-thermoelectric nanotweezers system.

What Can Glow Discharge Optical Emission Spectroscopy (GD-OES) Technique Tell Us about Perovskite Solar Cells?

The emerging broad range of applications of the glow discharge optical emission spectroscopy (GD-OES) technique in the field of perovskite solar cells (PSCs) research is reviewed. It can provide a large palette of information by easily and quickly tracking the depth distribution of light to heavy elements. After a discussion of the advantages and the limitations of the technique and a comparison with other analytical techniques, how GD-OES is employed to give structural information on perovskite solar cells is shown.

Design and Fabrication of a Tunable Optofluidic Microlens Driven by an Encircled Thermo-Pneumatic Actuator.

This paper presents the design, simulation, fabrication, assembly, and testing of a miniature thermo-pneumatic optofluidic lens. The device comprises two separate zones for air heating and fluid pressing on a flexible membrane. A buried three-dimensional spiral microchannel connects the two zones without pumps or valves.

Deciphering the Diversity of Mental Models in Neurodevelopmental Disorders: Knowledge Graph Representation of Public Data Using Natural Language Processing.

Background: Understanding how individuals think about a topic, known as the mental model, can significantly improve communication, especially in the medical domain where emotions and implications are high. Neurodevelopmental disorders (NDDs) represent a group of diagnoses, affecting up to 18% of the global population, involving differences in the development of cognitive or social functions. In this study, we focus on 2 NDDs, attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD), which involve multiple symptoms and interventions requiring interactions between 2 important stakeholders: parents and health professionals.

Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods.

Unlabelled: The exponential increase in the number of diabetics around the world has led to an equally large increase in the number of diabetic retinopathy (DR) cases which is one of the major complications caused by diabetes. Left unattended, DR worsens the vision and would lead to partial or complete blindness. As the number of diabetics continue to increase exponentially in the coming years, the number of qualified ophthalmologists need to increase in tandem in order to meet the demand for screening of the growing number of diabetic patients.

High Spatial and Temporal Resolution NIR-IIb Gastrointestinal Imaging in Mice.

Conventional biomedical imaging modalities, including endoscopy, X-rays, and magnetic resonance, are invasive and insufficient in spatial and temporal resolutions for gastrointestinal (GI) tract imaging to guide prognosis and therapy. Here we report a noninvasive method based on lanthanide-doped nanocrystals with ∼1530 nm fluorescence in the near-infrared-IIb window (NIR-IIb, 1500-1700 nm). The rational design of nanocrystals have led to an absolute quantum yield (QY) up to 48.

A Novel Epidemic Model Base on Pulse Charging in Wireless Rechargeable Sensor Networks.

As wireless rechargeable sensor networks (WRSNs) are gradually being widely accepted and recognized, the security issues of WRSNs have also become the focus of research discussion. In the existing WRSNs research, few people introduced the idea of pulse charging. Taking into account the utilization rate of nodes' energy, this paper proposes a novel pulse infectious disease model (SIALS-P), which is composed of susceptible, infected, anti-malware and low-energy susceptible states under pulse charging, to deal with the security issues of WRSNs.

Evaluation of methods to detect circular RNAs from single-end RNA-sequencing data.

Background: Circular RNA (circRNA), a class of RNA molecule with a loop structure, has recently attracted researchers due to its diverse biological functions and potential biomarkers of human diseases. Most of the current circRNA detection methods from RNA-sequencing (RNA-Seq) data utilize the mapping information of paired-end (PE) reads to eliminate false positives. However, much of the practical RNA-Seq data such as cross-linking immunoprecipitation sequencing (CLIP-Seq) data usually contain single-end (SE) reads.

Plasmonic and Graphene-Functionalized High-Performance Broadband Quasi-Two-Dimensional Perovskite Hybrid Photodetectors.

Quasi-two-dimensional (2D) layered organic-inorganic hybrid perovskites have attracted extensive attention, owing to their excellent optoelectronic tunability and moisture stability compared with three-dimensional perovskite counterparts and show great potential for application in photodetectors (PDs). However, owing to the unavoidable grain boundary defects of perovskite polycrystalline films, the photocurrent is limited by poor light absorption and charge mobility. Therefore, the preparation of quasi-2D perovskite films with strong light trapping and high charge mobility has been challenging.

Attitude and perception towards vaccination against poliomyelitis in Peshawar, Pakistan.

Objective: This research aimed to quantitatively assess the general public's awareness, attitude and perception of polio and its vaccination in Peshawar KPK, Pakistan.

Methods: We conducted a survey-based study to understand the surge in polio cases from 2015 to 2019 in the Peshawar city of the Khyber Pakhtunkhwa (KPK), Pakistan. A pre-tested questionnaire-based study was conducted in 2019 to assess the attitude and general perception of residents of Peshawar KPK towards polio vaccination.

Graphene-Based Opto-Thermoelectric Tweezers.

Since the discovery of graphene, its excellent physical properties have greatly improved the performance of optoelectronic devices and brought important technological advances to optical research and its applications. Here, graphene is introduced to the field of optical-tweezer technology and demonstrate a new graphene-based opto-thermoelectric tweezer. This technology not only reduces the incident light energy required by two orders of magnitude (compared with traditional optical tweezers), it also brings new advantages such as a much broader working bandwidth and a larger working area compared to those of widely researched gold-film-based opto-thermoelectric tweezers.