Search results for: directional sensor networks

Authors: A. K. M. Kafi, S. N. Nina, Mashitah M. Yusoff

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In this work, we have described a new 3-dimensional (3D) network of crosslinked Horseradish Peroxidase/Carbon Nanotube (HRP/CNT) on a thiol-modified Au surface in order to build up the effective electrical wiring of the enzyme units with the electrode. This was achieved by the electropolymerization of aniline-functionalized carbon nanotubes (CNTs) and 4-aminothiophenol -modified-HRP on a 4-aminothiophenol monolayer-modified Au electrode. The synthesized 3D HRP/CNT networks were characterized with cyclic voltammetry and amperometry, resulting the establishment direct electron transfer between the redox active unit of HRP and the Au surface. Electrochemical measurements reveal that the immobilized HRP exhibits high biological activity and stability and a quasi-reversible redox peak of the redox center of HRP was observed at about −0.355 and −0.275 V vs. Ag/AgCl. The electron transfer rate constant, KS and electron transfer co-efficient were found to be 0.57 s-1 and 0.42, respectively. Based on the electrocatalytic process by direct electrochemistry of HRP, a biosensor for detecting H2O2 was developed. The developed biosensor exhibits excellent electrocatalytic activity for the reduction of H2O2. The proposed biosensor modified with HRP/CNT 3D network displays a broader linear range and a lower detection limit for H2O2 determination. The linear range is from 1.0×10−7 to 1.2×10−4M with a detection limit of 2.2.0×10−8M at 3σ. Moreover, this biosensor exhibits very high sensitivity, good reproducibility and long-time stability. In summary, ease of fabrication, a low cost, fast response and high sensitivity are the main advantages of the new biosensor proposed in this study. These obvious advantages would really help for the real analytical applicability of the proposed biosensor.

Keywords: biosensor, nanomaterials, redox enzyme, thiol-modified Au surface

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Authors: Ahmad Alsolami, Darren Mundy, Manuel Hernandez-Perez

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Social media networks, such as Twitter, offer the perfect opportunity to either positively or negatively affect political attitudes on large audiences. A most important factor contributing to this effect is the existence of influential users, who have developed a reputation for their awareness and experience on specific subjects. Therefore, knowledge of the mechanisms to identify influential users on social media is vital for understanding their effect on their audience. The concept of the influential user is based on the pioneering work of Katz and Lazarsfeld (1959), who created the concept of opinion leaders' to indicate that ideas first flow from mass media to opinion leaders and then to the rest of the population. Hence, the objective of this research was to provide reliable and accurate structural mechanisms to identify influential users, which could be applied to different platforms, places, and subjects. Twitter was selected as the platform of interest, and Saudi Arabia as the context for the investigation. These were selected because Saudi Arabia has a large number of Twitter users, some of whom are considerably active in setting agendas and disseminating ideas. The study considered the scientific methods that have been used to identify public opinion leaders before, utilizing metrics software on Twitter. The key findings propose multiple novel metrics to compare Twitter influencers, including the number of followers, social authority and the use of political hashtags, and four secondary filtering measures. Thus, using ratio and percentage calculations to classify the most influential users, Twitter accounts were filtered, analyzed and included. The structured approach is used as a mechanism to explore the top ten influencers on Twitter from the political domain in Saudi Arabia.

Keywords: twitter, influencers, structured mechanism, Saudi Arabia

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Authors: Ganiyu Rasaq Omokeji

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The growing recognition of the pivotal role of universities in promoting socio-economic development has led to a focus upon the expansion of the sector around the world. As the economy and society become more ‘knowledge intensive’, the role of universities in development is more onerous than just teaching, research, and service. It is to help create the open society upon which the progress of ideas depends on. Driven to fulfill this role, universities are likely to become even more important in building regional networks of their host communities. Currently, there are about 129 universities in Nigeria, with a total number of 37 federal, 36 state, and 56 privately owned universities. Fountain University is among the private universities in Nigeria located in Osogbo, Osun State. The university is committed to the total development of men and women in an enabling environment, through appropriate teaching, research, and service to humanity, influenced by Islamic ethics and culture. The university focuses on educational development and growth that are relevant to the nation’s manpower needs and global competitiveness through a gradual but steady process. This paper examines the role of Private University in the socio-economic development of host community using Fountain University as a case study. The research methodology design for this paper has a total of 200 respondents. The research instrument of data collection was a questionnaire and in-depth interview (IDI). The finding reveals that Fountain University plays an important role in socio-economic and cultural development through their Islamic culture. The paper recommend that universities must bridge the gaps between creative individual with innovative ideas and the application of technology for economic progress and social betterment of their host communities. University also must serve as a bridge that carries the traffic of social and economic development.

Keywords: private university, socio-economic development, host communities, role of universities and community development

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Authors: Edeaghe Ehikhamenor, Jennifer Nnamdi

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Introduction: Gender-based violence (GBV) was a well-known global crisis before the COVID-19 pandemic. The pandemic burden only intensified the crisis. With prevailing lockdowns, increased poverty due to high unemployment, especially affecting females, and other mobility restrictions that have left many women trapped with their abusers, plus isolation from social contact and support networks, GBV cases spiraled out of control. Prevalence of economic with cultural disparity, which is greatly manifested in Nigeria, is a major contributory factor to GBV. This is made worst by religious adherents where the females are virtually relegated to the background. Our societal approaches to investigations and sanctions to culprits have not sufficiently applied forensic/DNA tools in combating these major vices. Violence against women or some rare cases against men can prevent them from carrying out their duties regardless of the position they hold. Objective: The main objective of this research is to highlight the origin of GBV, the victims, types, contributing factors, and the applications of forensics/DNA tools and remedies so as to minimize GBV in our society. Methods: Descriptive information was obtained through the search on our daily newspapers, electronic media, google scholar websites, other authors' observations and personal experiences, plus anecdotal reports. Results: Findings from our exploratory searches revealed a high incidence of GBV with very limited or no applications of Forensics/DNA tools as an intervening mechanism to reduce GBV in Nigeria. Conclusion: Nigeria needs to develop clear-cut policies on forensics/DNA tools in terms of institutional framework to develop a curriculum for the training of all stakeholders to fast-track justice for victims of GBV so as to serve as a deterrent to other culprits.

Keywords: gender-based violence, forensics, DNA, justice

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Authors: S. Shakibaei, P. Alpkokin

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The railway network is one of the major components of a transportation system in a country which may be an indicator of the country’s level of economic improvement. Since 2000s on, revival of national railways and development of High Speed Rail (HSR) lines are one of the most remarkable policies of Turkish government in railway sector. Within this trend, the railway age is to be revived and coming decades will be a golden opportunity. Indubitably, major infrastructures such as road and railway networks require sizeable investment capital, precise maintenance and reparation. Traditionally, governments are held responsible for funding, operating and maintaining these infrastructures. However, lack or shortage of financial resources, risk responsibilities (particularly cost and time overrun), and in some cases inefficacy in constructional, operational and management phases persuade governments to find alternative options. Financial power, efficient experiences and background of private sector are the factors convincing the governments to make a collaboration with private parties to develop infrastructures. Public-Private Partnerships (PPP or 3P or P3) and related regulatory issues are born considering these collaborations. In Turkey, PPP approaches have attracted attention particularly during last decade and these types of investments have been accelerated by government to overcome budget limitations and cope with inefficacy of public sector in improving transportation network and its operation. This study mainly tends to present a comprehensive overview of PPP concept, evaluate the regulatory procedure in Europe and propose a general framework for Turkish State Railways (TCDD) as an outlook on privatization, liberalization and deregulation of railway network.

Keywords: deregulation, high-speed railway, liberalization, privatization, public-private partnership

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Authors: Elham Fariborzi, Hoda Anvari Kazemabad

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Today, ICT plays an important role in all matters and it affects the development of creative cities. According to virtual space in this technology, it use especially for expand terms like smart schools, Virtual University, web-based training and virtual classrooms that is in parallel with the traditional teaching. Nowadays, the educational systems in different countries such as Iran are changing and start increasing creativity in the learning environment. It will contribute to the development of innovative ideas and thinking of the people in this environment; such opportunities might be cause scientific discovery and development issues. The creativity means the ability to generate ideas and numerous, new and suitable solutions for solving the problems of real and virtual individuals and society, which can play a significant role in the development of creative current physical cities or virtual borders ones in the future. The purpose of this paper is to study strategies to increase creativity in a virtual learning to develop a creative city. In this paper, citation/ library study was used. The full description given in the text, including how to create and enhance learning creativity in a virtual classroom by reflecting on performance and progress; attention to self-directed learning guidelines, efficient use of social networks, systematic discussion groups and non-intuitive targeted controls them by involved factors and it may be effective in the teaching process regarding to creativity. Meanwhile, creating a virtual classroom the style of class recognizes formally the creativity. Also the use of a common model of creative thinking between student/teacher is effective to solve problems of virtual classroom. It is recommended to virtual education’ authorities in Iran to have a special review to the virtual curriculum for increasing creativity in educational content and such classes to be witnesses more creative in Iran's cities.

Keywords: virtual learning, creativity, e-learning, bioinformatics, biomedicine

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Authors: Daniel M. Muntean, Viorel Ungureanu

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More than half of the urban population in Romania lives today in residential buildings made out of large prefabricated reinforced concrete panels. Since their initial design was made in the 1960’s, these housing units are now being technically and morally outdated, consuming large amounts of energy for heating, cooling, ventilation and lighting, while failing to meet the needs of the contemporary life-style. Due to their widespread use, the design of a system that improves their energy efficiency would have a real impact, not only on the energy consumption of the residential sector, but also on the quality of life that it offers. Furthermore, with the transition of today’s existing power grid to a “smart grid”, buildings could become an active element for future electricity networks by contributing in micro-generation and energy storage. One of the most addressed issues today is to find locally adapted strategies that can be applied considering the 20-20-20 EU policy criteria and to offer sustainable and innovative solutions for the cost-optimal energy performance of buildings adapted on the existing local market. This paper presents a possible adaptive design scenario towards sustainable retrofitting of these housing units. The apartments are transformed in order to meet the current living requirements and additional extensions are placed on top of the building, replacing the unused roof space, acting not only as housing units, but as active solar energy collection systems. An adaptive building envelope is ensured in order to achieve overall air-tightness and an elevator system is introduced to facilitate access to the upper levels.

Keywords: adaptive building, energy efficiency, retrofitting, residential buildings, smart grid

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Authors: Vixen Joshua Tan, Siyuan He

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Scanners have been implemented within single point laser rangefinders, to determine the ranges within an environment by sweeping the laser spot across the surface of interest. The research motivation is to exploit a smaller and cheaper alternative scanning component for the emitting portion within current designs of laser rangefinders. This research implements an FPCB (Flexible Programmable Circuit Board) Electromagnetic 1-Dimensional scanning micro-mirror as a scanning component for laser rangefinding by means of triangulation. The prototype uses a laser module, micro-mirror, and receiver. The laser module is infrared (850 nm) with a power output of 4.5 mW. The receiver consists of a 50 mm convex lens and a 45mm 1-dimensional PSD (Position Sensitive Detector) placed at the focal length of the lens at 50 mm. The scanning component is an elliptical Micro-Mirror attached onto an FPCB Structure. The FPCB structure has two miniature magnets placed symmetrically underneath it on either side, which are then electromagnetically actuated by small solenoids, causing the FPCB to mechanically rotate about its torsion beams. The laser module projects a laser spot onto the micro-mirror surface, hence producing a scanning motion of the laser spot during the rotational actuation of the FPCB. The receiver is placed at a fixed distance from the micro-mirror scanner and is oriented to capture the scanning motion of the laser spot during operation. The elliptical aperture dimensions of the micro-mirror are 8mm by 5.5 mm. The micro-mirror is supported by an FPCB with two torsion beams with dimensions of 4mm by 0.5mm. The overall length of the FPCB is 23 mm. The voltage supplied to the solenoids is sinusoidal with an amplitude of 3.5 volts and 4.5 volts to achieve optical scanning angles of +/- 10 and +/- 17 degrees respectively. The operating scanning frequency during experiments was 5 Hz. For an optical angle of +/- 10 degrees, the prototype is capable of detecting objects within the ranges from 0.3-1.2 meters with an error of less than 15%. As for an optical angle of +/- 17 degrees the measuring range was from 0.3-0.7 meters with an error of 16% or less. Discrepancy between the experimental and actual data is possibly caused by misalignment of the components during experiments. Furthermore, the power of the laser spot collected by the receiver gradually decreased as the object was placed further from the sensor. A higher powered laser will be tested to potentially measure further distances more accurately. Moreover, a wide-angled lens will be used in future experiments when higher scanning angles are used. Modulation within the current and future higher powered lasers will be implemented to enable the operation of the laser rangefinder prototype without the use of safety goggles.

Keywords: FPCB electromagnetic 1-D scanning micro-mirror, laser rangefinder, position sensitive detector, PSD, triangulation

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Authors: Adrian Niewiadomski, Barbara Surma, Katarzyna Kolodziejak, Dorota A. Pawlak

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Oxide-oxide eutectic is attracting increasing interest of scientific community because of their unique properties and numerous potential applications. Some of the most interesting examples of applications are metamaterials, glucose sensors, photoactive materials, thermoelectric materials, and photocatalysts. Their unique properties result from the fact that composite materials consist of two or more phases. As a result, these materials have additive and product properties. Additive properties originate from particular phases while product properties originate from the interaction between phases. MnTiO3-TiO2 eutectic is one of such materials. TiO2 is a well-known semiconductor, and it is used as a photocatalyst. Moreover, it may be used to produce solar cells, in a gas sensing devices and in electrochemistry. MnTiO3 is a semiconductor and antiferromagnetic. Therefore it has potential application in integrated circuits devices, and as a gas and humidity sensor, in non-linear optics and as a visible-light activated photocatalyst. The above facts indicate that eutectic MnTiO3-TiO2 constitutes an extremely promising material that should be studied. Despite that Raman spectroscopy is a powerful method to characterize materials, to our knowledge Raman studies of eutectics are very limited, and there are no studies of the MnTiO3-TiO2 eutectic. While to our knowledge the papers regarding this material are scarce. The MnTiO3-TiO2 eutectic, as well as TiO2 and MnTiO3 single crystals, were grown by the micro-pulling-down method at the Institute of Electronic Materials Technology in Warsaw, Poland. A nitrogen atmosphere was maintained during whole crystal growth process. The as-grown samples of MnTiO3-TiO2 eutectic, as well as TiO2 and MnTiO3 single crystals, are black and opaque. Samples were cut perpendicular to the growth direction. Cross sections were examined with scanning electron microscopy (SEM) and with Raman spectroscopy. The present studies showed that maintaining nitrogen atmosphere during crystal growth process may result in obtaining black TiO2 crystals. SEM and Raman experiments showed that studied eutectic consists of three distinct regions. Furthermore, two of these regions correspond with MnTiO3, while the third region corresponds with the TiO2-xNx phase. Raman studies pointed out that TiO2-xNx phase crystallizes in rutile structure. The studies show that Raman experiments may be successfully used to characterize eutectic materials. The MnTiO3-TiO2 eutectic was grown by the micro-pulling-down method. SEM and micro-Raman experiments were used to establish phase composition of studied eutectic. The studies revealed that the TiO2 phase had been doped with nitrogen. Therefore the TiO2 phase is, in fact, a solid solution with TiO2-xNx composition. The remaining two phases exhibit Raman lines of both rutile TiO2 and MnTiO3. This points out to some kind of coexistence of these phases in studied eutectic.

Keywords: compound materials, eutectic growth and characterization, Raman spectroscopy, rutile TiO₂

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Authors: S. Trafela, X. Xua, K. Zuzek Rozmana

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In this study, we used an accurate and precise method to measure the electrochemically active surface areas (Aecsa) of nickel electrodes. Calculated Aecsa is really important for the evaluation of an electro-catalyst’s activity in electrochemical reaction of different organic compounds. The method involves the electrochemical formation of Ni(OH)₂ and NiOOH in the presence of adsorbed oxalate in alkaline media. The studies were carried out using cyclic voltammetry with polycrystalline nickel as a reference material and electrodeposited nickel nanowires, homogeneous and heterogeneous nickel films. From cyclic voltammograms, the charge (Q) values for the formation of Ni(OH)₂ and NiOOH surface oxides were calculated under various conditions. At sufficiently fast potential scan rates (200 mV s⁻¹), the adsorbed oxalate limits the growth of the surface hydroxides to a monolayer. Although the Ni(OH)₂/NiOOH oxidation peak overlaps with the oxygen evolution reaction, in the reverse scan, the NiOOH/ Ni(OH)₂ reduction peak is well-separated from other electrochemical processes and can be easily integrated. The values of these integrals were used to correlate experimentally measured charge density with an electrochemically active surface layer. The Aecsa of the nickel nanowires, homogeneous and heterogeneous nickel films were calculated to be Aecsa-NiNWs = 4.2066 ± 0.0472 cm², Aecsa-homNi = 1.7175 ± 0.0503 cm² and Aecsa-hetNi = 2.1862 ± 0.0154 cm². These valuable results were expanded and used in electrochemical studies of formaldehyde oxidation. As mentioned nickel nanowires, heterogeneous and homogeneous nickel films were used as simple and efficient sensor for formaldehyde detection. For this purpose, electrodeposited nickel electrodes were modified in 0.1 mol L⁻¹ solution of KOH in order to expect electrochemical activity towards formaldehyde. The investigation of the electrochemical behavior of formaldehyde oxidation in 0.1 mol L⁻¹ NaOH solution at the surface of modified nickel nanowires, homogeneous and heterogeneous nickel films were carried out by means of electrochemical techniques such as cyclic voltammetric and chronoamperometric methods. From investigations of effect of different formaldehyde concentrations (from 0.001 to 0.1 mol L⁻¹) on electrochemical signal - current we provided catalysis mechanism of formaldehyde oxidation, detection limit and sensitivity of nickel electrodes. The results indicated that nickel electrodes participate directly in the electrocatalytic oxidation of formaldehyde. In the overall reaction, formaldehyde in alkaline aqueous solution exists predominantly in form of CH₂(OH)O⁻, which is oxidized to CH₂(O)O⁻. Taking into account the determined (Aecsa) values we have been able to calculate the sensitivities: 7 mA mol L⁻¹ cm⁻² for nickel nanowires, 3.5 mA mol L⁻¹ cm⁻² for heterogeneous nickel film and 2 mA mol L⁻¹ cm⁻² for heterogeneous nickel film. The detection limit was 0.2 mM for nickel nanowires, 0.5 mM for porous Ni film and 0.8 mM for homogeneous Ni film. All of these results make nickel electrodes capable for further applications.

Keywords: electrochemically active surface areas, nickel electrodes, formaldehyde, electrocatalytic oxidation

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Authors: Rasha Ahmadi

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Glioblastoma is one of the most frequent brain malignancies, having a high mortality rate and limited survival in individuals with this malignancy. Despite different treatments and surgery, recurrence of glioblastoma cancer stem cells may arise as a subsequent tumor. For this reason, it is crucial to research the markers associated with glioblastoma stem cells and specifically their microenvironment. In this study, using bioinformatics analysis, we analyzed and nominated genes in the microenvironment pathways of glioblastoma stem cells. In this study, an appropriate database was selected for analysis by referring to the GEO database. This dataset comprised gene expression patterns in stem cells derived from glioblastoma patients. Gene clusters were divided as high and low expression. Enrichment databases such as Enrichr, STRING, and GEPIA were utilized to analyze the data appropriately. Finally, we extracted the potential genes 2700 high-expression and 1100 low-expression genes are implicated in the metabolic pathways of glioblastoma cancer progression. Cellular senescence, MAPK, TNF, hypoxia, zimosterol biosynthesis, and phosphatidylinositol metabolism pathways were substantially expressed and the metabolic pathways were downregulated. After assessing the association between protein networks, MSMP, SOX2, FGD4 ,and CNTNAP3 genes with high expression and DMKN and SBSN genes with low were selected. All of these genes were observed in the survival curve, with a survival of fewer than 10 percent over around 15 months. hsa-mir-192-5p, hsa-mir-129-5p, hsa-mir-215-5p, hsa-mir-335-5p, and hsa-mir-340-5p played key function in glioblastoma cancer stem cells microenviroments. We introduced critical genes through integrated and regular bioinformatics studies by assessing the amount of gene expression profile data that can play an important role in targeting genes involved in the energy and microenvironment of glioblastoma cancer stem cells. Have. This study indicated that hsa-mir-192-5p, and hsa-mir-129-5p are appropriate candidates for this.

Keywords: Glioblastoma, Cancer Stem Cells, Biomarker Discovery, Gene Expression Profiles, Bioinformatics Analysis, Tumor Microenvironment

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Authors: Jonathan K. Hill

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The transportation networks throughout Africa remain the only transportation infrastructure system in the world that is attacked by terrorists at a high frequency, so the international community can learn from each attack. The targeting of transportation should be recognized as a direct attack against a civilian population, so the international community should work to better understand the types of attacks utilized, the types of improvised explosive device designs adapted to transportation targets, and the ways the various modes of transportation have been attacked throughout the continent. Some countries have seen grenade attacks that have resulted in only injuries, while some countries have experienced large vehicle bombings that have resulted in hundreds of injuries and numerous deaths. With insurgencies, explosive devices have been small, complex, and generally target an enemy of the insurgency. With terrorist bombings, the explosive devices have been large, brazen, and targeted at civilian populations. And, these civilian populations are easily targeted within the transportation system. The presentation provided by Assess Africa LLC is titled ‘Evolution of Bombings Against Transportation Infrastructure’ and covers improvised explosive device characteristics, how improvised explosive devices have been adapted to transportation targets in Africa, analyses recent incidents, and provides some advice for effective protective measures. A main component of the improvised explosive device characteristics portion of the presentation focuses on the link between explosive device components, the intelligence network, and the bomb-builder’s network. By understanding the components, how the use of various components can be linked to a terrorist group’s capabilities, and how the bomb-builder acquires materials, the analysis of improvised explosive device attacks takes on a new direction – one that focuses on defeating the network instead of merely reviewing incidents of the past.

Keywords: Africa, bombings, critical infrastructure protection, transportation security

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Authors: Halonen

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Covid-19 pandemic and the succeeding crises have profoundly shaken the accustomed ways of interaction and thereby challenged the customary engagement patterns among entrepreneurs Consequently, this has led to the experience of lack of collegial interaction for some. Networks and relationships are a crucial factor to strengthening resilience, being especially significant in non-ordinary times. This study aims to shed light on entrepreneurs’ resilience development in and through entrepreneurs’ communal and training space. The context for research is a communal training space in a municipality in Finland of which goal is to help entrepreneurs to experience of peer support and community as part of the "tribe" is strengthened, the entrepreneurs' well-being at work, resilience, ability to change, innovativeness and general life management is strengthened. This communal space is regarded as an example of a physical community of practice (CoP) of entrepreneurs. The research aims to highlight the importance of rediscovering the “new normal” communality as itself but as a key building block of resilience. The initial research questions of the study are: RQ1: What is the role of entrepreneurs’ CoP and communal space in nurturing resilience development among them? RQ2: What positive entrepreneurial outcomes can be achieved through established CoP. The data will be gathered starting from the launch of the communality space in September 2023 onwards. It includes participatory observations of training gatherings, interviews with entrepreneurs and utilizes action research as the method. The author has an active role in participating and facilitating the development. The full paper will be finalized by the fall 2024. The idea of the new normal communality in a CoP among entrepreneurs is to be rediscovered due to its positive impact on entrepreneur’s resilience and business success. The other implications of study can extend to wider entrepreneurial ecosystem and other key stakeholders. Especially emphasizing the potential of communality in CoP for fostering entrepreneurs’ resilience and well-being ensuing business growth, community-driven entrepreneurship development and vitality of the case municipality.

Keywords: resilience, resilience development, communal space, community of practice (CoP)

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Authors: Faiza Belbachir, Thibault Schienhinski

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The massive development of online social networks allows users to post and share their opinions on various topics. With this huge volume of opinion, it is interesting to extract and interpret these information for different domains, e.g., product and service benchmarking, politic, system of recommendation. This is why opinion detection is one of the most important research tasks. It consists on differentiating between opinion data and factual data. The difficulty of this task is to determine an approach which returns opinionated document. Generally, there are two approaches used for opinion detection i.e. Lexical based approaches and Machine Learning based approaches. In Lexical based approaches, a dictionary of sentimental words is used, words are associated with weights. The opinion score of document is derived by the occurrence of words from this dictionary. In Machine learning approaches, usually a classifier is trained using a set of annotated document containing sentiment, and features such as n-grams of words, part-of-speech tags, and logical forms. Majority of these works are based on documents text to determine opinion score but dont take into account if these texts are really correct. Thus, it is interesting to exploit other information to improve opinion detection. In our work, we will develop a new way to consider the opinion score. We introduce the notion of trust score. We determine opinionated documents but also if these opinions are really trustable information in relation with topics. For that we use lexical SentiWordNet to calculate opinion and trust scores, we compute different features about users like (numbers of their comments, numbers of their useful comments, Average useful review). After that, we combine opinion score and trust score to obtain a final score. We applied our method to detect trust opinions in TRIPADVISOR collection. Our experimental results report that the combination between opinion score and trust score improves opinion detection.

Keywords: Tripadvisor, opinion detection, SentiWordNet, trust score

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Authors: Yoganathan Karthik, Guhanathan Poravi

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Colorizing near-infrared (NIR) images poses unique challenges due to the absence of color information and the nuances in light absorption. In this paper, we present an approach to NIR image colorization utilizing a synthetic dataset generated from visible light images. Our method addresses two major challenges encountered in NIR image colorization: accurately colorizing objects with color variations and avoiding over/under saturation in dimly lit scenes. To tackle these challenges, we propose a Generative Adversarial Network (GAN)-based framework that learns to map NIR images to their corresponding colorized versions. The synthetic dataset ensures diverse color representations, enabling the model to effectively handle objects with varying hues and shades. Furthermore, the GAN architecture facilitates the generation of realistic colorizations while preserving the integrity of dimly lit scenes, thus mitigating issues related to over/under saturation. Experimental results on benchmark NIR image datasets demonstrate the efficacy of our approach in producing high-quality colorizations with improved color accuracy and naturalness. Quantitative evaluations and comparative studies validate the superiority of our method over existing techniques, showcasing its robustness and generalization capability across diverse NIR image scenarios. Our research not only contributes to advancing NIR image colorization but also underscores the importance of synthetic datasets and GANs in addressing domain-specific challenges in image processing tasks. The proposed framework holds promise for various applications in remote sensing, medical imaging, and surveillance where accurate color representation of NIR imagery is crucial for analysis and interpretation.

Keywords: computer vision, near-infrared images, automatic image colorization, generative adversarial networks, synthetic data

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Authors: Tesfaye Fenta Boka, Niu Zhendong

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Recommender systems play a crucial role in connecting individuals with the items they require, as is particularly evident in the rapid growth of online grocery shopping platforms. These systems predominantly rely on user-centered recommendations, where items are suggested based on individual preferences, garnering considerable attention and adoption. However, our focus lies on the item-centered recommendation task within the grocery shopping context. In the reverse next item period recommendation (RNPR) task, we are presented with a specific item and challenged to identify potential users who are likely to consume it in the upcoming period. Despite the ever-expanding inventory of products on online grocery platforms, the cold start item problem persists, posing a substantial hurdle in delivering personalized and accurate recommendations for new or niche grocery items. To address this challenge, we propose a Graph Neural Network (GNN)-based approach. By capitalizing on the inherent relationships among grocery items and leveraging users' historical interactions, our model aims to provide reliable and context-aware recommendations for cold-start items. This integration of GNN technology holds the promise of enhancing recommendation accuracy and catering to users' individual preferences. This research contributes to the advancement of personalized recommendations in the online grocery shopping domain. By harnessing the potential of GNNs and exploring item-centered recommendation strategies, we aim to improve the overall shopping experience and satisfaction of users on these platforms.

Keywords: recommender systems, cold start item recommendations, online grocery shopping platforms, graph neural networks

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Authors: Soheila Sadeghi

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In construction projects, predicting project performance metrics accurately is essential for effective management and successful delivery. However, conventional methods often depend on fixed baseline plans, disregarding the evolving nature of project progress and external influences. To address this issue, we introduce a distinct approach based on machine learning to forecast key performance indicators, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category within an urban road reconstruction project. Our proposed model leverages time series forecasting techniques, namely Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance by analyzing historical data and project progress. Additionally, the model incorporates external factors, including weather patterns and resource availability, as features to improve forecast accuracy. By harnessing the predictive capabilities of machine learning, our performance forecasting model enables project managers to proactively identify potential deviations from the baseline plan and take timely corrective measures. To validate the effectiveness of the proposed approach, we conduct a case study on an urban road reconstruction project, comparing the model's predictions with actual project performance data. The outcomes of this research contribute to the advancement of project management practices in the construction industry by providing a data-driven solution for enhancing project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, schedule variance, earned value management

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Authors: Katerina Koliou, Scott Parr, Evangelos Kaisar

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The surface transportation of freight is particularly vulnerable to storm and hurricane disasters, while at the same time, it is the primary transportation mode for delivering medical supplies, fuel, water, and other essential goods. To better plan for commercial vehicles during an evacuation, it is necessary to understand how these vehicles travel during an evacuation and determine if this travel is different from the general public. The research investigation used Florida's statewide continuous-count station traffic volumes, where then compared between years, to identify locations where traffic was moving differently during the evacuation. The data was then used to identify days on which traffic was significantly different between years. While the literature on auto-based evacuations is extensive, the consideration of freight travel is lacking. To better plan for commercial vehicles during an evacuation, it is necessary to understand how these vehicles travel during an evacuation and determine if this travel is different from the general public. The goal of this research was to investigate the movement of vehicles by classification, with an emphasis on freight during two major evacuation events: hurricanes Irma (2017) and Michael (2018). The methodology of the research was divided into three phases: data collection and management, spatial analysis, and temporal comparisons. Data collection and management obtained continuous-co station data from the state of Florida for both 2017 and 2018 by vehicle classification. The data was then processed into a manageable format. The second phase used geographic information systems (GIS) to display where and when traffic varied across the state. The third and final phase was a quantitative investigation into which vehicle classifications were statistically different and on which dates statewide. This phase used a two-sample, two-tailed t-test to compare sensor volume by classification on similar days between years. Overall, increases in freight movement between years prevented a more precise paired analysis. This research sought to identify where and when different classes of vehicles were traveling leading up to hurricane landfall and post-storm reentry. Of the more significant findings, the research results showed that commercial-use vehicles may have underutilized rest areas during the evacuation, or perhaps these rest areas were closed. This may suggest that truckers are driving longer distances and possibly longer hours before hurricanes. Another significant finding of this research was that changes in traffic patterns for commercial-use vehicles occurred earlier and lasted longer than changes for personal-use vehicles. This finding suggests that commercial vehicles are perhaps evacuating in a fashion different from personal use vehicles. This paper may serve as the foundation for future research into commercial travel during evacuations and explore additional factors that may influence freight movements during evacuations.

Keywords: evacuation, freight, travel time, evacuation

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Authors: Shativel S., Chandana B. R., Kavya B. C., Obli B. Vikram, Suganthi J., Nagendra Rao G.

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Satellite communication plays a pivotal role in modern global communication networks, serving as a vital link between terrestrial infrastructure and remote regions. The demand for reliable satellite reception systems, especially in UHF (Ultra High Frequency) and VHF (Very High Frequency) bands, has grown significantly over the years. This research paper presents the design and optimization of a high-gain, dual-band crossed Yagi-Uda antenna in CST Studio Suite, specifically tailored for satellite reception. The proposed antenna system incorporates a circularly polarized (Right-Hand Circular Polarization - RHCP) design to reduce Faraday loss. Our aim was to use fewer elements and achieve gain, so the antenna is constructed using 6x2 elements arranged in cross dipole and supported with a boom. We have achieved 10.67dBi at 146MHz and 9.28dBi at 437.5MHz.The process includes parameter optimization and fine-tuning of the Yagi-Uda array’s elements, such as the length and spacing of directors and reflectors, to achieve high gain and desirable radiation patterns. Furthermore, the optimization process considers the requirements for UHF and VHF frequency bands, ensuring broad frequency coverage for satellite reception. The results of this research are anticipated to significantly contribute to the advancement of satellite reception systems, enhancing their capabilities to reliably connect remote and underserved areas to the global communication network. Through innovative antenna design and simulation techniques, this study seeks to provide a foundation for the development of next-generation satellite communication infrastructure.

Keywords: Yagi-Uda antenna, RHCP, gain, UHF antenna, VHF antenna, CST, radiation pattern.

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Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: Vipul M. Patel, Hemantkumar B. Mehta

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Technological innovations in electronic world demand novel, compact, simple in design, less costly and effective heat transfer devices. Closed Loop Pulsating Heat Pipe (CLPHP) is a passive phase change heat transfer device and has potential to transfer heat quickly and efficiently from source to sink. Thermal performance of a CLPHP is governed by various parameters such as number of U-turns, orientations, input heat, working fluids and filling ratio. The present paper is an attempt to predict the thermal performance of a CLPHP using Artificial Neural Network (ANN). Filling ratio and heat input are considered as input parameters while thermal resistance is set as target parameter. Types of neural networks considered in the present paper are radial basis, generalized regression, linear layer, cascade forward back propagation, feed forward back propagation; feed forward distributed time delay, layer recurrent and Elman back propagation. Linear, logistic sigmoid, tangent sigmoid and Radial Basis Gaussian Function are used as transfer functions. Prediction accuracy is measured based on the experimental data reported by the researchers in open literature as a function of Mean Absolute Relative Deviation (MARD). The prediction of a generalized regression ANN model with spread constant of 4.8 is found in agreement with the experimental data for MARD in the range of ±1.81%.

Keywords: ANN models, CLPHP, filling ratio, generalized regression, spread constant

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Authors: Patricia Girrbach

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Currently, our society is facing a new and incremental upheaval technological revolution named digitalization. In order to face the relating challenges organizations have to be prepared. They need appropriate circumstances in order to cope with current issues concerning digital transformation processes. Nowadays digitalization emerged as top issues for companies and business leaders. In this context, it is a pressure on companies to have a positive, productive digital culture. And indeed, Organizations realize that they need to address this important issue. In this context 87 percent of organizations quote culture and engagement as one of their top challenges in terms of any change process, but especially in terms of the digital turn. Executives can give their company a competitive advantage and attract top talent by having a strong workplace culture that supports digitalization. Many current studies attest that fact. Digital-oriented companies can hire more easily, they have the lowest voluntary turnover rates, deliver better customer service, and are more profitable over the long run. Based on this background it is important to provide companies starting points and practical measurements how to reach this goal. The major findings are that firms need to make sense out of digitalization. In this context, they should focus on internal but also on external stakeholders. Furthermore, they should create certain working conditions and they should support the qualification of employees, e.g. by Virtual Reality. These measurements can create positive experiences in terms of digitalization in order to ensure the support of stuff in terms of the digital turn. Based on several current studies and literature research this paper provides concrete measurements for companies in order to enable the digital turn. Therefore, the aim of this paper is providing possible practical starting points which support both the education of employees by digitalization as well as the digital turn itself within the organization.

Keywords: digitalization, industry 4.0, education 4.0, virtual reality

Procedia PDF Downloads 158

Authors: Nurulhuda A. Manaf, Nor Najihah Zainal Abidin, Nur Amalina Jamaludin

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Coordinating service interactions are a vital part of developing distributed applications that are built up as networks of autonomous participants, e.g., software components, web services, online resources, involve a collaboration between a diverse number of participant services on different providers. The complexity in coordinating service interactions reflects how important the techniques and approaches require for designing and coordinating the interaction between participant services to ensure the overall goal of a collaboration between participant services is achieved. The objective of this research is to develop capability of steering a complex service interaction towards a desired outcome. Therefore, an efficient technique for modelling, generating, and verifying the coordination of service interactions is developed. The developed model describes service interactions using service choreographies approach and focusing on a declarative approach, advocating an Object Management Group (OMG) standard, Semantics of Business Vocabulary and Rules (SBVR). This model, namely, SBVR model for service choreographies focuses on a declarative deontic rule expressing both obligation and prohibition, which can be more useful in working with coordinating service interactions. The generated SBVR model is then be formulated and be transformed into Alloy model using Alloy Analyzer for verifying the generated SBVR model. The transformation of SBVR into Alloy allows to automatically generate the corresponding coordination of service interactions (service choreography), hence producing an immediate instance of execution that satisfies the constraints of the specification and verifies whether a specific request can be realised in the given choreography in the generated choreography.

Keywords: service choreography, service coordination, behavioural modelling, complex interactions, declarative specification, verification, model transformation, semantics of business vocabulary and rules, SBVR

Procedia PDF Downloads 151

Authors: Cécile Dufloux, Klaus Böttcher, Heike Oppermann, Jürgen Wollweber

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Hexagonal aluminum nitride (AlN) is a promising candidate for several wide band gap semiconductor compound applications such as deep UV light emitting diodes (UVC LED) and fast power transistors (HEMTs). To date, bulk AlN single crystals are still commonly grown from the physical vapor transport (PVT). Single crystalline AlN wafers obtained from this process could offer suitable substrates for a defect-free growth of ultimately active AlGaN layers, however, these wafers still lack from small sizes, limited delivery quantities and high prices so far.Although there is already an increasing interest in the commercial availability of AlN wafers, comparatively cheap Si, SiC or sapphire are still predominantly used as substrate material for the deposition of active AlGaN layers. Nevertheless, due to a lattice mismatch up to 20%, the obtained material shows high defect densities and is, therefore, less suitable for high power devices as described above. Therefore, the use of AlN with specially adapted properties for optical and sensor applications could be promising for mass market products which seem to fulfill fewer requirements. To respond to the demand of suitable AlN target material for the growth of AlGaN layers, we have designed an innovative technology based on reactive plasma spraying. The goal is to produce coarse grained AlN boules with N-terminated columnar structure and high purity. In this process, aluminum is injected into a microwave stimulated nitrogen plasma. AlN, as the product of the reaction between aluminum powder and the plasma activated N2, is deposited onto the target. We used an aluminum filament as the initial material to minimize oxygen contamination during the process. The material was guided through the nitrogen plasma so that the mass turnover was 10g/h. To avoid any impurity contamination by an erosion of the electrodes, an electrode-less discharge was used for the plasma ignition. The pressure was maintained at 600-700 mbar, so the plasma reached a temperature high enough to vaporize the aluminum which subsequently was reacting with the surrounding plasma. The obtained products consist of thick polycrystalline AlN layers with a diameter of 2-3 cm. The crystallinity was determined by X-ray crystallography. The grain structure was systematically investigated by optical and scanning electron microscopy. Furthermore, we performed a Raman spectroscopy to provide evidence of stress in the layers. This paper will discuss the effects of process parameters such as microwave power and deposition geometry (specimen holder, radiation shields, ...) on the topography, crystallinity, and stress distribution of AlN.

Keywords: aluminum nitride, polycrystal, reactive plasma spraying, semiconductor

Procedia PDF Downloads 280

Authors: Anjan Hazra, Nirjhar Dasgupta, Chandan Sengupta, Sauren Das

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Tea (Camellia sinensis) has received substantial attention from the scientific world time to time, not only for its commercial importance, but also for its demand to the health-conscious people across the world for its extensive use as potential sources of antioxidant supplement. These health-benefit traits primarily rely on some regulatory networks of different metabolic pathways. Development of microsatellite markers from the conserved genomic regions is being worthwhile for studying the genetic diversity of closely related species or self-pollinated species. Although several SSR markers have been reported, in tea the trait-specific Simple Sequence Repeats (SSRs) are yet to be identified, which can be used for marker assisted breeding technique. MicroRNAs are endogenous, noncoding, short RNAs directly involved in regulating gene expressions at the post-transcriptional level. It has been found that diversity in miRNA gene interferes the formation of its characteristic hair pin structure and the subsequent function. In the present study, the precursors of small regulatory RNAs (microRNAs) has been fished out from tea Expressed Sequence Tag (EST) database. Furthermore, the simple sequence repeat motifs within the putative miRNA precursor genes are also identified in order to experimentally validate their existence and function. It is already known that genic-SSR markers are very adept and breeder-friendly source for genetic diversity analysis. So, the potential outcome of this in-silico study would provide some novel clues in understanding the miRNA-triggered polymorphic genic expression controlling specific metabolic pathways, accountable for tea quality.

Keywords: micro RNA, simple sequence repeats, tea quality, trait specific marker

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Authors: K. Sathishkumar, V. Thiagarasu

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Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed.

Keywords: microarray technology, gene expression data, clustering, gene Selection

Procedia PDF Downloads 323

Authors: Debatosh Das, Moxian Chen, Jianhua Zhang, Caroline Gutjahr

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Arbuscular mycorrhiza (AM) is a mutualistic symbiosis between plant roots and Glomeromycotina fungi, which is activated under low but inhibited by high phosphate. The effect of phosphate on AM development has been observed for many years, but mechanisms regulating it under contrasting phosphate levels remain unknown. Based on previous observations that promoters of several AM functional genes contain PHR binding motifs, we hypothesized that PHR2, a master regulator of phosphate starvation response in rice, was recruited to regulate AM symbiosis development. We observed a drastic reduction in root colonization and significant AM transcriptome modulation in phr2. PHR2 targets genes required for root colonization and AM signaling. The role of PHR2 in improving root colonization, mycorrhizal phosphate uptake, and growth response was confirmed in field soil. In conclusion, rice PHR2, which is considered a master regulator of phosphate starvation responses, acts as a positive regulator of AM symbiosis between Glomeromycotina fungi and rice roots. PHR2 directly targets the transcription of plant strigolactone and AM genes involved in the establishment of this symbiosis. Our work facilitates an understanding of ways to enhance AMF propagule populations introduced in field soils (as a biofertilizer) in order to restore the natural plant-AMF networks disrupted by modern agricultural practices. We show that PHR2 is required for AM-mediated improvement of rice yield in low phosphate paddy field soil. Thus, our work contributes knowledge for rational application of AM in sustainable agriculture. Our data provide important insights into the regulation of AM by the plant phosphate status, which has a broad significance in agriculture and terrestrial ecosystems.

Keywords: biofertilizer, phosphate, mycorrhiza, rice, sustainable, symbiosis

Procedia PDF Downloads 133

Authors: Roman Kulikov, Svetlana Kolesnikova

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Last decade Cloud Computing technologies have been rapidly becoming ubiquitous. Each year more and more organizations, corporations, internet services and social networks trust their business sensitive information to Public Cloud. The data storage in Public Cloud is protected by security mechanisms such as firewalls, cryptography algorithms, backups, etc.. In this way, however, only outsider attacks can be prevented, whereas virtualization tools can be easily compromised by insider. The protection of Public Cloud’s critical elements from internal intruder remains extremely challenging. A hypervisor, also called a virtual machine manager, is a program that allows multiple operating systems (OS) to share a single hardware processor in Cloud Computing. One of the hypervisor's functions is to enforce access control policies. Furthermore, it prevents guest OS from disrupting each other and from accessing each other's memory or disk space. Hypervisor is the one of the most critical and vulnerable elements in Cloud Computing infrastructure. Nevertheless, it has been poorly protected from being compromised by insider. By exploiting certain vulnerabilities, privilege escalation can be easily achieved in insider attacks on hypervisor. In this way, an internal intruder, who has compromised one process, is able to gain control of the entire virtual machine. Thereafter, the consequences of insider attacks in Public Cloud might be more catastrophic and significant to virtual tools and sensitive data than of outsider attacks. So far, almost no preventive security countermeasures have been developed. There has been little attention paid for developing models to assist risks mitigation strategies. In this paper formal model of insider attacks on hypervisor is designed. Our analysis identifies critical hypervisor`s vulnerabilities that can be easily compromised by internal intruder. Consequently, possible conditions for successful attacks implementation are uncovered. Hence, development of preventive security countermeasures can be improved on the basis of the proposed model.

Keywords: insider attack, public cloud, cloud computing, hypervisor

Procedia PDF Downloads 361

Authors: Kasra Majbouri Yazdi, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, Saeed Saedy

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Fake news and false information are big challenges of all types of media, especially social media. There is a lot of false information, fake likes, views and duplicated accounts as big social networks such as Facebook and Twitter admitted. Most information appearing on social media is doubtful and in some cases misleading. They need to be detected as soon as possible to avoid a negative impact on society. The dimensions of the fake news datasets are growing rapidly, so to obtain a better result of detecting false information with less computation time and complexity, the dimensions need to be reduced. One of the best techniques of reducing data size is using feature selection method. The aim of this technique is to choose a feature subset from the original set to improve the classification performance. In this paper, a feature selection method is proposed with the integration of K-means clustering and Support Vector Machine (SVM) approaches which work in four steps. First, the similarities between all features are calculated. Then, features are divided into several clusters. Next, the final feature set is selected from all clusters, and finally, fake news is classified based on the final feature subset using the SVM method. The proposed method was evaluated by comparing its performance with other state-of-the-art methods on several specific benchmark datasets and the outcome showed a better classification of false information for our work. The detection performance was improved in two aspects. On the one hand, the detection runtime process decreased, and on the other hand, the classification accuracy increased because of the elimination of redundant features and the reduction of datasets dimensions.

Keywords: clustering, fake news detection, feature selection, machine learning, social media, support vector machine

Procedia PDF Downloads 176

Authors: Elucir Gir, Laelson Rochelle Milanês Sousa, Pedro Henrique Tertuliano Leoni, Carla Aparecida Arena Ventura, Ana Cristina de Oliveira e Silva, Renata Karina Reis

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Context and significance: Resilience is a protective agent for the physical and mental well-being of nursing professionals. Team members are constantly subjected to high levels of work stress that can negatively impact care performance and users of health services. Stress levels have been exacerbated with the COVID-19 pandemic. Objective: The aim of this study was to analyze the resilience of nursing professionals in Brazil during the COVID-19 pandemic. Method: Cross-sectional study with a quantitative approach carried out with professionals from nursing teams from all regions of Brazil. Data collection took place in the first year of the pandemic between October and December 2020. Data were obtained through an online questionnaire posted on social networks. The information collected included the sociodemographic characterization of the nursing professionals and the Brief Resilient Coping Scale was applied. Student's t-test for independent samples and analysis of variance (ANOVA) were used to compare resilience scores with sociodemographic variables. Results: 8,792 nursing professionals participated in the study, 5,767 (65.6%) were nurses, 7,437 (84.6%) were female and 2,643 (30.1%) were from the Northeast region of Brazil, 5,124 (58.8% ) had low levels of resilience. The results showed a statistically significant difference between the resilience score and the variables: professional category (p<0.001); sex (p = 0.003); age range (p<0.001); region of Brazil (p<0.001); marital status (p=0.029) and providing assistance in a field hospital (p<0.001). Conclusion: Participants in this study had, in general, low levels of resilience. There is an urgent need for actions aimed at promoting the psychological health of nursing professionals inserted in pandemic contexts. Descriptors: Psychological Resilience; Nursing professionals; COVID-19; SARSCoV-2.

Keywords: psychological resilience, nursing professionals, COVID-19, SARS-CoV-2

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