Empowering Patient Similarity Networks through Innovative Data-Quality-Aware Federated Profiling.

Continuous monitoring of patients involves collecting and analyzing sensory data from a multitude of sources. To overcome communication overhead, ensure data privacy and security, reduce data loss, and maintain efficient resource usage, the processing and analytics are moved close to where the data are located (e.g.

Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics.

Cybercriminals are becoming increasingly intelligent and aggressive, making them more adept at covering their tracks, and the global epidemic of cybercrime necessitates significant efforts to enhance cybersecurity in a realistic way. The COVID-19 pandemic has accelerated the cybercrime threat landscape. Cybercrime has a significant impact on the gross domestic product (GDP) of every targeted country.

Research on the Heating Process of CFRP Circular Tubes Based on Electromagnetic Induction Heating Method.

Based on the electromagnetic induction heating method, heating and curing of Carbon Fiber Reinforced Polymer (CFRP) have the advantages of high energy utilization and no pollution. However, in the heating process, both the material weaving structure and mold material can affect the temperature field. Therefore, in this study, an electromagnetic heating finite element analysis model for CFRP circular tubes was established based on the equivalent electromagnetic thermal characteristics of CFRP.

EnRDeA U-Net Deep Learning of Semantic Segmentation on Intricate Noise Roads.

Road segmentation is beneficial to build a vision-controllable mission-oriented self-driving bot, e.g., the Self-Driving Sweeping Bot, or SDSB, for working in restricted areas.

Unraveling Lifelong Brain Morphometric Dynamics: A Protocol for Systematic Review and Meta-Analysis in Healthy Neurodevelopment and Ageing.

A high incidence and prevalence of neurodegenerative diseases and neurodevelopmental disorders justify the necessity of well-defined criteria for diagnosing these pathologies from brain imaging findings. No easy-to-apply quantitative markers of abnormal brain development and ageing are available. We aim to find the characteristic features of non-pathological development and degeneration in distinct brain structures and to work out a precise descriptive model of brain morphometry in age groups.

Stifle Joint Arthrodesis for Treating Chronic-Osteoarthritis-Affected Dogs.

A two-year-old male Pomeranian dog was presented to a veterinary hospital due to the side effects of a surgical correction for patellar luxation. Stifle joint arthrodesis (SJA) was performed on the patient's right leg using autologous bone-grafting techniques. The right femur and tibial joint were angled 120-130°, and an SJA plate was fixed on the front of the two bones.

An Effective Hyperspectral Image Classification Network Based on Multi-Head Self-Attention and Spectral-Coordinate Attention.

In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) have been widely employed and achieved promising performance. However, CNN-based methods face difficulties in achieving both accurate and efficient HSI classification due to their limited receptive fields and deep architectures. To alleviate these limitations, we propose an effective HSI classification network based on multi-head self-attention and spectral-coordinate attention (MSSCA).

Comparative Evaluations on Real-Time Monitoring of Temperature Sensors during Endoscopic Laser Application.

Temperature sensors, such as Fiber Bragg Grating (FBG) and thermocouple (TC), have been widely used for monitoring the interstitial tissue temperature during laser irradiation. The aim of the current study was to compare the performance of both FBG and TC in real-time temperature monitoring during endoscopic and circumferential laser treatment on tubular tissue structure. A 600-µm core-diameter diffusing applicator was employed to deliver 980-nm laser light (30 W for 90 s) circumferentially for quantitative evaluation.

A Novel Diagnosis Scheme against Collusive False Data Injection Attack.

The collusive false data injection attack (CFDIA) is a false data injection attack (FIDA), in which false data are injected in a coordinated manner into some adjacent pairs of captured nodes of an attacked wireless sensor network (WSN). As a result, the defense of WSN against a CFDIA is much more difficult than defense against ordinary FDIA. This paper is devoted to identifying the compromised sensors of a well-behaved WSN under a CFDIA.

The Control Method of Autonomous Flight Avoidance Barriers of UAVs in Confined Environments.

This paper proposes an improved 3D-Vector Field Histogram (3D-VFH) algorithm for autonomous flight and local obstacle avoidance of multi-rotor unmanned aerial vehicles (UAVs) in a confined environment. Firstly, the method employs a target point coordinate system based on polar coordinates to convert the point cloud data, considering that long-range point cloud information has no effect on local obstacle avoidance by UAVs. This enables UAVs to effectively utilize obstacle information for obstacle avoidance and improves the real-time performance of the algorithm.

Breast cancer diagnosis using the fast learning network algorithm.

The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis.

Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring.

Wireless Sensor Networks (WSNs) have been adopted in various environmental pollution monitoring applications. As an important environmental field, water quality monitoring is a vital process to ensure the sustainable, important feeding of and as a life-maintaining source for many living creatures. To conduct this process efficiently, the integration of lightweight machine learning technologies can extend its efficacy and accuracy.

Software defined radio frequency sensing framework for intelligent monitoring of sleep apnea syndrome.

Healthy sleep is vital to all functions in the body. It improves physical and mental health, strengthens resistance against diseases, and develops strong immunity against metabolism and chronic diseases. However, a sleep disorder can cause the inability to sleep well.

An Alzheimer's disease category progression sub-grouping analysis using manifold learning on ADNI.

Many current statistical and machine learning methods have been used to explore Alzheimer's disease (AD) and its associated patterns that contribute to the disease. However, there has been limited success in understanding the relationship between cognitive tests, biomarker data, and patient AD category progressions. In this work, we perform exploratory data analysis of AD health record data by analyzing various learned lower dimensional manifolds to separate early-stage AD categories further.

Dopant-Free Hole-Transporting Material Based on Poly(2,7-(9,9-bis(N,N-di-p-methoxylphenylamine)-4-phenyl))-fluorene for High-Performance Air-Processed Inverted Perovskite Solar Cells.

It is a great challenge to develop low-cost and dopant-free polymer hole-transporting materials (HTM) for PSCs, especially for efficient air-processed inverted (p-i-n) planar PSCs. A new homopolymer HTM, poly(2,7-(9,9-bis(N,N-di-p-methoxylphenyl amine)-4-phenyl))-fluorene (denoted as PFTPA), with appropriate photo-electrochemical, opto-electronic and thermal stability, was designed and synthesized in two steps to meet this challenge. By employing PFTPA as dopant-free hole-transport layer in air-processed inverted PSCs, a champion power conversion efficiency (PCE) of up to 16.

High-Efficiency CsPbBr Light-Emitting Diodes using One-Step Spin-Coating In Situ Dynamic Thermal Crystallization.

All-inorganic perovskite materials (such as CsPbBr) have received widespread attention because of their better stability than hybrid counterparts, but their poor film morphology and crystalline quality limit their application in perovskite light-emitting devices (PeLEDs). Some previous studies have attempted to improve the morphology and crystalline quality of perovskite films by heating the substrate, but there are still some problems such as inaccurate temperature control, excessive temperature is not conducive to flexible applications, and the mechanism of action is not clear. In this work, we used a one-step spin-coating, low-temperature in situ thermally assisted crystallization process, in which the temperature was accurately monitored using a thermocouple in the range of 23-80 °C, and explored the effect of the in situ thermally assisted crystallization temperature on the crystallization of the all-inorganic perovskite material CsPbBr and the performance of PeLEDs.

Solving the Min-Max Clustered Traveling Salesmen Problem Based on Genetic Algorithm.

The min-max clustered traveling salesmen problem (MMCTSP) is a generalized variant of the classical traveling salesman problem (TSP). In this problem, the vertices of the graph are partitioned into a given number of clusters and we are asked to find a collection of tours to visit all the vertices with the constraint that the vertices of each cluster are visited consecutively. The objective of the problem is to minimize the weight of the maximum weight tour.

Photo- and Thermal-Induced Ion Migration and Phase Separation in Mn-Doped Two-Dimensional PEAPbX Perovskite.

Ion migration and phase separation in perovskite materials have negatively affected the solid-state lighting and display. Studying photo- and thermal-induced degradation is considered as a promising approach to understanding the luminescence mechanism and promoting practical applications. Herein, the Mn-doped two-dimensional PEAPbX (X = Cl, Br, I) microcrystals with changing halogen composition were synthesized by an acid-assisted post-processing strategy.

Ishophloroglucin A-based multifunctional oxidized alginate/gelatin hydrogel for accelerating wound healing.

This study investigated the potential applicability of wound dressing hydrogels for tissue engineering, focusing on their ability to deliver pharmacological agents and absorb exudates. Specifically, we explored the use of polyphenols, as they have shown promise as bioactive and cross-linking agents in hydrogel fabrication. Ishophloroglucin A (IPA), a polyphenol not previously utilized in tissue engineering, was incorporated as both a drug and cross-linking agent within the hydrogel.

Achievable rate as affected by active elements distribution in reconfigurable intelligent surfaces for wireless communication.

To increase constantly the achievable rate of reconfigurable intelligent surfaces (RISs)-assisted communication systems, the traditional approaches are to deploy a few active elements randomly on the passive RISs. However, the effect of the geometry distribution of the deployment of the active elements is ignored in performance analysis. In this article, three types of geometry distribution with active elements on RISs, denoted as random distribution, uniform distribution, and eight-queens distribution, are discussed to analyze the affect on achievable rate in RISs-assisted wireless communications.

A data-efficient zero-shot and few-shot Siamese approach for automated diagnosis of left ventricular hypertrophy.

Left ventricular hypertrophy (LVH) is a life-threatening condition in which the muscle of the left ventricle thickens and enlarges. Echocardiography is a test performed by cardiologists and echocardiographers to diagnose this condition. The manual interpretation of echocardiography tests is time-consuming and prone to errors.

Personalized federated learning for heterogeneous data: A distributed edge clustering approach.

Federated learning (FL) is a distributed machine learning technique that allows multiple devices (e.g., smartphones and IoT devices) to collaborate in the training of a shared model with each device preserving the privacy of its local data.

Resonance Analysis and Gain Estimation Using CMA-Based Even Mode Combination Method for Flexible Wideband Antennas.

This work presents an efficient design and optimization method based on characteristic mode analysis (CMA) to predict the resonance and gain of wideband antennas made from flexible materials. Known as the even mode combination (EMC) method based on CMA, the forward gain is estimated based on the principle of summing the electric field magnitudes of the first even dominant modes of the antenna. To demonstrate its effectiveness, two compact, flexible planar monopole antennas designed on different materials and two different feeding methods are presented and analyzed.