AROMA: Automatic Generation of Radio Maps for Localization Systems

WLAN localization has become an active research field recently. Due to the wide WLAN deployment, WLAN localization provides ubiquitous coverage and adds to the value of the wireless network by providing the location of its users without using any additional hardware. However, WLAN localization systems usually require constructing a radio map, which is a major barrier of WLAN localization systems' deployment. The radio map stores information about the signal strength from different signal strength streams at selected locations in the site of interest. Typical construction of a radio map involves measurements and calibrations making it a tedious and time-consuming operation. In this paper, we present the AROMA system that automatically constructs accurate active and passive radio maps for both device-based and device-free WLAN localization systems. AROMA has three main goals: high accuracy, low computational requirements, and minimum user overhead. To achieve high accuracy, AROMA uses 3D ray tracing enhanced with the uniform theory of diffraction (UTD) to model the electric field behavior and the human shadowing effect. AROMA also automates a number of routine tasks, such as importing building models and automatic sampling of the area of interest, to reduce the user's overhead. Finally, AROMA uses a number of optimization techniques to reduce the computational requirements. We present our system architecture and describe the details of its different components that allow AROMA to achieve its goals. We evaluate AROMA in two different testbeds. Our experiments show that the predicted signal strength differs from the measurements by a maximum average absolute error of 3.18 dBm achieving a maximum localization error of 2.44m for both the device-based and device-free cases.Comment: 14 pages, 17 figure

C-Reactive Protein to Albumin Ratio and Albumin to Fibrinogen Ratio in Rheumatoid Arthritis Patients

Objective: the albumin to fibrinogen ratio (AFR) and the C-reactive protein (CRP) to albumin ratio (CAR) have been proposed as markers of systemic inflammation. The goal of this study was to differentiate rheumatoid arthritis (RA) patients from healthy people and to study the association between AFR/CAR and DAS28 in RA.Patients and methods. A case control study including 30 RA patients and 30 healthy controls was performed. Fibrinogen, albumin, CRP and erythrocyte sedimentation rate (ESR) were measured. We calculated CAR and AFR in each group and compared them. Correlations of AFR, and CAR with disease activity were examined. Receiver operation characteristic (ROC) curves of AFR and CAR were also used to detect cutoffs for disease activity assessment.Results and discussion. CAR was higher while AFR was lower in RA patients than in control group. ROC curve analyses showed that CAR can be used to detect disease activity of RA at cut off 2.66 with sensitivity 81.3% and specificity 64.3% with an area under the curve (AUC) 0.78. So, CAR was a fair parameter to discriminate disease activity among RA patients. AFR has AUC 0.62, sensitivity 87.5% and specificity 42.9% at cutoff value 5.96. So, in our group AFR was a poor indicator to discriminate disease activity among RA patients.Conclusion. AFR and CAR have been recently proposed as inflammatory markers for assessment of disease activity in RA. AFR and CAR are simple, and inexpensive biomarkers, they also can be rapidly evaluated. CAR was found to be a fair parameter to depict disease activity in RA patients. AFR poorly depicted RA activity.Objective: the albumin to fibrinogen ratio (AFR) and the C-reactive protein (CRP) to albumin ratio (CAR) have been proposed as markers of systemic inflammation. The goal of this study was to differentiate rheumatoid arthritis (RA) patients from healthy people and to study the association between AFR/CAR and DAS28 in RA.Patients and methods. A case control study including 30 RA patients and 30 healthy controls was performed. Fibrinogen, albumin, CRP and erythrocyte sedimentation rate (ESR) were measured. We calculated CAR and AFR in each group and compared them. Correlations of AFR, and CAR with disease activity were examined. Receiver operation characteristic (ROC) curves of AFR and CAR were also used to detect cutoffs for disease activity assessment.Results and discussion. CAR was higher while AFR was lower in RA patients than in control group. ROC curve analyses showed that CAR can be used to detect disease activity of RA at cut off 2.66 with sensitivity 81.3% and specificity 64.3% with an area under the curve (AUC) 0.78. So, CAR was a fair parameter to discriminate disease activity among RA patients. AFR has AUC 0.62, sensitivity 87.5% and specificity 42.9% at cutoff value 5.96. So, in our group AFR was a poor indicator to discriminate disease activity among RA patients.Conclusion. AFR and CAR have been recently proposed as inflammatory markers for assessment of disease activity in RA. AFR and CAR are simple, and inexpensive biomarkers, they also can be rapidly evaluated. CAR was found to be a fair parameter to depict disease activity in RA patients. AFR poorly depicted RA activity

Towards Efficient Energy Usage at Ain Shams University Campus

In the light of global energy transition to renewable resources and energy efficiency usage, Ain Shams University (ASU) developed an ambitious plan to transform its campus into Green Campus. From an energy perspective, energy consumption data were continuously collected and audited to calculate the university campus carbon footprint. An energy usage strategy was established to tackle various pillars such as electrifying the campuses’ transportation system, improving energy efficiency usage, generating Renewable Energy (RE) for self-consumption, etc. Extensive research has been initiated on electric vehicles, wind and solar Photovoltaic (PV) energy generation with students’ activities/competitions. Thus, electric cars and buses were manufactured at the Faculty of Engineering (FoE) for elderly people and staff movement in ASU campus. Solar PV lighting poles with batteries were installed in the main campus. A small-scale Wind Turbine (WT) is manufactured and installed at the FoE and a pilot solar PV system is installed as well. Currently, an energy efficiency project is under implementation in various buildings/faculties and a parking lot that targets energy efficiency and solar PV energy generation. An energy efficiency measure is under implementation through replacing lamps with LED lamps, installing motion sensors, setting up a control center for monitoring and operation that is supported by Artificial Intelligence decision making algorithms. Rooftop solar PV energy systems are under design with smart meters. The project is targeting energy saving and bill reduction by at least 30% and as a result a reduction of carbon footprint will be achieved following the COP27 recommendations

HPV and Recurrent Respiratory Papillomatosis: A Brief Review

Recurrent Respiratory Papillomatosis (RRP) is a rare but severe manifestation of human papillomavirus (HPV). As our knowledge about HPV infections has expanded, it has become possible to understand the course of RRP disease and unravel plausible efficient methods to manage the disease. However, the surge in reports on HPV has not been accompanied by a similar increase in research about RRP specifically. In this paper, we review the clinical manifestation and typical presentation of the illness. In addition, the pathogenesis and progression of the disease are described. On the other hand, we discuss the types of treatments currently available and future treatment strategies. The role of vaccination in both the prevention and treatment of RRP will also be reviewed. We believe this review is essential to update the general knowledge on RRP with the latest information available to date to enhance our understanding of RRP and its management

Applications of Artificial Intelligence in Philadelphia-Negative Myeloproliferative Neoplasms.

Philadelphia-negative (Ph-) myeloproliferative neoplasms (MPNs) are a group of hematopoietic malignancies identified by clonal proliferation of blood cell lineages and encompasses polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). The clinical and laboratory features of Philadelphia-negative MPNs are similar, making them difficult to diagnose, especially in the preliminary stages. Because treatment goals and progression risk differ amongst MPNs, accurate classification and prognostication are critical for optimal management. Artificial intelligence (AI) and machine learning (ML) algorithms provide a plethora of possible tools to clinicians in general, and particularly in the field of malignant hematology, to better improve diagnosis, prognosis, therapy planning, and fundamental knowledge. In this review, we summarize the literature discussing the application of AI and ML algorithms in patients with diagnosed or suspected Philadelphia-negative MPNs. A literature search was conducted on PubMed/MEDLINE, Embase, Scopus, and Web of Science databases and yielded 125 studies, out of which 17 studies were included after screening. The included studies demonstrated the potential for the practical use of ML and AI in the diagnosis, prognosis, and genomic landscaping of patients with Philadelphia-negative MPNs

Applications of Artificial Intelligence in Thrombocytopenia

Thrombocytopenia is a medical condition where blood platelet count drops very low. This drop in platelet count can be attributed to many causes including medication, sepsis, viral infections, and autoimmunity. Clinically, the presence of thrombocytopenia might be very dangerous and is associated with poor outcomes of patients due to excessive bleeding if not addressed quickly enough. Hence, early detection and evaluation of thrombocytopenia is essential for rapid and appropriate intervention for these patients. Since artificial intelligence is able to combine and evaluate many linear and nonlinear variables simultaneously, it has shown great potential in its application in the early diagnosis, assessing the prognosis and predicting the distribution of patients with thrombocytopenia. In this review, we conducted a search across four databases and identified a total of 13 original articles that looked at the use of many machine learning algorithms in the diagnosis, prognosis, and distribution of various types of thrombocytopenia. We summarized the methods and findings of each article in this review. The included studies showed that artificial intelligence can potentially enhance the clinical approaches used in the diagnosis, prognosis, and treatment of thrombocytopenia

Artificial intelligence in sickle disease

Artificial intelligence (AI) is rapidly becoming an established arm in medical sciences and clinical practice in numerous medical fields. Its implications have been rising and are being widely used in research, diagnostics, and treatment options for many pathologies, including sickle cell disease (SCD). AI has started new ways to improve risk stratification and diagnosing SCD complications early, allowing rapid intervention and reallocation of resources to high-risk patients. We reviewed the literature for established and new AI applications that may enhance management of SCD through advancements in diagnosing SCD and its complications, risk stratification, and the effect of AI in establishing an individualized approach in managing SCD patients in the future. Aim: to review the benefits and drawbacks of resources utilizing AI in clinical practice for improving the management for SCD cases.Open Access funding provided by the Qatar National Library.Scopu

Chitosan edible coating: a potential control of toxic biogenic amines and enhancing the quality and shelf life of chilled tuna filets

Edible films and coatings offer great potential to support sustainable food production by lowering packaging waste, extending product shelf life, and actively preserving food quality. Using edible coatings containing plant extracts with antioxidant and antibacterial characteristics could help to enhance the quality and shelf life of fish products. In this study, the combination effect of chitosan with beetroot, curcumin, and garlic extracts on biogenic amines (BAs) reduction, biochemical quality [pH, thiobarbituric acid index (TBA), trimethylamine (TMA), and total volatile base (TVB)], shelf life and sensory characteristics of tuna filets was investigated over 14 days of refrigerated storage compared to control (uncoated) samples. The results showed that the coated samples experienced a lower increase in BAs levels than the control samples. Among the treated samples, chitosan incorporated with curcumin (CH-C) showed the highest reduction in BAs formation (1.45 – 19.33, 0.81 – 4.45, and 1.04 – 8.14 mg/kg), followed by chitosan with garlic (CH-G) (1.54 – 21.74, 0.83 – 5.77, and 1.08 – 8.84 mg/kg), chitosan with beetroot extract (CH-B) (1.56 – 31.70, 0.84 – 6.79, and 1.07 – 10.82 mg/kg), and chitosan without extract addition (CH) (1.62 – 33.83, 0.71 – 7.82 and 1.12 – 12.66 mg/kg) compared to control samples (1.62 – 59.45, 0.80 – 11.96, and 1.14 – 20.34 mg/kg) for histamine, cadaverine, and putrescine, respectively. In addition, the rate of increase in pH, TBA, TMA, and TVB of all coated treatments was lower than in the control samples. Sensory evaluation results revealed that chitosan-treated samples incorporated with beetroot, garlic, and curcumin extracts showed good quality and acceptability characteristics. Overall, chitosan edible coatings incorporated with beetroot, garlic, and curcumin extracts reduced the formation of biogenic amine, delayed biochemical deterioration, and extended the shelf life of tuna filets. Among the treated samples, CH-C demonstrated a remarkable superiority in all the studied parameters. Therefore, this study provides a promising strategy for the incorporation of active compounds in edible coatings to improve the quality and safety of foods during storage