bluuebunny/crossref_metadata_2025 huggingface dataset:
The promising role of bacteriophage therapy in managing total hip and knee arthroplasty related periprosthetic joint infection, a systematic review.
Authors: Khalifa Ahmed A. , Hussein Sarah M.
Published: September 2022

Background
Total hip and knee arthroplasty periprosthetic joint infection (PJI) poses a management dilemma owing to the emergence of resistant organisms. Bacteriophage therapy (BT) was introduced as a promising management option for PJI. The current review aimed to demonstrate the role and safety of using BT as an adjuvant to treat PJIs.
Methods
A systematic search was performed through four databases (Embase, PubMed, Web of Science, and Scopus) up to March 2022, according to the predetermined inclusion and exclusion criteria.
Results
our systematic review included 12 case reports of 17 patients in which 18 joints (15 TKAs and three THAs) were treated. The patients' average age was 76.3 years, underwent an average of 4 previous surgeries. The most common organism was the Staphylococcus aureus species. All patients underwent surgical debridement; 13 were treated with bacteriophages (eight received cocktail and five received monophage therapy), while in one report (four patients), all received bacteriophage-derived lysin. All patients received postoperative suppressive antibiotic therapy. After an average follow-up of 13.5 months, all patients had satisfactory outcomes. No recurrence of infection in any patient. Transaminitis complicating BT was developed in three patients, needed stoppage in only one, and the condition was reversible and non-life-threatening.
Conclusion
BT is an effective and safe option for treating resistant and relapsing PJIs; Further investigations are needed to clarify some issues related to BT's best route and duration; furthermore, new ethical regulations should be implemented to facilitate its widespread use.
Key factor for the formation of a di-superatomic molecule composed of silver-based icosahedral superatoms
Authors: Miyajima Sayuri , Hossain Sakiat , Ikeda Ayaka , Kosaka Taiga , Kawawaki Tokuhisa , Niihori Yoshiki , Iwasa Takeshi , Taketsugu Tetsuya , Negishi Yuichi
Published: September 2022

Metal nanoclusters composed of noble elements such as gold (Au) or silver (Ag) are regarded as superatoms. In recent years, the understanding of Au-based superatomic molecules has gradually progressed. However, there is still little information on Ag-based superatomic molecules. In the present study, we synthesise two di-superatomic molecules with Ag as the main constituent element and reveal the three essential conditions for the formation and isolation of a superatomic molecule comprising two Ag13−xMx structures (M = Ag or other metal; x = number of M) connected by vertex sharing. The effects of the central atom and the type of bridging halogen on the electronic structure of the resulting superatomic molecule are also clarified in detail. These findings are expected to provide clear design guidelines for the creation of superatomic molecules with various properties and functions.
The law of surrounding rock deformation and surface settlement of shield tunnel passing through upper soft and lower hard rock
Authors: Yu Guangming , Zhang Yongyi , Bai Yinqiu , Lei Jun , Yang Zihan , Li Gang , Peng Bin , Kuang Lijun , Liang Guanwen , Luo Weiting , Chen Ze
Published: September 2022

With the rapid increase in the number of urban subway construction in China, the geological distribution encountered in the tunnel construction process is gradually diversified, especially when the excavation is carried out in the upper soft and lower hard formations, there are certain differences in the physical and mechanical properties of the tunnel palm surface, the surrounding rock and the distribution position of the soft and hard rock junction surface, which can easily lead to the occurrence of large deformation or even collapse of the surrounding rock of the tunnel. In order to study the deformation of surrounding rock and the change of surface settlement when the shield tunnel crosses the upper soft and lower hard strata, relying on the actual project of the shield tunnel crossing the upper soft and lower hard strata of Shenzhen metro line 13. Firstly, based on the molar stress circle theory and the theory of elastic mechanics, the influence of internal friction angle and cohesion on the surrounding rock stress is analyzed, and the first yield location and distribution characteristics of the surrounding rock stress of the upper soft and lower hard strata are divided based on the calculation formula of the surrounding rock stress; Secondly, the inclination angle of the soft and hard rock junction surface of the above soft and hard formations is α variable, and the MIDAS GTS NX numerical simulation software is used to set seven working conditions of 0°, 15°, 30°, 45°, 60°, 75°, 90°, and analyze the influence of inclination on the surrounding rock stress of the tunnel, the plastic yield position of the surrounding rock, and the deformation of the surrounding rock. Finally, the surrounding rock deformation and surface settlement monitoring data after the completion of the construction of the soft and lower hard strata in the Liu ~ Bai section of Shenzhen Metro Line 13 are summarized, and the numerical simulation is compared and analyzed to verify the accuracy of the numerical simulation results, and the final research results can provide a certain reference for similar projects in the later stage.
The monocyte to high-density lipoprotein cholesterol ratio is a risk factor for frequent premature ventricular complexes: A retrospective cohort study
Authors: Wang Yunfei , Li Deming , Zhu Xuetao , Li Jing , Yue Cui , Wu Ling , Zhuan Qingqing , Dou Xiaomeng , Duan Wei
Published: September 2022

Background
Little is known about the link between the monocyte to high-density lipoprotein cholesterol ratio (MHR) and frequent premature ventricular complexes (PVCs). This investigation aimed to evaluate the link between the MHR and frequent PVCs in patients, as well as their outcomes, using the axis, burden, coupling interval–ventricular tachycardia (ABC-VT) risk score (ARS).
Methods
Two hundred patients with frequent PVCs and 70 controls were retrospectively enrolled, and their general data were gathered. The MHR and ARS were calculated. Then, patients developing frequent PVCs were classified into a medium-/high-risk subgroup and a low-risk subgroup according to ARS. The results were evaluated employing comparative statistical analyses, Spearman’s correlation, logistic regression analyses, and receiver operating characteristic (ROC) curves.
Results
The MHR in the controls was obviously lower than that in the frequent PVC group. In addition, the MHR was the lowest in the control group and highest in the medium-/high-risk subgroup, with that of the low-risk subgroup falling in the middle. Spearman’s correlation analyses showed that the MHR was positively correlated with the ARS (ρ = 0.307, P < 0.001). Ultimately, the MHR was found to be a risk factor for frequent PVCs in the multivariate analysis. In addition, an MHR cutoff point of 254.6 featured 67.50% sensitivity and 67.14% specificity for predicting frequent PVCs, and the area under the curve (AUC) reached 0.694 (95% confidence interval: 0.623–0.766) (P < 0.001).
Conclusions
The MHR is positively and independently correlated with frequent PVCs and can be used as a practical, cost-saving and simple biomarker of inflammation owing to its value in predicting frequent PVCs. In addition, the MHR is crucial to risk stratification and prognosis, which may give it clinical value in the prevention and management of frequent PVCs.
New compression point reducing memory size in field of characteristic different from 2 and 3
Authors: Assoujaa ismail , SIHAM EZZOUAK , HAKIMA MOUANIS
Published: September 2022

Compression point is a new method to compress the space memory and still havethe same data. In this paper, we will present a new method of compression points workwell with addition operation in elliptic curve, so instead of storing the value of two pointsP = (xP;yP), Q = (xQ;yQ), we will store the addition of the x-coordinates i,e(a = xP+xQ;yP;yQ) or the y-coordinates i,e (xP;xQ;b = yP+yQ).In this article, we show a new technique for compressing two points in elliptic curve withdifferent coordinate system: Affine, Projective and Jacobian in a field of characteristic different from 2& 3 , and show the cost of theses operations. This method can save if we work with affine,Projective or Jacobian coordinates, at least 25%, 17%, 17% of memory size respectively,and also see what happens in case if we take Edwards curve and Montgomery curve cases.
Measurement and evaluation of noise levels inside and outside the industrial area of Ferronikeli
Authors: Veliu Agron
Published: September 2022

The increasing awareness of the public of environmental issues such as CO2 emissions, carbon footprints and waste recycling has brought the issue of environmental noise to the fore. Many of the demands of modern society result in the creation of noise sources such as larger airports, additional power stations or heavy industry and higher road traffic levels. In addition to this, the expansion of towns and cities, driven by the demand for housing, has often resulted in the development of land near existing noise sources. These trends have meant that many more industries are now required to monitor their environmental noise impact. Environmental noise, caused by traffic, industrial and recreational activities is one of the main local environmental problems in many countries of Europe and the source of an increasing number of complaints from the public. Generally, however, action to reduce environmental noise has had a lower priority than that taken to address other environmental problems such as air and water pollution. In the past, the noise generated by different sources, including also the industries, is not so obvious in trends within Kosovo country. Gllogoc is a town which is a part of the Pristina region, located in the central part of the Republic of Kosovo. In this town is located the smelter plant ‘NewCo Ferronikeli’ which is well known as an industrial complex for ferronickel alloy production. This study was a simple assessment of the noise levels around the NewCo Ferronikeli company. However, this study is based on the Directive 2002/49/EC of The European Parliament and of the Council of 25 June 2002 relating to the assessment and management of environmental noise.
Nurses Barriers to Evidence-Based Practice in Palliative Care: A Systematic Review
Authors: Dakka Falah
Published: September 2022

Background
Research shows low EBP uptake among palliative care nurses, which is a global concern because the demand for palliative care services is rising, raising the urgent need to improve healthcare quality. Promoting EBP uptake in palliative care can improve healthcare quality. This systematic review investigated nurses’ barriers to EBP implementation in palliative care.
Method
PubMed and Google Scholar were used to identify six articles. Articles were included for review if published within the past ten years (English-only) and investigated barriers to EBP implementation in palliative care.
Results
Four barriers were identified: (a) time and resource constraints, (b) lack of readiness for organisational change, (c) negative attitudes towards palliative care, and (d) process-specific difficulties.
Conclusion
The findings of this systematic review can be used to inform policy changes to improve the uptake of EBP in palliative care.
Heterogeneous Graph Recommendation Model based on Graph Neural Network
Authors: Norton Van , Miller Mark , Meyer Pierre , Ward John , Agnew Gregory
Published: September 2022

With the development of the Internet in recent years, the network information is exploding, people have entered the era of big data, from the lack of information in the past to the great information overload nowadays. It has become an important challenge to filter the information that is beneficial to you in the ocean of information on the Internet. As a subset of the information filtering system, the recommendation system mainly relies on the historical interaction records of users' products and its own attribute information to explore the potential preferences and needs of users, which greatly reduces the time for users to filter information and is helpful for improving user experience and alleviating the information overload problem. Traditional recommendation algorithms represented by collaborative filtering often face the cold-start problem. They usually recommend products for users based on their purchase history or rating information of products, however, the performance of such recommendation algorithms decreases dramatically when the interaction history of users' products is sparse. Heterogeneous graph networks contain multiple types of nodes and edges due to their inclusion. It contains richer semantic information, and more general recommendation algorithms based on heterogeneous graphs are relatively less studied. Therefore, the purpose of this paper is to study graph neural network-based recommendation algorithms, mainly considering the heterogeneity of the network structure and the interaction order information between nodes, and then build a new recommendation model to cope with the cold start problem and improve the recommendation effect.
Gait-related cross-frequency coupling in the subthalamic nucleus of parkinsonian patients
Authors: Farokhniaee AmirAli , Palmisano Chiara , Vecchio Jasmin Del Vecchio Del , Volkmann Jens , Isaias Ioannis
Published: September 2022

Background
Analysis of coupling between the phases and amplitudes of neural oscillations has gained increasing attention as an important mechanism for large-scale brain network dynamics. In Parkinson’s disease (PD), preliminary evidence indicates abnormal beta-phase coupling to gamma-amplitude in different brain areas, including the subthalamic nucleus (STN).
Objective
To study gait-related, phase-amplitude coupling (PAC) of subthalamic local field potentials (LFPs) in parkinsonian patients.
Methods
We analyzed bilateral STN LFPs in eight subjects with PD chronically implanted with deep brain stimulation electrodes during upright quiet standing and unperturbed walking. PAC was computed using the Kullback-Liebler method, based on the Modulation Index. Neurophysiological recordings were correlated with clinical and kinematic measurements and individual molecular brain imaging studies ([123I]FP-CIT and single-photon emission computed tomography).
Results
We showed a dopamine-related increase in subthalamic beta-gamma PAC between standing and walking. Patients with poor PAC modulation between standing and walking and low PAC during walking spent significantly more time in the stance and double support phase of the gait cycle.
Conclusion
Cross-frequency coupling in the STN is a fundamental aspect of motor control for human locomotion, and could be used as an input signal for automated programming of novel stimulators.
Annotation of Helicobacter pylori mass peaks detected by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry
Authors: Ng Wenfa
Published: September 2022

Helicobacter pylori is an important human gastric pathogen that has been studied by the relatively new technique of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). This work attempts to annotate the mass peaks of two strains of H. pylori (J99 and ATCC 26695) that has been reported in the literature in 2010 out of pure curiosity. Reference proteome used in this work was H. pylori proteome deposited in UniProt, and the annotation process taken was aided by an in-house MATLAB mass peak annotation software. Briefly, the mass peaks reported were compared to the molecular weight of each protein in the H. pylori proteome to help find a match of lowest error in molecular weight. Results revealed that most of the annotated mass peaks were from ribosomal proteins, especially those in the large ribosome subunit. Other annotated proteins were uncharacterised proteins, which suggest that important facet of this important H. pylori pathogen remains unknown to the biology and biomedical community. Overall, annotation of the mass peaks of two strains of H. pylori should provide a useful resource for other works trying to understand the origins of the mass peaks and their physiological roles in the organism and in human pathogenesis.
Utilization of Maternal Health Services Among Women in Jiribam District, Manipur
Authors: Bhardwaj Prakash Chandra , Kannan Srinivasan
Published: September 2022

Introduction: Millennium Development Goal made significant progress, despite that major challenges such as maternal mortality, access to sexual and reproductive health and rights in the developing countries remain as a huge problem. The objective of the study was to find the proportion of institutional delivery and barriers of maternal health service utilization in Jiribam district, Manipur. Methods: Cross-sectional study conducted among women of Jiribam district, who had delivered during the period between November 2018 and October 2019. Using multi-stage random sampling, 215 women after consenting, participated in the study. Data was collected using KoBoCollect application. Using SPSS V 21.0. analysis performed for Chi-Square test for exploring association, and p-value < 0.05 was considered significant. Binary logistic regression model was built for predictors for the dependant variables. Results: The study found 61.4 percent (95% CI 54.7–67.7) had institutional delivery in Jiribam district during the year, 2018-19. Sixty percent of deliveries were conducted in Government facility and 40 percent in private facility. Thirty percent of institutional delivery was conducted inside the Jiribam district and 70 percent was conducted in the Cachar district, Assam state. Education level, ethnicity, and place of residence of women were important determinants of maternal health service utilization. Conclusion: The proportion of institutional delivery was 19.4 percent less compared to the NFHS-4 (2015-16) report of Imphal East district (till 2016, Jiribam was a sub-division of the district). Peripheral districts need more attention to maintain equity to access quality health care. Section II
Virtual Machine Migration Based on Coalition Formation Game
Authors: Qin Haiyan , Zhang Yonglong , Wu Yang , Li Bin
Published: September 2022

Virtualization has been regarded as a key technology to diversify computing resources and facilitate system management in cloud datacenters. However, the improper allocation way of virtual machines (VMs) results in the imbalanced resource allocation, and cause some physical machines (PMs) to overload while others to underutilize. Virtual machine migration is a method to help cloud service providers manage cloud resources effectively and automatically. We model the VM migration problem as coalition formation game (VMM-CFG), and propose a merge and split algorithm to implement VM migration. First of all, some VMs in the overloaded PMs are selected to be migrated. After that, the VMs to be migrated form effective coalitions independently and autonomously, and then the coalitions are mapping to the corresponding underloaded PMs. The simulation results show that VMM-CFG mechanism is well behaved in load-balance and communication costs.
Patient-Centric Machine Learning-based COVID Monitoring System
Authors: R.G Sangeetha , Hemanth C , Santhosha A , Madhumitha S
Published: September 2022

In this coronavirus epidemic period (COVID-19), vaccination is a highly recommended proactive measure. Millions of people are getting vaccination each day, and it isn’t easy to maintain physical records and monitor their health. In addition, most people are not aware of the availability of vaccines in the hospitals and their suitability to get themselves vaccinated. Moreover, post-vaccination effects need careful monitoring. But in today’s situation, frequent physical visits to hospitals may not be possible. If any complications developed post-vaccination may create health ailments. Therefore, there is a need for an appropriate system, mitigating the issues mentioned above in the vaccination process. We design and develop a Machine learning-based tool for early screening and closedloop real-time remote monitoring of coronavirus disease (COVID-19).
DESENVOLVIMENTO DE INTELIGÊNCIAS ARTIFICIAIS (IA’s) NA EDUCAÇÃO: UMA REVISÃO SISTEMÁTICA DE LITERATURA
Authors: Fernandes Carlos Eduardo Lima , Ribeiro Ana Paula de Medeiros , Vasconcelos Francisco Herbert Lima
Published: September 2022

A utilização de novas tecnologias computacionais no ensino, sobretudo, por meio da utilização de Inteligência Artificial (IA) pode se mostrar uma importante ferramenta no processo de ensino e aprendizagem. O presente artigo propõe um estudo sobre o desenvolvimento de IA’s voltadas para a educação, com enfoque no processo de ensino e aprendizagem. Para isso, a metodologia se deu em etapas distintas: a primeira, com o estabelecimento das palavras-chave e strings de busca; a segunda, buscas nos bancos de dados digitais dos artigos publicados entre os anos de 2017 e 2021. Após aplicação dos critérios de inclusão e exclusão, foram encontrados 20 artigos. Foi observado que houve uma prevalência do desenvolvimento de IA’s para o ensino superior, sobretudo, para a área do ensino de computação, bem como, ressalta-se a necessidade de implementação de políticas públicas de alfabetização digital e acesso às novas tecnologias, que possam ser importantes para minimizar: (i) a defasagem no desenvolvimento de IA’s para a educação básica; (ii) o domínio das IA’s voltadas apenas para o ensino de computação; (iii) e as diferenças entre as classes etárias, como fenômenos limitadores da implementação das IA’s na educação.
Rock Breaking Using Supercritical Carbon Dioxide (SC-CO2) Technology – A Safe, Efficient, and Sustainable Approach
Authors: Guan G X , Wang X , Ngai Weyman C T , Ng P L , Kwan A K H
Published: September 2022

Rock breaking by drill and blast using chemical explosives has been a dominant method in construction. However, blasting is hazardous and risky in nature: it involves the use of Category 1 Dangerous Goods; and it induces ground vibration and risks of fly rocks and air over pressure. Mechanical rock breaking, chemical expansion agent, and hydraulic fracturing techniques, complemented with hole drilling, wedging or splitting, are sometimes used as alternatives to drill and blast for rock breaking. However, these methods are extremely slow to match with construction progress and are also costly. In particular, mechanical rock breaking brings about continuous noise, dust and nuisances to the surroundings. As more and more construction works nowadays are in congested urban region, the construction industry needs to adopt a safe, efficient, and sustainable rock breaking approach. In view of this, rock breaking using supercritical carbon dioxide (SC-CO2) technology has been developed recently, and it has successfully been applied to numerous real projects.
The impact of Indonesian financial accounting standard implementation, credit risk, and credit restructuring on allowance for credit losses in Indonesia
Authors: Gusti Ngurah Agung Suaryana I , Noviari Naniek , Gusti Ayu Eka Damayanthi I
Published: September 2022

This study examines the impact of the implementation of the Indonesian Financial Accounting Standard, credit risk, and credit restructuring on the formation of Allowance for Credit Losses (ACL) of commercial banks listed on the Indonesia Stock Exchange. The formation of ACL is regulated in PSAK 71 which is part of the Indonesian Financial Accounting Standard. The implementation of PSAK 71, and credit risk are expected to increase the ACL of commercial banks, however, credit restructuring programs will reduce the ACL. The research population is commercial banks listed on the Indonesia Stock Exchange in 2019–2020. The research sample is the entire research population. This study uses panel data regression analysis to examine the effect of the application of PSAK 71, credit risk, and credit restructuring on ACL for commercial bank loans. The findings show that the implementation of PSAK 71 and credit risk have a positive effect on the ACL, meanwhile, credit restructuring has a negative effect on the ACL.
Gender Relations during Pandemic Era in Indonesia: Negotiation and Resistance of Urban Woman Workers at Work From Home (WFH) in Family
Authors: Setiyawan Radius , Setiyowati Arin , Maulida Maulida
Published: September 2022

This study discussed the condition of urban women workers during the COVID-19 pandemic in Indonesia, a new condition (new normal) that changes many human activities. Through this study, women were not only seen as objects of domestication, exploitation of workers, verbal or physical violence, and other discriminatory behavior. However, the study revealed another position of women as active subjects in negotiating between their domestic and public roles. The new condition during the pandemic (approximately two years) had certainly changed women's work from home activities with their families. Using qualitative methods with in-depth interviews, the researchers mapped out a new condition for women who could negotiate and resist during a pandemic in the family. This study was conducted in Surabaya, Sidoarjo, and Gresik with the subjects of working mothers. This study concluded that the practice of negotiating and resisting the role of urban woman workers often occurred. It meant that gender roles or relations were not static but were always in the process of being.
Come Back Stronger After Suffered for Child Sexual Abuse: A Case Study to Understand the Family Resilience Process
Authors: Ghani Mochammad Wahyu , Sitohang Marya Yenita
Published: September 2022

Background: Child sexual abuse (CSA) is quite prevalent in Indonesia, with an increased rate over several years. The negative impacts of CSA have been investigated in several studies and the coping strategies for CSA survivors. However, the resilience at the family level is rarely analyzed. This study investigates the resilience process of the CSA survivor's families after they were affected by the CSA incident. Methods: Using a qualitative approach, we collected the data by interviewing and observing a family suffering from a CSA incident, the Geulis family (informant pseudonym). Data then were analyzed using thematic analysis with a family resilience model developed by Henry et al. (2015). Results: The CSA incident has led to several adverse events in the Geulis (family, including unintended pregnancy, dropping out of school, hesitancy to stay in the hometown and loss of job and family income. With the support from extended family and some organizations, Geulis and her mother were able to find a new place to live and a new job for Geulis' mother, and Geulis was able to start a new relationship and continue her study for a while. The complex and continuous adaptation process of the Geulis family was also analyzed using the family adaptive systems component. Conclusion: The Geulis family could function normally after experiencing the negative impact of CSA with some adjustment in their life and dynamic process in the family. Environment, including extended family, government, and non-government organizations, plays a vital role in supporting family resilience.
The discriminant statistic based on MPE-MWPE relationship and non-uniform embedding
Authors: Petrauskiene Vilma , Ragulskiene Jurate , Zhu Huaxin , Wang Jie , Cao Maosen
Published: September 2022

The slope fitting line between MPE (multi-scale permutation entropy) and MWPE (multi-scale weighted permutation entropy) is recently proposed as a discriminant statistic for testing the nonlinearity of a time series. The main objective of this paper is to demonstrate that the selection of the optimal parameters of the non-uniform embedding is essential for the proposed discriminant statistic. In particular, the presented case studies indicate that the modified discriminant statistic based on non-uniform embedding can detect differences between such time series which remain indistinguishable if the original approach is used.
Peripheral or strategic? The border condition of the amazon frontier in Brazil
Authors: Rebelo Porto Jadson Luís , Superti Eliane
Published: September 2022

The objective of this article was to discuss the border condition of the Brazilian Amazon between the 20th and 21st centuries from its peripheral and strategic behavior. The guiding question was: how is the border condition configured through the peripheral/strategic behavior of frontier spaces in the Amazon region of Brazil? The methodological approach was the hypothetical-deductive based on three investigation strategies; qualitative analysis of documents, review of specialized literature and fieldwork observations. The results indicate that the Amazon frontier cannot be considered peripheral or strategic in isolation. These two adjectives qualify specific historical, political and economic uses that need to be considered together to understand the border condition of the Brazilian Amazon. We conclude that the peripheral/strategic behavior reveals not only the historical-economic construction of the border, but also the choice of national decision-making centers, reflected in the public policies implemented in the region.
Regional Anesthesia for Clavicle Fracture Surgery- What is the Current Evidence: A Systematic Review
Authors: Ding Xueqin
Published: September 2022

Introduction:

The sensory innervation of the clavicle remains controversial. It might come from both the cervical plexus and brachial plexus. Peripheral nerve blocks used to anesthetize the clavicle include cervical plexus blocks, brachial plexus blocks, and combined cervical and brachial plexus blocks.

Objectives:

The review was to determine whether there is a difference in pain scores and pain medication consumption intraoperatively and postoperatively among these blocks. Secondary endpoints were block success and serious adverse events.

Methods:

A comprehensive literature search of PubMed and Web of Science was performed. Only English-written randomized controlled studies were included.

Results:

Compared with patients with general anesthesia, patients with combined ultrasound-guided superficial cervical and interscalene brachial plexus block spent a shorter time in PACU (35.60 ± 5.59 min vs. 53.13 ± 6.95 min, P < 0.001), had a more extended pain-free period (324.67 ± 41.82 min vs. 185.27 ± 40.04 min, P < 0.001), and received less opioid consumption (Tramadol 213.33 ± 57.13 mg vs. 386.67 ± 34.57 mg, P < 0.001) in first 24 h postoperatively. Compared with patients with ultrasound-guided superficial cervical and interscalene brachial plexus block, patients with ultrasound-guided intermediate cervical and interscalene brachial plexus block had a higher success rate (100% vs. 80%) and longer duration of post-operative analgesia (7.5±0.8 h vs. 5.7± 0.4 h, P<0.001). Without ultrasound guidance, patients with combined superficial, deep cervical, and interscalene brachial plexus block had a higher success rate (96% vs. 60%), lower pain score at two h postoperatively (1.96±0.17 vs. 3.22±0.88, p=0.000), and a more extended pain-free period (1h vs 6h) compared to combined superficial cervical and interscalene brachial plexus block. There were no regional anesthesia-related complications reported in all studies.

Conclusion:

Patients with regional anesthesia have a more significant pain-free period and less intraoperative and postoperative opioid consumption than patients with general anesthesia. Combined intermediate or deep cervical plexus and Interscalene brachial plexus blocks provide better analgesia than combined superficial cervical plexus and Interscalene brachial plexus blocks. Ultrasound guidance improved the success rate of regional anesthesia. Combined cervical plexus and brachial plexus block can be used as sole anesthesia for clavicle fracture surgery.
Data Computation: Awareness, Architecture and Applications
Authors: Kansal Vani , Singh Sunil K.
Published: September 2022

There has been a tremendous revolution in computing technologies to handle
the vast amount of data in recent years. Big data is the large-scale complex data in
which real-time data is available and mushrooms the development of almost every
field. In recent years, the demand and requirement of big data produced an opportunity
to replace traditional data techniques due to their low efficiency and low accuracy. It
shows adequate responsiveness, absence of versatility, execution, and precision for
meeting the convolution of Big Data challenges. As an outcome, this created different
dispersions and innovations. Big data does not mean that the data is humongous but
additionally excessive in range and speed. This factor makes them tough to deal with
the usage of conventional gear and techniques. Decision-makers read the extension and
expansion of big data to understand and extract valuable information from rapidly
varying data using big data analytics. In this chapter, we can analyze big data tools and
techniques useful for big data. This chapter presents a literature survey covering
various applications and technologies that play an indispensable role in offering new
solutions dealing with large-scale, high-dimensional data. By summarizing different
available technologies in one place from 2011 to 2019, it covers highly ranked
international publications. Further, it extends in the context of computing challenges
faced by significant Data Healthcare, Clinical Research, E-Commerce, Cloud
Computing, Fog computing, Parallel Computing, Pervasive Computing,
Reconfigurable Computing, Green Computing, Embedded Computing, Blockchain,
Digital Image Processing and IoT and Computing Technology. The survey summarizes
the large-scale data computing solutions that help in directing future research in a
proper direction. This chapter shows that the popularity of data computing technology
has steeply risen in the year 2015, and before 2011, the core research was more
popular.
Role of Artificial Intelligence in Medicine and Health Care
Authors: Pandey Upasana , Kushwaha Arvinda
Published: September 2022

With the passing decades, Artificial Intelligence (AI) is gaining high
popularity in various domains. In this chapter, we aim to present the current scenario of
the application of AI in the field of medical science. Firstly, we will introduce the early
and basic role of AI in the medical field. We preceded the chapter with a summary of
the most current applications of AI in various areas of medicine and health care. In this
review, we have discussed the latest developments of applications of AI in biomedicine
while predicting the risk of disease. Estimating the success ratio of the therapy also
manages or reduces the severity of complications, taking care of ongoing patients,
living assistance, biomedical information processing, biomedical research, and medical
imagining. We also present a survey on AI techniques, which were used by many
authors with different objectives in medical science. Furthermore, we showcase the
effects of the usage of AI by highlighting the reduction in the rate of mortality, and fast
and accurate diagnostics which help in decreasing errors related to human fatigue and
lessening medical costs. Finally, we draw attention to some of the possible weaknesses,
apprehensions, and uncertainties in using AI in medical science. We briefly review the
efforts being made to improve the healthcare industry by offering various AI-based
healthcare products.
A Pattern Optimization for Novel Class in Multi.Class Miner for Stream Data Classification
Authors: Pratap Singh Harsh , Singh Vinay , Singh Divakar , Singh Rashmi
Published: September 2022

Stream data classification involves a predicament of new class generation
through pattern evaluation. The evaluation process of the pattern raised new ways of
data classification. The evolving decoration discrepancies dispensed the session for
rivulet data arrangement. Now, the twisted pattern fashions innovative classes for
cataloging progression. For this method of regulation, multi-class sapper method is
used. A catastrophic spread of new decorating appraisal methods for multiclass mine
workers is used nowadays. We cast off the pattern optimization performance using a
transmissible algorithm aiming at the group of patterns and their heightened process for
instructing multiclass. The enhanced pattern stables the new class while enhancing the
successful multiclass miners. For the empirical appraisal, we used health care data such
as cancer and some other deride for the evolutionary progression of the pattern
optimization process.
A Review on Automatic Plant Species Recognition System by Leaf Image Using Machine Learning in Indian Ecological System
Authors: Chakraverti Sugandha , Chakraverti Ashish Kumar , Kumar Jyoti , Bhushan Singh Piyush , Ranjan Rakesh
Published: September 2022

India is the land of agriculture with many varieties of plant species. These
species have different uses in the medical, food, and harvesting industries. Despite
having such a large collection of plants and agricultural assets, most of the Indian
population is not aware of the goodness and properties of these precious plants except
the usual ones. In this chapter, discussion and possibilities in this area are given and
explored for the awareness of Indian people regarding the Indian plants. In this area,
artificial intelligence and machine learning will likely develop an automated detecting
machine that can classify and describe the plants by their images of leaves, bark,
flowers, and stems. Looking forward in this direction, this chapter discusses an AI and
ML based technique to recognize vegetation by the image of its leaves. In this
approach, SIFT and ORB-based technique removes leaf image features and then tests
the data set to match with a trained data set. The system is trained with 32 plant leaves.
Henceforth, this system can recognize these plants by the image of their leaves. The
uniqueness of this system is its data set. In the data set, the image of the leaf is prepared
so that both sides of a leaf can be used to recognize the plant. This increase
distinguishes the image irrespective of its color and shape. The system is still in an
evolving phase that has the target of including all rare and useful plant information in
this dataset. This system is very useful to preserve the information of all users, rare
plants and those plants that are about to be extinct.
Shallow Cloud Classification using Deep Learning and Image Segmentation
Authors: Ahmad Amreen , Kumar Chanchal , Yadav Ajay Kumar , Guha Agnik
Published: September 2022

Shallow clouds play a significant role in the earth’s radiation balance, but
they’re still poorly represented in climatic models. Our project analyzes the cloud
images taken from satellites and attempts to build a deep learning model to classify
cloud patterns. This will help us to identify the cloud formations and help improve the
earth’s climate understanding. We will use various deep learning and image
segmentation techniques like UNet to produce a model which can classify the shallow
layers of clouds into various labels (fish, flower, gravel, and sugar). Various data
augmentation techniques are implemented to improve the proposed model.
Additionally, transfer learning is implemented by using ResNet backbones to improve
the performance of the segmentation model. This will help gain insights into the matter
of shallow cloud effects on the earth’s climate, there by helping in the development of
next-gen climate models without having to go through the tedious task of classifying
the clouds present in the images first.
Artificial Intelligence Based Lung Disease Classification By Using Evolutionary Deep Learning Paradigm
Authors: Kale Archana P. , Angre Ankita R. , Angre Ankita R. , Paranjape Dhanashree V.
Published: September 2022

Pattern classification is also called pattern reorganization. The classification
of pattern is one of the critical problems in Artificial Intelligence. COVID-19 is the
most viral lung disease that has put the whole world in such a difficult situation that it
has become necessary to develop a machine-learning algorithm to classify lung disease.
This research paper is ready to propose Artificial Intelligence Intelligence-based Lung
Disease Classification by using an Evolutionary Deep Learning Paradigm to solve the
said problem. It delineates an integrated bioinformatics approach in which different
aspects of information from a continuum of structured and unstructured data sources
are put together to form user-friendly platforms for physicians and researchers. The
main objective of the proposed system is to find the probability, diagnosis, and
treatment of the COVID-19 disease. Artificial Neural Network-based tool for
challenges is associated with COVID-19. There is some specification of our platform
as it includes various forms of input data containing medical as well as clinical data.
This helps in improving the performance of the system. Experimental results are
calculated and statistically analyzed. For benchmarking, the performance of the
proposed approach is compared and statistically analyzed.
Identification of Covid-19 Positive Cases Using Deep Learning Model and CT Scan Images
Authors: Kumar I. , Singh S.P , Mohd N. , Rawat J.
Published: September 2022

Today, the coronavirus has widely affected the entire world. In late 2019, a
virus with the pandemic potential was reported in the city of Wuhan, situated on the
mainland of China. In no time, the virus had spread all over the world, multiplying
from person to person. Undoubtedly, COVID-19 has become an important research
topic, and many research works are coming forward daily. Thus, COVID-19 patient’s
detection has become the most personified research for the researchers. CT scanning
has been an important and widely used approach for detecting COVID-19 patients. In
this work, the identification of COVID-19 patients is performed using two different
deep neural network methods. For the image accession, the dataset having 746 samples
was used. The entire dataset has been bifurcated into two different classes, i.e.,
COVID-19 and non-COVID-19. COVID-19 class contains samples of the COVID-19
positive cases, whereas the non-COVID-19 class contains the sample of COVID-19
negative cases. In total, 506 images are used for training purposes, whereas 240 images
are used for validation. The identification is performed using MobileNet-V2 and
Modified LeNet5 convolutional neural network (CNN) models having a fixed number
of convolutional and fully connected layers. The term modified is added before the
LeNet architecture because an extra convolutional layer was created for the experiment.
As per the details and requirements, the architecture for Modified LeNet was designed,
whereas, for the MobileNet-V2, it is imported from predefined libraries and is used
further as per the author’s need. After the successful completion of the experiment, it
has been found that the accuracy of MobileNet-V2 and Modified LeNet5 came out to
85.86%, and 84.38%, respectively.
Multimodal Genetic Optimized Feature Selection for Online Sequential Extreme Learning Machine
Authors: Kale Archana P. , Sonavane Shefali P. , Kale Shashwati P. , Wade Aditi R.
Published: September 2022

Extreme learning machine (ELM) is a rapid classifier evolved for batch
learning mode unsuitable for sequential input. Retrieving data from the new inventory
leads to a time-extended process. Therefore, online sequential extreme learning
machine (OSELM) algorithms were proposed by Liang et al.. The OSELM is able to
handle the sequential input by reading data 1 by 1 or chunk by chunk mode. The
overall system generalization performance may devalue because of the amalgamation
of the random initialization of OS-ELM and the presence of redundant and irrelevant
features. To resolve the said problem, this paper proposes a correspondence multimodal
genetic optimized feature selection paradigm for sequential input (MG-OSELM) for
radial basis function by using clinical datasets. For performance comparison, the
proposed paradigm is implemented and evaluated for ELM, multimodal genetic
optimized for ELM classifier (MG-ELM), OS-ELM, MG-OSELM. Experimental
results are calculated and analysed accordingly. The comparative results analysis
illustrates that MG-ELM provides 10.94% improved accuracy with 43.25% features
compared to ELM.
Fundamentals and Properties of Superconductors
Authors: Rizwan M.
Published: September 2022

Superconductors are evolutionary materials that have applications in energy harvesting, magnet technology, power generation and transmission, maglev transportation and many more due to its outstanding properties such as zero resistance, perfect conductivity and diamagnetism and Meissner effect. These potential applications are the key behind the boost in superconductor field and research. Properties and exploration of superconductors in depth is very crucial to decide its future and potential in many technologies. Superconductors are a fast field and thus are classified in many categories such as type I and II semiconductors, high and low Tc superconductors based on coherence length and critical temperature respectively.
Bulk Superconductors: Materials and Applications
Authors: Hitaish P.
Published: September 2022

Bulk or high temperature superconductors are materials that are not perfect diamagnetic, but attain zero resistivity or infinite conductivity below a well-defined temperature known as critical temperature (TC). In the presence of external magnetic field, such material exhibit two critical field values HC1 and HC2. These materials may exist in three different states: superconducting, vortex or mixed and normal state depending upon the intensity of the applied field. The principal application of such SCs is in compact strong and stable field permanent magnets, and the magnetic flux density of these magnets is much higher than conventional permanent magnets. This chapter has discussed various types of high-temperature superconductors, their physical properties, applications, and recent research in the field.
Soft Superconductors: Materials and Applications
Authors: Bugdayci M.
Published: September 2022

Superconductors emerge as materials that offer great benefits with the superior properties they offer. In this study, the general properties of superconducting materials, their history and properties of soft superconductors are emphasized. Superconductivity mechanisms are investigated through crystal structures and BCS theory. Superconductivity properties of A3B, perovskite, MMo6X8&M2A3X3 compounds were evaluated over crystal symmetries. In addition, research on the production methods and usage areas of superconductors are presented in this section.
Metallurgy of Lightweighting
Authors: Fisher David
Published: September 2022

The existential threat of global warming has triggered the need to reduce the energy consumption of vehicles. This can be achieved by reducing the weight of vehicles; a process known as lightweighting. The book reviews recent progress in this multifaceted discipline and discusses possible future developments. It references 214 original resources with their direct web links for in-depth reading.