Identification Stage Research Articles

SDS-Net: A Synchronized Dual-Stage Network for Predicting Patients Within 4.5-h Thrombolytic Treatment Window Using MRI.

Timely and precise identification of acute ischemic stroke (AIS) within 4.5h is imperative for effective treatment decision-making. This study aims to construct a novel network that utilizes limited datasets to recognize AIS patients within this critical window. We conducted a retrospective analysis of 265 AIS patients who underwent both fluid attenuation inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) within 24h of symptom onset. Patients were categorized based on the time since stroke onset (TSS) into two groups: TSS ≤ 4.5 h and TSS > 4.5 h. The TSS was calculated as the time from stroke onset to MRI completion. We proposed a synchronized dual-stage network (SDS-Net) and a sequential dual-stage network (Dual-stage Net), which were comprised of infarct voxel identification and TSS classification stages. The models were trained on 181 patients and validated on an independent external cohort of 84 patients using metrics of area under the curve (AUC), sensitivity, specificity, and accuracy. A DeLong test was used to statistically compare the performance of the two models. SDS-Net achieved an accuracy of 0.844 with an AUC of 0.914 in the validation dataset, outperforming the Dual-stage Net, which had an accuracy of 0.822 and an AUC of 0.846. In the external test dataset, SDS-Net further demonstrated superior performance with an accuracy of 0.800 and an AUC of 0.879, compared to the accuracy of 0.694 and AUC of 0.744 of Dual-stage Net (P = 0.049). SDS-Net is a robust and reliable tool for identifying AIS patients within a 4.5-h treatment window using MRI. This model can assist clinicians in making timely treatment decisions, potentially improving patient outcomes.

Methodology for extracting narratives from social media big data

Purpose: of the article is to present the experience in developing and testing the methodology for extracting a system of narratives on a socially significant phenomenon from authentic social network big data (using the example of narratives about COVID-19 vaccination in the Russian social network VKontakte during the pandemic).Methods: of automated data analysis were used by the tools of the PolyAnalyst analytical platform: topic modeling (PLSA method), text indexing algorithms with the sentence identification stage, clustering, data aggregation, data normalization, calculation of a quantitative index. The calculation of the measure of proximity of keywords using the Python, partial manual markup and data validation were also carried out.Results: 4.5 million messages relevant to the topic of COVID-19 vaccination published in VKontakte from 01.01.2020 to 01.03.2023 were reduced to 237 stable narratives. A popularity index was calculated for each narrative. For example, the following narrative turned out to be the most popular: “Employers put pressure on people to get vaccinated” (it was supported by 76,118 texts). As a result of the study, a dataset was obtained, including 237 narratives.Conclusions and Relevance: the developed toolkit is universal: the methodology can be adapted to any relevant topic, requiring only adjustments to the input parameters of thematic modeling. The obtained dataset is planned to be introduced into scientific circulation as an up-to-date material for studying public opinion on vaccination in Russia. The results contribute to international research on public opinion and communication in crises and can serve as a basis for practical actions aimed at improving the quality of public communications and decision-making at all levels of government.

Promoting Language and Scientific Literacy Through Children's Literature: A Systematic Literature Review

ABSTRACT Children's literature is widely recognized for its potential to enhance student development. Despite existing research and publications on the topic, a thorough study on integrating children's literature into language and scientific literacy has not been widely conducted. This systematic literature review aimed to comprehensively explore current literature on innovative strategies and the effects of integrating children's literature to improve language and scientific literacy. Utilizing the Systematic Literature Review (SLR) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methods, this review analyzed 54 studies from the Scopus database spanning 2010 to 2024. The Publish or Perish and VOSviewer applications supported the identification, screening, eligibility, and inclusion stages. Scopus-indexed articles totaling 275 were initially identified and filtered into 54 relevant pieces. The findings highlight that integrating children's literature into science education effectively bridges language and scientific literacy, enhancing cognitive development and comprehension. The potential of this interdisciplinary teaching model suggests practical applications for educators seeking to enrich their instructional strategies by employing diverse literary forms such as informational texts, biographies, comics, dramas, and science fiction. Ultimately, this review emphasizes children’s literature's significant role in advancing language and scientific literacy in educational contexts. Keywords: children’s literature, language literacy, scientific literacy

Management Challenges in Implementing the Merdeka Curriculum

This study focuses on implementing the Merdeka curriculum from the perspective of its management challenges, exploring the experiences and perceptions of stakeholders related to implementing the Merdeka Curriculum at MAN Jombang and using a qualitative study, interviews, observations, and documentation as data collection techniques. Data analysis used data reduction, coding, and theme identification stages. To ensure data validity, the researcher applied source triangulation. The study findings indicate that implementing the Merdeka Curriculum faces challenges related to technology access and teacher readiness. Technology limitations, including unequal access to devices, hinder effective project-based learning, and administrative staff acknowledge the need for infrastructure improvements. Students appreciate the curriculum's focus on personal interests but need more guidance to navigate independent learning. Teachers have received training in project-based learning, but many teachers need help implementing new methods effectively. Although progress has been made, continued support, increased resources, and collaborative efforts among stakeholders are critical to successfully implementing the Merdeka Curriculum and improving educational outcomes. Limitations of the study include limited observation time and potential bias in participant responses. This study has yet to explore the long-term impacts of implementing the Merdeka Curriculum. Stakeholder collaboration research can provide deeper insights into practical strategies to address challenges and improve educational outcomes in Islamic education.

Proof of concept study on early forecasting of antimicrobial resistance in hospitalized patients using machine learning and simple bacterial ecology data.

Antibiotic resistance in bacterial pathogens is a major threat to global health, exacerbated by the misuse of antibiotics. In hospital practice, results of bacterial cultures and antibiograms can take several days. Meanwhile, prescribing an empirical antimicrobial treatment is challenging, as clinicians must balance the antibiotic spectrum against the expected probability of susceptibility. We present here a proof of concept study of a machine learning-based system that predicts the probability of antimicrobial susceptibility and explains the contribution of the different cofactors in hospitalized patients, at four different stages prior to the antibiogram (sampling, direct examination, positive culture, and species identification), using only historical bacterial ecology data that can be easily collected from any laboratory information system (LIS) without GDPR restrictions once the data have been anonymised. A comparative analysis of different state-of-the-art machine learning and probabilistic methods was performed using 44,026 instances over 7 years from the Hôpital Européen Marseille, France. Our results show that multilayer dense neural networks and Bayesian models are suitable for early prediction of antibiotic susceptibility, with AUROCs reaching 0.88 at the positive culture stage and 0.92 at the species identification stage, and even 0.82 and 0.92, respectively, for the least frequent situations. Perspectives and potential clinical applications of the system are discussed.

Risk Analysis of Conveyor Material Handling in the Production Process of Crude Palm Oil and Palm Kernel Oil with FMECA and FTA Methods

Oil palm is one type of plantation crop that occupies an important position in the agricultural sector and the plantation sector, and is a mainstay commodity of Indonesia whose development is very rapid. Good Material Handling is minimizing material handling costs, minimizing disruption or risk and delays in the production process. One of the companies engaged in the palm oil industry is PT. BSI which is a company engaged in the palm fruit processing industry. The products produced are crude palm oil (CPO), kernel, and fiber which implement a material handling system. However, the material handling process that is applied still experiences disturbances whose risks have an impact on the production process. So that researchers want to know the value of material handling risks that occur at PT.BSI with FMECA and FTA methods. The results of this study are at the risk identification stage using the FMECA method obtained six lists of risks that have an RPN value above 200 this is included in the very high and Unacceptable category. Then after further analysis using the FMECA method also that the six risks have decreased where the RPN value is less than 180 so that it is included in the high and tolerable categories. Then for the FTA analysis that the problem of these risks, namely conveyor trips and conveyor blockages, needs to be checked on a regularly scheduled conveyor leaf and implement a routine maintenance schedule and provide training to operators before carrying out work.

Use of logical-graphic methods of analysis of risks in the water supply systems of thermal power plants

Today's urgent issues in the water supply of thermal power plants are increasing the efficiency of production processes, reducing energy costs, introducing innovative technologies, as well as ensuring environmental safety and sustainable development. The analysis of the causes of industrial accidents and damages shows that the occurrence and development of accidents, as a rule, is characterized by a combination of random local events that occur with different frequency at different stages of the accident. Logical-graphic methods of fault trees and events are used to identify cause-and-effect relationships between these events. In order to investigate the identification and analysis of cause-and-effect relationships that contribute to the occurrence of failures in the water treatment system using a fault tree for the further development of effective measures to minimize risks and increase the reliability and safety of the water supply systems of thermal power plants, it is proposed to use logical-graphic methods on the previous stage of identification and assessment of technogenic risks. The analysis of the obtained results showed that: 1) the fault tree is an instantaneous display of the system at a certain moment in time t and is a graphical representation of the cause-and-effect relationships obtained as a result of identifying dangerous situations in the system in a forward and reverse order in order to find possible causes of their occurrence; 2) a complex final event should be determined using the so-called «tree top». The top of the tree consists of the final event and additional undesirable events, including potential accidents and dangerous conditions that are direct causes of the final event. The final event and additional events must be carefully defined and all the most important causes of the final event identified. Therefore, the first five heuristic rules must be used when building a rejection tree; 3) construction of a fault tree using heuristic rules can be used when considering any element of the system, however, to simplify cumbersome constructions, it is more appropriate to build trees only for the main elements that cause emergency situations and have a negative impact on people's lives and health, as well as on environment; 4) when building a fault trees for the technological scheme of water treatment, it is possible to specify and quantify events, to find events with a very high probability and a very low probability, to limit the further development of the fault trees and to establish the causes of failures at each stage of the water treatment process; 5) the rejection tree can be used for quantitative assessments using Markov models.

The HZB F2X‐Facility—An Efficient Crystallographic Fragment Screening Platform

ABSTRACTCrystallographic fragment screening (CFS) has recently matured into an important method for the early stages of drug discovery projects. It is based on high‐throughput structure determination and thus requires a high degree of automation as well as specialized workflows and robust analysis tools. Consequently, large‐scale research facilities such as synchrotrons have embraced the method, and developed platforms to perform CFS campaigns with the help of crystallography experts and specific tools. The BESSY II synchrotron, operated by the Helmholtz–Zentrum Berlin (HZB), is one of these synchrotron facilities that offer a CFS platform, named the F2X‐facility. Here, the specialized F2X workflow is described along with the relevant differences to other existing CFS platforms, and the ongoing developments aimed at supporting users of the facility. The different stages of a CFS campaign including requirements, beamline capabilities, and the software environment are detailed and explained. A unique F2X‐GO kit is featured, which allows users the possibility of performing all sample preparation in their home laboratories. Furthermore, at the HZB a computational workflow has been built to support users beyond the hit identification stage. The advantages of the F2X‐facility at HZB are described and references are provided to successfully conduct CFS.

PSLBII-27 Investigating the emergence of antimicrobial resistance in Mycoplasma bovis from feedlot cattle

Abstract Bovine respiratory disease (BRD) is the most significant disease affecting feedlot cattle in North America. Mycoplasma bovis is among the main BRD pathogenic bacteria. Infection control of M. bovis is hampered by a lack of effective vaccines and increasing antimicrobial resistance (AMR). Integrative and conjugative elements (ICEs) have been documented as a mechanism of horizontal gene transfer (HGT) in mycoplasmas. Mycoplasma ICEs (MICE) do not carry AMR genes but have been associated with the HGT of genes that confer AMR through point mutations. Hence, there is a need to gain a better understanding of the role of MICE in M. bovis AMR. Randomly selected M. bovis isolates (n = 54) collected from Western Canadian feedlot cattle (AB and SK) underwent whole-genome sequencing by short-reads (Illumina), with a subset (n = 6) selected based on AMR and ICE profiles for Oxford Nanopore long-read sequencing. Sequences were screened for the presence of MICE. MICE circularization (cMICE) is the first step of conjugation, and an in-house collection of 472 M. bovis field isolates (feedlot cattle just from AB) was screened by PCR for the presence of cMICE as a proxy for conjugation capabilities. Environmental conditions that could impact cMICE quantity, including growth phase, temperature, cell density, pH, starvation, UV light, CO2 concentration, and sub-inhibitory concentrations of mitomycin C were investigated by qPCR, followed by growth of M. bovis isolates under the most favorable conditions identified for cMICE-detection and screening by a high throughput qPCR method that eliminated the need for DNA extraction. Nineteen different mating pairs were carried out and 4 different environmental factors were tested for their possible impact on conjugation occurrence (incubation time, incubation volume, conjugation parent proportions, atmosphere). All isolates carried MICEs with 23/54 having a structure/sequence comparable to previously described functional MICE in mycoplasma, 43/54 possessing non-functional ICE, and 23 having >1 ICE. The presence of more than one MICE/genome suggests a lack of MICE (entry) exclusion systems in M. bovis, a trait that may promote the flow of genetic information among M. bovis isolates including AMR. Even though some factors including growth phase (early stationary phase) and pH (9.0) generated greater levels (≥1.5 fold) of cMICE, none of the examined parameters were statistically significant. However, as early stationary growth phase generated the greatest cMICE quantity (2.3 fold), M. bovis isolates were cMICE-screened at this growth stage for the expeditious identification of M. bovis conjugation candidates. A total of 25.7% M. bovis were cMICE-positive. Of 19 mating pairs tested, transconjugants were recovered from only 2 different mating pairs, possibly reflective of the need for further optimization of conjugation conditions.

Teaching experience for process identification using first‐order‐plus‐time‐delay models

AbstractThis paper introduces an instructional framework for process identification, combining theoretical concepts with practical laboratory exercises, focusing particularly on the identification of first‐order‐plus‐time‐delay models. Our methodology emphasizes guiding students through the various stages involved in the system identification process, namely mastering techniques, such as data acquisition and preprocessing, identification and validation stages, and method comparison. The laboratory assignment is structured into three distinct stages: an initial prelab task working with simulated data, the hands‐on work with laboratory equipment, and the assignment report writing and oral presentation. The assessment of students' learning outcomes is conducted using a detailed rubric. Feedback from a focus group interview indicates that the majority of students appreciated the well‐balanced content, highlighting a strong link between theoretical concepts and practical application.

The impact of screen time on children’s well-being development: a scoping review

In the era of digitalisation, working parents today rely more than ever on electronic gadgets as digital babysitters and device-led playtime to entertain their children. The study suggests that parents and their interactions with the home environment may contribute to shaping children’s screen time. However, many struggle to keep up with the immense variety of mobile applications easily downloaded online. Thus, long screen time exposure inhibits children’s ability to engage actively in physical activities as well as affects children’s well-being development. This scoping review aims to identify parental perceptions of children’s exposure to screens for a long period of time and how screen time affects children’s well-being. Five databases, including Scopus, Web of Science (WOS), Bielefeld Academic Search Engine (BASE), Education Resources Information Center (ERIC), and ScienceDirect, were used in this research. In the initial identification stage, 218 articles were identified from the mentioned databases above. However, there were only 81 articles found to be assessed for eligibility. A total of 34 articles are eligible for analysis and reference after the exclusion and inclusion process for data collection. The findings show that the increased exposure to screens raised concerns about potential negative effects on children’s emotions and behaviour.

LOGISTICS SERVICE INFORMATION SYSTEM AUDIT USING COBIT 5 FRAMEWORK

KBN Prima Logistik is a subsidiary affiliated with PT. Kawasan Berikat Nusantara which operates in the field of logistics services. In carrying out its business processes, it has implemented an Information System in the form of using desktop-based supporting business applications (Client Server). Some of the current problems are that the applications used are not functioning optimally, IT Governance procedures are not well defined by standards, there is no IT Division in the Organizational Structure that is responsible for IT management and there is a shortage of competent human resources in the IT field. Therefore, it is necessary to measure the Maturity Level and Capability Level of IT governance. So, research was carried out using the COBIT 5 framework as an Information System Audit model. The research method goes through the stages of problem identification, literature study, and domain determination which focuses on the DSS and MEA domains. The data collection method was through interviews and distributing questionnaires filled in by 20 respondents. The research results show that the Maturity Level value in the DSS and MEA domains shows an average value of 1.94 or 194%. Capability Level is still at Level 2 (Managed Process) from the expected Target Level 3 (Established Process). This indicates that although the IT processes has been run and implemented regularly with planning and monitoring according to business process objectives, but the management is not yet optimal and not well standardized. The results of the gap assessment show that the average gap value is 1.06.

Current Tasks in Identifying Invalid Events in Critical Information Infrastructure

The purpose of the study is to develop an approach for identifying and processing invalid events in critical information infrastructure (CII) based on the concepts of taxonomy and categorization. The approach aims to improve the efficiency of identifying, classifying, and managing information security (IS) incidents. The article addresses the current tasks of ensuring the required level of CII protection and minimizing the negative consequences of information security incidents resulting from invalid events. The identification of these events is associated with the complexity of detecting such events, the need to process large volumes of data, insufficient speed in detecting IS events, as well as technological limitations.The relevance of identifying and classifying invalid events in information security, especially for CII, is driven by the need for timely detection and response to incidents that could lead to negative consequences. Understanding the nature and characteristics of such events allows for effective system protection and prevention of significant damage. To enhance the effectiveness of ensuring security, it is necessary to identify the class of invalid events among the numerous information security events by considering the characteristics that define invalid events.The novelty of the proposed approach lies in solving the task of identifying the class of invalid information security events based on taxonomy methods, involving the use of event categorization tools with the attributes of invalid events.Materials and methods. The approach to identifying invalid events in CII, based on the principles of information security event taxonomy, was used to solve the task. It was shown that identifying invalid information security events is directly related to solving the problem of searching for and analyzing their attributes, which represent the characteristics or parameters used to describe and classify security incidents. Based on the key principles of taxonomy, a model of the structure of the set of invalid events was developed to determine the characteristics that can be the basis for classifying invalid events. The process of identifying invalid information security events includes a sequence of stages: taxonomy, categorization, and classification, with appropriate methods and tools implemented at each stage.Results. Approaches to identifying invalid events in CII have been analyzed. Problems related to large data volumes, the complexity of event processing, the considerable time required for their detection, and technological limitations were considered. It was shown that the concept of taxonomy and categorization allows for effective identification and classification of information security incidents, ensuring efficient processing and response. The feasibility of applying taxonomy for describing and identifying the attributes of invalid events was justified, contributing to the development of effective protection strategies and improving security levels. A generalized scheme for processing invalid events was proposed, including a set of interconnected stages of identification, categorization, impact assessment, response, documentation, and analysis. An algorithm for structured description and classification of incidents was developed, allowing for more accurate and timely responses to information security threats.Conclusion. The results obtained increase the effectiveness of solving the task of classifying information security incidents by identifying invalid events, which reduces the level of negative consequences of incidents and enhances the security of CII objects.

Digital Dental Biometrics for Human Identification Based on Automated 3D Point Cloud Feature Extraction and Registration.

Intraoral scans (IOS) provide precise 3D data of dental crowns and gingival structures. This paper explores an application of IOS in human identification. We propose a dental biometrics framework for human identification using 3D dental point clouds based on machine learning-related algorithms, encompassing three stages: data preprocessing, feature extraction, and registration-based identification. In the data preprocessing stage, we use the curvature principle to extract distinguishable tooth crown contours from the original point clouds as the holistic feature identification samples. Based on these samples, we construct four types of local feature identification samples to evaluate identification performance with severe teeth loss. In the feature extraction stage, we conduct voxel downsampling, then extract the geometric and structural features of the point cloud. In the registration-based identification stage, we construct a coarse-to-fine registration scheme in order to realize the identification task. Experimental results on a dataset of 160 individuals demonstrate that our method achieves a Rank-1 recognition rate of 100% using complete tooth crown contours samples. Utilizing the remaining four types of local feature samples yields a Rank-1 recognition rate exceeding 96.05%. The proposed framework proves effective for human identification, maintaining high identification performance even in extreme cases of partial tooth loss.

Physical Characteristics and Somotatype of Myanmar National Hockey Players

Introduction: Performance in Field Hockey depends on different factors. Physical characteristics along with the Body shape and size of the players are one of them. The present study was aimed to find out the physical characteristics as well as Somatotypes of Myanmar National Field Hockey players. Methods: Twenty national Field Hockey players were measured for their height, weight, and Somatotype during their visit to a neighbuor country for a friendship tour. Health-Carter method (1967) was followed to calculate the Somatotype components. Results: The average Age, Height, and Weight of the Myanmar Hockey players of the present study were 21.7 (±1.1) yr, 164.8 (±4.6) cm, and 56.5 (±5.1) kg respectively. An average Somatotype of 1.7(±0.5)-3.8(±0.9)-2.9(±0.8) was observed for the present study. Conclusions: Most of the players (55 % ) were with Ectomorphic mesomorph body type, whereas 30 % of players were with Mesomorphic ectomorph body type. Rest were with Balanced mesomorph, Endomorphic mesomorph, and Mesomorph ectomorph body type. Compared to other countries, Myanmar Hockey players were shorter than their international counterparts. Thus, the height of the players should be considered during the selection of players at an early stage of Talent identification for good performance in Field Hockey.

Tindak Tutur Ekspresif dalam Novel Ranah 3 Warna karya Ahmad Fuadi

Research on expressive speech acts in novels is an important study to learn and understand. This research intends to discuss the types and functions of expressive speech in the novel Ranah 3 Warna by Ahmad Fuadi. The study of this novel is a descriptive method of qualitative research. The data of this research is in the form of characters' speech in the novel which relies on expressive speech acts. Data was collected using documentation techniques with the assistance of corpus linguistik technology, namely the korpus nusantara. The data was analyzed using computational linguistics technique through the stages of identification, classification, interpretation, and conclusion. This research found: (1) types of expressive speech acts: congratulating and thanking, expressing pleasure and anger, apologizing, praising, criticizing, insinuating, complaining, and insulting, (2) functions of expressive speech acts: convivial, collaborative, conflictive, and competitive functions.

Exploiting the capacity of deep networks only at the training stage for nonlinear black-box system identification

To benefit from the modeling capacity of deep models in system identification without worrying about inference time, this study presents a novel training strategy that uses deep models only during the training stage. For this purpose, two separate models with different structures and goals are employed. The first one is a deep generative model aiming at modeling the distribution of system output(s), called the teacher model, and the second one is a shallow basis function model, named the student model, fed by system input(s) to predict the system output(s). That means these isolated paths must reach the same ultimate target. As deep models show a great performance in modeling highly nonlinear systems, aligning the representation space learned by these two models makes the student model inherit the teacher model’s approximation power. The proposed objective function consists of the objective of each student and teacher model, adding up with a distance penalty between the learned latent representations. The simulation results on three nonlinear benchmarks show a comparative performance with examined deep architectures applied on the same benchmarks. Algorithmic transparency and structure efficiency are also achieved as byproducts.

Optimized Radio Frequency Footprint Identification Based on UAV Telemetry Radios.

With the widespread use of unmanned aerial vehicles (UAVs), the detection and identification of UAVs is a vital security issue for the safety of airspace and ground facilities in the no-fly zone. Telemetry radios are important wireless communication devices for UAVs, especially in UAVs beyond the visual line of sight (BVLOS) operating mode. This work focuses on the UAV identification approach using transient signals from UAV telemetry radios instead of the signals from UAV controllers that the former research work depended on. In our novel UAV Radio Frequency (RF) identification system framework based on telemetry radio signals, the EC-α algorithm is optimized to detect the starting point of the UAV transient signal and the detection accuracy at different signal-to-noise ratios (SNR) is evaluated. In the training stage, the Convolutional Neural Network (CNN) model is trained to extract features from raw I/Q data of the transient signals with different waveforms. Its architecture and hyperparameters are analyzed and optimized. In the identification stage, the extracted transient signals are clustered through the Self-Organizing Map (SOM) algorithm and the Clustering Signals Joint Identification (CSJI) algorithm is proposed to improve the accuracy of RF fingerprint identification. To evaluate the performance of our proposed approach, we design a testbed, including two UAVs as the flight platform, a Universal Software Radio Peripheral (USRP) as the receiver, and 20 telemetry radios with the same model as targets for identification. Indoor test results show that the optimized identification approach achieves an average accuracy of 92.3% at 30 dB. In comparison, the identification accuracy of SVM and KNN is 69.7% and 74.5%, respectively, at the same SNR condition. Extensive experiments are conducted outdoors to demonstrate the feasibility of this approach.

A systematic literature review on machine learning and deep learning-based covid-19 detection frameworks using X-ray Images

Coronavirus is an endangered disease to kills more than millions of people, but it has also put tremendous pressure on the whole medical system. The initial stage of identification of COVID-19 is necessary to isolate the patients with positive cases in order to stop the disease from spreading. The amalgamation of imaging techniques and deep learning algorithms takes less time and leads to more accurate outcomes for COVID-19 detection. Deep learning techniques have been employed by scientists to identify coronavirus infection in lung images during the COVID-19 worldwide epidemic. In this review, a review of the Covid-19 detection framework based on machine learning and deep learning techniques using X-ray images is done. First, the review of existing Covid-19 detection models is done. For this purpose, a detailed literature survey is carried out on Covid-19 detection papers from 2019 to 2023. Following the literature survey, the pre-processing procedures, the segmentation process, and the classification techniques used for Covid-19 detection using deep learning, machine learning, and optimization algorithms are reviewed and categorized. After that, the dataset and the implementation tool which are utilized for Covid-19 detection works are analyzed and grouped. Finally, the performance metrics validation such as accuracy, recall, F1-score, NPV, precision, sensitivity, and specificity is carried out. The research gaps in the existing Covid-19 detection techniques are provided further as references to aid in future works.

Penilaian Risiko menggunakan Metode Failure Mode and Effects Analysis (FMEA) terkait Kontaminasi Silang pada Area Pengemasan di Industri Farmasi “XYZ”

Cross-contamination occurs due to other previously produced products, other rooms or areas producing different products, and particles from machinery or equipment. Quality risk management can be a preventive measure in reducing the potential for cross-contamination that occurs in the pharmaceutical industry. One part of quality risk management is risk assessment. One of the most common and effective methods is the Failure Mode and Effect Analysis (FMEA) method. This risk assessment is carried out using the stages of risk identification, risk analysis, and risk evaluation using categorization based on the Risk Priority Number (RPN). Based on the risk assessment using the FMEA method regarding cross-contamination in the Packaging area in the "XYZ" pharmaceutical industry, it can be concluded that the results of observations and interviews with operators and Packaging Supervisors identified 19 risks which were categorized as low, medium, high, and very high. Based on the results of the risk assessment obtained, the risk of cross-contamination that needs to be controlled is those with an RPN value of 30 ≤ RPN ≤ 60 (medium); 75 ≤ RPN ≤ 100 (high); RPN ≥ 150 (very high). Therefore, the "XYZ" pharmaceutical industry needs to determine and implement risk control measures. By adequately controlling the potential risk of cross-contamination, it will ensure the safety and quality of the products produced.