YA-DA: YAng-Based DAta Model for Fine-Grained IIoT Air Quality Monitoring

With the development of industrialization, air pollution is also steadily on the rise since both industrial and daily activities generate a massive amount of air pollution. Since decreasing air pollution is critical for citizens' health and well-being, air pollution monitoring is becoming an essential topic. Industrial Internet of Things (IIoT) research focuses on this crucial area. Several attempts already exist for air pollution monitoring. However, none of them are improving the performance of IoT data collection at the desired level. Inspired by the genuine Yet Another Next Generation (YANG) data model, we propose a YAng-based DAta model (YA-DA) to improve the performance of IIoT data collection. Moreover, by taking advantage of digital twin (DT) technology, we propose a DT-enabled fine-grained IIoT air quality monitoring system using YA-DA. As a result, DT synchronization becomes fine-grained. In turn, we improve the performance of IIoT data collection resulting in lower round-trip time (RTT), higher DT synchronization, and lower DT latency.Comment: This paper has been accepted at the 4th Workshop on Future of Wireless Access and Sensing for Industrial IoT (FUTUREIIOT) in IEEE Global Communications Conference (IEEE GLOBECOM) 202

Network-Aware AutoML Framework for Software-Defined Sensor Networks

As the current detection solutions of distributed denial of service attacks (DDoS) need additional infrastructures to handle high aggregate data rates, they are not suitable for sensor networks or the Internet of Things. Besides, the security architecture of software-defined sensor networks needs to pay attention to the vulnerabilities of both software-defined networks and sensor networks. In this paper, we propose a network-aware automated machine learning (AutoML) framework which detects DDoS attacks in software-defined sensor networks. Our framework selects an ideal machine learning algorithm to detect DDoS attacks in network-constrained environments, using metrics such as variable traffic load, heterogeneous traffic rate, and detection time while preventing over-fitting. Our contributions are two-fold: (i) we first investigate the trade-off between the efficiency of ML algorithms and network/traffic state in the scope of DDoS detection. (ii) we design and implement a software architecture containing open-source network tools, with the deployment of multiple ML algorithms. Lastly, we show that under the denial of service attacks, our framework ensures the traffic packets are still delivered within the network with additional delays

TwinPot: Digital Twin-assisted Honeypot for Cyber-Secure Smart Seaports

The idea of next-generation ports has become more apparent in the last ten years in response to the challenge posed by the rising demand for efficiency and the ever-increasing volume of goods. In this new era of intelligent infrastructure and facilities, it is evident that cyber-security has recently received the most significant attention from the seaport and maritime authorities, and it is a primary concern on the agenda of most ports. Traditional security solutions can be applied to safeguard IoT and Cyber-Physical Systems (CPS) from harmful entities. Nevertheless, security researchers can only watch, examine, and learn about the behaviors of attackers if these solutions operate more transparently. Herein, honeypots are potential solutions since they offer valuable information about the attackers. It can be virtual or physical. Virtual honeypots must be more realistic to entice attackers, necessitating better high-fidelity. To this end, Digital Twin (DT) technology can be employed to increase the complexity and simulation fidelity of the honeypots. Seaports can be attacked from both their existing devices and external devices at the same time. Existing mechanisms are insufficient to detect external attacks; therefore, the current systems cannot handle attacks at the desired level. DT and honeypot technologies can be used together to tackle them. Consequently, we suggest a DT-assisted honeypot, called TwinPot, for external attacks in smart seaports. Moreover, we propose an intelligent attack detection mechanism to handle different attack types using DT for internal attacks. Finally, we build an extensive smart seaport dataset for internal and external attacks using the MANSIM tool and two existing datasets to test the performance of our system. We show that under simultaneous internal and external attacks on the system, our solution successfully detects internal and external attacks.Comment: Accepted on WS01 IEEE ICC 2023 Workshop on The Evolution of Digital Twin Paradigm in Wireless Communication

Prevalence, classification and dental treatment requirements of dens invaginatus by cone-beam computed tomography

Background. This study aimed the evaluation of the prevalence, characteristics, types of dens invaginatus (DI) and co-observed dental anomalies to understand dental treatment requirements in anterior teeth that are susceptible to developmental anomalies by using cone-beam computed tomography (CBCT). Methods. In this retrospective study, the anterior teeth of 958 patients were evaluated by using CBCT for the presence of DI. The demographic features, types of DI and treatment requirements were also recorded. The association between sex and the presence of DI was evaluated using chi-squared test. Results. Seventy-three DI anomalies were detected in the anterior teeth of 49 patients (18 females, 31 males). The frequency of DI was 5.11% and the most frequently involved teeth were lateral (57.53%). Forty-six teeth were classified as Type I (63.01%), 24 as Type II (32.87%), and three as Type III (4.10%). Apical pathosis was found to be 20.54% in all DIs detected and accounted for all Type III and one-third of Type II. Conclusions. CBCT imaging can be effective in the detection of dental anomalies such as DI and planning for root canal therapy and surgical treatments. Prophylactic interventions might be possible to prevent apical pathosis with the data obtained from CBCT images

Digital Twin-Enabled Intelligent DDoS Detection Mechanism For Autonomous Core Networks

Existing distributed denial of service attack (DDoS) solutions cannot handle highly aggregated data rates; thus, they are unsuitable for Internet service provider (ISP) core networks. This article proposes a digital twin-enabled intelligent DDoS detection mechanism using an online learning method for autonomous systems. Our contributions are threefold: we first design a DDoS detection architecture based on the digital twin for ISP core networks. We implemented a Yet Another Next Generation (YANG) model and an automated feature selection (AutoFS) module to handle core network data. We used an online learning approach to update the model instantly and efficiently , improve the learning model quickly, and ensure accurate predictions. Finally, we reveal that our proposed solution successfully detects DDoS attacks and updates the feature selection method and learning model with a true classification rate of ninety-seven percent. Our proposed solution can estimate the attack within approximately fifteen minutes after the DDoS attack starts

Solid cystic pseudopapillary tumor of pancreas with splenic metastasis: Case report and review of literature

Introduction: Solid-cystic pseudopapillary tumor of the pancreas is rare and most commonly seen in young women. We present a young women with solid-cystic pseudopapillary tumor of the pancreas and discuss the literature. Presentation of case: Thirty nine years old female patient with a mass about 12 cm in the pancreas with splenic invasion seen in our clinic. After having CT and PET-CT view, patient underwent surgery. Distal pancreatectomy with mass excision and splenectomy was performed. Microscopic examination result was solid cystic pseudopapillary tumor with spleen invasion. Discussion: Solid-cystic pseudopapillary tumor of the pancreas has cystic solid pseudopapillary structures. Prognosis of tumor is better than other pancreatic tumor. Complete resection of tumor with splenic inclusion is surgical treatment. Conclusion: In case of large slow growing pancreatic tumor with splenic metastasis, solid-cystic pseudopapillary tumor of the pancreas should be considered in the diagnosis. Complete surgical resection is associated with long-term survival even in the presence of metastatic disease. Close follow-up is necessary after surgery

TwinPort: 5G drone-assisted data collection with digital twin for smart seaports

Abstract Numerous ports worldwide are adopting automation to boost productivity and modernize their operations. At this point, smart ports become a more important paradigm for handling increasing cargo volumes and increasing operational efficiency. In fact, as ports become more congested and cargo volumes increase, the need for accurate navigation through seaports is more pronounced to avoid collisions and the resulting consequences. To this end, digital twin (DT) technology in the fifth-generation (5G) networks and drone-assisted data collection can be combined to provide precise ship maneuvering. In this paper, we propose a DT model using drone-assisted data collection architecture, called TwinPort, to offer a comprehensive port management system for smart seaports. We also present a recommendation engine to ensure accurate ship navigation within a smart port during the docking process. The experimental results reveal that our solution improves the trajectory performance by approaching the desired shortest path. Moreover, our solution supports significantly reducing financial costs and protecting the environment by reducing fuel consumption

Thyroid hormones and ovarian reserve: a comprehensive study of women seeking infertility care

Abstract Background Ovarian reserve is the number of oocytes remaining in the ovary and is one of the most important aspects of a woman’s reproductive potential. Research on the association between thyroid dysfunction and ovarian reserve has yielded controversial results. In our study, we aimed to investigate the relationship between thyroid-stimulating hormone (TSH) levels and ovarian reserve markers. Methods From 1443 women seeking infertility care, the data of 1396 women aged between 20–45 years old who had a body mass index between 18–30 kg/m2 were recruited for this retrospective study. The anti-Müllerian hormone (AMH) and TSH relationship was analyzed with generalized linear and polynomial regression. Results Median age, follicle-stimulating hormone (FSH), AMH, and TSH levels were 36.79 years, 9.55 IU/L, 3.57 pmol/L, and 1.80 mIU/L, respectively. Differences between TSH groups were statistically significant in terms of AMH level, antral follicle count (AFC), and age (p = 0.007 and p = 0.038, respectively). A generalized linear regression model could not explain age-matched TSH levels concerning AMH levels (p > 0.05). TSH levels were utilized in polynomial regression models of AMH, and the 2nd degree was found to have the best fit. The inflection point of the model was 2.88 mIU/L. Conclusions Our study shows a correlation between TSH and AMH values in a population of infertile women. Our results are as follows: a TSH value of 2.88 mIU/L yields the highest AMH result. It was also found that AMH and AFC were positively correlated, while AMH and FSH were negatively correlated

Hyperglycemia with or without insulin resistance triggers different structural changes in brain microcirculation and perivascular matrix

Both type-1 and type-2 DM are related to an increased risk of cognitive impairment, neurovascular complications, and dementia. The primary triggers for complications are hyperglycemia and concomitant insulin resistance in type-2 DM. However, the diverse mechanisms in the pathogenesis of diabetes-related neurovascular complications and extracellular matrix (ECM) remodeling in type-1 and 2 have not been elucidated yet. Here, we investigated the high fat-high sucrose (HFHS) feeding model and streptozotocin-induced type-1 DM model to study the early effects of hyperglycemia with or without insulin resistance to demonstrate the brain microcirculatory changes, perivascular ECM alterations in histological sections and 3D-reconstructed cleared brain tissues. One of the main findings of this study was robust rarefaction in brain microvessels in both models. Interestingly, the HFHS model leads to widespread non-functional angiogenesis, but the type-1 DM model predominantly in the rostral brain. Rarefaction was accompanied by basement membrane thickening and perivascular collagen accumulation in type-1 DM; more severe blood-brain barrier leakage, and disruption of perivascular ECM organization, mainly of elastin and collagen fibers' structural integrity in the HFHS model. Our results point out that the downstream mechanisms of the long-term vascular complications of hyperglycemia models are structurally distinctive and may have implications for appropriate treatment options