bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association of carbapenem and multidrug resistance with the expression of efflux pump-encoding genes in Pseudomonas aeruginosa clinical isolates
Authors: Yousefi Shaghayegh , Nazari Maryam , Ramazanzadeh Rashid , Sahebkar Amirhossein , Safarzadeh Elham , Khademi Farzad 
Published: May 2023

AbstractEfflux pumps play an important role in the emergence of antibiotic-resistant Pseudomonas aeruginosa strains. The present study aimed to assess the expression of the MexAB-OprM, MexCD-OprJ, MexEF-OprN, and MexXY-OprM efflux pumps in carbapenem-resistant and multidrug-resistant (MDR) P. aeruginosa strains isolated from clinical specimens between June 2019 and January 2022 in Ardabil city. The presence of efflux pump-encoding genes, i.e. mexA, mexC, mexE, and mexY, was assessed using the polymerase chain reaction (PCR) technique in 48 carbapenem-resistant and MDR P. aeruginosa strains. Real-time reverse transcription PCR was employed to evaluate the expression levels of mexA, mexC, mexE, and mexY genes. All 48 carbapenem-resistant and MDR P. aeruginosa strains harbored efflux pump-encoding genes including mexA, mexC, mexE, and mexY according to the PCR results. Overexpression of the MexAB-OprM, MexCD-OprJ, MexEF-OprN, and MexXY-OprM efflux pumps was detected in 75% (n = 36), 83.3% (n = 40), 10.4% (n = 5) and 41.6% (n = 20) of the clinical isolates of P. aeruginosa, respectively. This study revealed that the presence and overexpression of efflux pumps are associated with the emergence of carbapenem-resistant and MDR P. aeruginosa strains. Therefore, research on efflux pump inhibitors of P. aeruginosa will be a worthwhile endeavor to increase the clinical efficiency of available antibiotics and prevent ensuing treatment failure.
Penentuan Posisi Iradiasi Batu Topaz Dengan Kapsul dan Pengarah Boron Karbida (B4C) di Reaktor RSG-GAS Menggunakan Program BATAN 2-DIFF
Authors: Susanti Fitri 
Published: May 2023

PENENTUAN POSISI IRADIASI BATU TOPAZ DENGAN KAPSUL DAN PENGARAH BORON KARBIDA (B4C) DI REAKTOR RSG-GAS MENGGUNAKAN PROGRAM BATAN-2DIFF. Secara rutin iradiasi pewarnaan batu topaz di reaktor RSG-GAS dilakukan pada fasilitas irradiation position (IP). Adanya permintaan target iradiasi lain pada fasilitas yang sama, menyebabkan perlu adanya pengembangan fasilitas iradiasi baru yang dapat digunakan sebagai pengganti fasilitas iradiasi batu topaz. Hal ini dilakukan pada grid berilium yang digunakan sebagai reflektor dalam reaktor. Lamanya waktu paruh akibat interaksi material pengotor teraktivasi dengan neutron termal dalam iradiasi batu topaz merupakan hal lain yang juga perlu dipertimbangkan. Pengembangan teknik iradiasi batu topaz dengan kapsul dan pengarah B4C menjadi solusi karena sifat B4C sebagai penyerap neutron yang baik. Keuntungan dari pengembangan posisi iradiasi pada berilium yang jauh dari daerah bahan bakar menurunkan risiko gangguan distribusi fluk neutron di teras reaktor akibat penggunaan material B4C. Selain itu, penggunaan material B4C akan menurunkan interaksi neutron termal dan memperbesar interaksi neutron cepat dalam pewarnaan batu topaz. Penentuan posisi grid berilium yang baik digunakan dalam iradiasi batu topaz dengan kapsul dan pengarah B4C perlu dilakukan untuk menjamin keselamatan operasi reaktor RSG-GAS. Penelitian dilakukan dengan perhitungan keselamatan neutronik menggunakan program komputer code BATAN-2DIFF (2 dimensi) dan WIMSD5 untuk generasi tampang lintang. Hasil perhitungan menunjukan iradiasi batu topaz dengan kapsul dan pengarah B4C tidak dapat dilakukan pada grid berilium yang berdekatan langsung dengan bahan bakar karena akan menghasilkan gangguan reaktivitas yang besar terhadap teras reaktor. Perubahan reaktivitas terkecil antara kapsul tanpa sekat dan dengan sekat dihasilkan oleh grid berilium H-1 untuk 1 posisi sebesar 0,0485% ∆k/k dan 0,0431% ∆k/k. Pada variasi 2 posisi iradiasi, dihasilkan oleh kombinasi grid A-2 dan J-2 dengan nilai 0,2993% ∆k/k dan 0,2774% ∆k/k. Sedangkan pada variasi 3 posisi, dihasilkan oleh kombinasi grid A-2, J-2 dan H-2 dengan nilai 0,4138% ∆k/k baik pada kapsul tanpa sekat maupun dengan sekat. Selisih nilai perubahan reaktivitas terkecil antara kapsul tanpa sekat dan dengan sekat pada variasi grid 1 dan 2 posisi masing-masing sebesar 0,0054% dan 0,0219% ∆k/k. Sedangkan pada variasi grid 3 posisi menunjukan nilai yang sama. Keseluruhan nilai perubahan reaktivitas masih dibawah nilai batas yang ditentukan untuk pengoperasian reaktor RSG-GAS sebesar 0,5% ∆k/k. Kata kunci : batu topaz, boron karbida, berilium, BATAN-2DIFF, reaktor RSG-GAS
Implementasi Peraturan Menteri Kesehatan Republik Indonesia Nomor 24 Tahun 2022 Tentang Rekam Medis Terhadap Hasil Pemantauan Kesehatan Pekerja Radiasi di Kawasan Nuklir Serpong
Authors: Yunisca Frida , Chalimah Enny , Sitanggang Leonardo Oloan Agusta 
Published: May 2023

Rekam medis adalah berkas yang berisi catatan dan dokumen tentang identitas pasien, pemeriksaan, pengobatan, tindakan dan pelayanan lain yang telah diberikan kepada pasien, sebagaimana tercantum pada penjelasan pasal 46 ayat (1) Undang-Undang No 29 tahun 2004 tentang Praktik Kedokteran. Perkembangan teknologi digital perlu dimanfaatkan sebesar-besarnya dalam penyelenggaraan rekam medis. Pemerintah melalui Kementerian Kesehatan telah menerbitkan Peraturan Menteri Kesehatan Republik Indonesia Nomor 24 Tahun 2022 Tentang Rekam Medis yang mewajibkan setiap Fasilitas Pelayanan Kesehatan untuk menyelenggarakan Rekam Medis Elektronik. Peraturan ini perlu diimplementasikan untuk pengelolaan data hasil pemantauan kesehatan pekerja radiasi dalam berkas rekam medis. Untuk implementasi perlu disiapkan penyusunan perangkat lunak sistem elektronik yang akan digunakan serta sumber daya manusia pengelola, Hasilnya data kesehatan yang terbangun dapat terintegrasi dan dimanfaatkan dengan sebaik-baiknya dengan tetap memperhatikan keamanan, kerahasiaan data dan informasi, serta memenuhi ketentuan-ketentuan rekam jejak medis.
2021 Annual Report of the Kansas Poison Control Center at The University of Kansas Health System
Authors: Thornton Stephen L , Oller Lisa , White Kathy , Stockdale Robert , Silver Elizabeth 
Published: May 2023

Introduction. This is the 2021 Annual Report of the Kansas Poison Control Center (KSPCC) at The University of Kansas Health System. The KSPCC serves the state of Kansas 24-hours a day, 365 days a year with certified specialists in poison information and clinical and medical toxicologists.
Methods. Encounters reported to the KSPCC from 01/01/2021 through 12/31/2021 were analyzed. Data recorded includes caller demographics, exposure substance, nature and route of exposure, interventions, medical outcome, disposition, and location of care.
Results. The KSPCC logged 18,253 total encounters in 2021, including calls from every county in Kansas. A majority of human exposure cases (53.6%) were female. Approximately 59.8% were pediatric exposures (defined as 19 years of age or less). Most encounters occurred at a residence (91.7%) and most were managed there (70.5%). Unintentional exposures were the most common reason for exposures (70.5%). The most common reported substance in pediatric encounters was household cleaning products (n = 815) and cosmetics/personal care products (n = 735). For adult encounters, analgesics (n = 1,241) and sedative/hypnotics/antipsychotics (n = 1,013) were the most frequently reported. Medical outcomes were 26.0% no effect, 22.4% minor effect, 10.7% moderate effect, and 2.7% major effects. There were 22 deaths.
Conclusions. The 2021 KSPCC annual report demonstrated that cases were received from the entire state of Kansas. Pediatric exposures remained most common but cases with serious outcomes continue to increase. This report supports the continued value of the KSPCC to both public and health care providers in the state of Kansas.
Preparing Herbal Formulations through Indigenous and Modern Methods: An Experimental Study
Authors: Lall Shatakshi 
Published: May 2023

Traditional medicines can treat many problems because they are based on natural treatments. Because of their historical applications and useful treatments, numerous of these conventional medications have been the subject of extensive pharmacological research of their antibacterial, antiviral, and anti-inflammatory effects. Natural resources are frequently used as a primary or secondary source by academics and pharmaceutical corporations when developing new drugs. A wide variety of plants have long been used as a source of traditional medicine by people in many different cultures. Numerous research have examined the possible antibacterial and antiviral properties of these plants. Since there are so many different kinds of natural sources, including plants, choose the proper one as a starting point is crucial for accurate screening results. Due to their, “effectiveness in treating diseases and lower risk of side effects than synthetic treatments, the usage of plant-based medications has significantly expanded in the modern world. The current study was aimed to confirm the identity, quality and purity of some locally available potential medicinal plants such as Drymaria cordata (whole plant), Alstonia scholaris (bark), Hydrocotyle sibthorpioides (whole plant), Centella asiatica (whole plant), Senna hirsuta (leaf), Oroxylum indicum (bark),

Senna occidentalis (leaf), Stephania japonica (tuber) and Solanum indicum (root) in powdered form”.
The powdered plant components underwent preliminary phytochemical analysis as well as pharmacognostic tests, physical evaluation and heavy metal analysis. Initial phytochemical study of the various extracts indicated that triterpenoids were absent, but alkaloids, phenolics, carbohydrates and amino acids were present. The powder was studied under a microscope to reveal its, “distinguishing characteristics, including calcium oxalate crystals, fibres, stone cells, trichomes, stomata, xylem vessels, pitted spiral vessels, etc. The colour, smell, fragrance, and texture of the ground plant were all acceptable. The physical characteristics that affect the flow rate of the powder with respect to Carr's index and Hausner's ratio were found to be good to passable, with the exception of Hydrocotyle sibthorpioides (the complete plant) and Oroxylum indicum (bark), which were not easily passable. During the heavy metal test, lead, cadmium, and bismuth were not found. As a result, the current study may be utilised as a benchmark reference for the quality control analysis of the herbal medicine, either alone or in combination”.
Iot In Healthcare: Challenges and Opportunities for Improved Patient Outcomes
Authors: Chattopadhyay Saumitra 
Published: May 2023

The Internet of Things (IoT) has emerged as a promising technology to revolutionize healthcare by transforming the way medical services are delivered, improving patient outcomes, and reducing costs. IoT-enabled devices and systems offer immense potential for enhancing patient outcomes, improving healthcare delivery, and reducing costs. This paper presents an overview of the challenges and opportunities associated with IoT adoption in healthcare, emphasizing its potential to enhance patient care and streamline medical processes. This paper highlights the crucial role of IoT in transforming healthcare systems and emphasizes the need for multidisciplinary collaboration among stakeholders to ensure the successful implementation of IoT in healthcare.
Improving the Performance of Primary Storage Systems Using Leveraging Data Deduplication
Authors: Pattanshetti Mahantesh K 
Published: May 2023

Data deduplication is a specialised data compression technique used in computers to get rid of redundant copies of repeated data. Intelligent (data) compression and single-instance (data) storage are words that are similar and partly interchangeable. This method may be used to increase storage efficiency and the quantity of data that has to be sent should be reduced over networks. Unique data chunks, or byte patterns, are found and saved throughout the deduplication process through a process of analysis. Recent studies have demonstrated that main storage systems in the Cloud offer moderate to high levels of data redundancy. It demonstrates that as a result of the small I/O requests to redundant data's comparably high temporal access locality, data redundancy displays a substantially greater degree of intensity on the I/O path than that on discs. The system in this project suggests using a performance-oriented I/O deduplication technique known as POD. It is used to increase the I/O performance of main storage systems in the Cloud without compromising the latter's capacity savings.
Enabling Fine-Grained Multi-Keyword Search Supporting Classified Sub-Dictionaries Over Encrypted Cloud Data
Authors: Vats Satvik 
Published: May 2023

Abstract
Huge numbers of papers are transferred to the cloud for easier access and lower administration costs as cloud computing gains popularity. Individuals can utilize cloud computing to store their data on distant servers and grant public users access to the data via the cloud servers. Outsourced data are normally encrypted before being posted to the clod since they are likely to include sensitive privacy information. The difficulty of sifting through the encrypted data, however, severely restricts the utility of the outsourced data. In this research, we introduce the fine-grained multi-keyword search algorithms over encrypted clod data to overcome this problem. We made three original contributions. In order to provide exact keyword search and a tailored user experience, we initially present the relevance source plus preference factor upon keywords. Furthermore, we create a useful and very effective multi-keyword search strategy. The suggested system can enable complex logic searches using mixed "AND," "OR," and "NO" keyword operations. Thirdly, in order to improve the efficiency of index building and query generation, we also use the classified sub-dictionaries technique. Finally, we examine the security of the suggested strategies with regard to of document confidentiality, index alongside trapdoor privacy protection, as well as trapdoor unlinkability. We confirm that the suggested method performs better when it comes to of functionality, query complexity, yet efficiency than the ones currently in use plus can reach the same security level through rigorous tests on a real-world dataset. We confirm that the suggested method performs better with regard to functionality, query complexity, and efficiency than the existing ones and can reach the same security level through rigorous tests on a real-world dataset.
Exploring the Power of Transformer Models in Hospitality Domain
Authors: Parsola Jyoti 
Published: May 2023

ABSTRACT
Despite decades of medical advancements and a rising interest in precision healthcare, the great majority of diagnoses are made after patients start to exhibit observable symptoms of sickness. However, early disease indication and detection can give patients and caregivers the opportunity for early intervention, better disease management, and effective use of healthcare resources. Deep learning and other recent advancements in machine learning provide a fantastic chance to fill this unmet demand. Transformer designs are very expressive because they encode long-range relationships in the input sequences via self-attention methods. The models we offer in this work are Transformer-based (TB), and we provide a thorough description of each one in contrast to the Transformer's typical design. This study focuses on text-based task (TB) models used in Natural Language Processing (NLP). An examination of the key ideas at the core of the effectiveness of these models comes first. NLP's flexible architecture allows it to incorporate various heterogeneous concepts (such as diagnoses, treatments, measurements, and more) to further improve the accuracy of its predictions. Its (pre-)training results in disease and patient representations can also be helpful for future studies (i.e., transfer learning).
Predicting Equipment Failure in Manufacturing Plants: An AI-driven Maintenance Strategy
Authors: Dhyani Bijesh 
Published: May 2023

Failure of equipment can lead to costly repairs and downtime for manufacturing facilities. Traditional maintenance techniques can result in unexpected failures, which can cause revenue losses and production delays. Artificial Intelligence (AI)-driven methods use machine learning technologies to predict equipment malfunctions before they happen, which has gained widespread interest. Artificial intelligence-based maintenance strategies can help improve the reliability of equipment and reduce the cost of repairs in manufacturing facilities. This study explores the use of Kaggle's predictive maintenance dataset to predict equipment failure in a manufacturing plant. The research involves collecting and evaluating data, selecting machine learning models, and evaluating metrics. The findings of this study revealed the application of AI-driven maintenance techniques to predict equipment failure, highlighting their potential to improve the efficiency of the manufacturing process and reduce costs. This study serves as a valuable contribution to the field of predictive maintenance and provides relevant implications for the industry. The paper explores the application of AI-based maintenance strategies to predict the failure of equipment in manufacturing facilities. The study utilizes the Kaggle dataset, which contains machine sensor readings and process variables from a manufacturing facility that processes batches of a specific product. The research methodology includes data gathering, engineering, and model selection. It also compared the effectiveness of AI-driven and traditional maintenance approaches. The results indicated that the former led to better predicted equipment failure rates and lower costs. The study's findings provide valuable insight into the subject of predictive maintenance and the implications it has for the manufacturing sector. It also offers future research directions.
Optimized Traffic And Enhanced Lifetime Routing Algorithm In WSN
Authors: Painuly Sakshi 
Published: May 2023

Abstract:
Wireless Sensor Networks (WSNs) have gained significant traction as a viable solution for a diverse range of applications that necessitate the monitoring and regulation of distant environments. Wireless sensor networks (WSNs) are frequently constrained by the energy resources of the individual sensor nodes, resulting in network failures and reduced operational lifetimes. In response to this challenge, we present a proposed optimised traffic and lifetime routing algorithm for Wireless Sensor Networks (WSNs). The algorithm has been specifically designed to minimise network traffic and ensure equitable energy consumption across all sensor nodes. The algorithm utilises a fusion of clustering and network coding methodologies to leverage spatial diversity and redundancy within the network. The performance of the proposed algorithm is assessed through simulation experiments and is juxtaposed with various contemporary routing algorithms. The findings indicate that the algorithm we employed exhibits superior performance compared to other algorithms with respect to network longevity, network flow, and energy usage. The algorithm under consideration exhibits potential for utilisation in diverse Wireless Sensor Network (WSN) applications that necessitate dependable and energy-efficient data transmission.
Investigating the Use of Artificial Intelligence for Fault Detection in Electronic Circuits
Authors: Dubey Dheeraj 
Published: May 2023

Many control systems and industrial applications rely heavily on analogue electrical circuits. Diagnosing problems with such circuits the old-fashioned manner can be erroneous and time consuming, which can have a detrimental impact on the industrial product. Also Keeping up with the ever-increasing sophistication of modern electronics has made testing these systems a formidable challenge. Due to the complexity and amount of individual parts on a PCB, traditional simulation-based methods cannot be used, and instead, artificial intelligence-based methods must be prioritization intriguing area of study that has the potential to improve the safety and reliability of electronic systems is the use of artificial intelligence (AI) to the task of detecting faults in electronic circuits. Real-time defect identification, diagnosis, and localization are made possible by AI-based solutions, which can assist prevent system breakdowns and boost performance. Fault detection in electronic circuits has been approached using a wide variety of artificial intelligence (AI) based methods, each with their own set of benefits and drawbacks. The availability of labelled training data, the generalizability of AI models, and the interpretation of AI-based fault detection outcomes are just a few of the obstacles that must be overcome. Despite these limitations, problem detection based on AI has many potentials uses in industries like transportation, energy, healthcare, and more. In this study, we survey the landscape of available AI-based fault detection systems for electronic circuits, discussing their merits and shortcomings, difficulties, and possible applications. This study details an inquiry into the viability of utilising an AI approach to failure prediction and early detection in power systems. A machine-learning detector has been created to keep an eye on specific parts of electricity grids and anticipate any problems before they occur.The AI failure prediction system can quickly anticipate a system failure.
Using Data Mining to Improve Healthcare Decision-Making: A Systematic Review
Authors: Mishra Amit Kumar 
Published: May 2023

Data mining is a process utilized to analyze large databases to find patterns and relationships. It involves using various techniques such as machine learning and statistical analysis to identify trends and patterns. Due to the massive amount of medical data that is available, data mining has become an increasing trend in healthcare. This process involves extracting knowledge from these data sources. This review aims to analyze the current state of the research regarding the use of data mining in the healthcare industry. This review thoroughly searched for studies related to various aspects of healthcare diagnosis and prognosis. The results indicated that data mining techniques can help improve the accuracy of diagnoses and the prediction of treatment outcomes. Although the review acknowledged the potential of data mining in improving the quality of healthcare, it also highlighted various issues that need to be resolved in order for it to be successful. These include the need for standardization and privacy concerns. Despite these obstacles, the potential of this technology is still immense. The findings of this review provide a comprehensive analysis of the current state of the art in data mining in healthcare. It shows the potential of this technology to improve the quality of healthcare and inform clinical decisions. Future research must address the issues identified in this review in order to advance the use of this technology in the field.