The effects of serum granulin levels on anthropometric measures and glucose metabolism in infertile women with different ovarian reserve status

Introduction: Granulin (GRN) is an adipokine with proinflammatory features, which plays important role in glucose metabolism and insulin resistance pathogenesis. It has been reported that granulin precursors were localised in developing follicles in animal studies. The purpose of this study was to evaluate the association of granulin levels with anthropometric features, glucose metabolism, and ovarian reserve. Material and methods: A total of 109 infertile women were included in this cross-sectional, prospective study, who attended a tertiary clinic. All participants were categorised into diminished ovarian reserve (DOR) and normal ovarian reserve groups (NOR), in accordance with Bologna criteria. The demographic characteristics, including age, BMI, waist-hip circumferences, and biochemical parameters, were recorded. Serum granulin level was determined by enzyme-linked immunosorbent assay. Results: No significant difference was observed in the GRN levels (p = 0.229) between the groups. There was a positive correlation between GRN levels and BMI, WC, HC, and 75 g oral glucose tolerance values in NOR group (p < 0.01, p < 0.05, p < 0.01, and p < 0.05, respectively). Conclusions: Our results suggest that granulin is associated with anthropometric features in infertile patients and might be an important indicator of obesity and impaired glucose metabolism. Elevated levels of granulin may have a diabetogenic effect and predispose women to high glucose levels

Stimulated emission and ultrafast carrier relaxation in InGaN multiple quantum wells

Stimulated emission (SE) was measured from two InGaN multiple quantum well (MQW) laser structures with different In compositions. SE threshold power densities (I_th) increased with increasing QW depth (x). Time-resolved differential transmission measurements mapped the carrier relaxation mechanisms and explained the dependence of I_th on x. Carriers are captured from the barriers to the QWs in < 1 ps, while carrier recombination rates increased with increasing x. For excitation above I_th an additional, fast relaxation mechanism appears due to the loss of carriers in the barriers through a cascaded refilling of the QW state undergoing SE. The increased material inhomogeneity with increasing x provides additional relaxation channels outside the cascaded refilling process, removing carriers from the SE process and increasing I_th.Comment: submitted to Appl. Phys. Let

Defensive Distillation-Based Adversarial Attack Mitigation Method for Channel Estimation Using Deep Learning Models in Next-Generation Wireless Networks

Future wireless networks (5G and beyond), also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have dramatically grown with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those applications by improving and optimizing network functions. Artificial Intelligence (AI) has a high potential to achieve these requirements by being integrated into applications throughout all network layers. However, the security concerns on network functions of NextG using AI-based models, i.e., model poisoning, have not been investigated deeply. It is crucial to protect the next-generation cellular networks against cybersecurity threats, especially adversarial attacks. Therefore, it needs to design efficient mitigation techniques and secure solutions for NextG networks using AI-based methods. This paper proposes a comprehensive vulnerability analysis of deep learning (DL)-based channel estimation models trained with the dataset obtained from MATLAB\u27s 5G toolbox for adversarial attacks and defensive distillation-based mitigation methods. The adversarial attacks produce faulty results by manipulating trained DL-based models for channel estimation in NextG networks while mitigation methods can make models more robust against adversarial attacks. This paper also presents the performance of the proposed defensive distillation mitigation method for each adversarial attack. The results indicate that the proposed mitigation method can defend the DL-based channel estimation models against adversarial attacks in NextG networks

The Enlightening Role of Explainable Artificial Intelligence in Chronic Wound Classification

Artificial Intelligence (AI) has been among the most emerging research and industrial application fields, especially in the healthcare domain, but operated as a black-box model with a limited understanding of its inner working over the past decades. AI algorithms are, in large part, built on weights calculated as a result of large matrix multiplications. It is typically hard to interpret and debug the computationally intensive processes. Explainable Artificial Intelligence (XAI) aims to solve black-box and hard-to-debug approaches through the use of various techniques and tools. In this study, XAI techniques are applied to chronic wound classification. The proposed model classifies chronic wounds through the use of transfer learning and fully connected layers. Classified chronic wound images serve as input to the XAI model for an explanation. Interpretable results can help shed new perspectives to clinicians during the diagnostic phase. The proposed method successfully provides chronic wound classification and its associated explanation to extract additional knowledge that can also be interpreted by non-data-science experts, such as medical scientists and physicians. This hybrid approach is shown to aid with the interpretation and understanding of AI decision-making processes

Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction

6G – sixth generation – is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning (ML) algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous cars, and many more. Those algorithms have also been used in communication technologies to improve the system performance in terms of frequency spectrum usage, latency, and security. With the rapid developments of ML techniques, especially deep learning (DL), it is critical to consider the security concern when applying the algorithms. While ML algorithms offer significant advantages for 6G networks, security concerns on artificial intelligence (AI) models are typically ignored by the scientific community so far. However, security is also a vital part of AI algorithms because attackers can poison the AI model itself. This paper proposes a mitigation method for adversarial attacks against proposed 6G ML models for the millimeter-wave (mmWave) beam prediction using adversarial training. The main idea behind generating adversarial attacks against ML models is to produce faulty results by manipulating trained DL models for 6G applications for mmWave beam prediction. We also present a proposed adversarial learning mitigation method’s performance for 6G security in mmWave beam prediction application a fast gradient sign method attack. The results show that the defended model under attack’s mean square errors (i.e., the prediction accuracy) are very close to the undefended model without attack

Defensive Distillation-based Adversarial Attack Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks

Future wireless networks (5G and beyond), also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have dramatically grown with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those applications by improving and optimizing network functions. Artificial Intelligence (AI) has a high potential to achieve these requirements by being integrated into applications throughout all network layers. However, the security concerns on network functions of NextG using AI-based models, i.e., model poisoning, have not been investigated deeply. It is crucial to protect the next-generation cellular networks against cybersecurity threats, especially adversarial attacks. Therefore, it needs to design efficient mitigation techniques and secure solutions for NextG networks using AI-based methods. This paper proposes a comprehensive vulnerability analysis of deep learning (DL)-based channel estimation models trained with the dataset obtained from MATLAB’s 5G toolbox for adversarial attacks and defensive distillation-based mitigation methods. The adversarial attacks produce faulty results by manipulating trained DL-based models for channel estimation in NextG networks while mitigation methods can make models more robust against adversarial attacks. This paper also presents the performance of the proposed defensive distillation mitigation method for each adversarial attack. The results indicate that the proposed mitigation method can defend the DL-based channel estimation models against adversarial attacks in NextG networks.publishedVersio

Cybersecurity and Digital Privacy Aspects of V2X in the EV Charging Structure

With the advancement of green energy technology and rising public and political acceptance, electric vehicles (EVs) have grown in popularity. Electric motors, batteries, and charging systems are considered major components of EVs. The electric power infrastructure has been designed to accommodate the needs of EVs, with an emphasis on bidirectional power flow to facilitate power exchange. Furthermore, the communication infrastructure has been enhanced to enable cars to communicate and exchange information with one another, also known as Vehicle-to-Everything (V2X) technology. V2X is positioned to become a bigger and smarter system in the future of transportation, thanks to upcoming digital technologies like Artificial Intelligence (AI), Distributed Ledger Technology, and the Internet of Things. However, like with any technology that includes data collection and sharing, there are issues with digital privacy and cybersecurity. This paper addresses these concerns by creating a multi-layer Cyber-Physical-Social Systems (CPSS) architecture to investigate possible privacy and cybersecurity risks associated with V2X. Using the CPSS paradigm, this research explores the interaction of EV infrastructure as a very critical part of the V2X ecosystem, digital privacy, and cybersecurity concerns

Gossypiboma: Retained Surgical Sponge after a Gynecologic Procedure

We report on a case of gossypiboma. A 54-year-old woman was admitted to our hospital with abdominal mass. She had undergone a caesarean operation 23 years previously. The mass in the right abdominal quadrant was suspected by abdominal computed tomography and magnetic resonance imaging. The mass was removed by laparotomy excision and the final diagnosis was gossypiboma