Custom machine learning algorithm for large-scale disease screening - taking heart disease data as an example.

Heart disease accounts for millions of deaths worldwide annually, representing a major public health concern. Large-scale heart disease screening can yield significant benefits both in terms of lives saved and economic costs. In this study, we introduce a novel algorithm that trains a patient-specific machine learning model, aligning with the real-world demands of extensive disease screening. Customization is achieved by concentrating on three key aspects: data processing, neural network architecture, and loss function formulation. Our approach integrates individual patient data to bolster model accuracy, ensuring dependable disease detection. We assessed our models using two prominent heart disease datasets: the Cleveland dataset and the UC Irvine (UCI) combination dataset. Our models showcased notable results, achieving accuracy and recall rates beyond 95 % for the Cleveland dataset and surpassing 97 % accuracy for the UCI dataset. Moreover, in terms of medical ethics and operability, our approach outperformed traditional, general-purpose machine learning algorithms. Our algorithm provides a powerful tool for large-scale disease screening and has the potential to save lives and reduce the economic burden of heart disease.

Synchronization of Shop-Floor Logistics and Manufacturing Under IIoT and Digital Twin-Enabled Graduation Intelligent Manufacturing System.

Logistics interfaces with manufacturing throughout the entire production process need synchronous operations. For achieving integrated organization and operations between manufacturing and logistics, this article introduces the concept of shop-floor logistics and manufacturing synchronization with four principles, including: 1) synchronization-oriented manufacturing system; 2) synchronized information sharing; 3) synchronized decision making; and 4) synchronized operations. The marriage of the principles rendered the development of an overall framework of the Industrial Internet of Things (IIoT) and digital twin-enabled graduation intelligent manufacturing system (GiMS). A mixed-integer programming-based synchronization mechanism is proposed under GiMS. To meet the requirement of fast decision making in real-life shop-floor logistics and manufacturing synchronization problems, an equivalent constraint programming model is developed and tested. The observation and analysis of the case company show the advantage of the proposed concept and approach with the best performance regarding key performance indicators. The concept of synchronization provides an insight for understanding the interaction of logistics and manufacturing at the operational level. This article potentially enables manufacturers to reevaluate and develop their manufacturing planning and control strategies in the IIoT and digital twin-based manufacturing environment.

Fully Automatic Dual-Probe Lung Ultrasound Scanning Robot for Screening Triage.

Two-dimensional lung ultrasound (LUS) has widely emerged as a rapid and noninvasive imaging tool for the detection and diagnosis of coronavirus disease 2019 (COVID-19). However, image differences will be magnified due to changes in ultrasound (US) imaging experience, such as US probe attitude control and force control, which will directly affect the diagnosis results. In addition, the risk of virus transmission between sonographer and patients is increased due to frequent physical contact. In this study, a fully automatic dual-probe US scanning robot for the acquisition of LUS images is proposed and developed. Furthermore, the trajectory was optimized based on the velocity look-ahead strategy, the stability of contact force of the system and the scanning efficiency were improved by 24.13% and 29.46%, respectively. Also, the control ability of the contact force of robotic automatic scanning was 34.14 times higher than that of traditional manual scanning, which significantly improves the smoothness of scanning. Importantly, there was no significant difference in image quality obtained by robotic automatic scanning and manual scanning. Furthermore, the scanning time for a single person is less than 4 min, which greatly improves the efficiency of screening triage of group COVID-19 diagnosis and suspected patients and reduces the risk of virus exposure and spread.

A Subvision System for Enhancing the Environmental Adaptability of the Powered Transfemoral Prosthesis.

Visual information is indispensable to human locomotion in complex environments. Although amputees can perceive the environmental information by eyes, they cannot transmit the neural signals to prostheses directly. To augment human-prosthesis interaction, this article introduces a subvision system that can perceive environments actively, assist to control the powered prosthesis predictively, and accordingly reconstruct a complete vision-locomotion loop for transfemoral amputees. By using deep learning, the subvision system can classify common static terrains (e.g., level ground, stairs, and ramps) and estimate corresponding motion intents of amputees with high accuracy (98%). After applying the subvision system to the locomotion control system, the powered prosthesis can help amputees to achieve nonrhythmic locomotion naturally, including switching between different locomotion modes and crossing the obstacle. The subvision system can also recognize dynamic objects, such as an unexpected obstacle approaching the amputee, and assist in generating an agile obstacle-avoidance reflex movement. The experimental results demonstrate that the subvision system can cooperate with the powered prosthesis to reconstruct a complete vision-locomotion loop, which enhances the environmental adaptability of the amputees.

Environmental Features Recognition for Lower Limb Prostheses Toward Predictive Walking.

This paper aims to present a robust environmental features recognition system (EFRS) for lower limb prosthesis, which can assist the control of prosthesis by predicting the locomotion modes of amputees and estimating environmental features in the following steps. A depth sensor and an inertial measurement unit are combined to stabilize the point cloud of environments. Subsequently, the 2D point cloud is extracted from origin 3D point cloud and is classified through a neural network. Environmental features, including slope of road, width, and height of stair, were also estimated via the 2D point cloud. Finally, the EFRS is evaluated through classifying and recognizing five kinds of common environments in simulation, indoor experiments, and outdoor experiments by six healthy subjects and three transfemoral amputees, and databases of five healthy subjects and three amputees are used to validate without training. The classification accuracy of five kinds of common environments reach up to 99.3% and 98.5% for the amputees in the indoor and outdoor experiments, respectively. The locomotion modes are predicted at least 0.6 s before the switch of actual locomotion modes. Most estimation errors of indoor and outdoor environments features are lower than 5% and 10%, respectively. The overall process of EFRS takes less than 0.023 s. The promising results demonstrate the robustness and the potential application of the presented EFRS to help the control of lower limb prostheses.

Effect of Scanning Routes on the Stress and Deformation of Overhang Structures Fabricated by SLM.

Selective laser melting (SLM) is a promising manufacturing method for the construction of complicated precision parts. However, deformation of the overhang during the fabrication process and post treatment is still a common problem. In this paper, the effect of the scanning route on the residual stress and deformation of fabricated AlSi10Mg overhang specimens by SLM was investigated. Different scanning routes for the overhang including longitudinal direction, transverse direction, and the alternation between these two scanning routes in consecutive layers were studied by experiments within this study. Numerical simulation was utilized to measure the stress of the specimens while deformation prediction was used for the different scanning routes. Both the experimental and simulated results showed that the scanning route had a substantial influence on the residual stress and deformation of the specimens. The longitudinal scanning resulted in significant upward bending deformation of the overhang as it was cut from the baseplate. However, there was less deformation for the overhangs fabricated by transverse and alternating scanning routes. A transverse scanning route is helpful for the reduction of residual stress in the longitudinal direction and the corresponding deformation.

Atomistic insights into the exothermic self-sustained alloying of Al-shell/Ni-core nanoparticle triggered by laser irradiation.

By imposing a picosecond laser pulse irradiation on an Al-shell/Ni-core nanoparticle, an exothermic self-sustained alloying is triggered. Molecular dynamics simulation is implemented to get atomistic insights into the alloying process. The nanoparticle is composed by an equiatomic number of Al atoms in the shell and Ni atoms in the core. Due to the absorption of laser energy from the surface of the nanoparticle, atomic motion becomes active. The inter-diffusion of Ni and Al atoms results in thermal energy generation. It is found that the incident laser energy is responsible for controlling the degree of self-heating of the nanoparticle by governing the potential energy change during the inter-diffusion of Al-shell and Ni-core atoms.

Dynamic mechanical response and a constitutive model of Fe-based high temperature alloy at high temperatures and strain rates.

The effects of strain rate and temperature on the dynamic behavior of Fe-based high temperature alloy was studied. The strain rates were 0.001-12,000 s(-1), at temperatures ranging from room temperature to 800 °C. A phenomenological constitutive model (Power-Law constitutive model) was proposed considering adiabatic temperature rise and accurate material thermal physical properties. During which, the effects of the specific heat capacity on the adiabatic temperature rise was studied. The constitutive model was verified to be accurate by comparison between predicted and experimental results.