API System Research Articles

Resources for Teaching Operating Systems: A Survey of Instructors and a Literature Review

Objectives : Faculty new to teaching operating systems or those looking to refresh their course need to understand the state of the art in operating system education. Toward this goal, we conducted a survey of operating system instructors to understand how they approach teaching the course and what textbook and software platforms they use in their classrooms. We also conducted a literature review examining two decades of papers focused on teaching operating systems to undergraduates. Survey : We surveyed people who teach operating systems at the undergraduate level to determine which textbook they use, on which software they base projects, and how they approach teaching the course. Literature Review : We searched the ACM Digital Library for publications focused on undergraduate education about operating systems. We selected a total of 51 papers. For each selected paper, we identified which approach (i.e., concrete vs. abstract) and perspective (i.e., internal or external) on teaching operating systems the authors used, which Curriculum 2023 topics they covered, and on what type of system (e.g., educational, production, research) their projects were based. We also looked at the evaluation of the methods and the impact these papers have had on the field. Findings: Instructors teaching operating systems tend to balance both their approach and their perspective, with a slight leaning toward an internal perspective, whereas authors supporting operating system education overwhelmingly support an internal, concrete approach to the course. In addition, authors also tend to focus on a few key topics, including System Calls and Processes, Concurrency, Scheduling, Virtual Memory, File Systems API and Implementation, and Performance Evaluation. Conclusions : This work will help faculty teaching operating systems to consider which approach and perspective they wish to take in their course and to find the resources most relevant to their preferred approach and perspective. It can also help focus future research in the area of operating system education, to align it better with current practices.

Wasm-R3: Record-Reduce-Replay for Realistic and Standalone WebAssembly Benchmarks

WebAssembly (Wasm for short) brings a new, powerful capability to the web as well as Edge, IoT, and embedded systems. Wasm is a portable, compact binary code format with high performance and robust sandboxing properties. As Wasm applications grow in size and importance, the complex performance characteristics of diverse Wasm engines demand robust, representative benchmarks for proper tuning. Stopgap benchmark suites, such as PolyBenchC and libsodium, continue to be used in the literature, though they are known to be unrepresentative. Porting of more complex suites remains difficult because Wasm lacks many system APIs and extracting real-world Wasm benchmarks from the web is difficult due to complex host interactions. To address this challenge, we introduce Wasm-R3, the first record and replay technique for Wasm. Wasm-R3 transparently injects instrumentation into Wasm modules to record an execution trace from inside the module, then reduces the execution trace via several optimizations, and finally produces a replay module that is executable standalone without any host environment-on any engine. The benchmarks created by our approach are (i) realistic, because the approach records real-world web applications, (ii) faithful to the original execution, because the replay benchmark includes the unmodified original code, only adding emulation of host interactions, and (iii) standalone, because the replay benchmarks run on any engine. Applying Wasm-R3 to web-based Wasm applications in the wild demonstrates the correctness of our approach as well as the effectiveness of our optimizations, which reduce the recorded traces by 99.53% and the size of the replay benchmark by 9.98%. We release the resulting benchmark suite of 27 applications, called Wasm-R3-Bench, to the community, to inspire a new generation of realistic and standalone Wasm benchmarks.

AnLibsXAI: Android malware classification using library lists and explainable artificial intelligence with SHAP

In this study, we use various features, including permission lists, API system calls, and library lists, to create a system named AnLibsXAI to classify Android malware. We experimented with six machine learning models such as Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN) and Multilayer Perceptron (MLP). Additionally, this study applies explainable artificial intelligence platforms to assess the importance and impact of features in Android malware classification models. The evaluation results on the CICMalDroid 2020 dataset show that SVM has the highest accuracy, reaching 96%. The findings of this study can be applied to future research related to Android malware classification

Code Similarity Prediction Model for Industrial Management Features Based on Graph Neural Networks.

The code of industrial management software typically features few system API calls and a high number of customized variables and structures. This makes the similarity of such codes difficult to compute using text features or traditional neural network methods. In this paper, we propose an FSPS-GNN model, which is based on graph neural networks (GNNs), to address this problem. The model categorizes code features into two types, outer graph and inner graph, and conducts training and prediction with four stages-feature embedding, feature enhancement, feature fusion, and similarity prediction. Moreover, differently structured GNNs were used in the embedding and enhancement stages, respectively, to increase the interaction of code features. Experiments with code from three open-source projects demonstrate that the model achieves an average precision of 87.57% and an F0.5 Score of 89.12%. Compared to existing similarity-computation models based on GNNs, this model exhibits a Mean Squared Error (MSE) that is approximately 0.0041 to 0.0266 lower and an F0.5 Score that is 3.3259% to 6.4392% higher. It broadens the application scope of GNNs and offers additional insights for the study of code-similarity issues.

Solvent Replacement Strategies for Processing Pharmaceuticals and Bio-Related Compounds—A Review

An overview of solvent replacement strategies shows that there is great progress in green chemistry for replacing hazardous di-polar aprotic solvents, such as N,N-dimethylformamide (DMF), 1-methyl-2-pyrrolidinone (NMP), and 1,4-dioxane (DI), used in processing active industrial ingredients (APIs). In synthetic chemistry, alcohols, carbonates, ethers, eucalyptol, glycols, furans, ketones, cycloalkanones, lactones, pyrrolidinone or solvent mixtures, 2-methyl tetrahydrofuran in methanol, HCl in cyclopentyl methyl ether, or trifluoroacetic acid in propylene carbonate or surfactant water (no organic solvents) are suggested replacement solvents. For the replacement of dichloromethane (DCM) used in chromatography, ethyl acetate ethanol or 2-propanol in heptanes, with or without acetic acid or ammonium hydroxide additives, are suggested, along with methanol acetic acid in ethyl acetate or methyl tert-butyl ether, ethyl acetate in ethanol in cyclohexane, CO2-ethyl acetate, CO2-methanol, CO2-acetone, and CO2-isopropanol. Supercritical CO2 (scCO2) can be used to replace many organic solvents used in processing materials from natural sources. Vegetable, drupe, legume, and seed oils used as co-extractants (mixed with substrate before extraction) can be used to replace the typical organic co-solvents (ethanol, acetone) used in scCO2 extraction. Mixed solvents consisting of a hydrogen bond donor (HBD) solvent and a hydrogen bond acceptor (HBA) are not addressed in GSK or CHEM21 solvent replacement guides. Published data for 100 water-soluble and water-insoluble APIs in mono-solvents show polarity ranges appropriate for the processing of APIs with mixed solvents. When water is used, possible HBA candidate solvents are acetone, acetic acid, acetonitrile, ethanol, methanol, 2-methyl tetrahydrofuran, 2,2,5,5-tetramethyloxolane, dimethylisosorbide, Cyrene, Cygnet 0.0, or diformylxylose. When alcohol is used, possible HBA candidates are cyclopentanone, esters, lactone, eucalytol, MeSesamol, or diformylxylose. HBA—HBA mixed solvents, such as Cyrene—Cygnet 0.0, could provide interesting new combinations. Solubility parameters, Reichardt polarity, Kamlet—Taft parameters, and linear solvation energy relationships provide practical ways for identifying mixed solvents applicable to API systems.

Modeling Electromagnetic Field Radiation Variability for Minimization of Exposure Rate in Public Health Environments: A Machine Learning Approach

Public Health awareness of the exposure to radiofrequency electromagnetic fields (Rf-EMF) is necessary for epidemiological studies on possible bio-effects. The Knowledge will promote good health, reduce health risk factors, lower morbidity and mortality rates often associated with public health environment (PHE). Modeling EMF radiation variability and its application is vital given the prospective growth in both the number of mobile devices and equipment radiating electromagnetic fields (EMF) and the increasing concerns in the general public. The main goal of this work is to develop a framework based on Machine Learning (ML) approach to determine the exposure level on time-based variances within and during operational procedures of frequency bands used in mobile telecommunication, digital devices, transmitters, broadcasting terminals etc. The system adopts the measurements and instrumentation methodology, using a handheld Poniie pn8000 multi-field EMF metre and EME Spy exposimeter with a detection limit of 0.0066mW/m (2) with sampling at certain interval in a specific frequency ensemble. The measurements were synthesized as exposure coefficients with assessment based on EMF radiation from emitting devices and matching with International Commission on Non-Ionizing Radiation Protection (ICNIRP) standards endorsed by WHO. Consequently, obtained data was processed and segmented into training and test sets on 70:30 ratios respectively. Computing modeling of the system was performed with R programming language using data obtained from field measurements and records. The prosed system used Ensemble ML-Random Forest Algorithm and Decision Tree algorithm (DTA). Performance evaluation and results obtained clearly demonstrates 87% based on the Confusion Matrix (CM) outcomes for Random Forest and Decision Tree presents 56%. This justifies the capability of ML techniques in accurately classifying EMF radiation to mitigate exposure rate and improve healthcare throughput. The model was transformed and deployed in a production system API environment with Graphical User Interface (GUI) for flexibility. This work revealed relating potential health bio-effects associated with EMF exposure and the different parameters that are currently used for evaluating, limiting, and mitigating the impact of the exposure on the general public.

Design and development of a web-based patient management information system

This study aims to develop web-based hospital management software. Several previous studies discovered some lack, such as manual queue calling, limited patient registration, difficult access to medical records, and the absence of reminders. In this study, we developed queue, reservation, medical record, reminder, and medical action modules. We expected that facilities could improve the operational efficiency of hospital services. The research stage starts with problem formulation, literature study, design determination, system development with five main modules, system testing, and the creation of research articles. Some limitations were identified, including responsive design and the expense of API system.

Plant growth promoting potential of Arthrobacter spp., an early colonizer on the volcanic deposits of Mt. Merapi, Indonesia, as a restorative agent for degraded land

Exploration of novel and effective plant growth-promoting bacteria (PGPB) from unexplored environments is an intriguing option for restoring degraded land in Indonesia caused by mining activity. Due to their ability to develop in the absence of carbon, PGPB derived from volcanic environments may be a sustainable alternative. Previous research on the volcanic deposits of Mt. Merapi, Indonesia and Miyake-Jima, Japan, revealed that Arthrobacter spp. dominated the pioneer bacterial communities. Consequently, this research aimed to investigate the biochemical and physiological characteristics of volcanic Arthrobacter groups and assess their potential as PGPB. Using the conventional API system (20NE, 50CH, Zym), biochemical characteristics were evaluated. In vitro evaluations of plant growth-promoting activities such as siderophore production, phosphate solubilization, growth in media devoid of nitrogen, heavy metal resistance and weathering activity were conducted. The results showed broad substrate utilization and enzyme activity profiles of the Mt. Merapi isolates (BRU 37, BRU I.13, and BRU I.45). During 15 days of incubation on a minimal medium containing potassium feldspar, glucose, and NH4Cl, all of the isolates grew and produced acids. In nitrogen-free media, all of the isolates grew. These isolates showed positive reaction for the phosphate solubilization assay using Pikovskaya plates. After one day of incubation, all isolates produced yellow zones on the blue-green pigmented media (CAS-overlay media) and were therefore classified as potent siderophore producers. The heavy metal resistance assay showed that BRU37 grew in the heavy metal media. These results indicate the potential of selected indigenous Mt. Merapi Arthrobacter to enhance plant growth. Further characterization of plant growth-promoting traits is now in progress.

DL-AMDet: Deep learning-based malware detector for android

The Android operating system, with its market share leadership and open-source nature in smartphones, has become the primary target of malware. However, detecting malicious Android processes has become a significant challenge because of the complexity of size, length, and associations of various important and distinctive elements of Android applications, such as API calls and system calls. In this paper DL-AMDet, a deep learning architecture is proposed to detect Android malware applications based on its static and dynamic features. DL-AMDet consists of two main detection models the first one uses CNN-BiLSTM deep learning method for detecting malware using static analysis. The other model utilizes deep Autoencoders as an anomaly detection model to identify the malware based on dynamic analysis. The performance of the DL-AMDet architecture is evaluated using two different datasets. The results show that DL-AMDet achieves a competitive malware detection accuracy of 99.935 % for static and dynamic analysis models combined. Additionally, the results emphasize the significance of CNN-BiLSTM and Deep Autoencoders models used in DL-AMDet to outperform the existing state-of-the-art techniques.

HMS-REST v1.0: A plugin for the HEC-HMS model to provide RESTful services

Web technologies facilitating location-independent participation and broad knowledge sharing are critically important for open science and participative management and decision-making. Therefore, it is crucial to enable hydrological models to run in a web environment. In this study, a representational state transfer (REST) interface is proposed for the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The web service interface (named HMS-REST) was built based on the Java API and Jython script system of HEC-HMS. HMS-REST allows users to retrieve and write model inputs and outputs and perform model execution. These functionalities were evaluated via a case study of pseudoensemble flood forecasting using a data set of six historical flood events from the Chuanchang watershed, southeastern China. Satisfactory performance was achieved in pseudoensemble flood forecasting in terms of the peak flow, total flood volume, peak flow timing and overall hydrograph fitting. This case study indicates that HMS-REST and HEC-HMS can be effectively integrated and demonstrates that the use of HEC-HMS with a web-based interface can facilitate pragmatic applications of the HEC-HMS model in the fields of model calibration, flood simulation and forecasting, and participatory decision-making.

Knowledge graph-based data integration system for digital twins of built assets

The emergence of digital twin technologies offers a promising avenue for improving decision-making through the integrated use of up-to-date physical or synthetically simulated data. Nevertheless, the practical implementation of digital twins in the built environment remains a significant challenge. This paper describes a system that seamlessly integrates data into digital twins of built assets. The system uses a knowledge graph to achieve data integration, which is designed to be modular, flexible, and interoperable. The graph includes BIM models, metadata from an external IoT platform, and process-related information. The system is microservice-based and revolves around a graph database housing the knowledge graph. It employs dynamic operations to update the knowledge graph and is tested using civil engineering infrastructure examples. Results from this work can be used to create pipelines that extract and operate with data connecting computational agents integrated into the system as microservices or connected through the system API.

Treehouse: A Case For Carbon-Aware Datacenter Software

The end of Dennard scaling and the slowing of Moore's Law has put the energy use of datacenters on an unsustainable path. Datacenters are already a significant fraction of worldwide electricity use, with application demand scaling at a rapid rate. We argue that substantial reductions in the carbon intensity of datacenter computing are possible with a software-centric approach: by making energy and carbon visible to application developers on a fine-grained basis, by modifying system APIs to make it possible to make informed trade offs between performance and carbon emissions, and by raising the level of application programming to allow for flexible use of more energy efficient means of compute and storage. We also lay out a research agenda for systems software to reduce the carbon footprint of datacenter computing.

Evaluation of Antimicrobial Activity of Kitaibelia vitifolia Extract against Proven Antibiotic-Susceptible and Multidrug-Resistant (MDR) Strains of Bacteria of Clinical Origin.

The goal of the present research was to screen the antimicrobial activity of an ethanolic extract of Kitaibelia vitifolia against 30 multidrug-resistant (MDR) bacterial strains isolated from healthcare-associated infections. Minimum inhibitory concentrations (MICs) of the samples against the tested bacteria were determined using the microdilution method. MDR bacterial strains were characterized using standard biochemical tests and the commercial identification systems API 20 NE and API 20 E as: Klebsiella spp. (18 isolates-I); methicillin-resistant Staphylococcus aureus (MRSA)-3; Acinetobacter spp.-3; Pseudomonas aeruginosa-5; vancomycin-resistant Enterococcus (VRE)-1. The sensitivity of isolated bacterial strains was determined using the disc diffusion method against 25 commonly used antibiotics. The highest level of sensitivity to K. vitifolia extract was confirmed in 88.89% of Klebsiella spp. isolates, E. coli ATCC 25922, two strains of MRSA (1726, 1063), Acinetobacter spp. strain 1578, and VRE strain 30, like Enterococcus faecalis ATCC 29212 (MIC =< 2.44 μg/mL). The lowest sensitivity was exhibited by 75.00% of Acinetobacter spp. (strains 1577 and 6401), where the highest values for MICs were noted (1250 μg/mL). The results indicate that the extract of K. vitifolia could be a possible source for creating new, efficient, and effective natural medicines for combat against MDR strains of bacteria.

Molecular Epidemiology genes detection of Klebsiella pneumoniae Clinical Isolates from the Adult Patients with Comorbidities in Baghdad hospitals

Background Klebsiella species is one of the most worldwide opportunistic pathogens in the world that cause infections at multiple sites like lung, urinary tract, bloodstream, wound or surgical site, and brain. Klebsiella pneumoniae has many types of virulence factors that give the bacteria the ability to invade the host and cause infections. Methods A total of (105) human clinical samples were collected from different patients with different cases. Klebsiella pneumonia were isolated from 30 samples. The bacteria were identified by biochemical tests and certified by using VITEK 2 system and genetically by amplify using PCR for 16 rRNA gene. One of the Klebsiella pneumoniae virulence factors is the capsule that is responsible for the macrophage resistance and its also responsible for serotype. So to confirm the strains isolated, PCR Gene detection of, MagA, K2A, RmpA, and Kfu, where (magA, k2A, rmpA, kfu) gens are responsible for capsule synthesis of Klebsiella pneumonia have been done. Results There were 30 strains of Klebsiella pneumoniae were identified. Epidemiological studies show that infections are preceded by gastrointestinal colonization, with the gastrointestinal tract being the most important reservoir for transmission. The primary bacterial isolates were identified based on their cultural, microscopic, and biochemical traits. The API system (API 20E) and VITEK 2 systems were used to confirm the identity of the bacterial isolates. Colony morphology revealed that Klebsiella pneumonia was Gram-negative, non-motile, microscopic straight rods grouped singly or in pairs. Biochemical tests showed that neutral end products prevailed over acidic end products, with Klebsiella producing negative results for indole. The Kligler Iron Agar (KIA) test showed that Klebsiella isolates changed the color of the slant and butt, producing an acidic slant and acid butt, along with gas production. The urease test for Klebsiella distinguishes between Enterobacter and Klebsiella isolates, as Klebsiella can manufacture the urease enzyme. The indole test distinguishes K. pneumonia from K. pneumonia from K. mobiliz. The API 20E system was used for biochemical testing, and the VITEK 2 system was used for identification. The VITEK 2 system was used to test for biochemical and antibiotic susceptibility, and the results supported the findings from morphological and biochemical analyses. PCR amplification was used for molecular detection, and all nine isolates tested positive for the magA and k2A genes. The ferric iron uptake system gene (kfu) and the extracapsular polysaccharide synthesis regulator gene (rmpA) were amplified using specific primer pairs. The VITEK 2 system demonstrated promising outcomes for K. pneumoniae identification. However, standard diagnostic methods are costly and time-consuming. Conclusions Navigating the complex landscape of infectious diseases, collaborative research projects have become indispensable in unraveling the mysteries surrounding pathogens. The project in Baghdad, having stood as a shining example of such collaboration, provided valuable insights into the identification, characterization, and molecular aspects of Klebsiella pneumoniae strains. The findings, have not only contributed to the scientific community's understanding of bacterial infections but also paved the way for enhanced strategies in diagnosis and treatment.

Using Protein Fingerprinting for Identifying and Discriminating Methicillin Resistant Staphylococcus aureus Isolates from Inpatient and Outpatient Clinics.

In hospitals and other clinical settings, Methicillin-resistant Staphylococcus aureus (MRSA) is a particularly dangerous pathogen that can cause serious or even fatal infections. Thus, the detection and differentiation of MRSA has become an urgent matter in order to provide appropriate treatment and timely intervention in infection control. To ensure this, laboratories must have access to the most up-to-date testing methods and technology available. This study was conducted to determine whether protein fingerprinting technology could be used to identify and distinguish MRSA recovered from both inpatients and outpatients. A total of 326 S. aureus isolates were obtained from 2800 in- and outpatient samples collected from King Faisal Specialist Hospital and Research Centre in Riyadh, Saudi Arabia, from October 2018 to March 2021. For the phenotypic identification of 326 probable S. aureus cultures, microscopic analysis, Gram staining, a tube coagulase test, a Staph ID 32 API system, and a Vitek 2 Compact system were used. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), referred to as protein fingerprinting, was performed on each bacterial isolate to determine its proteomic composition. As part of the analysis, Principal Component Analysis (PCA) and a single-peak analysis of MALDI-TOF MS software were also used to distinguish between Methicillin-sensitive Staphylococcus aureus (MSSA) and MRSA. According to the results, S. aureus isolates constituted 326 out of 2800 (11.64%) based on the culture technique. The Staph ID 32 API system and Vitek 2 Compact System were able to correctly identify 262 (80.7%) and 281 (86.2%) S. aureus strains, respectively. Based on the Oxacillin Disc Diffusion Method, 197 (62.23%) of 326 isolates of S. aureus exhibited a cefoxitin inhibition zone of less than 21 mm and an oxacillin inhibition zone of less than 10 mm, and were classified as MRSA under Clinical Laboratory Standards Institute guidelines. MALDI-TOF MS was able to correctly identify 100% of all S. aureus isolates with a score value equal to or greater than 2.00. In addition, a close relationship was found between S. aureus isolates and higher peak intensities in the mass ranges of 3990 Da, 4120 Da, and 5850 Da, which were found in MRSA isolates but absent in MSSA isolates. Therefore, protein fingerprinting has the potential to be used in clinical settings to rapidly detect and differentiate MRSA isolates, allowing for more targeted treatments and improved patient outcomes.

Bacterial Species Causing Secondary Pneumonia Infection in Pregnant Women with COVID-19.

Sputum samples were taken from pregnant women infected with the COVID-19, where the study was conducted on 112 cases, and the results showed that 87 cases developed secondary bacterial infections at a rate of 78% and 25 cases were negative by 22%. The samples were cultured on solid media and incubated at 37°C. Then the samples were diagnosed by biochemical tests and using the API system. Six species of bcteria have been isolated (S. aureus, K.pneumonia P. auroginosa, H. influenza, S. pneumonia S. pyogens) by 31, 29, 12, 10, 7, and 5 isolates, respectively, where the result showed that the most common types of pneumonia were S. aureus with a percentage of 34%, followed by K. pneumonia with a percentage of 29%. The sensitivity of the isolates to eight types of common antibiotics was tested (Erythromycin, Trimethoprim, Ampicillin, Tetracycline, Augmentin, Nitrofurantoin, Meropenem, and Amikacin), where the isolates showed a high resistance to antibiotics (Erythromycin, Trimethoprim, Ampicillin), a high sensitivity of 100% to the Nitrofurantoin, and an average sensitivity to other antibiotics.

Scalable Data Processing Platform for Earth Observation Data Repositories

Earth observation (EO) satellite data is essential to environmental monitoring. At a national and regional level, the open data cubes harness the power of satellite data by providing application programming interfaces and services to the end-users. The volume and the complexity of satellite observations are increasing, demanding novel approaches for data storing, managing, and processing. High-performance computing (HPC) and cloud platforms may improve Big EO data processing performance. However, it is necessary to consider several vital aspects for efficient and flexible EO data processing, such as the interoperability from cloud-HPC and EO data repositories, automatic provisioning and scaling of cloud-HPC resources, cost-effectiveness, support of new EO data formats and open-source packages, or linkage with data cube platforms. The article proposes a scalable EO data processing platform interoperable from cloud-HPC and EO data repositories. The platform enables linking any data repository supporting web coverage service or SpatioTemporal Asset Catalog Application Programming Interfaces (STAC-API), and any cloud or HPC resource supporting scheduling system API for providing access to the cluster backends.

Development of an associated particle imaging system with pixel SiC detectors

The elemental analysis method, with fast neutron associated particle imaging, holds immense potential in identifying concealed explosives, nuclear materials, and container security inspection, owing to its superior sensitivity and three-dimensional analysis capability compared to traditional neutron activation analysis technology. However, the temperature and irradiation fragility of the traditional associated particle detector curtails its usage in harsh environments such as space missions. The adoption of wide-bandgap semiconductor detectors, such as SiC, is expected to address this constraint. In this work, an associated particle imaging system based on a 4 × 4 SiC pixel detector was designed, built, and tested. The SiC detector exhibited consistent energy resolution and time performance between 25–150 °C. The built API system achieved an intrinsic time resolution of 1.8 ns and spatial resolution of at least 10 cm in the neutron flight direction. By utilizing the coincidence time spectrum and pixel SiC detector, the built system can provide the element content distribution in three-dimensional space. The results demonstrate the feasibility of employing SiC detectors in associated particle imaging systems and pave the way for developing a new type of sealed neutron tube with a prolonged lifetime and high environmental adaptability.

Evaluation of the Effect of Crude Extracts of Fenugreek (Trigonella Foenum Graecum) on Resistant Isolates of Klebsiella Pneumoniae, and Pseudomonas Aeruginosa

The recent appearance of resistant bacterial species has generated alarm. Medicinal plants are still a good way to get bioactive substances with medical promise, and they can use to get a wide range of antibacterial substances. As an alternative to chemical agents, evaluated the effect of crude fenugreek seed extracts on resistant isolates of Klebsiella pneumoniae and Pseudomonas aeruginosa. This study was conducted from 2021 to 2022 in Karbala. Sputum samples were collected from 455 patients with severe COVID-19, samples were examined directly and cultured on microbiological culture media, and growing colonies were distinguished and preparing smears staining with Grams stain, API system for biochemical and fermentation differentiation, then disc diffusion method to distinguish the resistant isolates in accordance with clinical and laboratory standards institute 2020. Extraction method using the Soxhlet method for eight hours. Electric rotatory evaporators were used to evaporate the remaining solvents, then crude extracts were collected. The GC-MS is used to determine bioactive compounds. Determination of antimicrobial effects by well diffusion method using different concentrations of each extract as (25, 50, 100, and 150mg/ml). The isolated microorganisms were P. aeruginosa comprising 21 (4.6%) including 4 (0.9%) as resistant isolates. However, 50 (11.0%) of the isolates were K. pneumoniae, with 6 (1.3% being resistant) the remaining were other microorganisms. Methanolic extract indicated the heights contains of bioactive compounds followed by chloroform and aqueous respectively. Fenugreek seed methanolic extract at a MIC of 100 mg/ml significant effect on tested microorganisms, with inhibition zones of 10.67 and 9.67 mm, respectively. The methanolic extract contains the most bioactive compounds and the most effective antibacterial agent, followed by chloroform and aqueous extract respectively. P. aeruginosa was the most resistant isolate.

Smart Parking Application

Abstract: Today, most parking lots operate on a manual parking system, where vehicles enter, find an available space, pay for parking at the counter, etc. In contrast, parking booking apps allow users to save time and money. People use these sites to provide parking for Light Motor Vehicles (LMV). Users and owners of parking spaces will be located directly using the Global Positioning System (GPS) API. In addition, the software will assign a unique identification number (UID) to the parking space and display a live list of available parking spaces. This paper will discuss building an application that covers all the features and aspects of vehicle parking and the basics used to build it.