Ubiquitous Access Research Articles

Multivariate Broadcast Encryption with Group Key Algorithm for Secured IoT

The expanding and ubiquitous availability of the Internet of Things (IoT) have changed everyone’s life easier and more convenient. Same time it also offers a number of issues, such as effectiveness, security, and excessive power consumption, which constitute a danger to intelligent IoT-based apps. Group managing is primarily used for transmitting and multi-pathing communications that are secured with a general group key and it can only be decrypted by an authorized group member. A centralized trustworthy system, which is in charge of key distribution and upgrades, is used to maintain group keys. To provide longitudinal access controls, Software Defined Network (SDN) based security controllers are employed for group administration services. Cloud service providers provide a variety of security features. There are just a few software security answers available. In the proposed system, a hybrid protocols were used in SDN and it embeds edge system to improve the security in the group communication. Tree-based algorithms compared with Group Key Establishment (GKE) and Multivariate public key cryptosystem with Broadcast Encryption in the proposed system. When all factors are considered, Broadcast Encryption (BE) appears to become the most logical solution to the issue. BE enables an initiator to send encrypted messages to a large set of recipients in a efficient and productive way, meanwhile assuring that the data can only be decrypted by defining characteristic. The proposed method improves the security, efficiency of the system and reduces the power consumption and minimizes the cost.

Classification of News Texts by Categories Using Machine Learning Methods

In parallel with the advances in technology, digital journalism is preferred more than printed journalism day by day. Due to the fast and up-to-date sense of journalism provided by digital journalism and its ubiquitous accessibility features, it is read more by users. In addition to these advantages provided by digital journalism, it also has some difficulties compared to printed journalism. The stage of preparation and delivery of the news to the user requires more technological knowledge and equipment compared to printed journalism. The processes of title selection, text creation, photo selection and determination of the appropriate news category in the preparation phase of the news are designed to be both faster and user-friendly compared to printed publishing. The news created to be presented to the target audience may belong to one or more of different categories such as economy, politics, sports, technology, and health. The inclusion of the news in the appropriate category provides convenience in terms of reaching the right audience and archiving the news correctly. In this study, news texts were classified according to their categories based on the machine learning methods. In the study, news of five newspapers in three different categories were used. Bayesian classifier and decision tree methods were used to classify the news in the dataset including a total of 10.500 news. In the results of the study, it was observed that the Bayesian classifier classified the news more successfully according to their categories.

Crowdsourcing the (Un)Textbook: Rethinking and Future Thinking the Role of the Textbook in Open Pedagogy

In this paper, we adopt a critical lens to investigate educators' understanding of both traditional and alternative textbooks and examine how open pedagogy may call for a rethinking of textbooks and how they are used in a pedagogical setting. Within the context of open pedagogy, including open textbooks, we conducted workshops that involved faculty, instructional designers, educational developers, and academic administrators during three conferences in 2019: OER19 Conference held in Galway, Ireland; the Cascadia Open Education Summit held in Vancouver, British Columbia; and the Educational Technology Users Group held in Kamloops, British Columbia. Based on data collected during these three interactive workshops, combined with personal reflections from the project instigators, we discuss emerging issues and tensions in the use of textbooks as pedagogical agents/artefacts in teaching and learning, and their relation to open pedagogy. Specifically, we consider what aspects of the use and design of textbook may be rethought in the context of open pedagogy as increasingly ubiquitous access to knowledge and open licensing of content and data become more widely available. This is achieved by prompting educators to describe the best and worst features of the traditional textbook format and reflect on what they might imagine as a potential future for the textbook as a resource to support open pedagogy.

Investigating In-Situ Personal Health Data Queries on Smartwatches

Smartwatches enable not only the continuous collection of but also ubiquitous access to personal health data. However, exploring this data in-situ on a smartwatch is often reserved for singular and generic metrics, without the capacity for further insight. To address our limited knowledge surrounding smartwatch data exploration needs, we collect and characterize desired personal health data queries from smartwatch users. We conducted a week-long study (N = 18), providing participants with an application for recording responses that contain their query and current activity related information, throughout their daily lives. From the responses, we curated a dataset of 205 natural language queries. Upon analysis, we highlight a new preemptive and proactive data insight category, an activity-based lens for data exploration, and see the desired use of a smartwatch for data exploration throughout daily life. To aid in future research and the development of smartwatch health applications, we contribute the dataset and discuss implications of our findings.

Developing reliable urban flood hazard mapping from LiDAR data

Digital surface models (DSMs) are crucial in providing accurate urban flood hazard maps. The ubiquitous availability of LiDAR data (where accessible) makes constructing geometrically sound DSMs feasible. However, little attention has been paid to developing approaches for producing geometrically consistent DSMs. Herein is described an application-driven procedure for creating a geometrically robust DSM (DSM1). Two further DSMs were created, one for portraying streets using breaklines as ancillary information (DSM2) and the other through direct interpolation of LiDAR data (DSM3). The geometrical correctness and vertical accuracy of these DSMs were examined qualitatively and quantitatively by plotting longitudinal profiles and cross-sections onto major runoff pathways and determining statistical error. The effect of these DSMs’ geometric consistency on flood hazard maps was also evaluated. For this, hydraulic outputs from DSM1 were used as a benchmark to compare hydraulic outputs from DSM2 and DSM3. This comparison was conducted at two spatial resolutions: i) at the total area flooded using the F statistic; and ii) at the pixel level by employing global indices and category-level indices extracted from a confusion matrix. Our findings revealed that: 1) DSM1 defined the most geometrically coherent configurations for runoff pathways; 2) in urban areas with the most densely packed streets and buildings, DSM2 and particularly DSM3 featured the most unrealistic geometric representations of the urban domain, displaying fake water flow barriers and lower than real runoff pathway cross-sections; and 3) the geometric quality of the DSMs created had a significant impact on flood hazard maps reliability (i.e., the disagreement in flood hazard categories between DSM2 and DSM3 and DSM1 varied from 28% to 82%). These findings can be very valuable in achieving further reductions and better flood risk management.

License to look? The role of permissive beliefs, desire thinking, and self-control in predicting the use of social networking sites

Permissive beliefs are considered the most proximal predictor of actual behavior. Whereas they have frequently been researched in substance-use disorders, the field of social networking sites (SNS) use is missing the investigation as to how relevant permissive beliefs might be in this context, what might be causal precursors of permissive beliefs (i.e., desire thinking), and which processes might influence the effect that permissive beliefs have on actual behavior (i.e., self-control). To answer these questions, 116 people who use SNS participated in an online survey that contained an experimental manipulation of desire thinking and questionnaires measuring permissive beliefs, self-control, and severity of problematic SNS use. In a one-week follow-up, 85 participants reported their SNS usage times. Results showed that the association between permissive beliefs and tendencies to use SNS problematically was not significant. The experimental manipulation led to a decrease in permissive beliefs in the control condition, but did not increase permissive beliefs in the desire thinking condition. Permissive beliefs predicted SNS use in the follow-up assessment, which was not moderated by self-control. The results suggest that permissive beliefs seem to be unrelated to addictive tendencies of SNS use, but are nevertheless associated with daily use. Desire thinking does not appear to activate permissive beliefs in this study, possibly because the use of social networks is less often experienced as conflicting. Due to its ubiquitous availability, the use of social networks itself might quickly resolve the conflict between desires and possible regulatory attempts, making good reasons for use unnecessary.

Ten quick tips for sequence-based prediction of protein properties using machine learning.

The ubiquitous availability of genome sequencing data explains the popularity of machine learning-based methods for the prediction of protein properties from their amino acid sequences. Over the years, while revising our own work, reading submitted manuscripts as well as published papers, we have noticed several recurring issues, which make some reported findings hard to understand and replicate. We suspect this may be due to biologists being unfamiliar with machine learning methodology, or conversely, machine learning experts may miss some of the knowledge needed to correctly apply their methods to proteins. Here, we aim to bridge this gap for developers of such methods. The most striking issues are linked to a lack of clarity: how were annotations of interest obtained; which benchmark metrics were used; how are positives and negatives defined. Others relate to a lack of rigor: If you sneak in structural information, your method is not sequence-based; if you compare your own model to "state-of-the-art," take the best methods; if you want to conclude that some method is better than another, obtain a significance estimate to support this claim. These, and other issues, we will cover in detail. These points may have seemed obvious to the authors during writing; however, they are not always clear-cut to the readers. We also expect many of these tips to hold for other machine learning-based applications in biology. Therefore, many computational biologists who develop methods in this particular subject will benefit from a concise overview of what to avoid and what to do instead.

Comprehensive analysis of services towards enhancing security in IoT-based agriculture

The Internet's ever-increasing popularity may be attributed to its ubiquitous accessibility. At this point, IoT has reached its pinnacle, with a wide variety of devices and appliances effectively linked to sensors & Internet, ushering in a new age of convenience and connectivity. It is challenging to secure because of the diverse combination of sensors, the Internet, and objects. Using Internet of Things (IoT) sensors, farmers may control their crops and agricultural fields remotely. Recent studies have concentrated on finding ways to improve precision agriculture's security infrastructure. Prior efforts to improve security services in the case of precision agriculture enabled by IoT have been minimal. By resolving issues related to cluster key management and overcoming obstacles associated with web interface configuration, the proposed work has established a novel approach that enables cluster key management to enhance security. Additionally, a web interface for gathering information from sensor nodes has been developed. After an IoT sensor detects a signal, the user may handle notifications using a web interface. When dealing with the vast infrastructure of IoT, it may be challenging to ensure the security of individual sensor nodes, particularly when only partial details about the hardware and the capacity for deployment are available. As a result, key management has emerged as a critical issue, especially in light of the node-capturing assault. Using the EBS function Object() { [native code] } and the Chinese remainder theorem, this research provides a new method of cluster key management that may improve agricultural accuracy. After collecting information from the nodes, the Backend Server sends a filtered list, together with preloaded keys and an authentication code, to the Cluster Head. This system incorporates a secondary layer of encryption for further protection. Our proposed method outperforms existing strategies in terms of delivery success rate, energy efficiency, and packet loss.

The roots of female emancipation: Initializing role of Cool Water

The Cool Water condition is a climatic configuration that combines periodically frosty winters with mildly warm summers under the ubiquitous accessibility of fresh water. Historically, it embodied opportunity endowments that weakened fertility pressures, resulting in household formation patterns that empowered women and reduced gender inequality. Reviewing the literature on the deep historic roots of gender inequality, this paper theorizes and provides evidence for a trajectory that (1) originates in the Cool Water climatic configuration, (2) leads to late female marriages in preindustrial times, and (3) eventually paves the way for various gender-egalitarian patterns of the present.

NCOG-43. A RETROSPECTIVE ANALYSIS OF THE IMPACT OF THE COVID-19 INFECTION ON NEURO-ONCOLOGY CARE AND PATIENT OUTCOMES: A TWO-SITE STUDY

Abstract BACKGROUND COVID-19 radically changed neuro-oncology care. In this retrospective study, we examine the impact of COVID-19 infection on neuro-oncological care and clinical outcomes in two geographically separate populations. METHODS Descriptive statistics compared demographic and clinical history extracted from the medical records of COVID-19 positive patients with primary brain tumors treated between 3/1/2020 and 3/31/2021. All subjects were unvaccinated given our cohort pre-dates the ubiquitous availability of vaccines. Patients were treated at Washington University (WashU) in St. Louis, MO and Duke University in Durham, NC. Each site’s respective institutional review board approved the study, with a data transfer agreement in place. RESULTS We identified 62 total (WashU=13; Duke=49) subjects with positive COVID-19 infection. Patients were predominantly white (85.5%), male (56.5%), with KPS >=70 (82.3%) and never smoked (69.4%). WashU patients tended to be older with grade 4 tumors, but this was not significant. At the time of COVID infection 35.5% of patients were receiving cancer-directed therapy. Notably, 37.1% experienced delayed care due to a COVID-19 diagnosis, most often for scheduled systemic treatment or radiation treatment. A further 37.1% had an ER visit, hospitalization, or ICU stay attributed to COVID-19. Of the 17 patients who died during the study period, 4 deaths were attributed directly to COVID-19 and not to their underlying brain tumor or other cause. Finally, telehealth use differed between sites (84.6% at WashU vs 14.3% at Duke). However, this difference could not be attributed to patient age, performance status, or distance from treating institution. CONCLUSION COVID-19 infection led to treatment delays and death for a subset, but not the majority of neuro-oncology patients. Telehealth use varied between sites and was not associated with commonly held assumptions about patient distance or performance status, suggesting evolving practice norms following telehealth’s introduction. Study limitations include a small sample size

A Systematic Literature Review and Meta-Analysis of Studies on Online Fake News Detection

The ubiquitous access and exponential growth of information available on social media networks have facilitated the spread of fake news, complicating the task of distinguishing between this and real news. Fake news is a significant social barrier that has a profoundly negative impact on society. Despite the large number of studies on fake news detection, they have not yet been combined to offer coherent insight on trends and advancements in this domain. Hence, the primary objective of this study was to fill this knowledge gap. The method for selecting the pertinent articles for extraction was created using the preferred reporting items for systematic reviews and meta-analyses (PRISMA). This study reviewed deep learning, machine learning, and ensemble-based fake news detection methods by a meta-analysis of 125 studies to aggregate their results quantitatively. The meta-analysis primarily focused on statistics and the quantitative analysis of data from numerous separate primary investigations to identify overall trends. The results of the meta-analysis were reported by the spatial distribution, the approaches adopted, the sample size, and the performance of methods in terms of accuracy. According to the statistics of between-study variance high heterogeneity was found with τ2 = 3.441; the ratio of true heterogeneity to total observed variation was I2 = 75.27% with the heterogeneity chi-square (Q) = 501.34, the degree of freedom = 124, and p ≤ 0.001. A p-value of 0.912 from the Egger statistical test confirmed the absence of a publication bias. The findings of the meta-analysis demonstrated satisfaction with the effectiveness of the recommended approaches from the primary studies on fake news detection that were included. Furthermore, the findings can inform researchers about various approaches they can use to detect online fake news.

The effect of complementary music intervention on the patients’ quality of life after septoplasty and rhinoplasty

Purpose:Quality of life (QoL) assessment has emerged as an important evaluation tool for therapeutic treatments. The positive impact of complementary music interventions on QoL has been demonstrated in the literature, particularly in chronic and malignant diseases. However, its benefits during the perioperative period in head and neck patients have not been investigated thus far.Methods:Head and neck patients undergoing septoplasty and rhinoplasty were prospectively randomized and consecutively included in the trial. Passive music intervention (60 min per day) was applied to the intervention group. QoL was assessed using the Nasal Obstruction Symptom Evaluation (NOSE) questionnaire and the Functional Rhinoplasty Outcome Inventory 17 (FROI-17) questionnaire at three visits during the postoperative phase. Pain was measured using a visual analogue scale.Results:Forty-four patients were enrolled in the study. The NOSE score between the control group and the intervention group in the septoplasty arm differed significantly at visit #2 (p < 0.001) and visit #3 (p < 0.015). For the rhinoplasty study arm, significant differences in the FROI-17 score were also found at visit #2 and visit #3 (p = 0.04).Conclusion:Complementary music interventions can considerably improve patients’ QoL during the postoperative period. Furthermore, passive music interventions may be easily implemented in clinical practice as an additional cost-effective treatment with ubiquitous availability.

Geometrical Optimization of Printed Interdigitated Electrode Sensors to Improve Soil Moisture Sensitivity

Printing electronics allows us to fabricate low-cost, simple sensor networks. Distributed soil moisture sensors in agriculture industry will result in better water management practices; however, price and reliability do not allow for ubiquitous availability. Here, we demonstrate the fabrication of low-cost, flexible, interdigitated electrode (IDE) soil moisture sensors with the electrode widths of 537, 877, 1055, 1204, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1916~\mu \text{m}$ </tex-math></inline-formula> on polyimide (PI) substrates using a silver nanoparticle (Ag NP) ink. The sensors were tested in sandy textured soil with a volumetric water content ranging from 0.05 to 0.25 m3/m3. Analytically, the sensitivities of all the sensors are expected to be equal, as the proposed mechanisms dictate that the capacitance should depend linearly on the dielectric constant of the moist soil. However, we found the sensitivity for the widest IDE, which measured <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1916~\mu \text{m}$ </tex-math></inline-formula> to be 442 pF/H2O volumetric percentage, whereas the sensitivity for the sensor with 537- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> -wide electrodes to be only 221 pF/H2O volumetric percentage. We hypothesized that the increase in sensitivity can be directly related to the increase in the capacitive fringing field height, which results in increased sensing volume for the widest IDE. The increased sensing volume leads to increased water molecule polarization, increasing the overall capacitance change with respect to changing water content. To validate the hypothesis, we conducted electric fringing field experiments to observe the trend between the maximum fringing field height and the electrode width of an IDE. Results confirmed that increased sensing volume was caused by the increased fringing field height of the electrode in an IDE.

Using Software-Defined Networking for Data Traffic Control in Smart Cities with WiFi Coverage

The growth of smart cities is fueled by the vast rise in wireless smart gadgets and uninterrupted connectivity. WiFi is the dominant wireless technology, enabling Internet-of-Things (IoT) connectivity in smart cities due to its ubiquitous access points and low deployment cost. However, smart city applications offer a wide range of services with different quality-of-service (QoS) demands. This paper addresses packet delivery latency as one of the QoS metrics affecting many time-sensitive smart city services. Thus, the paper proposes employing software-defined networking (SDN) to control the traffic load of WiFi access points (APs), preserving its symmetry in a city-wide coverage of WiFi-connected IoT gateways or fog nodes. These gateways receive data packets from smart city/IoT devices via wireless links and forward them over a city-deployed WiFi network to their management entities or servers. Three SDN-based algorithms are devised to reduce the gateways’ packet-forwarding delay and keep a symmetric traffic load at the WiFi network APs. The algorithms are developed and tested using a real hardware setup constituting WiFi devices without additional requirements on the IoT gateways (WiFi clients) or the APs, such as support for a specific roaming protocol or bandwidth-consuming signaling such as sending probe packets. Extensive hardware experimentation shows that the SDN controller, via the proposed algorithms, can effectively reduce the packet forwarding latency of IoT gateways by carefully selecting the IoT gateway with the highest packet latency and seamlessly handing it over to the least-loaded covering AP.

Hierarchical multiscale crystal plasticity framework for plasticity and strain hardening of multi-principal element alloys

The multi-principal element alloys (MPEAs) exhibit the unprecedented combinations of the excellent mechanical properties, especially high strength and good ductility. However, the accurate and reasonable models for describing the mechanical behavior of MPEAs are still scarce due to their distinctive serious lattice distortion effects, which limit the performance prediction. Here, we develop a new general framework by combining the atomic simulation, discrete dislocation dynamics, and crystal plasticity finite element method, to study the strain-hardening behavior for MPEAs, which achieves the influence of the complex cross-scale factors, including the lattice distortion at the nanoscale and the dislocation hardening at the microscale, on the plastic deformation. Compared with the classic crystal plasticity finite element, the bottom-up hierarchical multiscale model could couple the underlying physical mechanisms from the nano-micron-meso scales and captures the inhomogeneous strain field induced by the serious lattice distortion, thus showing the high accuracy and ubiquitous availability for MPEAs. The result shows that the prediction of the strain-stress curve in the polycrystal MPEAs agrees well with the experimental result at the quasi-static tension, which verifies the accuracy of the proposed method. In addition to the dislocation evolution, the heterogeneous strain distribution combined with the significant change from the orientation of some grains could be an important reason for the enhanced strength at the micron scale. The present work not only gives an insight into the relationship between the multiscale microstructure and strain hardening considering the mechanistic linkages of the lattice distortion, dislocation behavior, and grain structure, but also provides a general approach to physically predict the mesoscopic mechanical response in MPEAs.

Edge-Assisted Intelligent Video Compression for Live Aerial Streaming

The ultra-low latency transmission, high speed broadband and ubiquitous access points in modern cities have greatly alleviated the last mile problem of live streaming services. Yet, the sophisticated video compression of high definition videos is still inevitable before transmission, which leads to overwhelming workloads in the light-weighted video collectors. In this paper, we are motivated to explore the computation offloading of live streaming video compression for UAVs, which are featured by limited capacity and fixed trajectory. In particular, the global motion model is utilized for inter frame residual coding instead of motion vector estimation, which usually takes more than 50% computation complexity in traditional H.264/265. We propose an edge-based Joint Video Coding (eJVC) scheme, which can save up to 84.04% encoding complexity of UAV video collector. Specifically, the attention network is used to distinguish foreground blocks and background blocks in the frame. LSTM neural network is used on the edge server to predict the auxiliary data needed by UAV video collector for video coding. In addition, our proposed solution can also accommodate the movement direction change when the control signal is notified in advance. Finally, the prototype system is implemented with the real world data set, and the experiment results show that our proposed solution can significantly save the computation time with little bitrate distortion performance loss.

Use of Modern Means of Monitoring the Knowledge of University Students in Distance and Mixed Learning Formats

The modern world provides more and more opportunities for using digital resources in the education system. The variety of digital tools and the ubiquitous availability of information anywhere and at any time create tremendous opportunities for the creative use of diverse approaches to assessing student learning in higher education. One of the widespread methods of online assessment used in higher education is online testing. Increasing the use of online tests requires the study of various resources and their capabilities in assessing learning. The article analyzes the role of online tests in higher education, their capabilities in assessing the results of students’ learning in distance and mixed formats, as well as the issues and problems they pose. The article discusses the stages of development of test tasks, as well as the rules that must be followed when compiling them. An analysis of some interactive tools for the development of various test materials has been carried out, on the basis of which several tools are proposed. In addition, the authors conclude that the limitations that are obvious when using online tests are not their inherent features, but are the result of poorly designed test tasks, an unsuccessful choice of tools for developing online tests.

Level of self-medication awareness to internet usage among selected, non-medical students in Centro Escolar university-Manila during COVID-19 pandemic

Self-medication is the act of using accessible substances to treat self-diagnosed health conditions. Due to ubiquitous access and innovations of smartphones, the internet has become more accessible than before. Hence, the internet has become a significant source of health-related information, leading to increased and uncontrolled internet self-medication. Additionally, because non-medical students typically have a higher level of education and ability to find online health resources, they are more likely to self-medicate. Thus, the purpose of the study was to verify the level of self-medication awareness to internet usage among selected, non-medical students from different colleges in Centro Escolar University-Manila during the COVID-19 pandemic, specifically the School of Accountancy and Management, School of Education, Liberal Arts, Music, and Social Work, School of Nutrition and Hospitality Management, and School of Science and Technology. The study applied a descriptive method and employed random sampling with a sample size of 327 respondents, using Slovin's formula. The research instrument used was survey questionnaires. After conducting the validity and reliability testing, the self-made questionnaire in Google forms was distributed to the respondents using their institutional email addresses. Results of the study have shown that there is a significant difference in self-medication awareness based on the respondents’ sociodemographic profiles, particularly their sex, year level, and degree program. Current findings also showed no significant relationship between the extent of internet usage and the level of self-medication awareness among the respondents.

Understanding the Importance of Social Media as a Citizen Science Platform by Using Tiger Sighting Photographs from Facebook

Availability of scientific data and public support for conservation actions are necessary tools in mitigating conservation issues (Forrester et al. 2017). According to Falk and Dierking (2010), informal learning may be considered as an important avenue to engage the public with both science and nature. Ballantyne et al. (2007) reported informal learning is a key strategy to maintain support for conservation. Citizen science is considered a useful method for collecting ecological data at large scales involving non-professional volunteers in scientific research (Tulloch et al. 2013). Dickinson et al. (2012) reported it is an effective form of informal education about the natural world. Citizen science is a unique tool that can be used as a vehicle for direct experience of nature as well as offering informal education. Ubiquitous and inexpensive availability of internet-connected devices leads the public to engage in social media platforms at a large scale. People from various age groups from a variety of professions regularly use social media platforms like Facebook, Instagram, Flickr etc., to share their activities and creativity, as well as engage in fruitful discussions. Facebook alone enjoys more than 2.35 billion monthly active users (Abu Al-Haija et al. 2019). Photo sharing platforms can help in generating occurrence datasets (Barve 2014) as well as understanding the spatial distribution of various organisms (Jiménez‐Valverde et al. 2019). In our current study (Barman et al. 2022) we have selected Facebook as a source to collect data and analyze it from a conservation perspective. On Facebook, common people, travelers, amateur scientists, and corporations, share their photos and observations of nature in a structured or unstructured manner, knowingly or unknowingly. By compiling data from communities on Facebook, we have attempted to co-relate various parameters like time and space, to understand the status and abundance of the Bengal tiger (Panthera tigris tigris), which is one of the largest and charismatic carnivorous megafauna (Kumar 2021), and plays a key role in conservation management plans as an umbrella species (Roberge and Angelstam 2004). Based on the dates that images were posted, we collected the data for a period of eighteen years (2004–2022). Individual tigers can be identified in the field based on their stripe pattern and are individually named by the staff of the forest department and tour naturalists. As the tiger is a critically endangered animal, it is important to have maximum data available for every individual, which can help in understanding, in a robust manner, the species ecology with respect to the landscape. Data compilation involved detailing the metadata recorded by the person posting and the comments on the post, as well as metadata evident from the photograph e.g., sex, life stage and activity. Geotagging was performed based on the place mentioned as the location of the photograph, to understand the extent of their area of mobility in a meaningful way. Data compiled for an individual tiger specimen, on the basis of photographs shared of various individuals over a period of time in social media platforms, has the potential to generate the same result involving radio telemetry, which is invasive and expensive in nature. It will then elevate the importance of photography as scientific documentation for ecological understanding and will generate interest through the participation in citizen science in conservation efforts involving megafauna. The sampled data shows a higher number of photographs available from protected areas that have a clear tourism protocol. We have also compared our distribution map, evolved out of this effort (Fig. 1) with the maps created by the Tiger Monitoring Cell*1 as a result of the Tiger Census activity, which happens in India every four years. This kind of cross referencing has the potential to bring out very interesting patterns in understanding coarse-scale ecological and geographic properties of a species (Peña-Aguilera et al. 2019, Soberón 2007).

Changing the economic paradigm for building a space elevator

The development of the traction Space Elevator concept started in 1959 with Yuri Artsutanov. From an early stage, researchers identified the traction cable or tether as the technical bottleneck of this space infrastructure, and it has marked the rhythm of its development. While this technical approach has generated great benefits, moving ahead with the product design and architecture, it has left out other important areas, such as the potential economic impact of Space Elevators. During Bradley Edward's studies for NASA in 2002–2003, the economic scope was only covered on two points: the estimated cost analysis for its construction; and how its cost per kilogram compared with rockets. The economics of Space Elevators has not progressed much since then, and new papers either refer to the 2003 paper or update the data of these exact two points. With the current global trends favoring a burgeoning space economy, it is even more crucial than ever to develop a long-term sustainable economic overview for Space Elevators to accelerate the development of this megaproject.This paper analyzes the economics of Space Elevators as infrastructure and a platform, utilizing relevant historical examples, such as the standardization of shipping containers, the transcontinental railroad, and the Panama Canal to explore its economic value and developmental impact. Infrastructure, at its core, provides value through the reduction of transaction costs. Therefore, trying to close a business case for infrastructure by charging high transaction costs is a doomed venture. However, expanding the picture to view the impact on the economy from increased access to value and more efficient markets through lower transaction costs and infrastructure becomes a very lucrative, stable, and reliable investment. Cost per kilogram is the language of rockets -- strategic investment, ubiquitous access, and uninterrupted exchange of resources are the staples of Space Elevators.Space Elevators are an evolution of launch technologies. Rockets are capable tools to lift payloads to orbit, including human cargo, but they are impractical tools for the mass quantities, distances, and timelines needed for our ongoing space ambitions. The focus of this paper is on establishing the value of a Space Elevator as part of a larger economic ecosystem and using that foundation as grounds for acquiring the financial leverage that will be required to construct it. To achieve this target, this paper presents a roadmap for the business development and financial milestones along the path to building this megaproject.