bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Pattern Based Customized Approach for Stock Market Trend Prediction With Big Data and Machine Learning Techniques
Authors: Verma Jai Prakash , Tanwar Sudeep , Garg Sanjay , Gandhi Ishit , Bachani Nikita H.
Published: July 2022

The stock market is very volatile and non-stationary and generates huge volumes of data in every second. In this article, the existing machine learning algorithms are analyzed for stock market forecasting and also a new pattern-finding algorithm for forecasting stock trend is developed. Three approaches can be used to solve the problem: fundamental analysis, technical analysis, and the machine learning. Experimental analysis done in this article shows that the machine learning could be useful for investors to make profitable decisions. In order to conduct these processes, a real-time dataset has been obtained from the Indian stock market. This article learns the model from Indian National Stock Exchange (NSE) data obtained from Yahoo API to forecast stock prices and targets to make a profit over time. In this article, two separate algorithms and methodologies are analyzed to forecast stock market trends and iteratively improve the model to achieve higher accuracy. Results are showing that the proposed pattern-based customized algorithm is more accurate (10 to 15%) as compared to other two machine learning techniques, which are also increased as the time window increases.
Application of Machine Learning Techniques in Healthcare
Authors: Sathya D.  , Sudha V.  , Jagadeesan D.
Published: July 2022

Machine learning is an approach of artificial intelligence (AI) where the machine can automatically learn and improve its performance on experience. It is not explicitly programmed; the data is fed into the generic algorithm and it builds logic based on the data provided. Traditional algorithms have to define new rules or massive rules when the pattern varies or the number of patterns increases, which reduces the accuracy or efficiency of the algorithms. But the machine learning algorithms learn new input patterns capable of handling complex situations while maintaining accuracy and efficiency. Due to its effectual benefits, machine learning algorithms are used in various domains like healthcare, industries, travel, game development, social media services, robotics, and surveillance and information security. In this chapter, the application of machine learning technique in healthcare is discussed in detail.
Machine Learning for Emergency Department Management
Authors: Benbelkacem Sofia , Kadri Farid , Atmani Baghdad , Chaabane Sondès
Published: July 2022

Nowadays, emergency department services are confronted to an increasing demand. This situation causes emergency department overcrowding which often increases the length of stay of patients and leads to strain situations. To overcome this issue, emergency department managers must predict the length of stay. In this work, the researchers propose to use machine learning techniques to set up a methodology that supports the management of emergency departments (EDs). The target of this work is to predict the length of stay of patients in the ED in order to prevent strain situations. The experiments were carried out on a real database collected from the pediatric emergency department (PED) in Lille regional hospital center, France. Different machine learning techniques have been used to build the best prediction models. The results seem better with Naive Bayes, C4.5 and SVM methods. In addition, the models based on a subset of attributes proved to be more efficient than models based on the set of attributes.
Early Stage Diagnosis of Eye Herpes (NAGIN) by Machine Learning and Image Processing Technique
Authors: Nikam Kakasaheb Rangnarh
Published: July 2022

Eyes are very important parts of the body. Automatic eye detection is must to diagnose various eye disease including Herpes (Nagin) in early stages. Type 1 Herpes Simplex Virus (HSV) may damaging the eye and causing permanent eyesight problems. Herpes keratitis, commonly known as eye herpes, is an inflammation of the cornea, the clear dome that covers the front part of the eye. The proposed method potentially reduce workload on eye doctors and increase the efficiency of limited healthcare resources.
Strategies to Improve B2B Customer Relationships Through Digitally Enabled Experiences
Authors: Colm Laura Ingrid Maria
Published: July 2022

Offering experiences—memorable exchanges and interactions with a brand—has become a top priority for companies of any sector and dimension over the last decades, especially in the light of the advent of digital technologies. However, to date both academic and practitioner attention is mostly limited to B2C contexts, thus calling for a deeper investigation of the opportunities that experiences offer also to B2B firms. After a brief review of the literature on customer experience and the role of digitalization—especially after COVID-19—this chapter focuses on digitally enabled experiences to improve B2B customer relationships. The chapter is based on a three case vignettes analysis that emphasizes how B2B firms can make use of digital tools to craft more effective and impactful customer experiences. Three digitally enabled B2B experiential strategies are identified—downstream empowerment, farsighted customization, and relational flexibility—that ultimately support B2B companies in improving their customer relationships in the long run.
Teleworking as an Emerging Strategy During COVID-19
Authors: Dogru Caglar
Published: July 2022

The world is facing one of its hardest pandemics ever with COVID-19. It has been a very tough challenge since the pandemic urged radical changes and transformations in every aspect of life and especially in the work life. During the pandemic, teleworking has gained a vital role for sustaining the production of goods and services. Although teleworking has both positive and negative outcomes, it has been a life jacket for not only organizations but also for employees all around the world during the COVID-19 pandemic. In this chapter, the role of teleworking in the human resources during the pandemic, between May 2020 and January 2022, is evaluated monthly on the basis of analyzing the descriptive statistics of people teleworking or working at home for pay in the United States, by their specific characteristics grouped as age, gender, full-time/part time job status and occupations.
COVID-19 Policy Measures and Reflections on the Turkish Economy and Foreign Trade
Authors: Bayraktutan Yusuf , Solmaz Ali Rıza
Published: July 2022

The first COVID-19 case in Turkey was seen on March 11, 2020. Various measures have been taken and restrictions have been introduced to reduce the rate of spread since then. International and intercity transport of goods and passengers were limited and/or stopped and curfews/restrictions were applied. The activities of the service sector were regulated/stopped, and their activities were allowed on the condition that certain criteria were met. Working style and hours in public and private sector were designed. The restrictions on economic and social life were gradually lifted. In addition to the loss of people and workforce caused by the pandemic, the reflections of the measures put in effect in terms of economic concerns on national economy have been clearly observed. In this chapter, the economic effects of the pandemic process in Turkey; the short-term results in selected/main indicators, like inflation, unemployment, GDP, trade, etc.; and economic recovery due to policy measures will be evaluated.
Performance Measurement Systems Threatened by Pandemic Opportunities in Retail
Authors: Girardi Andrea
Published: July 2022

Performance measurement systems such as KPIs are necessary managerial tools to set targets and monitor performances. During the outbreak of COVID-19, some companies experienced threats to traditional and consolidated business lines. The retail sector saw no decline in turnover. The aim of the chapter is to uncover and analyse the challenges imposed by COVID-19 to the retail sector as well as possible solutions implemented in order to overcome such difficulties. The chapter reports the case study of the largest retailer in Italy during the COVID-19 pandemic. Although seeing sales increase, the managers had many difficulties on the side of procurement and distribution of goods. The performance monitoring system was suddenly insufficient to manage the emergency. The solutions concerned the adjustment of the algorithms with dynamic components addition to KPIs and the conversion of some efficiency targets into effective ones. The chapter suggests insights on how performance measurement systems can be designed and developed in advance to allow more dynamism during emergencies.
Innovative Digital Transformation Strategies of Large Suppliers for Mexican Corner Stores During a Pandemic
Authors: Diaz Regina G. , Montalvo Raul F.
Published: July 2022

With the pandemic, digital transformation (DT) was accelerated, and consumers adopted new consumption patterns. Therefore, suppliers had to adapt their business models quickly. This study aims to understand the strategies of large suppliers on Mexican corner stores (CS) during the pandemic and show the perception of CS about these DT strategies. Many digital platform initiatives that were developed by both the government and large suppliers are introduced in this chapter. Additionally, interviews with large suppliers' executives were consulted to clarify their activities that were developed in favor of CS. By conducting 20 interviews with CS, the authors found their perception of large suppliers' DT actions. Findings reveal that the lack of training, investment, and incentives are key factors for the entire virtuous cycle to be fulfilled.
Turning a Crisis Into an Opportunity
Authors: Bonetti Francesca , Vecchi Alessandra
Published: July 2022

The impact of the COVID-19 pandemic has not been uniform across industries. In some, we have witnessed a remarkable degree of innovation which has seen the establishment of a vast array of business models. By contrast, in others innovation has been rather slack. These conflicting trends raise the questions of whether the COVID-19 pandemic has accelerated the demise of certain industries or hastened the emergence of others. By coupling the crisis management literature with organizational learning theory and by relying on 23 in-depth interviews, the chapter provides a taxonomy of the various types of innovation responses to the existing crisis and disruption. The taxonomy is instrumental to gain a better understanding of how companies across countries and industry sectors respond to disruption by innovating and the valuable lessons that can be drawn from this experience.
Sustainability Issues and Livelihood Coping Strategies in the COVID-19 Pandemic
Authors: Islam Md. Shafiqul
Published: July 2022

The COVID-19 pandemic in Bangladesh tells us that economic activities and alternative livelihoods have been disturbed. There are challenges in the areas of equitable distribution of resources and providing economic support to the disadvantaged groups (cash and kind support, food supports, social safety net). This research explores the short term livelihood coping strategies to respond to and recover from COVID-19 with a special focus on socio-demography, socio-economy, food security, and health aspect. As the crisis develops, future rounds of representative monitoring data on the same respondents will help us understand the evolving impacts and potential recovery.
Evaluation of Supply Chain Performance Using the Balanced Scorecard Approach During the COVID-19 Pandemic
Authors: Maryono Doni , Ambarwati Rita
Published: July 2022

The pandemic has an impact on almost all sectors of people's lives, both in the economic, political, and socio-cultural sectors. The government has implemented large-scale social restrictions (PSBB) as an effort to stop the spread of the COVID-19 virus. With the PSBB, it causes disruption to the hospital supply chain management. As a step to anticipate the hospital's impact on the PSBB, the hospital needs to evaluate the performance of supply chain management. The purpose of this chapter is to describe the performance evaluation of hospital supply chain management using a balanced scorecard approach. A new finding from this study is to measure the performance of hospitals experiencing various disruptions in their supply chain management caused by the COVID-19 pandemic with a balanced scorecard. The author concludes that measurements with the balanced scorecard approach can provide information about the performance of hospital supply chain management broadly in areas experiencing disruption due to the COVID-19 pandemic.
Using YouTube in Political Persuasion
Authors: Özoral Başak , Tam Mehmet Sinan
Published: July 2022

Political advertising is a form of campaigning that allows candidates to directly convey their message to voters and influence the political debate. South African political parties (African National Congress and the Democratic Alliance) also adopted the use of social media networks as an effective tool for political campaigning purposes and to encourage the public to engage in political discourse. The chapter investigates how both the South African public and the political parties are using YouTube for discussion, debate, and opinion formation during the 2019 South Africa elections. Sociological analysis method is used in the research. The subject is especially about Aristotle's concept of rhetoric, promises of political messages, and political persuasion in written form. The findings part of the study shows that the most prevailant issues in the election campaigns were corruption, failure to keep promises, and the effort to create a new leader identity in the DA party. By ANC, independence, Mandela's contributions and past actions, and the importance of voting were mentioned.
From an Empire to Brexit
Authors: Çarboğa Onur Serdan , Kaya Yıldırım Ece Nur
Published: July 2022

To comprehend how British advertising gained its unique identity, this study reviews historical events and phenomena such as imperialism and Brexit, discusses the cultural structure of British society, and considers the advertising industry's political-economic structure. It utilises a modern branch of critical discourse analysis called multimodal discourse analysis and extensively studies the HSBC TV commercial titled ‘Home To So Much More', focusing on how its visuals create and represent Britishness and the British way of making advertisements.
Consumer Expectations From Brands During COVID-19
Authors: Gupta Adarsh , Mandal Pratap Chandra
Published: July 2022

Consumer expectation is a crucial factor that leads to consumer satisfaction. In this competitive environment, brands must be aware of consumer expectations. The coronavirus pandemic is a worldwide crisis that has changed the lifestyle, has increased health concerns, and has made economies moribund leading to a change in consumer expectations. This research aims at understanding the consumer expectations from brands amid COVID-19 from a qualitative perspective. The researchers applied Grounded Theory for the analysis. The researchers conducted depth interviews to collect the data. The analysis used open coding of data, development of memos, generation of concepts, categories and subcategories identification, axial coding to identify relationships among the categories, and selective coding to ascertain the core category. The study generates concepts of consumer expectation amid pandemic, which will form the dimensions to evaluate consumer satisfaction. The study provides insights to brands based on which they can work to satisfy consumer expectations.
Digitalization of Lifecycle Management of Domestic Russian Tour Products Based on Problem-Oriented Digital Twins-Avatars, Supply Chain, 3D-Hybrid, Federated, and Coordinated Blockchain
Authors: Mkrttchian Vardan , Voronin Viacheslav
Published: July 2022

The Article fits into a moment of operational uncertainty and theoretical redevelopment of the nature of tourism in a society marked by geopolitical turmoil and declining international security, as well as rapid changes at the global level, including the pandemic, which is currently posing new challenges for the sector. Today, it is more relevant and appropriate than ever to reflect on them, with the new, digital energy of blockchain technology, using a fundamental approach to digitalizing the decentralized lifecycle management of the domestic Russian tour product with problem-oriented digital twin avatars, supply chain, volumetric hybrid and federated-consistent blockchain. The goal of the article is theoretical study and practical implementation, in the form of basic models and software modules, artificial intelligence algorithms in managing the life cycle of an internal Russian tour product use Laboratory for digitalization and management, using multi-agent models of intelligent digital twins-avatars, is being created, the purpose of these studies is to solve a scientific problem.
Analysis and Prediction of Diabetes Disease Using Machine Learning Methods
Authors: Samet Sarra , Laouar Mohamed Ridda , Bendib Issam , Eom Sean
Published: July 2022

To increase healthcare quality, early illness prediction helps patients prevent potentially life-threatening health issues before it is too late. Artificial intelligence is a rapidly evolving area, and its applications to diabetes, a worldwide epidemic, have the potential to revolutionize the way diabetes is diagnosed and managed. A total of six supervised machine learning algorithms based on patient data were used and compared to predict the diagnosis of diabetes mellitus. For experiments, the Pima Indians Diabetes Database was used, and their missing values were carefully handled by different techniques. For random train-test splits, the Random Forest classification algorithm achieved an accuracy rate of 92 percent. This model outperforms other state-of-the-art approaches due to the application of a combination of techniques for dealing with missing values (the mixture of imputing missing values techniques) that is proposed. With this approach, the models of this manuscript achieved better accuracy than prior work done with the Pima diabetes data.
A Hybrid PSO-ACO Algorithm to Facilitate Software Project Scheduling
Authors: Praveen Joe I. R.  , Malathy E. M.  , Aishwarya S.  , Akila R.  , Akshaya A.
Published: July 2022

While tailoring software applications, in the present day scenario, there is a huge demand for cost effective products however it is so challenging for the developers on how to make a trade-off between cost and quality as well as between quality and time. Late delivery, sometimes, has become inevitable due to the reasons like: improper time schedules, technical issues, changing customer requirements, miscommunication amongst team members, failure to analyse the risks involved, and underrating of resources like man cost, power, effort, etc. Here in this work, an attempt has been made to study the advantages of swarm based optimization techniques over traditional optimization approaches and thereby employ them efficiently to schedule projects. A hybrid PSO-ACO algorithm is proposed for scheduling purposes. The nuances of natural swarm cultures are appropriately incorporated in to the software scheduling scenario and the results are recorded and reported. It is observed that the readings are passible and are bench marked through relevant measures of quality.
A Survey for News Credibility in Social Networks
Authors: Yasser Farah , AbdelMawgoud Sayed AbdelGaber , Idrees Amira M.
Published: July 2022

Text mining has been a vital area that has been linked to some fields of research such as machine learning, data analysis and gathering, and information recovery. To extract knowledge and information, Natural Language Processing (NLP) was used alternative techniques. Text mining analyses unstructured data to provide critical data and information plans in a timely manner. Nowadays everyone uses online communication activities to keep in touch with others in their daily life. As a result, they're a great way to connect. Not sorting in a paragraph in a format suitable for word recognition has become a point of contention. intensity can cause a variety of inconsistencies, such as lexical, semantic, linguistic, and syntactic ambiguities, determining the proper data arrangement. Information and data are required for learning things and reaching knowledge. This paper covered how to use text mining to determine the credibility of news on social media. The findings of this study could be used as the basis for future text mining research.
Design of CPW-Fed Conformal T-Shaped Flexible Patch Antenna With Slits for Biomedical Applications
Authors: Naik Ketavath Kumar , Sailaja B. V. S.
Published: July 2022

In this paper, a conformal T-shaped flexible patch antenna is designed for biomedical applications. The proposed antenna designed using to resonate at 2.45 GHz operating frequency at ISM band. The proposed antenna operates in free space and muscle tissue. The proposed T-shaped patch antenna in free space resonates at 2.47GHz operating frequency with a bandwidth of 300MHz (2.45GHz and 2.75GHz).The antenna implanted in muscle tissue operates at 2.47GHz frequency with a bandwidth of 600MHz (2.25GHz – 2.85GHz). The return loss of -13.04dB is observed for T-shaped patch antenna in free space and the return loss for the antenna implanted in human tissue is -20.6dB respectively. The proposed flexible conformal patch antenna is compact in size, efficient in functioning, safe, and can effectively work within specified medical frequency bands.
Educational Technologies as Pedagogical Tools
Authors: Shambare Brian , Simuja Clement , Olayinka Theodorio Adedayo
Published: July 2022

One of the recent developments across education priorities in South Africa is the provision of Information and Communication Technologies (ICTs) to schools to achieve the objectives of the country’s National Development Plan (NDP) agenda 2030. With the current focus on adoption of technology for teaching and learning, it is imperative to fully understand the perceptions and attitudes of teachers towards the use of ICT in their practice. Thus, this study investigated the teachers’ perceptions and attitudes on the use of ICTs for teaching and learning. The findings are based on data collected through semi-structured interviews, journal reflections, and lesson observations. The results reveal that most teachers have positive perceptions towards the use of ICT for teaching and learning. It also indicated that the attitudes of teachers are varied based on the level of ICT training and support that they receive. The results of this research might be foremost to provide clear insights that could be contributing to the effective adoption of technology for teaching and learning.
Using Machine Learning in WSNs for Performance Prediction MAC Layer
Authors: Alaoui El Arbi Abdellaoui , Messai Mohamed-Lamine , Nayyar Anand
Published: July 2022

To monitor environments, Wireless Sensor Networks (WSNs) are used for collecting data in divers domains such as smart factories, smart buildings, etc. In such environments, different medium access control (MAC) protocols are available to sensor nodes for wireless communications and are of a paramount importance to enhance the network performance. Proposed MAC layer protocols for WSNs are generally designed to achieve a good performance in packet reception rate. Once chosen, the MAC protocol is used and remains the same throughout the network lifetime even if its performance decreases over time. In this paper, we adopt supervised machine learning techniques to predict the performance of CSMA/CA MAC protocol based on the packet reception rate. Our approach consists of three steps: experiments for data collection, offline modeling and performance evaluation. Our analysis shows that XGBoost prediction model is the better supervised machine learning technique to enhance network performance at the MAC layer level. In addition, we use SHAP method to explain predictions.
Application of Cold-Chain Logistics and Distribution Systems Using Deliver Schedule Management
Authors: Yin Qingwei , Tian Qian
Published: July 2022

Cold chain logistic firms have been motivated to decrease overall operating costs and carbon emissions to capture economic edge and maintain profitability by intense competition and financial energy requirements. C Our analysis develops a model cold-chain logistic and distribution system (CC-LDS) for logistics and transport firms working together to manufacture chilled and frozen goods by introducing carbon tax policies. The CC-LDS model provides a logistics and transport network. Virtual annealing (VA) algorithm for optimising the model is implemented based on actual customer information from multiple cold storage firms and 30 clients. The findings suggest that the second derivative is optimal compared to the individual distribution to slash overall expense and carbon pollution. The net cost is strongly associated with the cost of carbon, and energy consumption is similar to the price of carbon grows. Moreover, carbon caps have little effect on the direction of distribution.To best leverage social and technological capital to accomplish equal financial and ecological gains.
Heuristics' Library for Enterprise Architecture and Portfolio Alignment
Authors: Mesquita André , Vasconcelos André
Published: July 2022

It is a complex challenge to align and assess the strategic planning of organizations in the context of the Enterprise Architecture (EA) with the implementation of project portfolios. Thus, this research proposes a library of heuristics that allows evaluating the alignment of project portfolios to the desired qualities, given a strategy and a set of objectives. The heuristics related to the metrics are proposed to enable the measurement of qualities in EA such as reliability, availability, performance, security as well as alignment. Several heuristics are identified for each quality: regarding functionality, it is possible to identify three heuristics, for the quality reliability it is possible to identify one heuristic, for the quality of maintenance it is possible to identify four heuristics, for the quality alignment it is possible to identify nine heuristics and, finally, for the quality dimension it is possible to identify three heuristics. The solution is applied to government projects in the context of identification documents.
Metamorphic Testing of Image Classification and Consistency Analysis Using Clustering
Authors: Gudaparthi Hemanth , Naidu Prudhviraj , Niu Nan
Published: July 2022

Testing deep learning systems requires expensive labeled data. In recent years, researchers began to leverage metamorphic testing to address this issue. However, metamorphic relations on image data remain poorly understood. To gain a deeper understanding of these metamorphic relations, we survey common image operations modeling covariate shift, manually classify and categorize the underlying metamorphic relations, and conduct experiments to validate our classifications. In our experiments, we train three popular convolutional neural network architectures on an image classification task. Next, we apply metamorphic operations on input test images and measure the change in classification accuracy and cross-entropy loss. A hierarchical clustering algorithm cluster these results and plots a dendrogram. We compare the groups from manual classification and the clusters from the algorithm to provide key insights. We find that Affine and Noise relations are consistent. Furthermore, we recommend metamorphic relationships to save time and better test deep learning systems in the future.
The Role of Economic and Political Power in Preservation of Status-Quo and Markets Integration
Authors: Vlad Geogiana Daniela , Necula Anda-Ileana , Mieilă Mihai , Manole Andrei Marcel
Published: July 2022

After the end of the Cold War, and until September 11th, 2001, the academic and media sources generally considered that the military power reached its end, considering that the emerging economic power represents the power of the future. During this decade, the United States affirmed itself as the world’s dominant power, having the undisputed role of an economic superpower. This paper tries a brief examination of the concept of economic power, its semantics, and a possible application, at least in theory, to a possible future situation concerning China’s economic growth, as a threat to the current status of the United States. On the other hand, humankind is preparing for “The Great Reset”, towards a total transformation of the actual global status-quo, according to the programs drawn by the world leaders within the UNO and the Davos Economic Forum sessions. The decisions envisage the roadmap in order to deepen the social and economic world integration, aiming the elimination of boundaries, to a “planet village”, depicting unprecedented historical developments.
The Patentability of AI Invention
Authors: Almarzoqi Reem , Albakjaji Mohamad
Published: July 2022

Artificial intelligence has gained momentum in the recent past due to technological advancements. As a result, it has elicited an endless debate particularly in light of intellectual property laws. Artificial Intelligence is currently considered as an emerging issue. This make it as a challenging issue for the existing legal frameworks which are unable to govern and regulate it in a proper and effective way. KSA has put more effort and resources in streamlining the legal aspects to govern AI and related innovations. The current research aims to explore some of the potential difficulties encountered in patentability of AI inventions. It also aims to determine the legal challenges the researcher expect to emerge with the development of AI technology. The findings of this study indicate that KSA still finds a huge gap in terms of laws that governs AI generated innovations. This study is significant in the sense that it has pointed out some of the challenges experienced in enacting effective intellectual property laws to govern AI innovations.
Flow-Based Anomaly Detection Using BNN for Attack Mitigation on SDN
Authors: Lwin Nang May Phu , Win Su Thawda
Published: July 2022

Distributed Denial of Service (DDoS) attack remains one of the major issues that compromises the resources and services of the components in Software Defined Networks (SDN) environments. The implementation of intrusion prevention system (IPS) in OpenFlow-based SDN architecture has emerged to strengthen the security mechanisms by exploiting the concepts of SDN and OpenFlow protocols. This article provides the anomaly detection of the live traffic flow with Backpropagation Neural Network (BNN) for the online detection and mitigation of DDoS attacks. The dataset from the testbed is used to emulate the efficiency of the proposed method. The results achieve more than 90% detection accuracy with less than 6% false alarm rate. CUP utilization on the centralized controller is also measured by means of SYN and UDP flooding to calculate the effect of malicious traffic on the resources of the system.
Application of E-Commerce Recommendation Algorithm in Consumer Preference Prediction
Authors: Wang Wei
Published: July 2022

Through user characteristic information, user interaction behavior, commodity characteristic information, recommendation engine, and related technologies in data mining, this paper makes a more in-depth study, and analyzes the problems of "big data volume", "cold start" and "data sparsity" in the recommender system in modern business websites. In response to these problems, this paper transforms the problem of large data volume into the problem of large user groups. Then, after using the k-means clustering algorithm to divide the large user group into homogeneous user groups to alleviate the problem, a combination of collaborative filtering algorithm and content-based recommendation algorithm in the homogeneous user group is proposed to alleviate this problem. The experimental precision and recall are both around 0.4, and when W=0.8, the F value is the largest.
Reliability and factorial validity of long and brief versions of the inventory of personality organization in a Latvian sample
Authors: De Starceva-Apele Aija , Rascevska Malgozata
Published: July 2022

The aim of this study was to examine the construct validity and reliability of long and brief versions (57 and 16 items) of the Inventory of Personality Organization (IPO) in a Latvian non-clinical sample. The actuality of this study is dictated by the number of persons suffering from personality disorders, and the excessive need for reliable constructs for the assessment of personality in the normality-abnormality range. The total number of participants of the study was 1118, recruited in several subsamples, which were employed for the investigation of the factor structure, psychometric properties, test-retest, and convergent validity of the IPO-57 and IPO-16 scales. The 3-factor structure, obtained by confirmatory factor analysis, was replicated in the Latvian-speaking sample for long and brief IPO versions. Both constructs achieved adequate model fit psychometric indices and sufficient internal consistency, and the test-retest reliability was also approved. The convergent validity of IPO with aggressivity, negative affectivity, and non-adaptive personality measures was established. In the current study, we adapted a very fundamental psychodynamic/psychoanalytic construct in the Latvian language and replicated the previously empirically established IPO three-dimensional model, whereas the novelty in IPO construct convergent validity research was the establishment of convergent validity with non-adaptive personality traits measured by the Personality Inventory for DSM-5 Brief Form (PID-5-BF). Both versions of IPO are recommended for further use in the research in Latvian language samples, for clinical purposes to assess the level of psychic functioning, and for treatment planning and evaluation purposes.
Pomegranate seed extract enhances the inhibitory effect of adipose-derived mesenchymal stem cells on breast cancer cell line in co-culture conditions
Authors: Moradi-Gharibvand Nahid , Setayeshmehr Mohsen , Kazemi Mohammad , Safaee Azadeh , Khorsandi Laya Sadat , Nejad Darioush Bijan , Hasheminia Seyed Javad , Hashemibeni Batool
Published: July 2022

Background and purpose:

Pomegranate seed extract (PSE) possesses anticancer activities and healing effects. Adipose-derived stem cells (ADSCs) are being considered a new candidate for cancer treatment. The purpose of this study was to investigate the effect of PSE on the cell cycle and apoptosis of the MCF-7 cell line in the co-culture condition with ADSCs.

Experimental approach:

MCF-7 and ADSC cells (ratio 1/1) were cultured in a transwell plate with and without PSE (PSE-co-culture and co-culture groups). MCF-7 cells were cultured in monolayer without and with PSE (mono-culture and PSE-mono-culture groups). MCF-7 cell line was harvested on day 5 and cell viability, apoptotic activity, cell cycle, and gene expression were evaluated.

Findings / Results:

The results of the MTT assay indicated that PSE at 100 μg/mL has the highest cytotoxicity on the MCF-7 in the PSE-co-culture group. The cell cycle analysis revealed that ADSCs in combination with PSE significantly increased the population of MCF-7 cells in the G1 phase, resulting in the arrest of MCF-7 cells cycle in the G0/G1 transition. In addition, the most apoptotic MCF-7 cells (41.5%) were detected in the same group. Expression of BAX and caspase3 genes were upregulated while anti-apoptotic (BCL-2) and angiogenesis inducer (VEGF) genes were downregulated in the PSE-co-culture group compared with the other groups.

Conclusion and implications:

ADSCs reduced cell viability and proliferation of MCF-7 cells in co-culture conditions and adding PSE to the medium increased the apoptosis of cancer cells. This study suggests that ADSCs with PSE can suppress tumor cells.
Anti-nociceptive effect of black seed oil on an animal model of chronic constriction injury
Authors: Talaei Sayyed Alireza , Banafshe Hamid Reza , Moravveji Alireza , Shabani Mohammad , Tehrani Shiva Shirazi , Abed Alireza
Published: July 2022

Background and purpose:

Traditionally, Nigella sativa L. has been known as a medical intervention to treat numerous diseases. This study aimed at investigating the antihyperalgesic effect of black seed oil (BSO) in an experimental model of neuropathic pain.

Experimental approach:

Chronic constriction injury (CCI) was performed under anesthesia. The sciatic nerve was ligated with four loose ties. Two separate protocols were used to administer BSO. In chronic treatment, rats were given daily doses of BSO (250, 500, and 1000 mg/kg p.o.) from the 1st day until the 21st post-CCI day. While, in acute treatment, BSO (250, 500, and 1000 mg/kg p.o.) was administered only on the 7th, 14th, and 21st days. CCI and sham groups were given almond oil according to the same schedule. Behavioral scores were determined by evaluation of the paw withdrawal in the plantar, Von Frey, and acetone tests, on the 7th, 14th, and 21st days.

Findings/Results:

Our results showed that CCI leads to significant allodynia and hyperalgesia in the ipsilateral paw after surgery. Chronic administration of BSO (500 and 1000 mg/kg) obviously attenuated heat hyperalgesia and mechanical allodynia. However, daily administration of BSO did not alter cold allodynia. Nevertheless, when BSO was administered, 30 min before the pain assessment tests, hypersensitivity was not improved in the treated animals.

Conclusion and implications:

These results demonstrated BSO can inhibit neuropathic pain progression and suggests a potential use of BSO to manage hyperalgesia and allodynia. However, additional research is necessary to approve BSO effectiveness, in neuropathic pain conditions.
Design of two immunotoxins based rovalpituzumab antibody against DLL3 receptor; a promising potential opportunity
Authors: Ataee Mohammad Hossein , Mirhosseini Seyed Ali , Mirnejad Reza , Rezaie Ehsan , Hosseini Hamideh Mahmoodzadeh , Amani Jafar
Published: July 2022

Background and purpose:

The lack of a new effective treatment for small cell lung cancer (SCLC) is an unresolved problem. Due to the new identification of delta-like ligand 3 (DLL3) and its high expression in SCLC patients, the use of DLL3 in target therapy can be effective. The use of bacterial toxins belonging to the ADP-ribosyl transferase toxins family and human enzymes to remove cancerous cells has been effective in the structure of immunotoxins. In this study, single-chain fragment variable of rovalpituzumab antibody fused to granzyme B (Rova-GrB) and PltA of typhoid toxin (Rova-Typh) as immunotoxins were designed, and bioinformatics analysis was done.

Experimental approach:

In silico analysis including the physicochemical properties, evaluation of the secondary and tertiary structure, refinement and validation of 3D models, and docking were performed. Immunotoxin genes were cloned and expressed in the Escherichia coli BL21 (DE3) host, purified, subsequently confirmed by western blotting and their secondary structure was evaluated by the circular dichroism method.

Findings/Results:

The bioinformatics analysis showed that Rova-GrB and Rova-Typh had hydrophilic properties, their codon optimization parameters were standard, validation parameters were improved after immunotoxin refinement, and docking analysis showed that the binding domain of immunotoxins could bind the N-terminal region of DLL3. immunotoxins had high expression and after purification under denaturing condition by Ni-NTA column, the immunotoxins were dialyzed against PBS buffer.

Conclusion and implications:

The immunotoxins had the right structure and can be produced in a prokaryotic host. The recombinant immunotoxins against DLL3 can be promising therapeutic agents for SCLC cancer.
A pilot study of intraoperative intercostal nerve block during uniportal thoracoscopic wedge resection of the lung
Authors: Kang Do Kyun , Kang Min Kyun
Published: July 2022

BACKGROUND AND AIMS:

Uniportal thoracoscopic surgery has been reported to result in alleviating the postoperative pain when compared with traditional video-assisted thoracoscopic surgery (VATS). However, postoperative pain is still the main concerns associated with thoracic surgeries. The objective of this study is to evaluate the postoperative pain of patients undergoing uniportal VATS, especially wedge resection, with the use of intraoperative intercostal nerve block.

METHODS:

All consecutive patients undergoing the uniportal VATS wedge resection between January 2019 and March 2020 were reviewed retrospectively. Twenty consecutive patients in Group A underwent the uniportal VATS wedge resection without intraoperative intercostal nerve block. The other 20 consecutive patients in Group B underwent the uniportal VATS wedge resection with intraoperative intercostal nerve block. The numeric pain rating scale (NRS) scores were recorded at 1, 12, and 24 h, postoperatively. The number of opioid consumption was also recorded until the time to chest tube removal.

RESULTS:

There was no difference between groups with regard to sex, age, chest tube duration, length of stay, operative time, laterality time, and diagnosis. There was a significant difference in postoperative NRS scores at 1 h (P = 0.001) and 12 h (P = 0.022) between the groups. The opioid consumption was significantly in Group B lower than those in Group A (P = 0.025).

CONCLUSION:

The intraoperative intercostal nerve block with bupivacaine provided immediate postoperative pain relief with reducing the postoperative opioid consumption compared in patients who underwent uniportal VATS, especially wedge resection of the lung.
Continuous positive airway pressure therapy suppresses inflammatory cytokines and improves glucocorticoid responsiveness in patients with obstructive sleep apnea and asthma
Authors: Mahboub Bassam , Kharaba Zelal , Ramakrishnan Rakhee K. , Sharif Askari Narjes Saheb , Salameh Laila Ibraheem , Alhariri Hassan Saber , Vats Mayank G. , Erabia Wafa Taleb , Alshawamreh Esra’a Mohammad , Alfoteih Yassen , Mogas Andrea K. , Halwani Rabih , Hamid Qutayba
Published: July 2022

CONTEXT:

Asthma and obstructive sleep apnea (OSA) are prevalent respiratory disorders that frequently coexist. Continuous positive airway pressure (CPAP) therapy is the standard treatment for OSA. However, its effects on systemic inflammation and glucocorticoid responsiveness in OSA patients with asthma are largely unknown.

AIMS:

To examine the potential role of CPAP therapy in reducing systemic inflammation and improving glucocorticoid responsiveness in asthmatic patients with OSA.

SETTINGS AND DESIGN:

A case–control study was conducted at the respiratory and sleep clinics involving patients with OSA and patients with asthma and OSA.

METHODS:

The levels of inflammatory asthma biomarkers (interleukin [IL]-4, IL-17A, IL-8, IL-2, and interferon-γ [IFN-γ]), and glucocorticoid receptors (GR)-α and GR-β, were determined to compare systemic inflammation and glucocorticoid responsiveness between pre- and post-1-month CPAP treatment in both groups.

STATISTICAL ANALYSIS:

The Wilcoxon signed-rank test was used to compare inflammatory biomarkers before and after CPAP therapy. P < 0.05 considered statistically significant. The analysis was performed using SPSS.

RESULTS:

Recruited patients (n = 47), 51% (n = 24) had OSA and 49% (n = 23), had OSA with asthma. Interestingly, the blood levels of IL-17 and IL-8 were significantly decreased post-CPAP therapy in OSA patients, whereas IL-4, IL-17, and IFN-γ were significantly reduced post-CPAP treatment in OSA patients with asthma. Remarkably, CPAP therapy improved glucocorticoid responsiveness in asthmatic patients with OSA, but not in the OSA group and an increase in the GR-α/GR-β ratio was noted post-CPAP therapy.

CONCLUSIONS:

Continuous positive airway pressure therapy improved responsiveness to glucocorticoid treatment and demonstrated a suppressive effect on proinflammatory cytokines in asthmatics with OSA.
Comparison of radial endobronchial ultrasound-guided transbronchial lung biopsy with distance measurement versus with guide sheath in diagnosing peripheral pulmonary lesions with a diameter ≥3 cm by thin bronchoscope
Authors: Guan Shuhong , Zhou Jun , Zhang Qiudi , Xu Qianqian , Xu Xiong , Zhang Sujuan
Published: July 2022

OBJECTIVE:

This study aims to explore the diagnostic values of radial endobronchial ultrasound-guided transbronchial lung biopsy with distance (rEBUS-D-TBLB) measurement and with guide sheath (rEBUS-GS-TBLB) for peripheral pulmonary lesions (PPLs) with a diameter ≥3 cm by thin bronchoscope.

PATIENTS AND METHODS:

Six hundred and three patients with PPL (diameter ≥3 cm) were enrolled in this study. The subjects were divided into the rEBUS-D-TBLB and rEBUS-GS-TBLB groups by the random number table method. Patients were assigned to undergo rEBUS-D-TBLB or rEBUS-GS-TBLB, respectively. The histopathology, positive diagnosis rates, duration of the procedure, and postoperative adverse effects between the two groups were examined.

RESULTS:

A total of 569 patients were included in this study according to the inclusion and exclusion criteria, with 282 cases in the rEBUS-D-TBLB group and 287 cases in the rEBUS-GS-TBLB group. For malignant diseases, the positive diagnosis rates of PPL in the outer/inner-middle lung bands and the right-upper/-lower lung lobes by rEBUS-D-TBLB were noninferior to those of rEBUS-GS-TBLB. The duration of the procedure of rEBUS-D-TBLB was longer than that of rEBUS-GS-TBLB. There were 14 cases of hemorrhage >50 mL, 1 case of postoperative chest pain in the rEBUS-D-TBLB group, and 3 cases of hemorrhage >50 mL in the rEBUS-GS-TBLB group.

CONCLUSION:

REBUS-D-TBLB by thin bronchoscope has a high diagnostic value for PPL with a diameter ≥3 cm, which may be considered a useful alternative for rEBUS-GS-TBLB in the clinic.
Correlation between changes in corneal asphericity and progression of keratoconus in children
Authors: Tabl Ahmed A. , Elsayed Mohamed A. , Tabl Marwa A.
Published: July 2022

Clinical relevance

Early detection of keratoconus (KC) progression and timing for retreatment.

Background

Q value (coefficient of corneal asphericity) reflects the shape of the cornea, its refractive power, and spherical aberration. In this study, we aimed to analyze the correlation between changes in Q value and progression of KC in children.

Patients and methods

In this retrospective cross-sectional study, clinical data of 18 eyes of nine children that were diagnosed as bilateral progressive KC (four boys, five girls: mean age of 11.3±1.6 years) were extracted from a Scheimpflug camera (Pentacam) for analysis before (baseline) and annually after bilateral transepithelial corneal collagen cross-linking (TE-CXL) up to 5 years of follow-up.

Results

At all follow-up visits up to 5 years, Kmax improved significantly (mean change at 5 years was 1.6 D) (P<0.001); uncorrected visual acuity and best-corrected visual acuity showed significant changes at all follow-up visits (P<0.001). In seven (38.8%) eyes, KC had progressed by more than 1.0 D by the last follow-up visit, despite corneal cross-linking. In the last visit, there was a significant negative correlation between mean Q value and Kmax in both 6 and 8 mm anterior corneal zones (Pearson correlation coefficient, r=−0.847, P<0.001, r=−0.605, P<0.001, respectively).

Conclusion

There is significant negative correlations between changes in corneal asphericity and progression of KC in children, which could be considered with other parameters in early detection of disease progression and timing for retreatment after transepithelial corneal collagen cross-linking.