A protocol for harvesting biodiversity data from Facebook.

The expanding use of community science platforms has led to an exponential increase in biodiversity data in global repositories. Yet, understanding of species distributions remains patchy. Biodiversity data from social media can potentially reduce the global biodiversity knowledge gap. However, practical guidelines and standardized methods for harvesting such data are nonexistent. Following data privacy and protection safeguards, we devised a standardized method for extracting species distribution records from Facebook groups that allow access to their data. It involves 3 steps: group selection, data extraction, and georeferencing the record location. We present how to structure keywords, search for species photographs, and georeference localities for such records. We further highlight some challenges users might face when extracting species distribution data from Facebook and suggest solutions. Following our proposed framework, we present a case study on Bangladesh's biodiversity-a tropical megadiverse South Asian country. We scraped nearly 45,000 unique georeferenced records across 967 species and found a median of 27 records per species. About 12% of the distribution data were for threatened species, representing 27% of all species. We also obtained data for 56 DataDeficient species for Bangladesh. If carefully harvested, social media data can significantly reduce global biodiversity knowledge gaps. Consequently, developing an automated tool to extract and interpret social media biodiversity data is a research priority.

Un protocolo para recolectar datos sobre biodiversidad en Facebook Resumen El uso creciente de plataformas de ciencia comunitaria ha causado un incremento exponencial de los datos sobre biodiversidad en los repositorios mundiales. Sin embargo, el conocimiento sobre la distribución de las especies todavía está incompleto. Los datos sobre biodiversidad obtenidos de las redes sociales tienen el potencial para disminuir el vacío de conocimiento sobre la biodiversidad mundial. No obstante, no existe una guía práctica o un método estandarizado para recolectar dichos datos. Seguimos los protocolos de privacidad y protección de datos para diseñar un método estandarizado para extraer registros de la distribución de especies de grupos en Facebook que permiten el acceso a sus datos. El método consta de tres pasos: selección del grupo, extracción de datos y georreferenciación de la localidad registrada. También planteamos cómo estructurar las palabras clave, buscar fotografías de especies y georreferenciar las localidades de dichos registros. Además, resaltamos algunos retos que los usuarios pueden enfrentar al extraer los datos de distribución de Facebook y sugerimos algunas soluciones. Aplicamos nuestro marco de trabajo propuesto a un estudio de caso de la biodiversidad en Bangladesh, un país tropical megadiverso en el sureste de Asia. Reunimos casi 45,000 registros georreferenciados únicos para 967 especies y encontramos una media de 27 registros por especie. Casi el 12% de los datos de distribución correspondió a especies amenazadas, que representaban el 27% de todas las especies. También obtuvimos datos para 56 especies deficientes de datos en Bangladesh. Si los datos de las redes sociales se recolectan con cuidado, éstos pueden reducir de forma significativa el vacío de conocimiento para la biodiversidad mundial. Como consecuencia, es una prioridad para la investigación el desarrollo de una herramienta automatizada para extraer e interpretar los datos sobre biodiversidad de las redes sociales.

Fire risk assessments and fire protection measures for wind turbines: A review.

Wind turbine fires pose a significant global problem, leading to substantial financial losses. However, due to limited open discussions and lax regulations in the wind power industry, progress in addressing this issue has been hindered. This study aims to shed light on the fire risks associated with wind turbine nacelles and blades, while also exploring preventive measures and the latest fire detection and extinguishing technologies. The research conducted in this study involves a comprehensive investigation of various case studies, utilizing causal examination to identify common failure forms and their roles in fire incidents. Additionally, typical hazards, with a focus on fire incidents, in wind turbines are diagnosed. The primary causes of these fires were determined to be lightning strikes and hydraulic faults, often exacerbated by the presence of combustible materials. To conclude, the study includes a survey that encompasses education, knowledge analysis, and real-life accident experiences to assess fire risks and prevention measures in wind turbines. The participation of experts from wind farms, including those from the People's Republic of Bangladesh and other countries, adds valuable insights. The findings from this study serve as a crucial resource for enhancing safety standards and mitigating fire incidents within the wind power industry.

Spatiotemporal analysis and predicting rainfall trends in a tropical monsoon-dominated country using MAKESENS and machine learning techniques.

Spatiotemporal rainfall trend analysis as an indicator of climatic change provides critical information for improved water resource planning. However, the spatiotemporal changing behavior of rainfall is much less understood in a tropical monsoon-dominated country like Bangladesh. To this end, this research aims to analyze spatiotemporal variations in rainfall for the period 1980-2020 over Bangladesh at seasonal and monthly scales using MAKESENS, the Pettitt test, and innovative trend analysis. Multilayer Perception (MLP) neural network was used to predict the next 8 years' rainfall changes nationally in Bangladesh. To investigate the spatial pattern of rainfall trends, the inverse distance weighting model was adopted within the ArcGIS environment. Results show that mean annual rainfall is 2432.6 mm, of which 57.6% was recorded from July to August. The Mann-Kendall trend test reveals that 77% of stations are declining, and 23% have a rising trend in the monthly rainfall. More than 80% of stations face a declining trend from November to March and August. There is a declining trend for seasonal rainfall at 82% of stations during the pre-monsoon, 75% during the monsoon, and 100% during the post-monsoon. A significant decline trend was identified in the north-center during the pre-monsoon, the northern part during the monsoon, and the southern and northwestern portions during the post-monsoon season. Predicted rainfall by MLP till 2030 suggests that there will be little rain from November to February, and the maximum fluctuating rainfall will occur in 2025 and 2027-2029. The ECMWF ERA5 reanalysis data findings suggested that changing rainfall patterns in Bangladesh may have been driven by rising or reducing convective precipitation rates, low cloud cover, and inadequate vertically integrated moisture divergence. Given the shortage of water resources and the anticipated rise in water demand, the study's findings have some implications for managing water resources in Bangladesh.

Increasing biodiversity knowledge through social media: A case study from tropical Bangladesh.

Citizen science programs are becoming increasingly popular among naturalists but remain heavily biased taxonomically and geographically. However, with the explosive popularity of social media and the near-ubiquitous availability of smartphones, many post wildlife photographs on social media. Here, we illustrate the potential of harvesting these data to enhance our biodiversity understanding using Bangladesh, a tropical biodiverse country, as a case study. We compared biodiversity records extracted from Facebook with those from the Global Biodiversity Information Facility (GBIF), collating geospatial records for 1013 unique species, including 970 species from Facebook and 712 species from GBIF. Although most observation records were biased toward major cities, the Facebook records were more evenly spatially distributed. About 86% of the Threatened species records were from Facebook, whereas the GBIF records were almost entirely Of Least Concern species. To reduce the global biodiversity data shortfall, a key research priority now is the development of mechanisms for extracting and interpreting social media biodiversity data.

Wildfires in Australia: a bibliometric analysis and a glimpse on 'Black Summer' (2019/2020) disaster.

A wildfire, an unplanned fire that is mainly uncontrolled and originates in combustible vegetation in rural or urban settings, is one of the most pervasive natural catastrophes in some areas, such as Siberia, California and Australia. Many studies, such as standard reviews, have been undertaken to look into the works of literature on wildfires or forest fires and their effects on aquatic and terrestrial ecosystems. Regrettably, conventional literature reviews failed to identify the important researchers, evolving complexities, emerging research hotspots, trends and opportunities for further research on the ground of wildfire study. The present study employs bibliometric analysis to investigate this study area qualitatively and quantitatively. The Scopus database systems and Web of Science Core Collection yielded 78 qualifying papers, which were then evaluated using Biblioshiny (A bibliometrix tool of R-studio). According to the statistics, the discipline is expanding at a pace that is 13.68% faster than average. So far, three key periods of transformation have been documented: preliminary evolution (8 articles; 1999-2005), gentle evolution (14 articles; 2006-2013) and quick evolution (56 articles; 2014 to 2021). Forest Ecology and Management and Science journals have the highest number of publications, accounting for 7.70% of total wildfire-related articles published from 1999 to 2021. However, recent data indicate that investigators are shifting their focus to wildfires, with the term 'Australia' having the highest frequency (91) and 'wildfire' having the second highest (58) as the most appeared keywords. The present study will provide a foundation for future research on wildfire incidence and management by receiving information by synthesising previously published literature in Australia and around the world.

Eco-friendly scouring of cotton knit fabrics with enzyme and soapnut: An alternative to conventional NaOH and synthetic surfactant based scouring.

Eye-catching, aesthetic fashions often suppress its untold dark story of unsustainable processing including hazardous wet treatment. Considering the risks imposed by conventional cotton scouring and following the trend of scouring with enzymes, this study was undertaken to evaluate the bioscouring of cotton knit fabric involving saponin-enriched soapnut as a natural surfactant, applied from a bath requiring a few chemicals and gentle processing conditions, contributing to the eco-friendliness. The proposed application was compared to synthetic detergent engaged enzymatic scouring as well as the classic scouring with Sodium hydroxide. A cellulolytic pectate lyase enzyme (0.5%-0.8% o.w.f) was applied at 55 °C for 60 min at pH 5-5.5 with varying surfactant concentrations. A low concentration of soapnut extract (1 g/L to 2 g/L) was found sufficient to assist in the removal of non-cellulosic impurities from the cotton fabric after bioscouring with 0.5% o.w.f. enzyme, leading to good hydrophilicity indicated by an average wetting time of 4.86 s at the expense of 3.1%-3.8% weight loss. The scoured fabrics were further dyed with 1% o.w.f. reactive dye to observe the dyeing performance. The treated samples were characterized in terms of weight loss, wettability, bursting strength, whiteness index, and color value. The proposed application confronted level dyeing and the ratings for color fastness to washing and rubbing were 4-5 for all of the samples scoured enzymatically with soapnut. The study was also statistically analyzed and concluded.

Arsenic Elevated Groundwater Irrigation: Farmers' Perception of Rice and Vegetable Contamination in a Naturally Arsenic Endemic Area.

Arsenic (As) in groundwater and its accumulation in agricultural produces has caused serious threats to human health. The majority of current research on As mainly focuses on the technical aspects while bypassing the social perspectives. Farmers are the prime stakeholders as well as executors of agricultural strategies, and their adaptation largely depends on how they perceive the risk for which a mitigation strategy is proposed. This study aims to explore how rice and vegetable farmers perceive As accumulation in their rice and vegetables as well as explore current crop- and body-loading status, the subsequent health consequences of As, and alleviation possibilities with mitigation strategies and to investigate if there is an association between their socioeconomic status and their level of perception. Results reveal that one-fourth of the farmers gave a positive message regarding the As-contamination scenario in rice and vegetables. Although 10 farmers' socioeconomic characteristics were positively significant, distinctive emphasis should be given to five predictor variables explaining 88% variances: knowledge, direct participation in farming, information sources used, participant education, and organizational participation. Path analysis depicts that direct participation in farming presents the highest positive total effect (0.855) and direct effect (0.503), whereas information sources show the highest positive indirect effect (0.624). The mean As content in all five locations was statistically significant at the 5%, 5%, 0.1%, 1%, and 1% probability levels in scalp hairs, rice, vegetables, soils, and irrigation water, respectively. The first principal component (PC1) explains 92.5% of the variation. Significant variations were primarily explained by As levels in irrigation water, rice grain, and soil. Farmers' perception is far behind the actual field status of As level and its transfer. Therefore, intensified priorities should be administered on the farmers' characteristics contributing to variances in perception. The findings can be utilized for policy formulation in all As-endemic nations. More multidisciplinary research can be undertaken on farmers' attitude towards adopting As-mitigation techniques, with a focus on the socioeconomic position found to influence farmers' perceptions.

Depression, anxiety and stress among people infected with COVID-19 in Dhaka and Chittagong cities.

Background: Covid-19 is an acute respiratory infectious disease caused by novel coronavirus 2019. Many individuals have suffered or are experiencing psychological symptoms due to feelings of isolation, insecurity and instability triggered by Covid-19. This study aimed to explore the perceived psychological distress and associated factors among people infected with Covid-19 in Dhaka and Chittagong cities. Methods: Using the face-to-face interview method, a survey was conducted from 23 April to 22 May 2021 on a questionnaire on depression, anxiety and stress scale (DASS-21), socio-demographic, economic and health factors. Among those who had ever been infected with Covid-19, a total of 2092 respondents (1180 from Dhaka and 912 from Chittagong) were randomly selected and interviewed. χ 2 test for independence of attributes was employed to observe the association of various socioeconomic, cultural, demographic and health factors with psychological distress. Moreover, multinomial logistic regression analysis was performed to explore the factors that contributed to psychological distress. Results: Among participants from Dhaka, of three mental health conditions, the prevalence was higher for anxiety (80.0%), followed by stress (64.2%) and depression (59.8%), respectively. Anxiety was also more prevalent (57.3) among respondents in Chittagong, followed by depression (47.7%) and stress (39.5%). As the coexistence of symptoms, 52.8% of respondents in Dhaka, 34.4% in Chittagong experienced depression, anxiety and stress simultaneously. Moreover, in both Dhaka and Chittagong, all three psychological symptoms were strongly correlated in pairs. Multivariate analysis revealed that the most consistent factors associated with mild to moderate (MM), and severe to extremely severe (SES) depression were respondents from Chittagong who were illiterate or primary educated (OR = 0.166, CI: 0.076-0.364 for MM and OR = 0.041, CI: 0.013-0.131 for SES), male (OR = 0.999, CI: 0.666-1.496 for MM and OR = 0.395, CI: 0.249-0.625 for SES), single (OR = 0.393, CI: 0.157-0.982 for MM and OR = 0.121, CI: 0.049-0.303 for SES) and married (OR = 0.403, CI: 0.177-0.916 for MM and OR = 0.075, CI: 0.033-0.167 for SES), had a family of size ≤ 4 (OR = 0.253, CI: 0.140-0.458 for MM and OR = 0.114, CI: 0.059-0.218 for SES) and a family of size 5-6 (OR = 0.151, CI: 0.084-0.272), and no family members at risk being infected with Covid-19 (OR = 0.699, CI: 0.487-1.002 for MM and OR = 0.332, CI: 0.199-0.522 for SES). The analysis yielded similar findings for the other two mental health subscales, such as anxiety and stress. For respondents in Dhaka, the effect of factors other than sex on psychological distress was the opposite in Chittagong. Overall, psychological distress during the outbreak was greater among respondents in Dhaka than in Chittagong if respondents were not classified based on various characteristics. Conclusion: This study showed that in both Dhaka and Chittagong, a substantially large portion of Covid-19-infected respondents experienced all three psychological distress (e.g., depression, anxiety and stress). Regardless of the dissimilarity between the results in Dhaka and Chittagong, better mental health support was needed for women in both cities.

Protected areas in South Asia: Status and prospects.

Natural ecosystems globally have been disrupted by anthropogenic activities, and the current biodiversity extinction rate exceeds the natural extinction rate by 1,000-fold. Protected areas (PAs) help insulate samples of biodiversity from these human-induced threats; however, assessments of the factors threatening biodiversity in PAs are scarce in South Asia - one of the key global epicentres of human population growth. Here, by synthesizing published literature and analysing the current configuration of the PA estate, we discuss the trends and biases in existing knowledge, identify research gaps, measure the level of PA coverage and growth patterns, and discuss the threats to South Asian biodiversity inside PAs. We showed that published studies focused mainly on documenting species distributions in PAs, were heavily biased toward vertebrates, and had been mostly conducted in India. Nearly 70% of studies focused on the distribution of organisms, while only 9% performed conservation assessments or devised strategies to manage PAs; 70% of studies cover vertebrates, while only two studies focused on marine fauna; 50% of studies focused on India, with only a handful from Afghanistan. Only three (Bhutan, Nepal, Sri Lanka) of the eight countries already meet a terrestrial PA representation target of 17%, while no country meets a marine representation target of 10%. Most PAs were very small, with nearly 80% below 100 km2, and 22% below 1 km2. We identified that South Asian PAs are facing a broad range of anthropogenic threats - about three in five studies reported threats inside protected areas. Due to extensive anthropogenic pressures, biodiversity in South Asia is facing an existential crisis, and society-wide collaborative efforts are needed to arrest and reverse the declines. We hope this review will stimulate efforts to capitalise on the opportunity for efficient PA growth in the region on the eve of the post-2020 global biodiversity targets.

Self-Sustained Autonomous Wireless Sensor Network with Integrated Solar Photovoltaic System for Internet of Smart Home-Building (IoSHB) Applications.

Conventional wireless sensor networks (WSNs) in smart home-building (SHB) are typically driven by batteries, limiting their lifespan and the maximum number of deployable units. To satisfy the energy demand for the next generation of SHB which can interconnect WSNs to make the internet of smart home-building (IoSHB), this study introduces the design and implementation of a 250 mW to 2.3 W energy harvesting device. The proposed device is dynamically autonomous owing to the integration of embedded solar photovoltaic (PV) modules and power storage through a supercapacitor (SC; 5 V, 0.47 F) capable of powering WSNs for 95 s (up to 4.11 V). The deployed device can harvest indoor and outdoor ambient light at a minimum illumination of 50 lux and a maximum illumination of 200 lux. Moreover, the proposed system supports wireless fidelity (Wi-Fi) and Bluetooth Low Energy (BLE) to do data transfer to a webserver as a complete internet of things (IoT) device. A customized android dashboard is further developed for data monitoring on a smartphone. All in all, this self-powered WSN node can interface with the users of the SHBs for displaying ambient data, which demonstrates its promising applicability and stability.

An Adaptive TE-PV Hybrid Energy Harvesting System for Self-Powered IoT Sensor Applications.

In this paper, an integrated thermoelectric (TE) and photovoltaic (PV) hybrid energy harvesting system (HEHS) is proposed for self-powered internet of thing (IoT)-enabled wireless sensor networks (WSNs). The proposed system can run at a minimum of 0.8 V input voltage under indoor light illumination of at least 50 lux and a minimum temperature difference, ∆T = 5 °C. At the lowest illumination and temperature difference, the device can deliver 0.14 W of power. At the highest illumination of 200 lux and ∆T = 13 °C, the device can deliver 2.13 W. The developed HEHS can charge a 0.47 F, 5.5 V supercapacitor (SC) up to 4.12 V at the combined input voltage of 3.2 V within 17 s. In the absence of any energy sources, the designed device can back up the complete system for 92 s. The sensors can successfully send 39 data string to the webserver within this time at a two-second data transmission interval. A message queuing telemetry transport (MQTT) based IoT framework with a customised smartphone application 'MQTT dashboard' is developed and integrated with an ESP32 Wi-Fi module to transmit, store, and monitor the sensors data over time. This research, therefore, opens up new prospects for self-powered autonomous IoT sensor systems under fluctuating environments and energy harvesting regimes, however, utilising available atmospheric light and thermal energy.