Previous Stage Research Articles

Nurse’s Knowledge Sharing in Time of COVID-19 Pandemic: A Systematic Review and Expert Opinion

This study aimed to review the effective factors, tools and methods of knowledge sharing used by nurses during the COVID-19 pandemic. This mix-methods study was conducted in 2023 and included two stages. In the first stage, a systematic review of relevant literature was conducted. In the second stage, a panel of 10 experts justified and appended the findings of the previous stage. The main source of knowledge during the COVID-19 epidemic was social networks followed by official authorities, online training courses, media and colleagues. Different factors affecting knowledge sharing among nurses categorised into process-oriented factors, Human-oriented factors, Technology-oriented factors and organisation-oriented factors. Various known and innovative methods have been used by nurses to share knowledge during the COVID-19 pandemic including utilising social networks, Telementoring, team approaches for new staff and continuous training in line with the latest scientific findings. Nurses used both formal and informal sources to explore evidence supporting evolving clinical practices and social networks were a prominent source of knowledge during the pandemic. By recognising the significance of affecting factors, healthcare organisations can enhance nurses’ ability to effectively share knowledge, ultimately improving patient outcomes and overall healthcare response during similar crises in the future.

Comparing matching prescriptions between pre-equilibrium and hydrodynamic models in high-energy nuclear collisions

State-of-the-art simulations of high-energy nuclear collisions rely on hybrid setups, involving in particular a pre-equilibrium stage to let the system evolve from a far-from-equilibrium initial condition towards a near-equilibrated state after which fluid dynamics can be applied meaningfully. A known issue is the mismatch between the equation of state in the fluid-dynamical evolution and the effective one in the previous stage, which leads to discontinuities at the interface between the two models. Here we introduce a new matching prescription at this interface, based on the entropy, and we compare it with the standard one relying on local energy conservation. We study the behavior of various quantities at the switching time between the models and investigate a number of final-state hadronic observables. For the latter, we show that they are not modified significantly by the choice of matching prescription, provided an appropriate normalization is chosen for the initial state. In turn, our approach reduces sizeably the ratio of bulk over thermodynamic pressure at the beginning of the fluid-dynamical stage.

First experience of endovascular balloon occlusion of the aorta in the wounded with hemorrhagic shock during the war in Ukraine

Background. Resuscitative endovascular balloon occlusion of the aorta (REBOA) is used worldwide to stop bleeding in trauma and wounds in patients with hemorrhagic shock. REBOA allows for the hemodynamic stabilization, patient transportation and final bleeding control. The study aims to demonstrate the capabilities of REBOA as a method of bleeding control in the wounded with hemorrhagic shock in the context of war in Ukraine. Materials and methods. Five AFU servicemen who sustained mine-blast wounds in the Eastern Ukraine as a result of shelling by Russian troops were delivered with signs of internal bleeding and hemorrhagic shock to the primary and secondary levels of medical care (Role 1, 2). The REBOA balloon catheter was inserted and inflated by surgeons working in stabilization centers or advanced surgical teams. Upon admission, all patients underwent chest and abdominal ultrasound according to the FAST protocol. We used COBRA-OS 4 Fr and ER-REBOA 7 Fr balloon catheters. The positioning of the balloon in the aorta was performed according to external anatomical landmarks (3 cases) and ultrasound (2 cases). In all clinical cases, the balloon catheter was placed in zone I. In one case, the balloon was moved from zone I to zone III (control of the source of bleeding). The duration of aortic occlusion ranged from 15 to 50 minutes. Immediately after removal of the introducer, manual compression of the puncture site was used (3 cases) or suturing of the arteriotomy (2 cases). Results. The sources of bleeding in our clinical cases were injuries of the iliac vessels (2 cases), the renal artery (1 case), and injuries of the inferior vena cava and liver (2 cases). Four patients were evacuated to a higher level of medical care after the REBOA was installed. In one case, there were complications from vascular access (femoral artery thrombosis). We were unable to save one wounded soldier, most likely due to the delay in evacuation from the previous stage, the severity of injury, and massive blood loss. Conclusions. As our observations have shown, the use of REBOA in combat conditions allows not only to stabilize hemodynamics in the wounded with severe combined trauma and bleeding but also to conduct a successful evacuation to a higher level of medical care to stop bleeding ultimately. REBOA can be successfully used at the prehospital stage. This requires clearly defined indications for the technique’s use, training of medical personnel, and provision of balloon catheters to medical units of the Armed Forces of Ukraine.

Segmentation of MR images for brain tumor detection using autoencoder neural network

Medical images often require segmenting into different regions in the first analysis stage. Relevant features are selected to differentiate various regions from each other, and the images are segmented into meaningful (anatomically significant) regions based on these features. The purpose of this study is to present a model for segmenting and identifying the local tumor formation in MR images of the human brain. The proposed system operates in an unsupervised manner to minimize the intervention of expert users and to achieve an acceptable speed in the tumor classification process. The proposed method includes several steps of preprocessing for different brain image classify that Perform the normalization task. These preprocessing steps lead to more accurate results in high-resolution images and ultimately improve the accuracy and sensitivity of tumor separation from brain tissue. The output of this stage is applied to a self-encoding neural network for image zoning. By nature of self-encoding networks, leads to reduce the dimensionality of tumor pixels from the surrounding healthy environment, which significantly helps remove regions incorrectly extracted as tumors. Finally, by extracting features from the previous stage's output through Otsu thresholding, the surrounding area and type of tumor are also extracted. The proposed method was trained and tested using the BRATS2020 database and evaluated by various performance metrics. The results based on the Dice Similarity Coefficient (DSC) show an accuracy of 97% for the entire MR image and improved detection accuracy compared to other methods, as well as a reduction in the cost of the diagnostic process.

Research on Obstacle-Avoidance Trajectory Planning for Drill and Anchor Materials Handling by a Mechanical Arm on a Coal Mine Drilling and Anchoring Robot

At present, China’s coal mine permanent tunneling support commonly uses mechanized drilling and anchoring equipment; there are low support efficiency, labor intensity, and other issues. In order to further improve the support efficiency and liberate productivity, this paper further researches the trajectory planning of the drilling and anchoring materials of the robotic arm for the drilling machine “grasping–carrying–loading–unloading” on the basis of the drilling and anchoring robotic system designed by the team in the previous stage. Firstly, the kinematic model of the robotic arm with material was established by improving the D-H parameter method. Then, the working space of the robotic arm with the material was analyzed using the Monte Carlo method. The singular bit-shaped region of the robotic arm was restricted, and obstacles were removed from the working space. The inverse kinematics was utilized to solve the feasible domain of the robotic arm with material. Secondly, in order to avoid blind searching, the guidance of the Bi-RRT algorithm was improved by adding the target guidance factor, and the two-way tree connection strategy for determining the feasible domain was combined with the Bi-RRT algorithm’s feasible domain judgment bi-directional tree connection strategy to improve the convergence speed of the Bi-RRT algorithm. Then, in order to adapt to the dynamic environment and avoid the global planning algorithm from falling into the local minima, on the basis of the above planning methods, an improved Bi-RRT trajectory planning algorithm incorporating the artificial potential field was proposed, which takes the planned paths as the guiding potential field of the artificial potential field to make full use of the global information and avoid falling into the local minimization. Finally, a simulation environment was built in a ROS environment to compare and analyze the planning effect of different algorithms. The simulation results showed that the improved Bi-RRT trajectory planning algorithm incorporating the artificial potential field improved the optimization speed by 69.8% and shortened the trajectory length by 46.6% compared with the traditional RRT algorithm.

Impacts of human resources management strategies and practices on workers performance in construction industry: a review

Human resource management (HRM) plays a vital role in the growth and sustainability of companies and in achieving company objectives, as HRM relates to the workers’ practices and their functional roles. The main objective of this paper is to identify the HRM strategies, practices and their impact on worker’s performance in the Architecture, Engineering, and Construction (AEC) Industry. The method which was used in this study was based on three stages. After the completion of the database and web engine search, the total number of sources found were 149. Next, the sources’ titles and abstracts were reviewed and those marked as relevant to the review were chosen to be retrieved and thoroughly reviewed. The sources were chosen based on the following inclusion criteria: (a) the sources implied in HRM strategies in the AEC industry, (b) the sources released between 2010 and 2023, (c) the online sources, and (d) the English-language sources. Also, the selected sources are reviewed to extract the factors using the content analysis method which is a thorough and systematic study of the contents of a specific body of material. In addition, content analysis is carried out to extract practices of HRM strategies and their impact on worker performance. This can be followed up and observed through the figures and tables that come later. As a final result, which concludes the outputs of the previous stages which results in a first-round total of 31 HRM strategies practice but in the second-round result of reaching 39 different sources from 149 related sources. After that, discussing the most popular between them based on the appearance of it in the sources which reviewed beside that, the impact of it on worker’s performance were viewed in the AEC industry. Based on that, it was found the following 5 categories of strategies: (1) Human Resources Planning; (2) Polarization and Recruitment; (3) Training and Development; (4) Human Resources Following-up; (5) Career Planning. At the same time the most popular practices are based on appearance in sources: In terms of Human Resources Planning Strategy, “The company’s management analyses the functions accurately”. In Polarization and Recruitment Strategy, “The company’s internal resources are the best to provide its human resource needs” and “Selection and recruitment policies are in line with the company’s current and future needs”. In Training and Development of Human Resources Strategy, “The company evaluates the results of development and training programs to achieve the purpose of feedback”. In Human Resources Following-up Strategy, “There is a fair incentive system in the company”. In Career Planning Strategy, “There is a clear description of the relationship of workers to each other”. Finally, human resource management strategies and practices are one of the major players in the AEC industry, which is evidenced by its impact on the performance of workers. First published online 22 October 2024

A pH-Sensitive cRGD-PEG-siRNA Conjugated Compound Targeting Glioblastoma.

Glioblastoma ranks among the most prevalent primary intracranial tumors, characterized by high mortality and poor prognosis. Chemotherapy remains a key treatment strategy for gliomas, though most current drugs suffer from limited efficacy and significant toxicity. This study focuses on a cRGD-siEGFR coupling compound synthesized in a previous stage. Prior research indicated that cRGD-siEGFR molecules exhibited certain targeting and antitumor properties but faced issues of inadequate targeting, low efficacy, and high renal toxicity. To enhance antitumor efficacy and mitigate side effects, a pH-responsive, long-circulating, and highly targeted siRNA delivery system, the cRGD-PEG-siEGFR conjugate, was developed. The targeting, antitumor effects, and biological distribution of cRGD-PEG-siEGFR were examined. The results demonstrated that cRGD-PEG-siEGFR was effectively taken up by αvβ3-positive U87MG cells, specifically silenced EGFR gene expression, and exhibited antitumor effects. In normal physiological conditions, it avoided uptake by normal cells, thereby reducing side effects. Furthermore, in vivo biodistribution experiments revealed that cRGD-PEG-siEGFR, compared to cRGD-siEGFR, significantly decreased renal accumulation and exhibited prolonged circulation. Consequently, cRGD-PEG-siRNA emerges as a promising drug candidate with attributes of long circulation, high targeting, pH responsiveness, and substantial antitumor efficacy.

Genomic insights into Aspergillus tamarii TPD11: enhancing polyphyllin production and uncovering potential therapeutic applications.

The excavation and utilization of endophytic fungi from medicinal plants is highly important for the development of new drugs. The endophytic fungus Aspergillus tamarii TPD11, which was isolated and obtained by the authors in the previous stage, can produce a variety of polyphyllins with important potential applications in hemostasis, inflammation and antitumor activities; however, the genomic information of TPD11 is still unknown. In this study, we sequenced and assembled the whole genome of the endophytic fungus A. tamarii TPD11, resolved the genome evolutionary relationships of 24 Aspergillus strains, and phylogenetic analysis of the genomes of 16 strains revealed the evolutionary differences between Aspergillus and Penicillium and the mechanisms of genome expansion and contraction. CAZy annotation analysis revealed that TPD11 obtains nutrients mainly by ingesting starch from the host plant. TPD11 has a biosynthesis-related gene cluster for the synthesis of squalestatin S1, and the silencing of this biosynthesis-related gene cluster might increase the content of polyphyllin. Annotation of 11 UDP-glycosyltransferase genes helps to further reveal the biosynthetic pathway of polyphyllin. In addition, secondary metabolism gene cluster and CAZy analyses confirmed the potential probiotic, insecticidal and antimicrobial activities of TPD11 on host plants. This study reveals the intrinsic mechanism by which endophytic fungi increase the content of polyphyllin, which provides a basis for the synthetic synthesis of the natural product polyphyllin.

Tractable optimal experimental design using transport maps*

Abstract We present a flexible method for computing Bayesian optimal experimental designs (BOEDs) for inverse problems with intractable posteriors. The approach is applicable to a wide range of BOED problems and can accommodate various optimality criteria, prior distributions and noise models. The key to our approach is the construction of a transport-map-based surrogate to the joint probability law of the design, observational and inference random variables. This order-preserving transport map is constructed using tensor trains and can be used to efficiently sample from (and evaluate approximate densities of) conditional distributions that are required in the evaluation of many commonly-used optimality criteria. The algorithm is also extended to sequential data acquisition problems, where experiments can be performed in sequence to update the state of knowledge about the unknown parameters. The sequential BOED problem is made computationally feasible by preconditioning the approximation of the joint density at the current stage using transport maps constructed at previous stages. The flexibility of our approach in finding optimal designs is illustrated with some numerical examples inspired by disease modeling and the reconstruction of subsurface structures in aquifers.

How Data is Documented: An Analysis of Metadata Schemas for Research Data

The importance of metadata has long been understood by those in the fields of librarianship and research data management. Currently, there are many research data service providers that allow deposit of research data or gather metadata for research data housed elsewhere, following various metadata schemas to describe their collections. These schemas were designed to make their datasets organized, discoverable, and reusable. As data curators initially recognize the need for and begin working on metadata schemas for research data, most previous studies in this field were conducted from their perspectives. However, few of these studies discuss a schema from the perspective of data (re)users. This study aims to take a small step towards filling this gap by evaluating four influential metadata schemas in light of researchers' information-seeking behaviors for research data. This study explores the factors that affect researchers’ efforts in seeking, understanding, and evaluating (re)usable data, and examines how these factors are represented across various fields in the current metadata schemas for research data. The methodology for this study consists of three parts: a meta-analysis of relevant research on researcher’s seeking behaviors for (re)usable data, a crosswalk of the metadata schemas, and a comparison of findings from the previous stages. The findings from this study will offer insights into the development of research data metadata schema in the future, which will enhance the infrastructure for data sharing, ultimately facilitating more effective (re)use of existing data.

METHOD OF RECOGNITION OF FPV-UAV RADIO SIGNALS FORMED ACCORDING TO CROSSFIRE AND EXPRESSLRS STANDARDS

Unmanned aerial vehicles (UAVs) with the First Person View (FPV) system are the most common type of attack UAVs used at the tactical level by the armed forces of the russian federation. Timely detection of facts of their using by the enemy and countering them are important tasks solved by units of the Ukrainian Defense Forces. An effective countermeasure way for FPV-UAVs is to create interference in their radio control channels frequency bands. To increase the radio suppression range, it is necessary to use interference matched in frequency and structure, the necessary condition for the formation of which is the presence of a priori information about the structure of the radio signal. Analysis of the available information on the construction of enemy FPV-UAVs shows that their radio control and telemetry channels are usually combined, have time division multiple access, the signals are formed according to CrossFire and ExpressLRS standards and have fixed sets of parameter values that can be used as features in recognition. The proposed FPV-UAV radio signal recognition technique is based on decision tree and minimum metric methods and uses a priori information on frequency, time and modulation parameters of radio signals. The combination of the two decision-making methods made it possible to reduce the computational complexity by eliminating operations for determining the parameters of radio signals that do not correspond to the decisions taken at the previous stages, and provides the possibility of adding new time parameters without changing the developed recognition technique. The method consists of a sequence of operations for estimation radio signal parameters, comparing them with known values, making decisions about the type of standard and command- telemetry radio line operation mode. Only one fragment with a duration of one frequency element is sufficient to recognize the FPV-UAV radio signal, which reduces the technical requirements for detection means in which the method can be used.

Leveraging Incremental Learning for Dynamic Modulation Recognition

Modulation recognition is an important technology used to correctly identify the modulation modes of wireless signals and is widely used in cooperative and confrontational scenarios. Traditional modulation-recognition algorithms require the assistance of expert experiences, which constrains their applications. With the rapid development of artificial intelligence in recent years, deep learning (DL) is widely advocated for intelligent modulation recognition. Typically, DL-based modulation-recognition algorithms implicitly assume a relatively static scenario in which the signal samples of all the modulation modes can be completely collected in advance. In practical situations, the radio environment is quite dynamic and the signal samples with new modulation modes may appear sequentially, in which the current DL-based modulation-recognition algorithms may require unacceptable time and computing resource consumption to re-train the optimal model from scratch. In this study, we leveraged incremental learning (IL) and designed a novel IL-based modulation-recognition algorithm that consists of an initial stage and multiple incremental stages. The main novelty of the proposed algorithm lies in the new loss function design in each incremental stage, which combines the distillation loss of recognizing old modulation modes and the cross-entropy loss of recognizing new modulation modes. With the proposed algorithm, the knowledge of the signal samples with new modulation modes can be efficiently learned in the current stage without forgetting the knowledge learned in the previous stages. The simulation results demonstrate that the proposed algorithm could achieve a recognition accuracy close to the upper bound with a much lower computing time and it outperformed the existing IL-based benchmarks.

Value model of political communications of the Russian elite in conflictual and deviant polysubject environment

The article, based on the results of a comprehensive political analysis, substantiates that in the modern socio-political reality, the formation of a multipolar world, the problem of the effectiveness of political communications are closely related to the potential of political and administrative power elites. The communicative qualities of a modern politician are enhanced by value-civilizational and moral parameters, the ability of strategic vision and the ability to maintain civilizational and value discourse in the political and economic system and direct it towards sustainable advanced development of society. This, in turn, involves the analysis and development of parameters for diagnosing the quality of modern Russian political and administrative elites, their moral and professional adequacy and aptitude; so is the actualization and solution of the problem of deviations at the elite level of political power. In order to form a value model of political communications of the Russian elite, it is necessary to comprehensively consider management systems based on specific values (including management of public values), identify and position public values as determinants of socio-political development, develop mechanisms to counteract the value degradation and marginalization of national elites, take into account the propensity of the Russian government to identify internal and external enemies in the previous stages of development, and also to the uncritical borrowing of the models of democracy alien to the Russian cultural and civilizational code, and the image imitation of elite qualities.

New evidence of Holocene pyroclastic density currents at Galeras volcano, Colombia

The Galeras Volcanic Complex (GVC) is a composite volcano located between the Central and Western cordilleras in southern Colombia. It is considered one of the most active volcanoes in Colombia, with typically Vulcanian eruptions. The complex has been divided into different stages based on stratigraphic and geochronological differences. The most recent one is called the Galeras stage and is considered to have started ca. 4500 years. This study presents the results of new stratigraphic and geochronological analysis of the deposits that outcrop along the El Barranco river valley, NW of the GVC, which allowed us to identify newer and older deposits within the most recent stage (i.e., the Galeras stage). The deposits were analyzed via distribution, stratigraphy, geochronology, componentry and granulometry, as well as vesicularity and microtexture of the pumice fragments. We identified three previously unreported deposits, which results in a total of eight pyroclastic density current (PDC) deposits in the valley, which discordantly overlie andesitic lava flows of the previous stage (i.e., Genoy stage; 150–40 ka) of the GVC. They were named from Unit U1 to Unit U8, with units U1, U2 and U8 representing events not previously recorded in the eruptive history of Galeras volcano. Units U1 (8303 ± 97 and 8284.5 ± 90.5 cal BP), U2 (7667 ± 78 cal BP), U3 (5082 ± 198, 5096.5 ± 226.5, 5801.5 ± 481.5 and 5966 ± 235 cal BP) and U6 (2215.5 ± 101.5 and 2033 ± 91 cal BP) are interpreted as formed by PDCs generated by pumice-rich flows linked to the collapse of Subplinian eruptive columns, while units U5 (3201.5 ± 129 cal BP), U7 (1066.5 ± 109.5 and 944.5 ± 115.4 cal BP) and U8 (390.5 ± 80.5 cal BP) are interpreted as formed by PDCs generated by block and ash flows linked to the destruction of intracrater domes and the collapse of Vulcanian eruptive columns. U4 (4625 ± 181 cal BP) is associated with a “blast” type event, caused by the overpressure exerted by the gases of a viscous magma. The above mentioned record allows us to propose that the eruptive history of Galeras volcano began at the beginning of the Holocene (∼8300 years BP), and not 4500 BP, as previously established, which has hazard implications. Unit U8 in the town of La Florida as well as the nearby recorded deposits, indicates a probable PDC hazard for this population.

Modified local Granger causality analysis based on Peter‐Clark algorithm for multivariate time series prediction on IoT data

AbstractClimate data collected through Internet of Things (IoT) devices often contain high‐dimensional, nonlinear, and auto‐correlated characteristics, and general causality analysis methods obtain quantitative causality analysis results between variables based on conditional independence tests or Granger causality, and so forth. However, it is difficult to capture dynamic properties between variables of temporal distribution, which can obtain information that cannot be obtained by the mean detection method. Therefore, this paper proposed a new causality analysis method based on Peter‐Clark (PC) algorithm and modified local Granger causality (MLGC) analysis method, called PC‐MLGC, to reveal the causal relationships between variables and explore the dynamic properties on temporal distribution. First, the PC algorithm is applied to compute the relevant variables of each variable. Then, the results obtained in the previous stage are fed into the modified local Granger causality analysis model to explore causalities between variables. Finally, combined with the quantitative causality analysis results, the dynamic characteristic curves between variables can be obtained, and the accuracy of the causal relationship between variables can be further verified. The effectiveness of the proposed method is further demonstrated by comparing it with standard Granger causality analysis and a two‐stage causal network learning method on one benchmark dataset and two real‐world datasets.

Efficient Bayesian updating for deformation prediction of high rock slopes induced by excavation with monitoring data

This study develops an efficient Bayesian updating method with monitoring data for predicting the deformation of high rock slopes induced by excavation. The importance ranking based on random forest is introduced to identify the key rock parameters as random variables in the Bayesian updating. The surrogate models using support vector machine are constructed to approximate the physical numerical models using FLAC3D for evaluating slope deformation. A practical example involving deformation prediction of the excavated left-bank rock slope for the well-known Baihetan hydropower station in southwest China is presented. The results indicate that the developed Bayesian updating method can efficiently and accurately update the posterior distributions of rock parameters and predict the deformation of high rock slopes induced by excavation. Incorporating the monitoring data of displacement into the Bayesian updating can effectively reduce the uncertainty of rock parameters and displacement prediction. As a result, the displacement predictions made by the Bayesian updating are closer to the monitoring data than the prior displacement predictions. In addition, incorporating more monitoring data of displacement from the previous excavation stages produces more accurate displacement predictions for subsequent excavation stages.

Digging into Old English Legal Compounds

Word-formation in Old English has long been subject to several important studies, both from a theoretical and textual. Special attention has been given to compounding especially as a means to cope with the new concepts and notions present in Latin texts. Although often quoted, compounds in the Anglo-Saxon laws has been so far neglected. The present paper aims to partially fill the gap. Focussing on the early legal codes, from Æthelberht to Ine, we will categorise compounds in laws in a sort of typology and analyse them according their formal features. The difficulty in the determination of the semantic relationship between their constituents lead us to the hypothesis that, due to the close intertwining between law and its society, compounds are a mirror either backwards previous stages of the language (e.g. drihtinbeag ‘fine for slaying a freeman’ where drihtin retains its pre-Christian meaning) or forwards to future changes (e.g. the loss of grammatical gender, as in liblac ‘witchcraft’), but are also the results of a long process to develop a legal terminology. Therefore, to understand them one has to dig into each individual law.

Development of Digital Forensic Framework for Anti-Forensic and Profiling Using Open Source Intelligence in Cyber Crime Investigation

Abstract. Cybercrime is a crime that increases every year. The development of cyber crime occurs by utilizing mobile devices such as smartphones. So it is necessary to have a scientific discipline that studies and handles cybercrime activities. Digital forensics is one of the disciplines that can be utilized in dealing with cyber crimes. One branch of digital forensic science is mobile forensics which studies forensic processes on mobile devices. However, in its development, cybercriminals also apply various techniques used to thwart the forensic investigation process. The technique used is called anti-forensics. Purpose: It is necessary to have a process or framework that can be used as a reference in handling cybercrime cases in the forensic process. This research will modify the digital forensic investigation process. The stages of digital forensic investigations carried out consist of preparation, preservation, acquisition, examination, analysis, reporting, and presentation stages. The addition of the use of Open Source Intelligence (OSINT) and toolset centralization at the analysis stage is carried out to handle anti-forensics and add information from digital evidence that has been obtained in the previous stage. Methods/Study design/approach: This research will modify the digital forensic investigation process. The stages of digital forensic investigations carried out consist of preparation, preservation, acquisition, examination, analysis, reporting, and presentation stages. The addition of the use of Open Source Intelligence (OSINT) and toolset centralization at the analysis stage is carried out to handle anti-forensics and add information from digital evidence that has been obtained in the previous stage. By testing the scenario data, the results are obtained in the form of processing additional information from the files obtained and information related to user names. Result/Findings: The result is a digital forensic phase which concern on anti-forensic identification on media files and utilizing OSINT to perform crime suspect profiling based on the evidence collected in digital forensic investigation phase. Novelty/Originality/Value: Found 3 new types of findings in the form of string data, one of which is a link, and 7 new types in the form of usernames which were not found in the use of digital forensic tools. From a total of 408 initial data and new findings with a total of 10 findings, the percentage of findings increased by 2.45%.

The history of post-Soviet Armenia through its political myths and symbols

We address the semiotics of conceptual shifts in the dominant discursive political and sociocultural practices of the Post-Soviet Armenia. The political history of independent Armenia is expressed through dominant political symbols and concepts, with a heterogeneous mixture of mythologized ancient history, Soviet and anti-Soviet ideologies. Attempts to create new symbols or re-interpret old ones led to significant transformations in the main discursive practices, which could form the foundation for the legitimization of the Armenian political identity. In general, if we depart from the substantive aspects, semiotic operations are governed by two main transformational principles: recursion and inversion, precisely those that Levi-Strauss described in relation to myth. At the same time, different cycles of this process can coexist in the same socio-cultural space. The political post-Soviet history of Armenia is divided into four periods, each of which is associated with a specific political regime (1990 - 1998; 1998 - 2018; 2018 - 2020; 2020 - up to present). Each of them gives rise to different political myths, both dominant (official) and oppositional, but in general, they are all based on the basic vocabulary that was formed in the late 1980s and early 1990s. Therefore, from the point of view of symbolic conceptual patterns, they are all based on the transformation (negation, inversion, and recursion) of the previous stages. Thus, the initial stage was characterized by a revaluation of values: everything Soviet was to be denied, and the Republic of Armenia of 1918-1920 was considered as a model. However, the new authorities intended to establish themselves through the myth of the creation of a fundamentally new - namely, a political nation, therefore, a tension has emerged between the concepts of reincarnation and creation. Soviet Armenia is contrasted with non-Soviet Armenia, which is then split into two opposed concepts: the First vs. the Third Republic. Then their “reconciliation” took place. It is reflected in the metanarrative of the Three Republics, and the Soviet Armenia occupies a worthy place as an intermediate link from incomplete to full-fledged independent statehood. In the 1900s, primordial narratives were revived, asserting the immutability of Armenia, starting with the legendary progenitor Hayk. At present, there is an obvious attempt to create the concept of the “Fourth Republic” based on the negation of the previous ones, up to the rejection of the Declaration of Independence as a Soviet legacy and the annulment of previous symbols (the coat of arms, the anthem, the Mount Ararat).

Sex-Based Differences in Lung Cancer Incidence: A Retrospective Analysis of Two Large US-Based Cancer Databases

Background/Objectives: Non-small cell lung cancer (NSCLC) has seen a relative rise in incidence among females versus males in recent years, although males still have a higher overall incidence. However, it is unclear whether this trend is consistent across all populations. Therefore, we retrospectively examined this relationship in two large high-risk clinical cohorts. Methods: First, we analyzed lung cancer incidence among individuals with a smoking history of over 40 pack-years in the National Lung Screening Trial (NLST). Then, we investigated the incidence of second primary NSCLC in patients who underwent lobectomy for previous stage I lung cancer using the Surveillance, Epidemiology, and End Results (SEER) database. We performed both univariate and multivariable time-to-event analyses to investigate the relationship between sex and lung cancer incidence. Results: In the NLST cohort (n = 37,627), females had a higher risk of developing primary NSCLC than males (HR = 1.11 [1.007–1.222], p = 0.035) after adjusting for age and pack-year history. In the SEER cohort (n = 19,327), females again exhibited an increased risk of developing a second primary lung cancer (HR = 1.138 [1.02–1.269], p = 0.021), after adjusting for age, race, grade, and histology. Conclusions: Our analysis reveals that females have a modestly higher lung cancer incidence than males in high-risk populations. These findings underscore the importance of further researching the underlying cellular processes that may cause sex-specific differences in lung cancer incidence.