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Recent advancements of Industrial IoT (IIoT) have revolutionized modern urbanization and smart cities. While IIoT data contains rich events and objects of interest, processing a massive amount of IIoT data and making predictions in real-time is challenging. Recent advancements in AI allow processing such a massive amount of IIoT data and generating insights for further decision-making processes. In this paper, we propose several key aspects of AI-enabled IIoT data for smart city monitoring. Firstly, we have combined a human-intelligence-enabled crowdsourcing application with that of an AI-enabled IIoT framework to capture events and objects from IIoT data in real-time. Secondly, we have combined multiple AI algorithms that can run on distributed edge and cloud nodes to automatically categorize the captured events and objects and generate analytics, reports, and alerts from the IIoT data in real-time. The results can be utilized in two scenarios. In the first scenario, the smart city authority can authenticate the AI-processed events and assign these events to the appropriate authority for managing the events. In the second scenario, the AI algorithms are allowed to interact with humans or IIoT for further processes. Finally, we will present the implementation details of the scenarios mentioned above and the test results. The test results show that the framework has the potential to be deployed within a smart city.

Perfusion collateral index (PCI) has been recently defined as a promising measure of collateral status. We sought to compare collateral status assessed via CT-PCI in comparison to single-phase CTA and their relationship to outcome measures including final infarction volume, final recanalization status and functional outcome in ELVO patients.ELVO patients with anterior circulation large vessel occlusion who had baseline CTA and CT perfusion and underwent endovascular treatment were included. Collateral status was assessed on CTA. PCI from CT perfusion was calculated in each patient and an optimal threshold to separate good vs insufficient collaterals was identified using DSA as reference. The collateral status determined by CTA and PCI were assessed against 3 measured outcomes: 1) final infarction volume; 2) final recanalization status defined by TICI scores; 3) functional outcome measured by 90-day mRS.A total of 53 patients met inclusion criteria. Excellent recanalization defined by TICI ≥2C was achieved in 36 (68%) patients and 23 patients (43%) had good functional outcome (mRS ≤2). While having good collaterals on both CTA and CTP-PCI was associated with significantly (p0.05) smaller final infarction volume, only good collaterals status determined by CTP-PCI was associated with achieving excellent recanalization (p = 0.001) and good functional outcome (p = 0.003).CTP-based PCI outperforms CTA collateral scores in determination of excellent recanalization and good functional outcome and may be a promising imaging marker of collateral status in patients with delayed presentation of AIS.

Chemical energy is not effectively converted into mechanical energy by an internal combustion engine. Major portion of heat energy get dissipated with exhaust and coolant. Use of thermoelectric generator (TEG) technology is regarded as an environment friendly technique for recovering the waste heat, with which heat energy directly converts into electrical energy. In this study, an experiment is conducted in which TEG technology has been used to recover waste heat going out through exhaust gas from the diesel engine. Thermoelectric modules were fixed on the surface of stainless steel made pipe having square cross-section. It has been found that the power output of thermoelectric generator enhances with the increase in engine load. The maximum electrical power output of TEG has been obtained as 37 W at maximum load of 6 kg. Overall thermal efficiency of diesel engine has also been improved with the use of TEG type of waste heat recovery system.

Chronic obstructive pulmonary disease (COPD) is a progressive lung dysfunction caused mainly by inhaling toxic particles and cigarette smoking (CS). The continuous exposure to ruinous molecules can lead to abnormal inflammatory responses, permanent damages to the respiratory system, and irreversible pathological changes. Other factors, such as genetics and aging, influence the development of COPD. In the last decade, accumulating evidence suggested that mitochondrial alteration, including mitochondrial DNA damage, increased mitochondrial reactive oxygen species (ROS), abnormal autophagy, and apoptosis, have been implicated in the pathogenesis of COPD. The alteration can also extend to epigenetics, namely DNA methylation, histone modification, and non-coding RNA. This review will discuss the recent progressions in COPD pathology, pathophysiology, and molecular pathways. More focus will be shed on mitochondrial and epigenetic variations related to COPD development and the role of nanomedicine as a potential tool for the prevention and treatment of this disease.

This is a single center, retrospective, observational study carried out in Ohud hospital, the main referral hospital for SARS-CoV-2 infections in the region of Madinah, Kingdom of Saudi Arabia. The study was carried on hospitalized patients with moderate to severe symptoms, including critically ill patients in the intensive care unit mostly of them requiring oxygen or mechanical ventilator support. Medical records from 432 cases were investigated showing that the majority of infected population were adults with an average age of 48 years, where 68.3% were males and the mortality rate was 5.6%. Duration of the disease was determined as the period between the first positive and the first negative PCR results. Patients who received antibiotics or Metoclopramide showed shorter duration of the disease time course while those who received Hydroxychloroquine, Omeprazole or Calcium exhibited longer durations before obtaining a negative PCR result. Regression analysis furtherly confirmed that antibiotics administration was associated with shorter course of disease while hydroxychloroquine or omeprazole were correlated with longer duration of the disease. Antiviral drugs, however, showed no correlations to the COVID-19 duration of stay in hospital. Finally, combining antibiotics and antiviral agents did not result in a better outcome, suggesting that the use of antibacterial agents helps in the recovery of SARS-CoV-2 patients.

To determine the impact of magnetic resonance imaging (MRI) on fracture classification for low lumbar fractures (LLFs) compared to CT alone. This study was a retrospective review of 41 consecutive patients with LLFs who underwent CT and MRI within 10 days of injury. Three reviewers classified all fractures according to AOSpine Classification and the Thoracolumbar Injury Classification (TLISS). Posterior ligamentous complex (PLC) injury in MRI was defined by black stripe discontinuity and in CT by the presence of: vertebral body translation, facet joint malalignment, horizontal laminar or spinous process fracture, and interspinous widening. The proportion of patients with AO type A/B/C and with TLISS

Objectives Intrathecal baclofen (ITB) is a cost-effective therapy for patients with severe spasticity. The most common complications are catheter-related complications (CRCs) including kinking/occlusion, blockage, migration, fracture, disconnection, and CSF leak. Our objective was to determine the CRC rate in a large cohort of adults with newly implanted ITB pump systems with polymer reinforced silicone catheters. Materials and methods This is a retrospective study of a prospectively maintained database consisting of patients who had undergone implantation of ITB pump systems with Ascenda (Medtronic, Minneapolis) catheters from 2013 to 2020. Over this seven-year period, 141 patients underwent ITB pump system implantations; 126 of which had a minimum of one-year follow-up. Results The 126 patients with a minimum of one year follow-up (average 43 month; range 12-89), had an average age of 51 years (63% male). Severe spasticity was due to spinal cord injury (38%), traumatic brain injury (15%), cerebral palsy (13%), multiple sclerosis (11%), stroke (10%), and other (13%). Nine (7.1%) CRCs occurred in 7 (5.6%) patients (median 6 mo. post-implant): 5 intrathecal catheter occlusions (range 3-52 months post-implant), two fractures in one patient (6 months), one disconnection at the catheter pump interface (2 months), and one due to kinking at 84 months No migrations occurred. Conclusions Reported CRCs have been high for ITB pump systems. Ours is the first large cohort, long-term study of CRCs related to reinforced catheters; additionally, our low CRC rate compares favorably to previously published data. Thus, implantation of reinforced catheters may be associated with a low CRC rate.

Background Neurosurgery requires the communication of radiological imaging. Smartphones are increasingly used for this purpose because of the efficiency and convenience of integrated cameras and native Multimedia Messaging Service (MMS) functionality. There is inconsistency among hospitals regarding policies addressing this use as it relates to the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule. Some hospitals offer a HIPAA-compliant secure messaging application (SMA) as a substitute. The authors hypothesized that the use of smartphones for sharing radiological imaging would be commonplace among residents. We sought to characterize usage patterns, resident awareness of policies and HIPPA, and the effectiveness of SMAs as a means of avoiding HIPAA violations. Methods An electronic dynamic questionnaire was sent to all 116 Accreditation Council for Graduate Medical Education–accredited US neurosurgery residency program directors and coordinators to be forwarded to their residents. Results A total of 100 responses were received, representing 49 residency programs. Ninety-two (92%) residents reported using MMS to transmit radiological imaging. Twenty-six (26%) reported doing so with patient identifiers. Roughly half (48%) of residents were unaware of policies regarding imaging transmission via MMS at their institutions. Among the 17 (35%) programs providing SMAs, only 3 of 27 (11%) residents in these programs did not use MMS for image transmission. Conclusions The data suggest that there is widespread resident use of MMS for image transmission, regardless of policy and the availability of alternative HIPAA-compliant applications. Knowledge of local institutional policies and HIPAA privacy rules is poor. Alternative strategies are needed to prevent HIPAA-noncompliant transmission of imaging by residents.

Aim: To assess the knowledge, attitude and practice of healthcare professionals regarding the anaphylaxis in Medina, Saudi Arabia. Settings and Design: This was a population-based epidemiological study using a survey composed of 20 questions about the causes, symptoms, and treatment of anaphylaxis. Methods: This survey was distributed to healthcare professionals who represented different job categories including consultants, specialists, interns, residents, nurses, and medical students. Statistical Analysis Used: One-way ANOVA is used to compare the knowledge score across different job categories. SPSS V26 software was used for the analysis and a p value of

The degenerative disease of the intervertebral disc and back pain are chronic conditions that are caused by several factors and represent an important cause of morbidity and mortality in everyday clinical practice. The study aims to summarize the updated evidence regards epidemiology, pathophysiology, clinical manifestation, diagnosis, and management of degenerative disc diseases. The incidence of low back pain, which is the main symptom in Intervertebral Disc (IVD) disease, varies widely among different reports. It is the fifth most common cause for the visit to the doctor and affects 7.6 to 37% of patients. IVD degeneration is attributed to a complex interplay between environmental and genetic factors. DDD is a process that includes a progressive decrease in disk nutrient supply and changes in extracellular matrix (ECM) composition, which weakens the tissue strength and alters the cell metabolism.Degenerative lumb disc disease patients typically present with mechanical lower back pain, which is worse on forward flexion and when carrying heavy load. The pain is relieved with rest. Diagnosis of DDD is done by various methods, computed tomography (CT) scan, magnetic resonance imaging (MRI), and provocative discography. These methods should be used in conjunction with the patient history, physical examination and specific biomarker to monitor the response to treatment. There are three major lines of management of DDD; Treatment Options for Relief of Pain in Conservative Therapy.Treatment with Aims of Restoration, Repair, and Regeneration of Intervertebral Disc Diseases: Molecular Therapy. Reconstructive Strategies: Percutaneous Intervertebral Disc Techniques. Definitive Treatment for Intervertebral Disc Diseases :( surgical management).

Graphical abstract, Introduction The Internet of Things (IoT) comprises of various smart devices for the sharing of sensed data through online services. People will be directly contacted to check their health parameters and the reports will be collected centrally through smart devices. The requirement is protection of messages during the exchange of data between sender and receiver in order to tackle human malicious attacks. Various signature-based schemes are discussed in the literature to provide secure communication. Smart devices however require lightweight tasks by ensuring critical safety strengths. An important problem in the signature based method is that it incurs more computational expenses for signing and verification process in large numbers. Objectives In this study, we introduced an efficient Short Signature Scheme (SSS) that uses Fractional Chaotic Map (FCM) for secure communication in IoT based smart devices, the security of which is closely related to a random oracle based on FCM assumption. Methods In this study, we have designed new short signature scheme using FCM. The presented scheme consist of four sub-algorithm as follows: setup, key generation, signing and verification. We have used less rigorous operations based on the FCM to carry out signing and verification procedures, similar to human signing on valid documents and then verifying them as per witness. Results The proposed SSS offers a better security assurance than currently established signature schemes. The key advantage of the SSS over the DSA schemes is that at the verification stage and signing period it takes less computation; it retains the degree of protection. The presented SSS takes less bandwidth for storage, communication, and computing resources; particularly applicable to wireless devices and smart cards. Conclusion We concluded that the uses of fractional chaotic maps is more effective for secure communication in human-centered IoT to present a provably secure short signature technique.

Brucellosis is an endemic disease in many countries of the middle east including Iraq, despite the high attempts to control the disease in animals using vaccination programs. The study was conducted from January - March 2012, to determine the prevalence of bovine brucellosis in the Al-Najaf province in Iraq, which represent risk factors associated with brucellosis in cattle. Blood samples were collected from 172 cows and 15 bulls of 20 dual-purpose cattle herds. Serological examinations are the most useful and widely used tool for the diagnosis of brucellosis in man and animals. All animal serum samples were tested by the Rose Bengal plate test (RBT), and the positive cases were confirmed by competitive enzyme-linked immunosorbent assay (c-ELISA). The overall prevalence of brucellosis was 5.81% divided between 5.81% in females and 0% in males, and 40% as herd numbers level. Females showed a higher seropositive reaction to the disease than males. In conclusion, this study demonstrated the presence of the disease and detected the seropositive reactions in animal samples in different farms of the province, with a difference in infection rate regarding the risk factor. Sanitary measures must be taken to avoid the occurrence and spread of the disease in humans and animals.

Internet of Things (IoT) paved the way for devices and machine communication using TCP/IP protocol. Lightweight and stateless communication is imperative especially in a situation requiring conserv...

Since early 2020, the whole world has been facing the deadly and highly contagious disease named coronavirus disease (COVID-19) and the World Health Organization declared the pandemic on 11 March 2020. Over 23 million positive cases of COVID-19 have been reported till late August 2020. Medical images such as chest X-rays and Computed Tomography scans are becoming one of the main leading clinical diagnosis tools in fighting against COVID-19, underpinned by Artificial Intelligence based techniques, resulting in rapid decision-making in saving lives. This article provides an extensive review of AI-based methods to assist medical practitioners with comprehensive knowledge of the efficient AI-based methods for efficient COVID-19 diagnosis. Nearly all the reported methods so far along with their pros and cons as well as recommendations for improvements are discussed, including image acquisition, segmentation, classification, and follow-up diagnosis phases developed between 2019 and 2020. AI and machine learning technologies have boosted the accuracy of Covid-19 diagnosis, and most of the widely used deep learning methods have been implemented and worked well with a small amount of data for COVID-19 diagnosis. This review presents a detailed mythological analysis for the evaluation of AI-based methods used in the process of detecting COVID-19 from medical images. However, due to the quick outbreak of Covid-19, there are not many ground-truth datasets available for the communities. It is necessary to combine clinical experts’ observations and information from images to have a reliable and efficient COVID-19 diagnosis. This paper suggests that future research may focus on multi-modality based models as well as how to select the best model architecture where AI can introduce more intelligence to medical systems to capture the characteristics of diseases by learning from multi-modality data to obtain reliable results for COVID-19 diagnosis for timely treatment .

The cytokines IFN-γ, interleukin IL-1β, and TNF-α each up regulate the expression of major histocompatibility complex (MHC) and are therefore considered as inflammatory markers. The present study aimed to measure the serum levels of IFN-γ, interleukin IL-1β, and TNF-α in mice after induction of diabetes with streptozotocin and to correlate their levels to lipid and glycaemic profiles. The study included 40 Swiss Albino mice (20 males and 20 females), half of each male and female groups were made diabetic by intraperitoneal injection of streptozotocin. After 48 hours, the serum levels of INF-γ, IL1-β as well as TNF-α were measured with ELISA and their levels were correlated to lipid and glycaemic profiles. The levels of the cytokines, IFN-γ, IL1-β and TNF-α were measured and correlated to lipid profile, blood glucose and insulin. The serum levels of the three studied cytokines, IFN-γ, IL 1-β and TNF-α were statistically significantly higher among diabetic mice compared to the control group. Diabetic male mice (M-STZ mice) group showed significantly higher lipid profile compared to the control group (M-control). Cholesterol level was significantly higher among female control (F-control) compared to the M-control group. Cholesterol level was significantly higher among diabetic female mice (F-STZ mice) compared to the F-Control group. Regarding IFN-γ, IL 1-β and TNF-α levels, there were significant linear correlations with the glycemic profile (Glucose, insulin) reflected as positive correlation with blood glucose level and negative correlation with insulin level.

The rural-isolated agricultural region in Iraq and some developing countries lack access to electricity, and thus the primarily alternative is to use diesel water pumping systems to irrigate their crops. However, due to a rise in the oil's price on the international market, toxic pollution from burning oil, high maintenance costs, and short lifespans have been challenged to create more viable alternatives. Renewable energy can limit the use of fossil fuels, particularly by using the solar-powered water pumping system. This article aimed at finding an optimal design for a direct-coupled photovoltaic water pumping system in Iraq. The article presents the significant design aspect for an optimal system, such as the groundwater aquifer depth, installation aspect, cost, and irrigation efficiency. The design offers a combination of sprinkler and environmentally sustainable and cost-effective photovoltaic technology to reduce electricity and water use. A deep well to the storage system is adopted with, 40m maximum well depth, 90m3 reservoir, 1200W submersible pump, and 1800 Wp, which can supply water to 12 greenhouses for three days. The yearly results show pumping efficiency is increased up to 42.6%, used water need is achieved at 10950 m3, and unused energy is reduced by 48.8%.

Electrocardiogram (ECG) signal utilization for medical image encryption (ImgEnc) algorithm was proposed in this manuscript. It will utilize the chaotic logistic map and the generalized Arnold map (...

Convolutional neural networks (CNNs) have dominated image recognition and object detection models in the last few years. They can achieve the highest accuracies with several applications such as automotive and biomedical applications. CNNs are usually implemented by using Graphical Processing Units (GPUs) or generic processors. Although the GPUs are capable of performing the complex computations needed by the CNNs, their power consumption is huge compared to generic processors. Moreover, current generic processors are unable to cope up with the growing CNNs demand for computation performance. Therefore, hardware accelerators are the best choice to provide the required computation performance needed by the CNNs as well as affordable power consumption. Several techniques are adopted in hardware accelerators such as pruning and quantization. In this paper, a low-power dedicated CNN hardware accelerator is proposed based on GoogLeNet CNN as a case study. Weights pruning and quantization are applied to reduce the memory size by $57.6\times $ . Consequently, only FPGA on-chip memory is used for weights and activations storage without using offline DRAMs (Dynamic Random Access Memories). In addition, the proposed hardware accelerator utilizes zero DSP (Digital Signal Processing) units as all multiplications are replaced by shifting operations. The accelerator is developed based on a time-sharing/pipelined architecture, which processes the CNN model layer by layer. The architecture proposes a new data fetching mechanism that increases data reuse. Moreover, the proposed accelerator units are implemented in native RTL (Register Transfer Logic). The accelerator classifies 25.1 frames per second (fps) with 3.92W only, which is more power-efficient than other GoogLeNet implementations on FPGA in the literature. In addition, the proposed accelerator achieves an average classification efficiency of 91%, which is significantly higher than comparable architectures. Furthermore, this accelerator surpasses the popular CPUs such as Intel Core-i7 and GPUs such as GTX 1080Ti in terms of the number of frames processed per Watt.

Water and energy shortages came due to rapid population growth, living standards and rapid development in the agriculture and industrial sectors. Desalination tends to be one of the most promising water solutions; however, it is a process of intense energy. Membrane Capacitive Deionization (MCDI) has received considerable interest as a promising desalination technology, and MCDI research has increased significantly over the last 10 years. In addition, there are no guidelines for the design of Capacitive Deionization (CDI) implementation strategies for individual applications. This study, therefore; provides an alternative of CDI’s recent application developments, with emphasis placed on hybrid systems to address the technological needs of different relevant fields. The MCDI’s energy consumption is compared with the reverse osmosis literature data based on experimental data from laboratory-scale system. The study demonstrates that MCDI technology is a promising technology in the next few years with an extreme competition in water recovery, energy consumption and salt removal for reverse osmosis.

As COVID-19 pandemic continued to propagate, millions of lives are currently at risk especially elderly, people with chronic conditions and pregnant women. Iraq is one of the countries affected by the COVID-19 pandemic. Currently, in Iraq, there is a need for a self-assessment tool to be available in hand for people with COVID-19 concerns. Such a tool would guide people, after an automated assessment, to the right decision such as seeking medical advice, self-isolate, or testing for COVID-19. This study proposes an online COVID-19 self-assessment tool supported by the internet of medical things (IoMT) technology as a means to fight this pandemic and mitigate the burden on our nation’s healthcare system. Advances in IoMT technology allow us to connect all medical tools, medical databases, and devices via the internet in one collaborative network, which conveys real-time data integration and analysis. Our IoMT framework-driven COVID-19 self-assessment tool will capture signs and symptoms through multiple probing questions, storing the data to our COVID-19 patient database, then analyze the data to determine whether a person needs to be tested for COVID-19 or other actions may require to be taken. Further to this, collected data can be integrated and analyzed collaboratively for developing a national health policy and help to manage healthcare resources more efficiently. The IoMT framework-driven online COVID-19 self-assessment tool has a big potential to prevent our healthcare system from being overwhelmed using real-time data collection, COVID-19 databases, analysis, and management of people with COVID-19 concerns, plus providing proper guidance and course of action.

Background: Stroke is defined as a health problem due to obstruction in blood flow through cerebrovascular vessels resulting in a state of acute shock. Thereby, this study aimed to assess the level of awareness of general public population toward early manifestation of stroke in Saudi Arabia and to explore the relationship between level of awareness and different socio-demographic factors. Methodology: This was a cross-sectional study conducted among the general public population of Saudi Arabia. Pretested questionnaire was used in the data collection. The questionnaire included questions about socio-demographic factors and awareness of stroke. Results: A total of 740 participants completed the questionnaire. Almost 64.9% were females. It was found that 478 (64.6%) participants had good awareness about early manifestation of stroke. Majority of the participants (93.1%) had already heard about stroke and the main primary source of information was family/friends (29.5%). Furthermore, no statistically significant association between gender, age, nationality, marital status, occupational status, and region of residence was found with the level of awareness regarding stroke. On contrary, a significant difference was observed between level of education and level of awareness about stroke (p = 0.002). Conclusion: About two thirds of participants had good general awareness about stroke and its risk factors and its early signs, but there were few knowledge gaps regarding specific aspects of stroke, which needs to be fixed.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder. It generates joint inflammation and synovitis. Which influence more or less 20 % of the population in India. The arthritis symptoms are created step by step. The major symptom of RA is a pain in the joint. The high level of rheumatoid factor ( RF ) in the blood is most often associated with autoimmune disease such as rheumatoid arthritis & sjogren's syndrome. The Normal range of RF is from 0-20 IU / ml. The anti-arthritis activity evaluation can be studied by using an animal model. The anti-arthritis activity can be done using pithecellobium dulce leaves. The pithecellobium dulce is also called Manila tamarind. PD leaves are also used as astringent to treat antidiabetic activity. The phytoconstituent is existing in leaves are alkaloids, phenol, afezilin, glycoside, saponin, telephenoid, quericitin, dulcitol and laempferol. The fresh leaves are collected and dried to prepared the extract. The dried leaves were ground by using a blender has fine powder. In pithecellobium dulce leaf extract, various methods of extraction like methanol, ethanol, and aqueous extract used for many activities such as CNS depressant, Analgesic, Antidiarrhoeal, Anti-inflammatory and Anti arthritis activity. The ethanol extract of pithecellobium dulce has a significant Anti arthritis activity.

Background: Injuries of the upper cervical spine are a major cause of morbidity and mortality due to associated spinal cord and head injuries. The injury patterns of the upper cervical spine are numerous, and the neurologic sequelae are diverse. The axis (C2) is the most commonly fractured vertebra in the upper cervical spine; its unique anatomy and architecture pose difficulties in the diagnosis and the management of its fractures. Methods: All cases of acute spinal injuries at Prince Mohammed Bin Abdulaziz Hospital in Riyadh, Saudi Arabia, were screened for fractures of C2 vertebrae. These patients underwent computerized tomography (CT) imaging of the cervical spine with special attention paid to the cranio-cervical junction. Magnetic resonance imaging (MRI) and angiography of the neck were performed to exclude ligamentous tears and vascular injuries. Unstable fractures were fixed surgically. In the remaining cases, a conservative trial was given. All patients were followed up once every 3 months for a period of 1 year. During follow-up, some patients underwent additional CT imaging of the cervical spine to monitor the healing of fractures. Results: Out of 230 spinal trauma patients, 43.5% suffered from cervical spine injury. C2 fractures were recorded in 26% cases, and fractures of the C2 vertebral body, including pedicles, laminae, lateral masses, and articular processes, were found in many cases, followed by odontoid fractures (50%). No case of atlanto-axial or atlanto-occipital dislocation was recorded. Road traffic accidents were found to be responsible for 92% of cases. The majority of patients were young males, and 96% of patients had no neurological deficit. Only 15% of the patients required surgery for their unstable fractures. Half of the patients attended outpatient follow -up appointments, all of whom underwent CT scanning of the cervical spine 9 months after the accident or operation. Conclusions: The axis (C2) is the most commonly affected vertebra in cervical spine trauma, and odontoid fractures make up 50% of all C2 fractures. C2 fractures rarely cause any neurological deficit or vascular injury, and the majority of affected patients can be managed conservatively; only a small proportion requires surgical intervention. Surgical intervention leads to early and complete healing.

In the absence of information on radiation doses in mammography in the Gulf countries, this study was designed to assess patient dose in terms of entrance surface air kerma and average glandular dose (AGD) in three mammography units in Qatar that covers 21% of all mammography systems in the country. The study of 150 patients involving 600 projections indicated that the average value of AGD in patients was 2.2 mGy for cranio-caudal and 2.5 mGy for mediolateral-oblique views, respectively. Dose assessment was also performed for polymethyl methacrylate phantoms of thicknesses, ranging from 20 to 80 mm. Comparing the patient dose values with several other publications in literature for full-field digital mammography, our values are typically higher, which can be likely attributed to the larger compressed breast thickness.

The impact of the solar photovoltaic cell temperature on module output performance parameters is investigated experimentally under conditions of Baghdad city climate in the present study. The tests are conducted using a test rig for Polycrystalline silicon solar panel with rated capacity 300W and equipped with measuring devices. Test conditions are based on three months duration, June, July and August of year 2019 through the period from 8AM to 4PM per day. This period represents the highest ambient temperatures through summer months in Baghdad city in Iraq. The results have shown that, under the test conditions for cell temperature with range 45-65°C a significant reduction in the output power, efficiency and fill factor with module temperature rise were observed. The loss in the open circuit voltage and output power of the photovoltaic module was about -0.104/°C and -1.3/°C respectively. The reductions in the current and voltage at maximum output power with temperature increasing were about -5.24% and -5.45% respectively. The drops in the efficiency and fill factor of the photovoltaic module due to the module temperature increase were 9.62% and 12.96% respectively compared to that at standard conditions. The temperature coefficient of the maximum power was -0.52% for the solar photovoltaic module under test conditions.

Convolutional Neural Networks get increasingly importance nowadays as they enable machines to interact with the surrounding environment, which paves the way for computer vision applications. FPGA implementations of CNN architectures have higher speed and lower power consumption compared to GPUs and CPUs. This paper proposes a high-speed hardware accelerator on FPGA for SqueezeNet CNN to accelerate its processing without decreasing the classification accuracy. Several ideas are applied to solve the memory bottleneck issue such as using Ping-Pong memory and deploying several FIFOs in the design. The architecture is built as a pipelined unit to process SqueezeNet CNN layer by layer. Different parallelism techniques are applied while processing the convolution layers to speedup layers processing. Moreover, the proposed accelerator classifies 248.76 fps at a frequency of 100MHz, and 427.4 fps at a frequency of 172 MHz. The proposed accelerator is implemented on Virtex-7 FPGA, and overcomes Geforce RTX 2080Ti GPU and several SqueezeNet FPGA implementations.

INTRODUCTION: Covid-19 has exposed the necessitate for the rapid acceptance of increasingly pioneering digital health technologies, especially remote health monitoring. The digital revolution in Healthcare is dynamic ease of use of inexpensive concern solutions, enhancing patient care, reducing complications, improving effectiveness, and authorizing healthcare decision-makers with intelligence insight at the point of care. OBJECTIVES: The primary objective of this work is to depict the need to recognize wearable sensors as a prerequisite for supporting paradigms in monitoring patients in real-time and enabling access information from the cloud.METHODS: Internet of Things (IoT) is the association of substantial objects where information and communication tools connect various embedded devices to the internet for gathering and switching over data. The combinations of embedded devices with cloud servers recommend extensive pertinence of IoT to several areas of human life. This paper has proposed a method through cloud-based IoT healthcare sensors to formulate patient monitoring remotely. In combination with the implementation of various inbuilt capabilities, internet-enabled heterogeneous wearable sensors can be used for the collection of biomedical data to transmit patient data directly to cloud severe systems to monitor health remotely.RESULTS: Smart healthcare monitoring includes channels of communication, embedded internal and external sensors, IoT server, and cloud storage. The health parameters activities are done at various levels of refining named application layer, management layer, network layer and layer of device. Different data sensors have been collected by wireless media from nodes. It is saved as an unstructured dataset in the cloud. For security with username and password, a patient database is created. Authorized individuals have access to the cloud in order to monitor cloud sensor data in data log, analogue log, digital input and digital output.CONCLUSION: Patient physical parameters like heart beat respiration ,high temperature and stress are calculated via sensors and can be progressed by WIFI unit in the cloud. From this healthcare practitioner will analyze medical to have effective medication. For face-to-face consultation between doctors and patients, the video feature can be added in the future work.

Choroidal melanoma, although rare, is the most common primary intraocular malignancy among adults, with an incidence ranging from 6 to 7.5 cases per million per year globally. This tumor originates from the uveal tract’s melanocytes in the eye. Melanomas usually arise from the sixth decade of age with increasing incidence with progressive age. A 60-year-old male presented with a right-sided sudden painless decrease in vision due to a grade 4 vitreous hemorrhage. B-scan ultrasonography and CT scan of the orbits revealed a mushroom-shaped choroidal mass highly suggestive of choroidal melanoma, which was managed surgically with pars plana endoresection, endodiathermy, and endolaser. Endoresection can be an effective method for the management of solitary non-metastasized choroidal tumors, especially those that have become symptomatic. Pre and post-surgical imaging are necessary, along with careful follow-up to detect any recurrence.

One essential utilization of phase change materials as energy storage materials is energy saving and temperature control in air conditioning and indirect solar air drying systems. This study presents an experimental investigation evaluating the characteristics and energy savings of multiple phase change materials subjected to internal flow in an air heating system during charging and discharging cycles. The experimental tests were conducted using a test rig consisting of two main parts, an air supply duct and a room model equipped with phase change materials (PCMs) placed in rectangular aluminum panels. Analysis of the results was based on three test cases: PCM1 (Paraffin wax) placed in the air duct was used alone in the first case, PCM2 (RT–42) placed in the room model was used alone in the second case, and in the third case, the two PCMs (PCM1 and PCM2) were used at the same time. The results revealed a significant improvement in the energy savings and room model temperature control for the air heating system incorporated with multiple PCMs compared with that of a single PCM. Complete melting during the charging cycle occurred at temperatures in the range of 57–60 °C for PCM1 and 38–43 °C for PCM2, respectively, thereby validating the reported PCMs’ melting–solidification results. Multiple PCMs maintained the room air temperature at the desired range of 35–45.2 °C in the air heating applications by minimizing the air temperature fluctuations. The augmentation in discharging time and improvement in the room model temperature using multiple PCMs were about 28.4% higher than those without the use of PCMs. The total energy saving using two PCMs was higher by about 29.5% and 46.7% compared with the use of PCM1 and PCM2, respectively. It can be concluded that multiple PCMs have revealed higher energy savings and thermal stability for the air heating system considered in the current study.

Testing and evaluation of the photovoltaic array are important matters for researchers and students in the renewable engineering field. The collected data from the PV power plant gives a clear vision for the power plant production, array efficiency and fault detection. The presented system offers an electronic load connected to the PV array which is slightly increased from zero to the maximum designated value. It displays and stores all the voltage, current, and power measurements. It also calculates the voltage and current at the maximum power point, and the fill factor. All the measured data are documented within an Excel file on the computer; curves are plotted automatically to give the user a complete vision for the array behavior, power faults and the mismatching of the PV modules can be easily detected from these curves. The presented system describes all the electronic circuit schematics, how to select the appropriate electronic components with the proper rated values and offers a low-cost design that can be implemented with ease.

The tomato crop is an important staple in the ?market and it is one of the most common crops daily ?consumed. Plant or crop diseases cause reduction of quality and quantity of the production; therefore detection and classification of these diseases are very necessary. There are many types of diseases that infect ?tomato plant like (bacterial spot, late blight, sartorial leaf ?spot, tomato mosaic and yellow curved). Early detection of plant diseases increases production and improves its quality. Currently, intelligent ?approaches have been widely used to detect and classify these ?diseases. This approach helps the farmers to identify the types? of diseases that infect crop. The main object of the current work is ?to apply a modern technique to identify and classify the ?disease. Intelligent technique is based on using convolution ?neural network (CNN) which is a part of machine learning to ?obtain an early detection about the situation of plants. CNN ?method depends on feature extraction (such as color, leaves ?edge, etc.) from input image and on this basis the decision of ?classification is done. A Matlab m-file has been used to build ?the CNN structure. A dataset obtained from plant village has ?been used for training the network (CNN). The suggested ?neural network has been applied to classify six types of tomato ?leaves situation (one healthy and five types of leave plant ?diseases). The results show that the convolution neural ?network (CNN) has achieved a classification accuracy ??of 96.43%. Real images are used to validate the ability of ?suggested CNN technique for detection and classification, and obtained using a 5-megapixel camera from a real ?farm because most common diseases which infect the planet are similar.

The intersection between cardiovascular disease and cancer care is relevant to patients with cancer across the health care system. Cancer and cardiovascular disease are the leading causes of death in the United States ([1][1]). In the United States, 48% of adults have cardiovascular disease ([2][2

Background Machine learning is a branch of Artificial Intelligence that is concerned with the design and development of algorithms, and it enables today’s computers to have the property of learning. Machine learning is gradually growing and becoming a critical approach in many domains such as health, education, and business. Methods In this paper, we applied machine learning to the diabetes dataset with the aim of recognizing patterns and combinations of factors that characterizes or explain re-admission among diabetes patients. The classifiers used include Linear Discriminant Analysis, Random Forest, k–Nearest Neighbor, Naïve Bayes, J48 and Support vector machine. Results Of the 100,000 cases, 78,363 were diabetic and over 47% were readmitted.Based on the classes that models produced, diabetic patients who are more likely to be readmitted are either women, or Caucasians, or outpatients, or those who undergo less rigorous lab procedures, treatment procedures, or those who receive less medication, and are thus discharged without proper improvements or administration of insulin despite having been tested positive for HbA1c. Conclusion Diabetic patients who do not undergo vigorous lab assessments, diagnosis, medications are more likely to be readmitted when discharged without improvements and without receiving insulin administration, especially if they are women, Caucasians, or both.

The prediction of a difficult cholecystectomy has traditionally been based on certain pre-operative clinical and imaging factors. Most of the previous literature reported small patient cohorts and have not used an objective measure of operative difficulty. The aim of this study was to develop a pre-operative score to predict difficult cholecystectomy, as defined by a validated intra-operative difficulty grading scale. Two cohorts from prospectively maintained databases of patients who underwent laparoscopic cholecystectomy were analysed: the CholeS Study (8755 patients) and a single surgeon series (4089 patients). Factors potentially predictive of difficulty were correlated to the Nassar intra-operative difficulty scale. A multivariable binary logistic regression analysis was then used to identify factors that were independently associated with difficult laparoscopic cholecystectomy, defined as operative difficulty grades 3 to 5. The resulting model was then converted to a risk score, and validated on both internal and external datasets. Increasing age and ASA classification, male gender, diagnosis of CBD stone or cholecystitis, thick-walled gallbladders, CBD dilation, use of pre-operative ERCP and non-elective operations were found to be significant independent predictors of difficult cases. A risk score based on these factors returned an area under the ROC curve of 0.789 (95% CI 0.773–0.806, p

In this paper, a deep convolutional neural network (CNN) is proposed for accurate segmentation of retinal blood vessels. This method plays a significant role in observing many eye diseases. A strided-CNN model is proposed for accurate segmentation of retinal vessels, especially the tiny vessels. The model is a fully convolutional model consisting of an encoder part and a decoder part where the pooling layers are replaced with strided convolutional layers. The strided convolutional layer approach was chosen over the pooling layers approach as the former can be trained. The morphological mappings along with the Principal Component Analysis (PCA)- based pre-processing steps are used to generate contrast images for training dataset. Skip connections are implemented to concatenate features from the encoder part and the decoder part to enhance the vessels segmentation especially the tiny vessels and to make the vessel’s edges sharper. We used a class balancing loss function to train and optimize the proposed model to improve vessel image quality. The impact of the proposed segmentation method is evaluated on four databases namely DRIVE, STARE, CHASE-DB1 and HRF. Overall model performance, particularly with respect to tiny vessels, is primarily influenced by sensitivity and accuracy metrics. We demonstrate that our model outperforms other models with a sensitivity of 0.87, 0.808, 0.886 and 0.829 on DRIVE, STARE, CHASE_DB1 and HRF respectively, along with respective accuracies of 0.956, 0.954, 0.976 and 0.962.

Previous studies in Euro-American countries have shown that patients with chronic hepatitis C virus infection have increased levels of neuropsychiatric symptoms. While chronic hepatitis C virus infection has been reported in Arab countries such as Iraq, there is little studies about the neuropsychological burden associated with chronic hepatitis C among patients in the Arab region. The aim of the current study was to measure the prevalence and level of severity of depression, anxiety and stress among a sample of chronic hepatitis C patients in AL-Najaf province /Iraq. The current study was cross-sectional study carried out on (110) already diagnosed chronic viral hepatitis C patients who attended the Gastroenterology and Hepatology Center/Al-Sader Medical City/Najaf/Iraq during November 2018 to May 2019. In addition, (100) apparently healthy subjects were included as a control group. The Arabic version of the Depression Anxiety Stress Scale (DASS-21) was used to assess mental health. Both the prevalence rates and severity levels of depression, anxiety and stress were significantly higher in chronic hepatitis C patients compared to the control group (P

This paper introduces design and FPGA implementation of sound encryption system based on a fractional-order chaotic system. Also, it presents the FPGA implementation of Tang, Yalcin, and Ozoǧuz fractional order chaotic systems. The Grunwald-Letnikov (GL) definition is used to generalize the investigated systems into the fractional-order domain. Also, the variation of parameters for each system is investigated against the window size of the GL definition. Xilinx ISE 14.5 is used to simulate the proposed design. Also, some hardware reduction techniques are applied to decrease hardware utilization and increase throughput. Moreover, Yalcin system is employed as a chaotic generator in a speech encryption algorithm. Security analysis techniques are presented to show the robustness of the proposed algorithm. They include basic perceptual and statistical aspects, the NIST tests, key space analysis, mean square error (MSE) and key sensitivity. Statistical analyses including correlation, histogram, spectrogram, and entropy analysis are presented. The system is implemented on Artix-7 FPGA with one clock latency. Finally, experimental results are illustrated.

This paper presents an optimal controller design using three different algorithms for trajectory tracking of Two Wheeled Balancing Mobile Robot TWBMR All the proposed algorithms have been investigated through simulations under the influence of different inputs tracking and exogenous disturbance This attempts to validate the significance of these algorithms in balance regulation and tracking trajectory These algorithms are represented by PID PID based parallel dual feed back serial dual feed back and hybrid optimal control using Feed forward PID and Feed back LQR Simulation results are provided to demonstrate that the hybrid controller can achieve a better robust performance in comparing to the other two design algorithms The graphical user interface GUI software has been used to show the simulation results in more convenient way

Extending the useful life of bridges through better design, construction, and management is a shared effort among the bridge management community. Data in the National Bridge Inventory (NBI) is valuable for understanding the behavior of bridges throughout their service lives. While the primary reason for bridge replacement, intuitively, would be condition, research has indicated that bridge replacement may not necessarily be driven by the condition of the bridge. The estimated median service life for bridges, 53 years, is much lower than the desired service life of 75 years. This paper summarizes the results of an NCHRP Project which identified the main drivers for bridge decommissioning in the United States, based on findings from three complementary analyses using historic NBI data files, select agency records, and data from old and new structure pairs. A common finding in previous studies was that a significant portion (15–30%) of decommissioning could not be associated with any particular reason. Although poor condition is a significant factor, the major driver of bridge decommissioning is functional improvement, and this explains the majority of the unexplained cases. Structures replaced due to functional reasons tend to be replaced at a younger age, leading to a decrease in the overall decommissioning age. While decisions on functional improvement projects are not led by bridge offices, bridge networks are substantially affected by these decisions. Coordinating functional improvement decisions at the agency level and integrating relevant information with decision support tools can improve financial planning and asset management processes.

Language impairment in Parkinson’s disease (PD) may be attributable to motor and action/event knowledge deficits. We predicted that cognitively intact PD participants would be impaired in anticipating objects in sentences from event-based thematic fit information. Twenty-four PD and 24 healthy age-matched participants completed comprehensive neuropsychological assessments. We recorded participants’ eye movements as they heard predictive (The fisherman rocks the boat) and non-predictive baseline sentences (Look at the bathtub). Predictive sentences contained target, agent-related, verb-related, and unrelated images. Baseline sentences used phonologically and semantically unrelated distractors. We tested effects of group (PD/control) on gaze using growth curve models. There were no significant differences between PD and control participants in either sentence type, suggesting that PD participants successfully and rapidly use combinatory thematic fit information to predict upcoming language. Additionally, we conducted an exploratory analysis contrasting PD and controls’ performance on low motion content versus high motion content verbs. This analysis revealed fewer predictive fixations in high-motion sentences only for healthy older adults, suggesting that people with Parkinson’s disease may adapt to their disease by relying on spared, non-action-simulation-based language prediction and processing mechanisms. Given that multiple studies have shown that individuals with PD have difficulty processing verbs, it is highly surprising that they match healthy adults in their ability to use verb meaning to predict upcoming nouns.

This case series aims to evaluate the use of alternating perpendicular biplanar fluoroscopy in percutaneous nephrostomies/percutaneous nephrolithotripsies (PCNs/PCNLs) to approach renal stones in patients with horseshoe kidneys. Between January 2012 and December 2019, PCNs/PCNLs were done for six patients with horseshoe kidneys having renal stones. Skin and renal calyceal entry points were determined by alternating perpendicular biplanar fluoroscopy using a portable C-arm machine in the conventional fluoroscopy unit. The site of renal access, postoperative complications, and residual stones was assessed. The mean age of the patients was 36.8 years. The mean stone size was 6.1 cm (2.1-16.05cm). In five out of six (5/6; 83%) patients, there was one access site. Four patients had their access site through the upper calyx, and one patient had it through the lower calyx. The stone-free rate was four out of six (4/6; 66.6%). One patient had a mild drop in hemoglobin postoperatively. There were no major complications reported. The implementation of alternating biplanar fluoroscopy was found safe and helpful in providing a better appreciation of renal anatomy and stone location in patients with horseshoe kidneys. This technique helps in approaching horseshoe kidney stones in PCN/PCNL without moving the patient or fluoroscopy machine, with a potential decrease in operation time and radiation exposure.

A Mobile Ad-hoc Network (MANET) is a self-directed group of mobile handlers that communicate over relatively bandwidth constrained wireless channels. Many types of data could be transferred in MANET such as data, voice, and video streaming which is required sufficient packet routing and scheduling mechanisms. These scheduling algorithms have the responsibility to guarantee the different quality of service classes such as Unsolicited Grant Service (UGS), Real-Time Polling Service (RTPS), Non-Real-Time Polling Service (NRTPS), and Best Effort (BE). The demand for performance evaluation for different scheduling algorithms is imposed to this project, in which four famous MANET scheduling algorithms are selected and investigated. These algorithms are Round Robin (RR), Strict Priority (SP), Weighted Fair (WF), and Weighted Round Robin (WRR). The MANET scenario which is consisting of 50 random mobile nodes is built using network simulator QualNet 2.0.1. The results show the performance metrics of the network such as the throughput and the end-end delay as well as queuing metrics such as peak queue size, average queue length, the average time in queue, and total packets dropped. Regrading throughput, the SP algorithm has higher throughput than WF, RR, and WRR by 4.5%, 2.4%, and 1.42%, but WRR has outperformed others regarding the end-to-end delay. Moreover, WRR represents the best scheduling algorithm regarding both peak queue size since its higher than RP, WF, and WRR by 10.13%, 9.6%, and 5.32%, in order, and average output queue length.in contrast, WRR worst more time in queuing but it is the best in preventing the packets from dropping.

The internet of things (IoT) is one of the most advanced technologies that have emerged in the last decade. In recent years, IoT has been used in many medical fields. With the emergence of the Coronavirus pandemic, some IoT technologies were employed to serve the health sector to make quick and accurate decisions to save people's lives. However, there are still many ideas and works not yet implemented that could be applied in many aspects to preserve people's lives. Therefore, it is necessary to collect works and ideas that depend on IoT to produce modern systems quickly to serve the health sector. In this paper, a review of the most recent technologies of IoT against Coronavirus disease (COVID-19) has been done. A comparative and analysis among the previous works have been done to reach the most efficient depending on comparing the services that each work has provided. Besides that, suggest several new ideas that can be adopted as systems use IoT technologies and the expected advantages that can be gain from applying these ideas. A framework for a proposed idea to build a comprehensive monitoring system based on IoT technologies on the patient and hospital sides and expected advantages of implementing the system has been done.

This paper analyzes the properties of a continuous-time epidemic model used to describe infectious diseases such as COVID-19 and Tuberculosis (TB) and how controlled and tracked it using optimal control theory. The dynamic behavior of the epidemiological model is analyzed using the SIR (Susceptible-Infected-Recovery) model, where different values of parameters of the epidemic model will be simulated using MATLAB program to better understand and to extract useful information about possible situations. The behavior of the infectious disease COVID-19 and TB is investigated with real data in Libya and controlled using a Pontryagin Minimum Principle (PMP) which characterizes optimality around the optimal solution to minimizing the infected people and maximizing the recovery process. The simulation results indicate that the designed control strategy has a positive effect. In addition, the Graphical User Interface (GUI) was designed to know the behavior of other epidemics and how they can be controlled and thus by increasing or decreasing one of the parameters of that disease without the need to use mathematical operations to solve differential equations.

Brain-Computer Interfaces (BCI) or Neural Control Interface (NCI), is a distinct and global technology that has revolutionized the world of control and signal processing. This technology has helped link humans and computers to achieve goals that are difficult to achieve for certain patients or people. This work combines the cutting edge BCI strategies into one single framework to control a 3D arm. In the proposed experiment, brain signals are utilized to move the automated arm and perform various assignments such as move any finger of the hand. To provide movement to the 3D arm robot in real-time, we acquired EEG data based 10-20 international system and forwarded these signals to the processing computer using OpenBCI Wi-FI, OpenBCI cyton board, and controlling the servo motors using OpenBCI GUI and Arduino Uno. The BCI designed system was evaluated by a real-time experiment study and applied on man and woman were induced by opening and closing hands, also the validation process has been done with different frequency and voltages where the experimental results demonstrate that the process will operate as designed with extreme precision and high performance.