Power control with Machine Learning Techniques in Massive MIMO cellular and cell-free systems

This PhD thesis presents a comprehensive investigation into power control (PC) optimization in cellular (CL) and cell-free (CF) massive multiple-input multiple-output (mMIMO) systems using machine learning (ML) techniques. The primary focus is on enhancing the sum spectral efficiency (SE) of these systems by leveraging various ML methods. To begin with, it is combined and extended two existing datasets, resulting in a unique dataset tailored for this research. The weighted minimum mean square error (WMMSE) method, a popular heuristic approach, is utilized as the baseline method for addressing the sum SE maximization problem. It is compared the performance of the WMMSE method with the deep Q-network (DQN) method through training on the complete dataset in both CL and CF-mMIMO systems. Furthermore, the PC problem in CL/CF-mMIMO systems is effectively tackled through the application of ML-based algorithms. These algorithms present highly efficient solutions with significantly reduced computational complexity [3]. Several ML methods are proposed for CL/CF-mMIMO systems, tailored explicitly to address the PC problem in CL/CF-mMIMO systems. Among them are the innovative proposed Fuzzy/DQN method, proposed DNN/GA method, proposed support vector machine (SVM) method, proposed SVM/RBF method, proposed decision tree (DT) method, proposed K-nearest neighbour (KNN) method, proposed linear regression (LR) method, and the novel proposed fusion scheme. The fusion schemes expertly combine multiple ML methods, such as system model 1 (DNN, DNN/GA, DQN, fuzzy/DQN, and SVM algorithms) and system model 2 (DNN, SVM-RBF, DQL, LR, KNN, and DT algorithms), which are thoroughly evaluated to maximize the sum spectral efficiency (SE), offering a viable alternative to computationally intensive heuristic algorithms. Subsequently, the DNN method is singled out for its exceptional performance and is further subjected to in-depth analysis. Each of the ML methods is trained on a merged dataset to extract a novel feature vector, and their respective performances are meticulously compared against the WMMSE method in the context of CL/CF-mMIMO systems. This research promises to pave the way for more robust and efficient PC solutions, ensuring enhanced SE and ultimately advancing the field of CL/CF-mMIMO systems. The results reveal that the DNN method outperforms the other ML methods in terms of sum SE, while exhibiting significantly lower computational complexity compared to the WMMSE algorithm. Therefore, the DNN method is chosen for examining its transferability across two datasets (dataset A and B) based on their shared common features. Three scenarios are devised for the transfer learning method, involving the training of the DNN method on dataset B (S1), the utilization of model A and dataset B (S2), and the retraining of model A on dataset B (S3). These scenarios are evaluated to assess the effectiveness of the transfer learning approach. Furthermore, three different setups for the DNN architecture (DNN1, DNN2, and DNN3) are employed and compared to the WMMSE method based on performance metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Moreover, the research evaluates the impact of the number of base stations (BSs), access points (APs), and users on PC in CL/CF-mMIMO systems using ML methodology. Datasets capturing diverse scenarios and configurations of mMIMO systems were carefully assembled. Extensive simulations were conducted to analyze how the increasing number of BSs/APs affects the dimensionality of the input vector in the DNN algorithm. The observed improvements in system performance are quantified by the enhanced discriminative power of the model, illustrated through the cumulative distribution function (CDF). This metric encapsulates the model's ability to effectively capture and distinguish patterns across diverse scenarios and configurations within mMIMO systems. The parameter of the CDF being indicated is the probability. Specifically, the improved area under the CDF refers to an enhanced probability of a random variable falling below a certain threshold. This enhancement denotes improved model performance, showcasing a greater precision in predicting outcomes. Interestingly, the number of users was found to have a limited effect on system performance. The comparison between the DNN-based PC method and the conventional WMMSE method revealed the superior performance and efficiency of the DNN algorithm. Lastly, a comprehensive assessment of the DNN method against the WMMSE method was conducted for addressing the PC optimization problem in both CL and CF system architectures. In addition to, this thesis focuses on enhancing spectral efficiency (SE) in wireless communication systems, particularly within cell-free (CF) mmWave massive MIMO environments. It explores the challenges of optimizing SE through traditional methods, including the weighted minimum mean squared error (WMMSE), fractional programming (FP), water-filling, and max-min fairness approaches. The prevalence of access points (APs) over user equipment (UE) highlights the importance of zero-forcing precoding (ZFP) in CF-mMIMO. However, ZFP faces issues related to channel aging and resource utilization. To address these challenges, a novel scheme called delay-tolerant zero-forcing precoding (DT-ZFP) is introduced, leveraging deep learning-aided channel prediction to mitigate channel aging effects. Additionally, a cutting-edge power control (PC) method, HARP-PC, is proposed, combining heterogeneous graph neural network (HGNN), adaptive neuro-fuzzy inference system (ANFIS), and reinforcement learning (RL) to optimize SE in dynamic CF mmWave-mMIMO systems. This research advances the field by addressing these challenges and introducing innovative approaches to enhance PC and SE in contemporary wireless communication networks. Overall, this research contributes to the advancement of PC optimization in CL/CF-mMIMO systems through the application of ML techniques, demonstrating the potential of the DNN method, and providing insights into system performance under various scenarios and network configurations

Patterns of Cervical Lymph Node Metastases in Primary and Recurrent Papillary Thyroid Cancer

The incidence of thyroid cancer is rising in the United States with papillary thyroid cancer (PTC) being the most common type. We performed a retrospective study of 49 patients with PTC who underwent 57 lateral neck dissections (NDs). The extent of NDs varied, but 29 of 57 (51%) consisted of levels II–V. Twelve of 57 (21%) NDs consisted of levels I–V. Twelve of 57 (21%) NDs consisted of levels II–IV. One of 57 (1.8%) necks involved only levels I–IV. One of 57(1.8%) necks involved only levels I–V. One of 57(1.8%) necks involved only levels III–V. Two (3.5%) double-level (III–IV) neck surgeries were also performed. Metastatic PTC adenopathy was confirmed pathologically in 2%-level-I, 45%-level-II, 57%-level-III, 60%-level-IV, and 22%-level-V necks. Level-V was positive in 21% of primary and 24% of recurrent groups (P = 0.76). Comparing primary and recurrent disease, there was no difference in nodal distribution or frequency for levels I, II, III, and V. Level-IV was more common in the recurrent cases (P = 0.05). Based on the pathologic distribution of nodes, dissection should routinely include levels II–IV and extend to level-V in primary and recurrent cases. Our data does not suggest routine dissection of level-I

Inequities as a social determinant of health: Responsibility in paying attention to the poor and vulnerable at risk of COVID-19

To the Editor In December 2019, in Wuhan, Hubei province, China, a disease of viral origin was identified among people working in or around the seafood and animal market. Covid-19 is a causative agent of acute respiratory syndrome.  Almost 26-36% of patients need special care and about 4-15% of them die. Due to the lack of vaccines and effective treatment, the best way to prevent its spread is to quarantine patients and track their contact with other people in the community, trying to reduce mortality and protect the elderly, the vulnerable and special patients..

Formulation and Physicochemical Characterization of Magnetic Nanoparticles Containing Brimonidine for Ophthalmic Drug Delivery

Introduction: Recently, magnetic nanoparticles (MNPs) drew a great attention for application in drug delivery systems. Due to their biocompatibility and non-toxic properties, they have potential to create versatile drug delivery systems. Brimonidine (a relatively selective alpha-2 adrenergic receptor agonist) has a significant effect on lowering intraocular pressure in glaucoma. In this study MNPs were coated with alginate and chitosan and loaded by brimonidine to prepare a drug delivery system applicable in glaucoma treatment. Methods and Results: Brimonidine, sodium alginate and MNPs have been prepared as a dispersion. Chitosan solution was added dropwise to the previous dispersion. The dispersion was centrifuged and the absorbance of the supernatant analyzed by UV spectrophotometer at the λmax of 246 nm. The final dispersion was freeze-dried. The morphological studies of chitosan alginate MNPs(C-A-MNPs) have been done by using transmission electronic microscope (TEM). The release rate of brimonidine tartrate was evaluated by Franz diffusion cell through cellophane membrane. Results showed that more than 93% of the brimonidine tartrate was loaded on the C-A-MNPs. The formulation prepared was stable at room temperature protected from light. Release study showed that less than 40% of the brimonidine was released after 2 hours compared to simple formulation of brimonidine solution which showed more than 80% release after 2 hours. This finding showed sustained release in C-A-MNPs formulation. Kinetic of drug release from C-A-MNPs was slower than blank and followed zero order. The stability of formulation was more than 2 years. Conclusions: It can be concluded that loading of brimonidine on C-S-MNPs may decrease the frequency of administration and increase the efficacy of the product

The Impact of Managerial Credibility on Affective Organizational Commitment: An Empirical Study in the Sport Sector of Iran

The paper aims to study the impact of credibility of manager on employees’ affective commitment. Data were collected using a questionnaire including managerial credibility and affective commitment measures. A sample of 212employeesfrom a number of organizations operating in the sport sector of Iran was used. Confirmatory factor analysis (CFA) and a linear regression analysis were used to test the relationship between managerial credibility and affective commitment. In addition, ANOVA analysis was used to determine the effect of demographic characteristics on perceptions of manager credibility. The findings indicated that the relationship between and affective commitment is positive and significant. Moreover, when people perceive manager credibility, they feel more affectively attached to their organizations, experience a sense of obligation/loyalty towards them, and feel less instrumentally committed. Sampling was one of the limitations identified in this study. The fact that convenience sampling was used meant that results were not immediately transferable to the general working population. If samples were drawn from a wider range of demographics, then the results become more meaningful. By utilizing credibility, managers can promote affective organizational commitment and, thus, individual and organizational performance. It allows them to experience senses of purpose, self-determination, enjoyment and belonging. The paper contributes by filling a gap in the organization and management literature, in which empirical studies on managerial credibility as an antecedent of affective organizational commitment have been scarce until now.Key words: Credibility; Affective commitment; Employe

PRIORITIZATION AND VALUATION FACTORS AFFECTING ON BRAND EQUITY BASED ON THE GILL MODEL: A CASE STUDY ON BUYERS OF LG HOME PRODUCTS IN KERMANSHAH CITY, IRAN

ABSTRACT Brand equity (BE) is measured in different international brands by international institutions each year. However, this valuation important for companies to see the customer, because their survival and success depends on their customers. In current study, effective factors on brand equity (price, advertisement, promotions and family) were investigated, using their effects on aspects of brand equity (brand image, awareness of it, loyalty to it and its perceived qua lity). In fact model of Gill et al (2007) was used to study effective factors on brand equity from a consumers' point of view. In this model for the first time family variable is studied along with others. Statistical population was LG home-product consumers in whole Kermanshah city. Since the number of members in this population was infinite, a sample size of 384 persons was derived from Morgan's table; these were selected by cluster sampling. Statistical significance of Pearson's correlation coefficients were tested at alpha= 0.01. The results indicated that the loyalty factor was the most effective variable factors affecting brand equity (r= 0.729). Regarding model of Gill et al. the largest positive correlation was found between the positive information about the brand in the family and its perceived quality (r= 0.642). This means that the family is an effective source of power on behavior and perception of the consumer

Decreased Risk of Squamous Cell Carcinoma of the Head and Neck in Users of Nonsteroidal Anti-Inflammatory Drugs

We evaluated the chemopreventive effect of nonsteroidal anti-inflammatory drug (NSAID) use in head and neck squamous cell carcinomas (HNSCC) by conducting a case-control study based on the administration of a standardized questionnaire to 71 incident HNSCC cases and same number of healthy controls. NSAID use was associated with a 75% reduction in risk of developing HNSCC. A significant risk reduction was noted in association with frequency of NSAID use. Restricting the analysis to aspirin users revealed a significant 90% reduction in risk of developing HNSCC. This study provides evidence for a significant reduction in the risk of developing HNSCC in users of NSAIDs, and specifically aspirin users

A comparison of the efficacy of amoxicillin and nasal irrigation in treatment of acute sinusitis in children

Purpose: The efficacy of antibiotic therapy for acute sinusitis is controversial. This study aimed to compare the efficacies of amoxicillin with nasal irrigation and nasal irrigation alone for acute sinusitis in children. Methods: This randomized, double-blind, controlled study included 80 children aged 4–15 years with a clinical presentation of acute sinusitis. Patients were randomly assigned to receive either amoxicillin (80 mg/kg/day) in 3 divided doses orally for 14 days with saline nasal irrigation (for 5 days) and 0.25 phenylephrine (for 2 days) or the same treatment without amoxicillin. Clinical improvements in their initial symptoms were assessed on days 3, 14, 21, and 28. Results: On day 3, patients in the amoxicillin with nasal irrigation group showed significant clinical improvement (P =0.001), but there was no significant difference in the degree of improvement between the amoxicillin with nasal irrigation and nasal irrigation alone groups during follow-up (P >0.05). In addition, no significant differences were seen in age, sex, and degree of improvement between groups (P > 0.05). Conclusion: High-dose amoxicillin with saline nasal irrigation relieved acute sinusitis symptoms faster and more often than saline nasal irrigation alone. However, antibiotic treatment for acute sinusitis confers only a small therapeutic benefit over nasal irrigation. © 2014 by The Korean Pediatric Society

Intravenous Morphine vs Intravenous Ketofol for Treating Renal Colic; a Randomized Controlled Trial

Introduction: The main purpose of emergency department (ED) management for renal colic  is prompt pain relief. The present study aimed to compare the analgesic effects of intravenus (IV) ketofol with morphine in management of ketorolac persistent renal colic. Methods: This study is a single blind randomized, clinical trial, on patients who were presented to ED with renal colic, whose pain was resistant to 30 mg IV ketorolac. The patients were randomly assigned to either IV morphine (0.1 mg/kg) or IV ketofol (0.75 mg/kg propofol and 0.75 mg/kg) and the measures of treatment efficacy were compared between the groups after 5 and 10 minutes. Results: 90 patients with mean age of 38.01 ± 9.78 years were randomly divided into 2 groups of 45 (66.7% male). Treatment failure rate was significantly lower in ketofol group after 5 (20% vs 62.2%, p < 0.001) and 10 minutes (11.1% vs 44.4%, p < 0.001). ARR and NNT for ketofol after 5 miutes were 42.22% (95% CI: 23.86 – 60.59) and 3 (95% CI: 1.7 - 4.2), respectively. After 10 minutes, these measures reached 33.33 (95% CI:16.16 – 50.51) and 4 (95% CI: 2.0 - 6.2), respectively. NNH and ARI for hallucination or agitation were 12 (95%CI: 5.8 - 174.2) and 8.89% (0.57 - 17.20), respectively. Conclusion: The results of the present study, showed the significant superiority of ketofol (NNT at 5 minute = 3 and NNT at 10 minute = 4)  in ketorolac resistant renal colic pain management. However, its NNH of 12, could limit its routine application in ED for this purpose