FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA

Federated Learning (FL) has emerged as a promising approach for privacy preservation, allowing sharing of the model parameters between users and the cloud server rather than the raw local data. FL approaches have been adopted as a cornerstone of distributed machine learning (ML) to solve several complex use cases. FL presents an interesting interplay between communication and ML performance when implemented over distributed wireless nodes. Both the dynamics of networking and learning play an important role. In this article, we investigate the performance of FL on an application that might be used to improve a remote healthcare system over ad hoc networks which employ CSMA/CA to schedule its transmissions. Our FL over CSMA/CA (FLCC) model is designed to eliminate untrusted devices and harness frequency reuse and spatial clustering techniques to improve the throughput required for coordinating a distributed implementation of FL in the wireless network. In our proposed model, frequency allocation is performed on the basis of spatial clustering performed using virtual cells. Each cell assigns a FL server and dedicated carrier frequencies to exchange the updated model's parameters within the cell. We present two metrics to evaluate the network performance: 1) probability of successful transmission while minimizing the interference, and 2) performance of distributed FL model in terms of accuracy and loss while considering the networking dynamics. We benchmark the proposed approach using a well-known MNIST dataset for performance evaluation. We demonstrate that the proposed approach outperforms the baseline FL algorithms in terms of explicitly defining the chosen users' criteria and achieving high accuracy in a robust network

A clinicopathological analysis of 151 odontogenic tumors based on new WHO classification 2022: A retrospective cross-sectional study

Background: Odontogenic tumors are a diverse group of lesions with a variety of clinical behavior and histopathologic subtypes, from hamartomatous and benign to malignant. The study aimed to examine the clinical and pathological features of odontogenic tumors in Baghdad over the last 11 years (2011–2021). Materials and Methods: The present retrospective study analyzed all formalin-fixed, paraffin-embedded tissue blocks of patients diagnosed with an odontogenic tumor that were retrieved from archives at a teaching hospital/College of Dentistry in Baghdad University, Iraq, between 2011 and 2021. The diagnosis of each case was confirmed by examining the hematoxylin and eosin stained sections by two expert pathologists. Data from patients' case sheets were collected, including age, gender, location, and histopathological information. The type of lesions was evaluated based on the World Health Organization's most recent classification (March 2022). Results: There were 151 odontogenic tumor during this period. The most common type (39.1%) was Solid ameloblastoma. The mandibular tumors (76.8%) were more than the maxillary tumors (23.2%). The female to male ratio was 1.1:1. The most cases are found between the 2nd and 5th decades of life. Conclusions: Solid ameloblastoma was the most common odontogenic tumor, while primordial odontogenic tumor was the rarest, Odontogenic tumors were slightly more common in females than in males, the most common cases occur in the mandible., the outcome of the study gives valuable information regarding the patients' profile and type of odontogenic tumors over 11 years, which could aid in the early diagnosis and enhance the intervention

Internet-of-vehicles network for CO₂ emission estimation and reinforcement learning-based emission reduction

The escalating impact of vehicular Carbon Dioxide (CO2) emissions on air pollution, global warming, and climate change necessitates innovative solutions. This paper proposes a comprehensive Internet-of-Vehicles (IoV) network for real-time CO2 emissions estimation and reduction. We implemented and tested an on-board device that estimates the vehicle’s emissions and transmits the data to the network. The estimated CO2 emissions values are close to the standard emissions values of petrol and diesel vehicles, accounting for expected discrepancies due to vehicles’ age and loading. The network uses the aggregate emissions readings to inform the Reinforcement Learning (RL) algorithm, enabling the prediction of optimal speed limits to minimize vehicular emissions. The results demonstrate that employing the RL algorithm can achieve an average CO2 emissions reduction of 11 kg/h to 150 kg/h

The Electroencephalogram as a Biomarker Based on Signal Processing Using Nonlinear Techniques to Detect Dementia

Dementia being a syndrome caused by a brain disease of a chronic or progressive nature, in which the irreversible loss of intellectual abilities, learning, expressions arises; including memory, thinking, orientation, understanding and adequate communication, of organizing daily life and of leading a family, work and autonomous social life; leads to a state of total dependence; therefore, its early detection and classification is of vital importance in order to serve as clinical support for physicians in the personalization of treatment programs. The use of the electroencephalogram as a tool for obtaining information on the detection of changes in brain activities. This article reviews the types of cognitive spectrum dementia, biomarkers for the detection of dementia, analysis of mental states based on electromagnetic oscillations, signal processing given by the electroencephalogram, review of processing techniques, results obtained where it is proposed the mathematical model about neural networks, discussion and finally the conclusions

Cellular pharmacology studies of anticancer agents: recommendations from the EORTC-PAMM group

An increasing number of manuscripts focus on the in vitro evaluation of established and novel anti-tumour agents in experimental models. Whilst the design of such in vitro assays is inherently flexible, some of these studies lack the minimum information necessary to critically evaluate their relevance or have been carried out under unsuitable conditions. The use of appropriate and robust methods and experimental design has important implications for generating results that are reliable, relevant, and reproducible. The Pharmacology and Molecular Mechanisms (PAMM) group of the European Organization for Research and Treatment of Cancer (EORTC) is the largest group of academic scientists working on drug development and bundle decades of expertise in this field. This position paper addresses all researchers with an interest in the preclinical and cellular pharmacology of anti-tumour agents and aims at generating basic recommendations for the correct use of compounds to be tested for anti-tumour activity using a range of preclinical cellular models of cancer

Correlation of amyloid and ameloblast‐associated proteins to odontogenic cysts and tumors: A cross‐sectional study

Abstract Background and Aims Odontogenic cysts and tumors often form hard and soft structures that resemble odontogenesis. It is well known that amyloid is produced in Pindborg tumors; however, it is still debatable whether it is also formed in other odontogenic tumors and cysts. This study aimed to detect the presence of amyloid in different odontogenic cysts and tumors in correlation to matrix proteins secreted during enamel formation; namely amelogenin and odontogenic ameloblast‐associated protein. Methods This study included formalin fixed paraffin embedded tissue blocks of 106 different types of odontogenic cysts and tumors. Congo red and thioflavin T were performed to confirm the presence of amyloid; immunohistochemistry was used to detect amelogenin and odontogenic ameloblast‐associated protein. Results Amyloid was detected in pindborg tumors (conventional), adenomatoid odontogenic tumors, odontogenic fibroma (Amyloid variant), follicular solid and unicystic ameloblastomas, radicular cysts, dentigerous cysts, dentinogenic ghost cell odontogenic tumor, ameloblastic fibroma, calcifying odontogenic cyst, and primordial Odontogenic tumor. Amelogenin was detected in 95.3% of the cases, while odontogenic ameloblast‐associated protein was detected in 93.4% of the cases. The association between odontogenic ameloblast‐associated protein and amyloid was highly significant at p  0.05. Conclusion Although pindborg tumor is the bonafide example of amyloid deposition in odontogenic tumors, this study concluded that amyloid may be deposited in traces to massive amounts in various odontogenic cysts and tumors, and it is significantly linked to odontogenic ameloblast‐associated protein but not amelogenin matrix protein, since all amyloid cases were odontogenic ameloblast associated protein positive

Investigation of Strain in II-VI Semiconductor Superlattices Using Electron Paramagnetic Resonance of Mn++\text{}^{++}

We explore the possibility of using electron paramagnetic resonance (EPR) of Mn$\text{}^{++}$ for measuring uniaxial strain in II-VI superlattices. This work is motivated by the fact that the EPR spectrum of Mn$\text{}^{++}$ is very strongly affected by crystalline fields. Changes in a crystalline field which arise from strain are thus automatically expected to have a profound effect on the EPR spectrum. Consistent with this expectation, we have observed giant crystal field splittings of Mn$\text{}^{++}$ EPR lines in ZnTe/MnTe, CdTe/MnTe, and ZnTe/MnSe superlattices. The EPR spectra observed in these systems are ascribed to isolated Mn$\text{}^{++}$ ions diffused into the ZnTe or the CdTe layers from the respective MnTe or MnSe layers. In addition to providing precise information oii the magnitude and the sign of strain produced by lattice mismatch between the superlattice constituents, we show that the EPR spectrum also provides a direct measure of strain fluctuations in the layered medium

Proton pump inhibitors and peptic ulcer management: Antioxidant mechanisms

Peptic ulcer (P.U.) is the gastrointestinal tract's most frequent disorder affecting mainly the stomach and duodenum. Surgical intervention, ingested materials and microbial infections may trigger inflammation that further predispose to oxidative stress. Proton pump inhibitors (PPIs) are group of compounds established for suppressions of gastric acid secretions profoundly and permanently over a reasonably long period of time. Oxidative stress has been shown to be involved in the pathophysiology of various diseases and disorders, including P.U. Particularly when H. pylori infection accompanies it. In addition to the colonization of this microorganism, gastric mucosa may be subjected to extreme oxidative stress with large levels of inflammatory cell aggregation, which may eventually predispose to the disorder. PPIs exert several effects other than gastric acid suppression that can be used to treat Helicobacter pylori infections, disorders of the respiratory tract, viral infections, and other conditions related to dysfunction of endothelium by activating endogenous antioxidant protection and reducing the release of cytokine. Recent therapeutic protocols have recommended PPIs as gastro-protective compounds not only because of their acid suppression properties, but also because of their potent antioxidant and anti-inflammatory properties. Keywords: Proton Pump Inhibitors, Peptic Ulcer, Oxidative Stress, H. pylori&nbsp