Evaluation of the Clinical Efficacy and Trust in AI-Assisted Embryo Ranking: Survey-Based Prospective Study.

BACKGROUND: Current embryo assessment methods for in vitro fertilization depend on subjective morphological assessments. Recently, artificial intelligence (AI) has emerged as a promising tool for embryo assessment; however, its clinical efficacy and trustworthiness remain unproven. Simulation studies may provide additional evidence, provided that they are meticulously designed to mitigate bias and variance. OBJECTIVE: The primary objective of this study was to evaluate the benefits of an AI model for predicting clinical pregnancy through well-designed simulations. The secondary objective was to identify the characteristics of and potential bias in the subgroups of embryologists with varying degrees of experience. METHODS: This simulation study involved a questionnaire-based survey conducted on 61 embryologists with varying levels of experience from 12 in vitro fertilization clinics. The survey was conducted via Google Forms (Google Inc) in three phases: (1) phase 1, an initial assessment (December 23, 2022, to January 22, 2023); (2) phase 2, a validation assessment (March 6, 2023, to April 5, 2023); and (3) phase 3 an AI-guided assessment (March 6, 2023, to April 5, 2023). Inter- and intraobserver assessments and the accuracy of embryo selection from 360 day-5 embryos before and after AI guidance were analyzed for all embryologists and subgroups of senior and junior embryologists. RESULTS: With AI guidance, the interobserver agreement increased from 0.355 to 0.527 and from 0.440 to 0.524 for junior and senior embryologists, respectively, thus reaching similar levels of agreement. In a test of accurate embryo selection with 90 questions, the numbers of correct responses by the embryologists only, embryologists with AI guidance, and AI only were 34 (38%), 45 (50%), and 59 (66%), respectively. Without AI, the average score (accuracy) of the junior group was 33.516 (37%), while that of the senior group was 35.967 (40%), with P<.001 in the t test. With AI guidance, the average score (accuracy) of the junior group increased to 46.581 (52%), reaching a level similar to that of the senior embryologists of 44.833 (50%), with P=.34. Junior embryologists had a higher level of trust in the AI score. CONCLUSIONS: This study demonstrates the potential benefits of AI in selecting embryos with high chances of pregnancy, particularly for embryologists with 5 years or less of experience, possibly due to their trust in AI. Thus, using AI as an auxiliary tool in clinical practice has the potential to improve embryo assessment and increase the probability of a successful pregnancy.

Secure and Reliable Big-Data-Based Decision Making Using Quantum Approach in IIoT Systems.

Nowadays, the industrial Internet of things (IIoT) and smart factories are relying on intelligence and big data analytics for large-scale decision making. Yet, this method is facing critical challenges regarding computation and data processing due to the complexity and heterogeneous nature of big data. Smart factory systems rely primarily on the analysis results to optimize production, predict future market directions, prevent and manage risks, and so on. However, deploying the existing classical solutions such as machine learning, cloud, and AI is not effective anymore. Smart factory systems and industries need novel solutions to sustain their development. On the other hand, with the fast development of quantum information systems (QISs), multiple sectors are studying the opportunities and challenges of implementing quantum-based solutions for a more efficient and exponentially faster processing time. To this end, in this paper, we discuss the implementation of quantum solutions for reliable and sustainable IIoT-based smart factory development. We depict various applications where quantum algorithms could improve the scalability and productivity of IIoT systems. Moreover, we design a universal system model where smart factories would not need to acquire quantum computers to run quantum algorithms based on their needs; instead, they can use quantum cloud servers and quantum terminals implemented at the edge layer to help them run the desired quantum algorithms without the need of an expert. To prove the feasibility of our model, we implement two real-world case studies and evaluate their performance. The analysis shows the benefits of quantum solutions in different sectors of smart factories.

Facile Achievement of Complementary Resistive Switching in Block Copolymer Micelle-Based Resistive Memories.

Interest in resistive random access memory (RRAM) has grown rapidly in recent years for realizing ultrahigh density data storage devices. However, sneak currents in these devices can result in misreading of the data, thus limiting the applicability of RRAM. Complementary resistive switching (CRS) memory consisting of two antiserial RRAMs can considerably reduce sneak currents; however, complicated device architectures and manufacturing processes still remain as challenges. Herein, an effective and simple approach for fabricating CRS memory devices using self-assembled block copolymer micelles is reported. Cu ions are selectively placed in the core of polystyrene-block-poly(2-vinylpyridine) spherical micelles, and a hexagonally packed micelle monolayer is prepared through spin-coating. The micelle monolayer can be a symmetrical resistive switching layer, because the micelles and Cu act as dielectric and active metals in memory devices, respectively. The locally enhanced electric field and Joule heating achieved by the structured Cu atoms inside the micelles promote metal ionization and ion migration in a controlled manner, thus allowing for position selectivity during resistive switching. The micelle-based memory device exhibits stable and reliable CRS behavior, with a nonoverlapping and narrow distribution of threshold voltages. Therefore, this approach is promising for fabricating CRS memory devices for high-performance and ultrahigh-density RRAM applications.

Blockchain-Based Distributed Information Hiding Framework for Data Privacy Preserving in Medical Supply Chain Systems.

Medical supply chain communication networks engender critical information and data. Notably in the COVID era, inner personal and private information is being shared between healthcare providers regarding the medical supply chain. In recent years, multiple cyber-attacks have targeted medical supply chain communication networks due to their lack of security measures. In the era where cyber-attacks are cheaper and easier due to the computational power and various algorithms available for malicious uses, security, and data privacy requires intensive and higher measures. On the other hand, Information Hiding Techniques (IHT) compromise various advanced methods to hide sensitive information from being disclosed to malicious nodes. Moreover, with the support of Blockchain, IHT can bring higher security and the required privacy levels. In this paper, we propose the implementation of Blockchain and smart contract with the information hiding technique to enhance the security and privacy of data communication in critical systems, such as smart healthcare supply chain communication networks. Results show the feasibility of the framework using Hyperledger smart contract along with the desired security level.

A Blockchain-Enabled Secure Digital Twin Framework for Early Botnet Detection in IIoT Environment.

Resource constraints in the Industrial Internet of Things (IIoT) result in brute-force attacks, transforming them into a botnet to launch Distributed Denial of Service Attacks. The delayed detection of botnet formation presents challenges in controlling the spread of malicious scripts in other devices and increases the probability of a high-volume cyberattack. In this paper, we propose a secure Blockchain-enabled Digital Framework for the early detection of Bot formation in a Smart Factory environment. A Digital Twin (DT) is designed for a group of devices on the edge layer to collect device data and inspect packet headers using Deep Learning for connections with external unique IP addresses with open connections. Data are synchronized between the DT and a Packet Auditor (PA) for detecting corrupt device data transmission. Smart Contracts authenticate the DT and PA, ensuring malicious nodes do not participate in data synchronization. Botnet spread is prevented using DT certificate revocation. A comparative analysis of the proposed framework with existing studies demonstrates that the synchronization of data between the DT and PA ensures data integrity for the Botnet detection model training. Data privacy is maintained by inspecting only Packet headers, thereby not requiring the decryption of encrypted data.

Etching-Assisted Crumpled Graphene Wrapped Spiky Iron Oxide Particles for High-Performance Li-Ion Hybrid Supercapacitor.

From graphene oxide wrapped iron oxide particles with etching/reduction process, high-performance anode and cathode materials of lithium-ion hybrid supercapacitors are obtained in the same process with different etching conditions, which consist of partially etched crumpled graphene (CG) wrapped spiky iron oxide particles (CG@SF) for a battery-type anode, and fully etched CG for a capacitive-type cathode. The CG is formed along the shape of spikily etched particles, resulting in high specific surface area and electrical conductivity, thus the CG-based cathode exhibits remarkable capacitive performance of 210 F g-1 and excellent rate capabilities. The CG@SF can also be ideal anode materials owing to spiky and porous morphology of the particles and tightly attached crumpled graphene onto the spiky particles, which provides structural stability and low contact resistance during repetitive lithiation/delithiation processes. The CG@SF anode shows a particularly high capacitive performance of 1420 mAh g-1 after 270 cycles, continuously increases capacity beyond the 270th cycle, and also maintains a high capacity of 170 mAh g-1 at extremely high speeds of 100 C. The full-cell exhibits a higher energy density up to 121 Wh kg-1 and maintains high energy density of 60.1 Wh kg-1 at 18.0 kW kg-1 . This system could thus be a practical energy storage system to fill the gap between batteries and supercapacitors.

Development of A Textile Capacitive Proximity Sensor and Gait Monitoring System for Smart Healthcare.

Gait is not only one of the most important functions and activities in daily life but is also a parameter to monitor one's health status. We propose a single channel capacitive proximity pressure sensor (TCPS) and gait monitoring system for smart healthcare. Insole-type TCPS (270 mm in length) was designed consisting of three layers including two shield layers and a sensor layer. Analyzing the step count and stride time are the basic indicators in gait analysis, thus they were selected as evaluation indicators. A total of 12 subjects participated in the experiment to evaluate the resolution of our TCPS. To evaluate the accuracy of TCPS, step count and its error rates were simultaneously detected by naked eye, ZIKTO Walk (ZIKTO Co., Korea), and HJ-203-K pedometer (Omron Co., Japan) as reference. Results showed that the error rate of 1.77% in TCPS was lower than those of other devices and correlation coefficient was 0.958 (p-value = 0.000). ZIKTO Walk and pedometer do not provide information on stride time, therefore it was detected by F-scan (Tekscan, USA) to evaluate the performance of TCPS. As a result, error rate of stride time measured by TCPS was found to be 1% and the correlation coefficient was 0.685 (p-value = 0.000). According to these results, our proposed system may be helpful in development of gait monitoring and measurement system as smart healthcare.

Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network.

Wireless mesh networks (WMNs) have been considered as one of the key technologies for the configuration of wireless machines since they emerged. In a WMN, wireless routers provide multi-hop wireless connectivity between hosts in the network and also allow them to access the Internet via gateway devices. Wireless routers are typically equipped with multiple radios operating on different channels to increase network throughput. Multicast is a form of communication that delivers data from a source to a set of destinations simultaneously. It is used in a number of applications, such as distributed games, distance education, and video conferencing. In this study, we address a channel assignment problem for multicast in multi-radio multi-channel WMNs. In a multi-radio multi-channel WMN, two nearby nodes will interfere with each other and cause a throughput decrease when they transmit on the same channel. Thus, an important goal for multicast channel assignment is to reduce the interference among networked devices. We have developed a minimum interference channel assignment (MICA) algorithm for multicast that accurately models the interference relationship between pairs of multicast tree nodes using the concept of the interference factor and assigns channels to tree nodes to minimize interference within the multicast tree. Simulation results show that MICA achieves higher throughput and lower end-to-end packet delay compared with an existing channel assignment algorithm named multi-channel multicast (MCM). In addition, MICA achieves much lower throughput variation among the destination nodes than MCM.

A Reliable TTP-Based Infrastructure with Low Sensor Resource Consumption for the Smart Home Multi-Platform.

With the ICT technology making great progress in the smart home environment, the ubiquitous environment is rapidly emerging all over the world, but problems are also increasing proportionally to the rapid growth of the smart home market such as multiplatform heterogeneity and new security threats. In addition, the smart home sensors have so low computing resources that they cannot process complicated computation tasks, which is required to create a proper security environment. A service provider also faces overhead in processing data from a rapidly increasing number of sensors. This paper aimed to propose a scheme to build infrastructure in which communication entities can securely authenticate and design security channel with physically unclonable PUFs and the TTP that smart home communication entities can rely on. In addition, we analyze and evaluate the proposed scheme for security and performance and prove that it can build secure channels with low resources. Finally, we expect that the proposed scheme can be helpful for secure communication with low resources in future smart home multiplatforms.

Lightweight Sensor Authentication Scheme for Energy Efficiency in Ubiquitous Computing Environments.

The Internet of Things (IoT) is the intelligent technologies and services that mutually communicate information between humans and devices or between Internet-based devices. In IoT environments, various device information is collected from the user for intelligent technologies and services that control the devices. Recently, wireless sensor networks based on IoT environments are being used in sectors as diverse as medicine, the military, and commerce. Specifically, sensor techniques that collect relevant area data via mini-sensors after distributing smart dust in inaccessible areas like forests or military zones have been embraced as the future of information technology. IoT environments that utilize smart dust are composed of the sensor nodes that detect data using wireless sensors and transmit the detected data to middle nodes. Currently, since the sensors used in these environments are composed of mini-hardware, they have limited memory, processing power, and energy, and a variety of research that aims to make the best use of these limited resources is progressing. This paper proposes a method to utilize these resources while considering energy efficiency, and suggests lightweight mutual verification and key exchange methods based on a hash function that has no restrictions on operation quantity, velocity, and storage space. This study verifies the security and energy efficiency of this method through security analysis and function evaluation, comparing with existing approaches. The proposed method has great value in its applicability as a lightweight security technology for IoT environments.

[From Bioartificial Liver Development to Laboratory Medicine -In Vitro and In Vivo Stable Isotope-Labeled Glucose Breath Tests -].

The urea breath test was developed for the diagnosis of Helicobacter pylori infection. In this test, we measure exhaled ¹³CO2 using POCone developed in Japan, which is an infrared spectrometry photometer. Based on the urea breath test, we subsequently developed the in vitro ¹³C-glucose exhaling test. In this test, ¹³C-glucose is added in the circulation culture medium, and ¹³CO2 generated with a bioartificial liver is measured by the POCone. The quantity of discharged ¹³CO2 reflects the energy-producing ability of the bioartifi- cial liver. Therefore, we can simply and easily observe the viability of a bioartificial liver and the effect of drugs on carbohydrate metabolism in the bioartificial liver in real time. According to the results of a bioartificial liver study, we developed the fasting ¹³C-glucose breath test, which can quickly determine hepatic insu- lin resistance in humans with marked sensitivity. The bioartificial liver study unexpectedly led to the development of a new stable isotope breath test. [Review].

Complex chiral colloids and surfaces via high-index off-cut silicon.

Silicon wafers are commonly etched in potassium hydroxide solutions to form highly symmetric surface structures. These arise when slow-etching {111} atomic planes are exposed on standard low-index surfaces. However, the ability of nonstandard high-index wafers to provide more complex structures by tilting the {111} planes has not been fully appreciated. We demonstrate the power of this approach by creating chiral surface structures and nanoparticles of a specific handedness from gold. When the nanoparticles are dispersed in liquids, gold colloids exhibiting record molar circular dichroism (>5 × 10(9) M(-1) cm(-1)) at red wavelengths are obtained. The nanoparticles also present chiral pockets for binding.

A rhythm-based authentication scheme for smart media devices.

In recent years, ubiquitous computing has been rapidly emerged in our lives and extensive studies have been conducted in a variety of areas related to smart devices, such as tablets, smartphones, smart TVs, smart refrigerators, and smart media devices, as a measure for realizing the ubiquitous computing. In particular, smartphones have significantly evolved from the traditional feature phones. Increasingly higher-end smartphone models that can perform a range of functions are now available. Smart devices have become widely popular since they provide high efficiency and great convenience for not only private daily activities but also business endeavors. Rapid advancements have been achieved in smart device technologies to improve the end users' convenience. Consequently, many people increasingly rely on smart devices to store their valuable and important data. With this increasing dependence, an important aspect that must be addressed is security issues. Leaking of private information or sensitive business data due to loss or theft of smart devices could result in exorbitant damage. To mitigate these security threats, basic embedded locking features are provided in smart devices. However, these locking features are vulnerable. In this paper, an original security-locking scheme using a rhythm-based locking system (RLS) is proposed to overcome the existing security problems of smart devices. RLS is a user-authenticated system that addresses vulnerability issues in the existing locking features and provides secure confidentiality in addition to convenience.

Time pattern locking scheme for secure multimedia contents in human-centric device.

Among the various smart multimedia devices, multimedia smartphones have become the most widespread due to their convenient portability and real-time information sharing, as well as various other built-in features. Accordingly, since personal and business activities can be carried out using multimedia smartphones without restrictions based on time and location, people have more leisure time and convenience than ever. However, problems such as loss, theft, and information leakage because of convenient portability have also increased proportionally. As a result, most multimedia smartphones are equipped with various built-in locking features. Pattern lock, personal identification numbers, and passwords are the most used locking features on current smartphones, but these are vulnerable to shoulder surfing and smudge attacks, allowing malicious users to bypass the security feature easily. In particular, the smudge attack technique is a convenient way to unlock multimedia smartphones after they have been stolen. In this paper, we propose the secure locking screen using time pattern (SLSTP) focusing on improved security and convenience for users to support human-centric multimedia device completely. The SLSTP can provide a simple interface to users and reduce the risk factors pertaining to security leakage to malicious third parties.

DS-ARP: a new detection scheme for ARP spoofing attacks based on routing trace for ubiquitous environments.

Despite the convenience, ubiquitous computing suffers from many threats and security risks. Security considerations in the ubiquitous network are required to create enriched and more secure ubiquitous environments. The address resolution protocol (ARP) is a protocol used to identify the IP address and the physical address of the associated network card. ARP is designed to work without problems in general environments. However, since it does not include security measures against malicious attacks, in its design, an attacker can impersonate another host using ARP spoofing or access important information. In this paper, we propose a new detection scheme for ARP spoofing attacks using a routing trace, which can be used to protect the internal network. Tracing routing can find the change of network movement path. The proposed scheme provides high constancy and compatibility because it does not alter the ARP protocol. In addition, it is simple and stable, as it does not use a complex algorithm or impose extra load on the computer system.

Maximizing throughput in NOMA-enable industrial IoT networks using digital twin and reinforcement learning.

INTRODUCTION: Increased deployment of heterogeneous and complex Industrial Internet of Things (IIoT) applications such as predictive maintenance and asset tracking places a substantial strain on the limited computational and communication resources. To cater to the rigorous demands of these applications, it is imperative to devise an adaptive online resource allocation method to enhance the efficiency of the current network operations. Multiaccess edge computing (MEC) and digital twins (DTs) are promising solutions that facilitate the realization of edge intelligence and find applications in various industrial applications. Yet, little is known about the advantage the two technologies offer to IIoT networks. OBJECTIVE: This study presents a joint optimization of offloading and resource allocation approach where MEC-server DT is created at the edge, and nonorthogonal multiple access (NOMA) communication is considered between IIoT devices and the industrial gateways (IGWs) for spectral efficiency. Our proposed framework is tailored to reduce mean task completion latency and enhance overall IIoT network throughput. METHOD: To achieve our objective, we jointly optimize the computation resource allocation (RA), subchannel assignment (SA), and offloading decisions (OD). Given the inherent complexity of the problem, we further divide it into RA and SA/OD sub-problems. Employing Deep Reinforcement Learning (DRL), we have formulated a solution delineating the most efficient RA strategy and leveraged DT for optimal SA/OD strategies. RESULTS: Simulation results demonstrate the superior efficiency of our framework, realizing up to 92 % of the efficiency of the exhaustive search method while reducing computation and action decision time. CONCLUSION: In light of system dynamics considered for our work, the proposed framework perfomance showcase its robustness and potential application in real-world IIoT networks.

Improved prediction of clinical pregnancy using artificial intelligence with enhanced inner cell mass and trophectoderm images.

This study aimed to assess the performance of an artificial intelligence (AI) model for predicting clinical pregnancy using enhanced inner cell mass (ICM) and trophectoderm (TE) images. In this retrospective study, we included static images of 2555 day-5-blastocysts from seven in vitro fertilization centers in South Korea. The main outcome of the study was the predictive capability of the model to detect clinical pregnancies (gestational sac). Compared with the original embryo images, the use of enhanced ICM and TE images improved the average area under the receiver operating characteristic curve for the AI model from 0.716 to 0.741. Additionally, a gradient-weighted class activation mapping analysis demonstrated that the enhanced image-trained AI model was able to extract features from crucial areas of the embryo in 99% (506/512) of the cases. Particularly, it could extract the ICM and TE. In contrast, the AI model trained on the original images focused on the main areas in only 86% (438/512) of the cases. Our results highlight the potential efficacy of using ICM- and TE-enhanced embryo images when training AI models to predict clinical pregnancy.

Regulation of differentiation potential of human mesenchymal stem cells by intracytoplasmic delivery of coactivator-associated arginine methyltransferase 1 protein using cell-penetrating peptide.

Recent studies suggest that epigenetic modifications, such as DNA methylation and histone modification, can alter the differentiation potential of stem cells or progenitor cells. Specifically, coactivator-associated arginine methyltransferase 1 (CARM1) is known to act as a coactivator for various transcription factors and to regulate gene expression by chromatin remodeling through histone methylation. Here, for the first time, we have used direct protein delivery of CARM1 using cell-penetrating peptide (CPP) to regulate the differentiation potential of human mesenchymal stem cells (hMSCs). Immunofluorescence showed that the CPP-CARM1 protein is successfully delivered into the nuclei of hMSCs. Further experiments using immunofluorescence and Western blotting showed that the delivered CARM1 protein can effectively methylate the arginine 17 residue of histone H3 in both bone marrow (BM)- and adipose-derived (AD)-hMSCs, thus suggesting that the CARM1 protein delivered by the CPP system is biologically active in hMSCs. Chromatin immunoprecipitation (ChIP) assay and genome-wide gene expression profiling supported the result that delivered CARM1 protein can cause chromatin remodeling through histone methylation. Finally, the CPP-CARM1 protein efficiently elevated the differentiation efficiency of BM-hMSCs and AD-hMSCs into adipogenic, osteogenic, and myogenic cell lineages in vitro. Altered expression of critical genes after hMSC differentiation was reconfirmed by real-time reverse transcription polymerase chain reaction (qRT-PCR). Collectively, our results suggest that CPP-CARM1 can elevate the differentiation potential of hMSCs into various cell types, and that this system using CPP is a useful tool for exogenous protein delivery in clinical applications of cell-based therapy.

Federated Learning-Based Secure Electronic Health Record Sharing Scheme in Medical Informatics.

Medical Cyber-Physical Systems support the mobility of electronic health records data for clinical research to accelerate new scientific discoveries. Artificial Intelligence improves medical informatics, but current centralized data training and insecure data storage management techniques expose private medical data to unauthorized foreign entities. In this paper, a Federated Learning-based Electronic Health Record sharing scheme is proposed for Medical Informatics to preserve patient data privacy. A decentralized Federated Learning-based Convolutional Neural Network model trains data locally in the hospital and stores results in a private InterPlanetary File System. A secondary global model is trained at the research center using the local models. Private IPFS secures all medical data stored locally in the hospital. The novelty of this study resides in securing valuable hospital biomedical data useful for clinical research organizations. Blockchain and smart contracts enable patients to negotiate with external entities for rewards in exchange for their data. Evaluation results demonstrate that the decentralized CNN model performs better in accuracy, sensitivity, and specificity, similar to the traditional centralized model. The performance of the Private IPFS exceeds the Blockchain-based IPFS based on file upload and download time. The scheme is suitable for promoting a secure and privacy-friendly environment for sharing data with clinical research centers for biomedical research.

Lightweight Authentication Scheme for IoT based E-Healthcare Service Communication.

Remote Patient Monitoring (RPM) using Electronic Healthcare (E-health) is a growing phenomenon enabling doctors predict patient health such as possible cardiac arrests from identified abnormal arrythmia. Remote Patient Monitoring enables healthcare staff to notify patients with preventive measures to avoid a medical emergency reducing patient stress. However weak authentication security protocols in IoT wearables such as pacemakers, enable cyberattacks to transmit corrupt data, preventing patients from receiving medical care. In this paper we focus on the security of wearable devices for reliable healthcare services and propose a Lightweight Key Agreement (LKA) based authentication scheme for securing Device-to-Device (D2D) communication. A Network Key Manager on the edge builds keys for each device for device validation. Device authentication requests are verified using certificates, reducing network communication costs. E-health empowered mobile devices are store authentication certificates for future seamless device validation. The LKA scheme is evaluated and compared with existing studies and exhibits reduced operation time for key generation operation costs and lower communication costs incurred during the execution of the device authentication protocol compared with other studies. The LKA scheme further exhibits reduced latency when compared with the three existing schemes due to reduced communication costs.