The integration of intelligent reflecting surfaces (IRS) and non-orthogonal multiple access (NOMA) within the Open Radio Access Network (O-RAN) offers significant opportunities for 6G Internet of Things (IoT) systems but also raises new challenges in security, reliability, and resource efficiency. In this paper, we propose a cross-layer secure sum-rate maximization framework that jointly addresses physical-layer secrecy and network-layer packet loss in IRS-NOMA O-RAN environments. We analytically derive closed-form expressions for secrecy rates and packet loss under a cascaded Rician fading model, and formulate a secrecy sum-rate optimization problem that accounts for IRS phase shifts, NOMA power allocation, and energy-harvesting constraints. The resulting problem is NP-hard due to non-convex coupling across layers. To overcome this, we develop SecureO-RAN-SAC, a deep reinforcement learning algorithm based on Soft Actor-Critic v2, which learns near-optimal policies in real time. Simulation results demonstrate that SecureO-RAN-SAC achieves comparable or superior performance to grid-based search (GBS) while requiring only similar to 10% of its computational cost for a 64-element IRS. These findings highlight the scalability and efficiency of our approach, establishing a new paradigm for secure, resource-aware, and ML-driven cross-layer optimization in O-RAN-enabled IoT networks.

We explore THz communication uplink multi-access with multi-hop Intelligent reflecting surfaces (IRSs) under correlated channels. Our aims are twofold: 1) enhancing the data rate of a desired user while dealing with interference from another user and 2) maximizing the combined data rate. Both tasks involve non-convex optimization challenges. For the first aim, we devise a sub-optimal analytical approach that focuses on maximizing the desired user’s received power, leading to an over-determined system. We also attempt to use approximate solutions utilizing pseudo-inverse (Pinv) and block solution (BLS) based methods. For the second aim, we establish a loose upper bound and employ an exhaustive search (ES). We employ deep reinforcement learning (DRL) to address both aims, demonstrating its effectiveness in complex scenarios. DRL outperforms mathematical approaches for the first aim, with the performance improvement of DDPG over the block solution ranging from 8% to 57.12%, and over the pseudo-inverse ranging from 41% to 190% for a correlation-factor equal to 1. Moreover, DRL closely approximates the ES for the second aim. Furthermore, our findings show that as channel correlation increases, DRL’s performance improves, capitalizing on the correlation for enhanced statistical learning.

THz technology is considered a key element in 6G wireless communication because it provides ultra-high bandwidths, considerable capacities, and significant gains. However, wireless systems operating at high frequencies are faced with uncertainty and highly dynamic channels. Reflecting intelligent surfaces (RISs) can increase the range of the THz communication links and boost the rate at the receiver. In contrast to the existing literature, we investigate the scenario of multiple access multi-hop (cascaded) RISs uplink THz networks in a correlated channel environment. We show that our inspected cascaded RIS system is over-determined and that the rate maximization optimization problem is non-convex. To this end, we derive a closed-form expression of the received power and derive an analytical solution based on pseudo-inverse to obtain optimum RISs’ phase shifts that maximize the received signal power and hence increase the rate. In addition, we utilize deep reinforcement learning (DRL), which is capable of solving non-convex optimization problems, to obtain the optimum cascaded RISs’ phase shifts at the receiver taking into account the situation of the spatially correlated channels. Simulation results demonstrate that the DRL algorithm achieves higher rates than the mathematical sub-optimal method and the case of randomized phases.

We propose a framework that enables the cluster head (CH) to harvest energy from uplink transmission by Internet of Things (IoT) nodes employing data compression under nonorthogonal multiple access (NOMA) scheme. Our framework enables the CH to maximize the harvested energy while meeting constraints on outage probability, consumed energies by the transmitting IoT nodes and compression and distortion ratios. We provide necessary analysis for our framework and derive an expression for the outage probability and average harvested energy under the NOMA scheme. We formulate an optimization problem with NOMA factors, simultaneous wireless information and power transfer (SWIPT) factors, and NOMA user distances as optimization parameters. We first solve the optimization problem and find the optimized values using a grid-based search. Then, we exploit a deep reinforcement learning algorithm to solve the optimization problem more efficiently. Throughout this work, we prove the feasibility of such framework and deliver key observation that will help the CH scheduling different IoT nodes such that the harvested energy is maximized while the constraints are met.

In recent times, Big data is modifying the style life of workplaces and thinking by improved performance in knowledge discovering and decision making ever-greater volumes of data are being produced data due to the network of sensors and communication technologies Heterogeneous data is a category of unstructured data with an unknown pace in several ways. Current data analysis techniques are inadequate to handle the huge volumes of data produced, this data difficult to manage, store, handle, interpret, analyze using traditional techniques. Deep learning (DL) is extremely popular among many data scientists and experts thanks to the high precision in speech recognition, image handling, and data analytics. DL has become much more important because it can be used for largescale heterogeneous data. DL has been applied efficiently in several fields and has exceeded most of the traditional techniques, DL algorithmic can study large unclassified data with the ability to select features. This study concentrates on the discussion of a variety of new algorithms that handle this data and DL models that provide greater accuracy for heterogeneous data.

One of the highly promising radio access strategies for enhancing performance in the next generation cellular communications is non-orthogonal multiple access (NOMA). NOMA offers a number of advantages including better spectrum efficiency. This paper focuses primarily on proposing an energy efficient system for transmitting medical data, such as electroencephalogram (EEG), collected from patients for the sake of continuous monitoring. The framework proposes the use of deep reinforcement learning (DRL) to provide smart data compression in uplink-NOMA protocol. DRL enforces the data compression ratios for the nodes in order to avoid outage constraints at any sensor node. Jointly, it optimizes the power consumption of these sensor nodes. The data compression for such sensor network is vital in order to minimize the power every sensor consumes to maximize its service lifetime. We minimize the expected distortion under practical channel realization and outage probability constraints using NOMA-uplink protocol. Meanwhile, we optimize the power efficiency of the user node in order to increase the battery lifetime.

In diabetes patients with chronic ≥3 vessel disease, coronary artery bypass grafting (CABG) holds a class I recommendation in the American College of Cardiology and American Heart Association (ACC/AHA) 2011 guidelines, and this classification has not changed to date. Much of the literature has focused upon whether CABG or percutaneous coronary intervention (PCI) produces better outcomes; there is a paucity of data comparing the odds of receiving these procedures. A secondary analysis was conducted in a de-identified database comprised of 30,482 patients satisfying the entry criteria. Odds of occurrence (CABG, PCI) were determined as the binary dependent variable in period 1, (17 October 2009 through 31 December 2011), and period 2 (1 January 2013 through 16 March 2015), before and after the 2011 guidelines, while controlling for gender, ethnicity/race, and ischemic heart disease as covariates. The odds of performing CABG rather than PCI in period 2 were not statistically significantly different than in period 1 (p = 0.400). The logistic regression model chi-square statistic was statistically significant, with χ2 (7) = 308.850, p < 0.0001. The Wald statistic showed that ethnicity/race (African American, Caucasian, Hispanic and Other), gender, and heart disease contributed significantly to the prediction model with p < 0.05, but ethnicity ‘Unknown’ did not. The odds of CABG versus PCI in period 2 were 0.98 times those in period 1 95% confidence interval (CI) = (0.925, 1.032), statistically controlling for covariates. There was no significant rise in the odds of undergoing a CABG among this dataset of high-risk patients with diabetes and multivessel coronary heart disease. Modern practice has evolved regarding patient choice and additional variables that impact the final revascularization method employed. The degree to which odds of occurrence of procedures are a reliable surrogate for provider compliance with guidelines remains uncertain.

With the rapid development of Big Data and the necessity for analyzing their huge volumes, the issue of Unstructured Data analysis in social media was appeared. The Data analysis process is very important in all fields as to make decisions at the right time and over certain facts. The usage of social media has become the latest trend in today's world in which users send, read posts known as ‘message’ and communicate with various groups. Users are sharing their regular life, posting their views on everything like products and locations. This data is extremely unstructured, making it hard to analyze. Machine learning technology offers important data preparation techniques for processing large-scale data to extract knowledge, e.g., classifying data. Extract useful information from social media data is essential to success in the big data age. Therefore, fresh strategies are needed for handling huge quantities of unstructured data and finding the hidden information in these data and achieving better data analysis outcomes, In this paper, the proposed framework recommends the construction of a machine-learning model capable of analyzing unstructured text data with highly accuracy compared to other machine learning algorithms.

Acute peritonitis (AP) is a common and devastating complication in end-stage renal disease patients on peritoneal dialysis (PD). We are reporting an epidemiologic study of AP in Qatar over 8-year follow-up. We retrospectively reviewed medical records of all PD patients in Qatar from 2007 to 2014. The analysis was conducted to report epidemiology, outcome, and associated risk factors of AP. We had 318 AP episodes in 180 patients between 2007 and 2014. Six (3.3%) patients died as a result AP. Six cases of fungal peritonitis were reported. AP rate has decreased from 1 episode/29.7 PD-months in 2007 to 1/43.7 PD-months in 2014. Ninety-nine (55%) patients had single AP while 81 (45%) patients had 2 episodes or more (multiple AP). Patients on automated PD carried a higher risk of developing multiple AP [odds ratio (OR) = 1.46, 95% confidence interval (CI): 1.01-1.71]. The first episode of AP caused by Gram-positive cocci carried a significant risk of multiple AP (OR = 4.3, 95 % CI: 2.2-8.2). Negative-culture AP carried a significant protective role from multiple AP (OR = 0.35, 95% CI: 0.19-0.66). Most deaths occurred with the first episode of AP (4 out of 6). In this 8-year follow-up, epidemiologic study from Qatar, fungal peritonitis and mortality rate were very low, AP rate improved overall, multiple AP was prevalent (45%), and its risk increases with Gram-positive cocci infections. Our results signify the importance of implementing more efficient care bundles to prevent multiple AP.

Due to the peculiarity of wireless sensor networks (WSNs), where a group of sensors continuously transmit data to other sensors or to the fusion center, it is crucial to compress the transmitted data in order to save the consumed power, which is paramount in the case of portable devices. There exists several techniques for data compression such as discrete wavelet transform (DWT) based, which fails to achieve high compression ratio for an acceptable distortion ratio. In this paper, we explore exploiting Walsh transform with a moving average filtering (MAF) for data compression in WSNs. One application of WSN is wireless body sensor networks. We apply Walsh transform on real Electroencephalogram (EEG) data collected from patients. Furthermore, we compare our results to DWT and show the superiority of exploiting Walsh transform for data compression. We show that using MAF with Walsh transform enhances the compression ratio for up to 30% more than that achieved by DWT.

Wireless body sensor networks (WBSN) provide an appreciable aid to patients who require continuous care and monitoring. One key application of WBSN is mobile health (mHealth) for continuous patient monitoring, acquiring vital signs e.g. EEG, ECG, etc. Such monitoring devices are doomed to be portable, i.e., batter powered, and agile to allow for patient mobility, while providing sustainable, energy-efficient hardware platforms. Hence, EEG data compression is critical in reducing the transmission power, hence increase the battery life. In this paper, we design and implement a complete hardware model based on discrete wavelet transform (DWT) for vital signs data compression and reconstruction on a field programmable gate array (FPGA) based platform. We evaluate the performance of our DWT compression FPGA implementation under different practical parameters including filter length and the compression ratio. We investigate the hardware and computational complexity of our design in terms of used resource blocks for future comparison with state-of-the-art techniques. Our results show the efficiency of the proposed hardware compression and reconstruction model at different system parameters, including the high pass filter coefficients, and DWT type, and DWT threshold.

The “Union of the Comoros” is a volcanic archipelago nation in the Indian Ocean, consisting of three islands. About half the population lives below the international poverty line of US$1.25 a day. The interiors of the islands vary from steep mountains to low hills with mostly damaged or unconstructed roads to interconnect the several islets. This situation represents an extraordinary field test: the geographical isolation from other Countries, among the islands and within the same island, the development level of the population and the lack of infrastructures can be efficiently mitigated if a high capacity ICT infrastructure is realized, in order to provide social services (distance learning, telemedicine, financial administration) to the population, together with the possibility to access basic information on the Internet. In this context, we have put into practice a wireless network based on the use of low cost components, optimized for the transportation of large amount of bandwidths (minimum measured throughput is 65 Mb/s). The infrastructure has been made available at no cost for the people, since an extremely small percentage of them can afford regular Internet subscriptions. The network provides interconnectivity among the islets and local Hot Spot facilities at the islet level, free of cost for the end users. The local population has been recruited during the design and installation phase, in order to develop sufficient know how that allows them to maintain and even extend the infrastructure after its completion. Accessibility is guaranteed over the whole Country. To this aim, technical tests have been made in order to verify the affordability and reliability of the solution in the medium and long terms. The project is now being used to vehicle public finance management among the multitude of islets of the islands, under the endorsement of the World Bank.

A seedy female jojoba shrub of good traits (seeds of high oil content) was selected as a source of scions and propagated in two dates, April and August during 2010 and 2011 seasons by two grafting methods namely top wedge and veneer grafting. Twelve seedy mature male or non-productive female jojoba shrubs (15 years old) were used as rootstocks. Data showed that grafting practiced in April were significantly higher in success percentage and sprout length than that of August while, grafting in August showed significantly lower number of days for sprouting. Top wedge grafting scored significantly higher success percentage and sprout length. On the other hand, veneer grafting scored significantly lower number of days for sprouting. April was the recommended time for jojoba grafting and top wedge grafting as well.

Background Breast cancer is the most common cancer among women worldwide. Aim The aim of the study to evaluate the immunohistochemical expression of stromal CD 10 and to correlate with the histopathological variables and available performed hormonal profile. Materials and methods The present study was conducted on 50 female patients with duct carcinoma in situ and invasive duct carcinoma. Results All studied patients with invasive duct carcinoma were graded as grade I, II, and III according to Elston/Nottingham modification of Bloom–Richardson system. All patients diagnosed as duct carcinoma in situ, with or without focal invasion, as invasive duct carcinoma grade II, and most of the invasive duct carcinoma grade I patients showed weak stromal immunostaining to CD10 as opposed to invasive duct carcinoma grade III in which 71.4% of the patients showed strong immunostaining. Conclusion There was a highly significant correlation between CD10 immunostaining intensity and tumor grade. No significant correlation could be achieved between CD10 immunostaining intensity and patients age, tumor size, lymph node metastasis, or hormone profile.

The increased popularity of high power microwave systems and the various sources to drive them is the motivation behind the work to be presented. A stand-alone, self-contained explosively driven high power microwave pulsed power system has been designed, built, and tested at Texas Tech University's Center for Pulsed Power and Power Electronics. The system integrates four different sub-units that are composed of a battery driven prime power source utilizing capacitive energy storage, a dual stage helical flux compression generator as the main energy amplification device, an integrated power conditioning system with inductive energy storage including a fast opening electro-explosive switch, and a triode reflex geometry virtual cathode oscillator as the microwave radiating source. This system has displayed a measured electrical source power level of over 5 GW and peak radiated microwaves of about 200 MW. It is contained within a 15 cm diameter housing and measures 2 m in length, giving a housing volume of slightly less than 39 l. The system and its sub-components have been extensively studied, both as integrated and individual units, to further expand on components behavior and operation physics. This report will serve as a detailed design overview of each of the four subcomponents and provide detailed analysis of the overall system performance and benchmarks.

The paper presents a methodology developed and implemented for the assessment of human exposure to Indoor Base Stations compliant with the IEEE 802.11 Standards. An efficient procedure that combines simulations and measurements has been introduced and verified in a selected and controlled environment. The procedure is based on a precise simulation of the electromagnetic ambience where the access points are deployed, by means of a combination of physical and ray optics. A statistical description of the obstacles is implemented to sped up the computational time. Data have been statistically analyzed and compared to measurements, to identify conservative exposure coefficients that minimize the number of measuring points, without introducing an excessive overestimate.

Introduction: This study was conducted at Hamad General Hospital to determine the incidence of fungal peritonitis and to describe its clinical and microbiological findings in patients undergoing continuous ambulatory peritoneal dialysis in Qatar. Methodology: The medical records of these patients between 1 January 2005 and 31 December 2008 were retrospectively reviewed and the collected data were analysed. Results: During the study period, 141 episodes of peritonitis were observed among 294 patients. In 14 of these episodes (9.9%), fungal peritonitis was reported in 14 patients with a rate of 0.05 episodes per patient year, while the bacterial peritonitis rate was 0.63 per patient year. Thirteen (93%) patients had one or more previous episodes of bacterial peritonitis that was treated with multiple broad-spectrum antibiotics, 11 (85%) had received broad-spectrum antibiotics within the preceding month, 12 (92%) within three months, and 8 (62%) within six months. Candida species were the only fungal species isolated from the dialysate with predominance of non-albicans Candida species (especially Candida parapsilosis). Therapeutic approach was immediate catheter removal, followed by systemic antifungal therapy and temporary haemodialysis. Nine patients (64.3%) were continued on haemodialysis, whereas five patients (35.7%) died. Conclusions: Prior antibiotic use was an important risk factor predisposing patients to the development of fungal peritonitis. Early detection of fungal peritonitis would lead to early institution of appropriate therapy and prevention of complications.

This paper discusses two different approaches to assess exposure to indoor WiFi access points at two different frequency bands: 2.4GHz and 5GHz. The first analyzes the electromagnetic propagation in almost empty environments by applying advanced simulation tools. The second is based on measurements performed in real scenarios. All measurements are compared to simulations. Results show good agreement and open the door to a possible exploitation of quasi-heuristic methods for the characterization of complex environments.

Recent efforts at the Center for Pulsed Power and Power Electronics at Texas Tech University have been focused on the development of a compact and explosively driven High Power Microwave, HPM, system. The primary energy source (other than the seed energy source) driving the microwave load in this system is a mid-sized, dual-stage helical flux compression generator, HFCG. The HFCG has a constant stator inner diameter of 7.6 cm, a length of 26 cm, with a working volume of 890 cm3. Testing at the Center has revealed energy gains in the 30's and 40's with output energy levels in the kilo-joules regime into loads of several micro-Henries. Over the last few years, close to one hundred shots have been taken with these generators into various loads consisting of dummy inductive loads, power conditioning systems, and HPM sources. Throughout these tests, the working volume of the HFCG, i.e. the volume in between the wire stator and the explosive-filled aluminum armature, was filled with SF6 at atmospheric pressure. This was primarily done do avoid electrical breakdown in the generator volume during operation, resulting in flux loss. Recent design updates enable pressurizing the generator volume to pressures up to 0.5 MPa, which is needed, for instance, to replace the SF6 with other gases such as air or nitrogen. The performance of the dual-stage HFCG with pressurized working volume (SF6 and N2) is presented in this paper along with an analysis of the maximum electric field amplitude held off in the volume during operation. The design technique to seal the HFCG will also be briefly discussed.