An Internet of Things Based Air Pollution Detection Device for Mitigating Climate Changes

Climate Change, a key stabilizing factor, has now exceeded critical thresholds. The high energy consumption of cities is a major contributor to climate change because of CO2 emissions. In addition to the rise in urban populations throughout the worldwide, the complexity of todays cities and the strain they put on limited resources means that the causes and consequences of climate changes become even more concentrated. Internet of Things (IoT) advancements provide several possibilities for reducing the effects of climate change by merging existing information, design techniques, and breakthrough technology. The current state of monitoring technology is subpar; it is insensitive, inaccurate, and requires laboratory examination. Consequently, new, and better methods of surveillance are required. Air pollution is one of the main causes of climate change. We suggest a new IoT-based monitoring device for air pollution to address the shortcoming of the current setup. Gas sensors, Arduino IDE, and Wi-Fi module were used to assemble the IoT kit. The air is analyzed by the gas sensors, and the results are sent to the Arduino software development environment. By using a WiFi module, the Arduino IDE may send data to the monitor. The resulting device may be deployed in different cities to monitor the levels of air pollution with little cost, easy to use and high accuracy

Microstructures and textures comparison of conventional and high niobium API 5L X80 line pipe steel using EBSD

The development of microstructure and crystallographic texture of conventional and high Niobium API 5L X80line pipe steels has been studied using Electron Backscattered Diffraction (EBSD) technique. The selectiveuse of micro-alloying elements like niobium in alloy design, increases the recrystallization stop temperature,which facilitates rolling at higher temperatures compared to conventional thermo-mechanical processing. Themeasurement tools available with EBSD, such as image quality values and precise grain boundary misorientationangles, provide new approaches to characterize and compare both microstructures. The texture analysis, inaddition, shows high intensification of {112}<110>, {554}<255> and rotated cube {001}<110> for both processes.However, the Goss {110}<001> and the rotated Goss {110}<110> has been only observed in the conventionalprocessing which is a sign that the austenite was recrystallized prior to transformation. This shows the ease ofprocessing experienced with High Temperature Processing (HTP) chemistry compared to the conventional one

Evaluación de la prescripción y uso de medicamentos supresores de ácido en hospitales centrales en la región de Abha, Arabia Saudita

Objective: The aim of this study was to study and assess the indications of acid suppressive drugs and to find out percentage of irrational prescriptions with acid suppressive drugs. Material/Methods: It is a prospective observational study conducted in the Armed Forces Hospitals Southern Region and Abha Maternity Hospital, both in Abha in Assir region (Saudi Arabia). The sample size of study was 185 patients. The case sheets of the patients’ prescription order were reviewed for acid suppressive drugs prescription and relevant data was taken. Patients’ age above 18 were identified. The duration of study was 8 weeks, between May and June 2017. Results: Our results showed that the majority of the prescriptions of proton pump inhibitors (68.1%) were unjustifiable and that proton pump inhibitor was the most commonly prescribed acid suppressive drugs for the patients (97.8%). The frequency of prescribing for the autism spectrum disorders in our study was found to be higher in patients with an existing risk factor and was mostly recommended by physicians as concomitant medications (67.6%). The most common concomitant medications used with the proton pump inhibitors were non-steroidal anti-inflammatory drugs (29.2%) in which aspirin composed 13.5% of the non-steroidal anti-inflammatory drugs prescribed followed by antimicrobials (9.2%). Conclusion: Acid suppressive drugs are the most commonly prescribed drugs with no proper indications hence irrational. Based on the results of this study, creating awareness about reasonable use of acid suppressive drugs is a necessity.Objetivo: El objetivo de este estudio fue estudiar y evaluar las indicaciones de los medicamentos supresores de ácidos y averiguar el porcentaje de recetas irracionales con medicamentos supresores de ácidos. Material / Métodos: es un estudio observacional prospectivo realizado en los Hospitales de las Fuerzas Armadas del Sur y en el Hospital de Maternidad Abha, ambos en Abha en la región de Assir (Arabia Saudita). El tamaño muestral del estudio fue de 185 pacientes. Se revisaron las hojas de casos de orden de prescripción de los pacientes para la prescripción de medicamentos supresores de ácido y se tomaron los datos pertinentes. Se identificó la edad de los pacientes mayores de 18 años. La duración del estudio fue de 8 semanas, entre mayo y junio de 2017. Resultados: nuestros resultados mostraron que la mayoría de las prescripciones de inhibidores de la bomba de protones (68,1%) eran injustificables y que este era el fármaco supresor de ácido más comúnmente prescrito para los pacientes (97,8%). La frecuencia de prescripción para los trastornos del espectro autistas en nuestro estudio, fue mayor en pacientes con un factor de riesgo existente y fue recomendada principalmente por los médicos como medicamentos concomitantes (67,6%). Los medicamentos concomitantes más comunes que se usaron con los inhibidores de la bomba de protones fueron los antiinflamatorios no esteroideos (29.2%) en los cuales la aspirina supuso el 13,5% de los antiinflamatorios no esteroideos prescritos, seguidos por los antimicrobianos (9.2%) Conclusión: los medicamentos supresores de ácido son los medicamentos más comúnmente recetados sin indicaciones adecuadas, por lo que son irracionales. Basado en los resultados de este estudio, crear conciencia sobre el uso razonable de los medicamentos supresores del ácido es una necesidad

Executing Quality Management Tools to Enhance Customer’s Journey at a Clothes Laundry Firm

Purpose: This project aims to assess and enhance the customer’s journey at a laundry company in Saudi Arabia from when the customer arrives at the parking lot of the laundry store until receiving the services and payment. Design, methodology, approach: A mixed-method approach was employed, in which qualitative data were gathered from focus group interviews and solo interviews, and quantitative data were gathered through the survey. Moreover, Quality Management Tools were used to build the action plan and offer conclusions and recommendations that would enhance the customer’s journey and improve satisfaction. Findings: Services quality issues are categorized into five categories: promotions, human resources; services; detergent products, and facilities. House of Quality represents the highest 15 prioritized solutions. These recommended solutions' relative weights range between 9% to 4%. The use of these tools highlights areas for improvement and the root causes of each issue. The seven quality tools are trustworthy tools to conquer challenges faced by the company and may be effective in improving service quality to positively strengthen organizational performance, customer satisfaction, and success. Originality, value: There are limited studies practically employing the seven Quality Management Tools to enhance customer satisfaction and improve their journey. The Saudi laundry market specifically has a dearth of this type of study. Furthermore, this market has seen rapid growth since it is known as a part of the SME sector in recent few years

Executing Quality Management Tools to Enhance Customer’s Journey at a Clothes Laundry Firm

Purpose: This project aims to assess and enhance the customer’s journey at a laundry company in Saudi Arabia from when the customer arrives at the parking lot of the laundry store until receiving the services and payment. Design, methodology, approach: A mixed-method approach was employed, in which qualitative data were gathered from focus group interviews and solo interviews, and quantitative data were gathered through the survey. Moreover, Quality Management Tools were used to build the action plan and offer conclusions and recommendations that would enhance the customer’s journey and improve satisfaction. Findings: Services quality issues are categorized into five categories: promotions, human resources; services; detergent products, and facilities. House of Quality represents the highest 15 prioritized solutions. These recommended solutions' relative weights range between 9% to 4%. The use of these tools highlights areas for improvement and the root causes of each issue. The seven quality tools are trustworthy tools to conquer challenges faced by the company and may be effective in improving service quality to positively strengthen organizational performance, customer satisfaction, and success. Originality, value: There are limited studies practically employing the seven Quality Management Tools to enhance customer satisfaction and improve their journey. The Saudi laundry market specifically has a dearth of this type of study. Furthermore, this market has seen rapid growth since it is known as a part of the SME sector in recent few years

Detection and classification of power quality disturbances based on Hilbert-Huang transform and feed forward neural networks

This paper presents a hybrid detection method and classification Technique based on Hilbert-Huang Transform (HHT) and Feed Forward Neural Networks (FFNNs) to improve the efficient delivery and ensure accurate detection of quality disturbances in the electrical power grids. First, quantities characteristics of power quality disturbances (PQDs) are introduced according its parametrical conditions. Thereafter, a detection and recognition algorithm is used for single and multiple disturbances. Then, a decomposition process and features extraction using Empirical Mode Decompensation (EMD) is conducted for each of these distorted waveforms into Intrinsic Mode Functions (IMFs). Finally, these features are constructed using signal amplitude and frequency and then after fed to one of the powerful Artificial Intelligence Techniques in this field for training, evaluating and testing using (FFNNs) classifier to verify and confirm the effectiveness of the detection methodology

Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network

The Industrial Internet of Things (IIoT) refers to the employment of the Internet of Things in industrial management, where a substantial number of machines and devices are linked and synchronized with the help of software programs and third platforms to improve the overall productivity. The acquisition of the industrial IoT provides benefits that range from automation and optimization to eliminating manual processes and improving overall efficiencies, but security remains to be forethought. The absence of reliable security mechanisms and the magnitude of security features are significant obstacles to enhancing IIoT security. Over the last few years, alarming attacks have been witnessed utilizing the vulnerabilities of the IIoT network devices. Moreover, the attackers can also sink deep into the network by using the relationships amidst the vulnerabilities. Such network security threats cause industries and businesses to suffer financial losses, reputational damage, and theft of important information. This paper proposes an SDN-based framework using machine learning techniques for intrusion detection in an industrial IoT environment. SDN is an approach that enables the network to be centrally and intelligently controlled through software applications. In our framework, the SDN controller employs a machine-learning algorithm to monitor the behavior of industrial IoT devices and networks by analyzing traffic flow data and ultimately determining the flow rules for SDN switches. We use SVM and Decision Tree classification models to analyze our framework’s network intrusion and attack detection performance. The results indicate that the proposed framework can detect attacks in industrial IoT networks and devices with an accuracy of 99.7%

Risk and diagnostic factors and therapy outcome of neonatal early onset sepsis in ICU patients of Saudi Arabia: a systematic review and meta analysis

BackgroundNeonatal early onset sepsis (NEOS) is a serious and potentially life-threatening condition affecting newborns within the first few days of life. While the diagnosis of NEOS was based on clinical signs and symptoms in the past, recent years have seen growing interest in identifying specific diagnostic factors and optimizing therapy outcomes. This study aims to investigate the diagnostic and risk factors and therapy outcomes of neonatal EOS in ICU patients in Saudi Arabia, with the goal of improving the management of neonatal EOS in the country.MethodsThis method outlines the protocol development, search strategy, study selection, and data collection process for a systematic review on neonatal early onset sepsis in Saudi Arabian ICU patients, following the PRISMA 2020 guidelines. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is a well-established guideline that provides a framework for conducting systematic reviews and meta-analyses in a transparent and standardized manner. It aims to improve the quality and reporting of such research by ensuring clear and comprehensive reporting of study methods, results, and interpretations. The search strategy included electronic databases (PubMed, Embase, Google Scholar, Science Direct, and the Cochrane Library) and manual search of relevant studies, and data were extracted using a standardized form.ResultsThe systematic review included 21 studies on neonatal sepsis in Saudi Arabia, with varying study designs, sample sizes, and prevalence rates of sepsis. Group B streptococcus and E. coli were the most commonly isolated pathogens. Various diagnostic factors and risk factors were reported, including hematological parameters, biomarkers, and blood cultures. The quality of the included studies was assessed using the Newcastle-Ottawa Scale and Joanna Briggs Institute critical checklist.ConclusionsThe review identified a number of risk and diagnostic factors and therapy outcomes for neonatal sepsis. However, most of the studies were having small scale cohort groups. Further research with controlled study designs is needed to develop effective prevention and management strategies for neonatal sepsis in Saudi Arabia

Antidiabetic potential of Moringa oleifera Lam. leaf extract in type 2 diabetic rats, and its mechanism of action

Purpose: To explore the antidiabetic potential of Moringa oleifera leaf extract in type 2 diabetic rats, and the underlying mechanisms.Methods: Streptozotocin (STZ) at a dose of 40 mg/kg was given to high fat diet (HFD)- fed rats to induce type 2 diabetes. M. oleifera leaf extract at doses 100, 200 and 400 mg/kg were given to 3 groups of type 2 diabetic rats. The area under curve (AUC) of glucose and homeostasis model assessment of insulin resistance (HOMA-R) were calculated using appropriate formulas, whereas levels of glucose,insulin, peroxisome proliferator activated receptor-γ (PPARγ, dipeptidyl peptidase-IV (DPP-IV) and inflammatory cytokines (IL-6, IL-1β and TNFα) were assayed using ELISA kits.Results: The leaf extract of M. oleifera significantly reduced the levels of glucose, insulin and cytokines in treated type 2 diabetic groups (p < 0.05). DC group had significantly increased AUC for glucose, whereas the extract-treated groups showed significant  decrease in glucose AUC. There was significant decrease in insulin sensitivity parameters, as indicated by increase in HOMA-R and decrease in PPARγ levels in the DC group (p < 0.05). However, treatment with the M. oleifera extract reversed this trend via marked decrease in HOMA-R level and significant rise in PPARγ level. In contrast, the extract had no effect on DPP-IV concentration in diabetic treated groups (p < 0.05).Conclusion: These results indicate that M. oleifera leaf extract mitigates hyperglycemia in type 2 DM by modulating hyperinsulinemia, PPARγ and inflammatory cytokines. Thus, the extract is a potential source of drug for the management of type 2 DM. Keywords: Moringa oleifera, Diabetes mellitus, Streptozotocin, Peroxisome proliferator activated receptor-γ, Dipeptidyl peptidase I