Large Geographical Area Research Articles

Deep-learning-based multi-timescale load forecasting in buildings: opportunities and challenges from research to deployment

Electricity load forecasting for buildings and campuses is becoming increasingly important as the penetration of distributed energy resources (DERs) grows. Efficient operation and dispatch of DERs require reasonably accurate predictions of future energy consumption in order to conduct near-real-time optimized dispatch of on-site generation and storage assets. Electric utilities have traditionally performed load forecasting for load pockets spanning large geographic areas, and therefore, forecasting has not been a common practice by buildings and campus operators. Given the growing trends of research and prototyping in the grid-interactive efficient buildings domain, characteristics beyond simple algorithm forecast accuracy are important in determining the algorithm’s true utility for smart buildings. Other characteristics include the overall design of the deployed architecture and the operational efficiency of the forecasting system. In this work, we present a deep-learning-based load forecasting system that predicts the building load at 1-hour intervals for 18 hours in the future. We also discuss challenges associated with the real-time deployment of such systems as well as the research opportunities presented by a fully functional forecasting system that has been developed within the National Renewable Energy Laboratory’s Intelligent Campus program.

Near real‐time monitoring of wading birds using uncrewed aircraft systems and computer vision

Wildlife population monitoring over large geographic areas is increasingly feasible due to developments in aerial survey methods coupled with the use of computer vision models for identifying and classifying individual organisms. However, aerial surveys still occur infrequently, and there are often long delays between the acquisition of airborne imagery and its conversion into population monitoring data. Near real‐time monitoring is increasingly important for active management decisions and ecological forecasting. Accomplishing this over large scales requires a combination of airborne imagery, computer vision models to process imagery into information on individual organisms, and automated workflows to ensure that imagery is quickly processed into data following acquisition. Here we present our end‐to‐end workflow for conducting near real‐time monitoring of wading birds in the Everglades, Florida, USA. Imagery is acquired as frequently as weekly using uncrewed aircraft systems (aka drones), processed into orthomosaics (using Agisoft metashape), converted into individual‐level species data using a Retinanet‐50 object detector, post‐processed, archived, and presented on a web‐based visualization platform (using Shiny). The main components of the workflow are automated using Snakemake. The underlying computer vision model provides accurate object detection, species classification, and both total and species‐level counts for five out of six target species (White Ibis, Great Egret, Great Blue Heron, Wood Stork, and Roseate Spoonbill). The model performed poorly for Snowy Egrets due to the small number of labels and difficulty distinguishing them from White Ibis (the most abundant species). By automating the post‐survey processing, data on the populations of these species is available in near real‐time (<1 week from the date of the survey) providing information at the time scales needed for ecological forecasting and active management.

Emissions from air travel and major football tournaments

ABSTRACT Research Question This paper explores the environmental impact of air travel during two international football tournaments – the Mens FIFA World Cup and UEFA European Championships – by estimating CO2 emissions from the movements of competing teams. Specifically, we consider how travel associated with major international football tournaments has evolved over time. Research Methods We access data for 895 international football matches (June 1990–July 2024). Our data focuses on the travel associated with players and management travelling to, and within, host countries and includes the movement of more than 16,000 people. Estimates of CO2 emissions per passenger (per kilometre) are used to examine the environmental impact of travelling to host countries, and competing, during the tournament. Results and Findings Our findings are the first attempt to demonstrate the CO2 emissions from team travel to and within major football tournaments. Despite improvements in aeroplane fuel efficiency, the carbon footprint from team travel has generally not been reduced. The decision to expand these tournaments, locate matches across large geographic areas, and use co-host countries has exacerbated this issue. Implications Our findings can support sustainability initiatives and could be adopted by both federations to curb CO2 emissions which appear to undermine genuine attempts to become more environmentally friendly.

Scaling Terrain-Aware Spatial Machine Learning for Flood Mapping on Large Scale Earth Imagery Data

The accurate and prompt mapping of flood-affected regions is important for effective disaster management, including damage assessment and relief efforts. While high-resolution optical imagery from satellites during disasters presents an opportunity for automated flood inundation mapping, existing segmentation models face challenges due to noises like cloud cover and tree canopies. Thanks to the digital elevation model (DEM) data readily available from sources such as United States Geological Survey (USGS), terrain guidance was utilized by recent graphical models such as hidden Markov trees (HMTs) to improve segmentation quality. Unfortunately, these methods either can only handle a small area where water levels at different locations are assumed to be consistent, or require restricted assumptions such as there is only one river channel. This paper presents an algorithm for flood extent mapping on large-scale Earth imagery, applicable to a large geographic area with multiple river channels. Since water level can vary a lot from upstream to downstream, we propose to detect river pixels in order to partition the remaining pixels into localized zones, each with a unique water level. In each zone, water at all locations flow to the same river entry point. Pixels in each zone are organized by an HMT to capture water flow directions guided by elevations. Moreover, a novel regularization scheme is designed to enforce inter-zone consistency by penalizing pixel-pairs of adjacent zones that violate terrain guidance. Efficient parallelization is made possible by coloring the zone adjacency graph to identify zones and zone-pairs that have no dependency and hence can be processed in parallel, and incremental one-pass terrain-guided scanning is conducted wherever applicable to reuse computations. Experiments demonstrate that our solution is more accurate than existing solutions, and can efficiently and accurately map out flooding pixels in a giant area of size 24,805 × 40,129. Despite the imbalanced workloads caused by a few large zonal HMTs dominating the serial computing time, our parallelization approach is effective and manages to achieve up to 14.3 × speedup on a machine with Intel Xeon Gold 6126 CPU @ 2.60GHz (24 cores, 48 threads) using 32 threads.

A comparative biomonitoring study of trace metals and organic compounds bioaccumulation in marine biofilms and caged mussels along the French Mediterranean coast

The bioaccumulation potential of contaminants in marine environments was investigated in biofilms and compared with caged mussels for a wide range of both organic and metallic contaminants across a large geographic area. Marine biofilms were sampled after three months of sub-surface immersion at 49 locations along the 1800 km of the French Mediterranean coast. Ten chemical elements (i.e. As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, and Zn) and 57 organic compounds (i.e., 18 polycyclic aromatic hydrocarbons (PAHs), 8 dioxin-like and 6 non-dioxin-like polychlorinated biphenyls (PCBs) and 25 organochlorine pesticides (OCPs)) were quantified in triplicates, revealing different multi-contaminated profiles depending on sites. Most of contaminants exhibited higher concentrations in biofilms than in mussels. Moreover, a remarkable significant and positive correlation between the concentrations in both biological matrices was observed for PAHs and PCBs, and more contaminant-dependent for OCPs and metals. These results highlighted the potential of biofilms as relevant bioindicators of the marine chemical contamination.

Assessment of pollution and other impairments of US reservoirs based on expert opinion

A comprehensive understanding of the prevailing pollution and other impairments to reservoirs is necessary at the national level to analyze patterns and causes as well as allocate national resources effectively. Most of these impairments are associated with sedimentation, nutrient contamination, and other pollution factors. The costs involved in conducting on-site surveys at multiple locations are potential obstacles for obtaining empirical data on large-scale spatial impairments. In such cases, inputs from specialists may be required to offset the absence of empirical data for determining the impairment statuses of thousands of geographically dispersed reservoirs. Thus, our aim was to examine whether expert opinion could offer a comprehensive review of the impairment statuses of numerous reservoirs in the United States. We designed and executed an internet-based survey of reservoir specialists to gather their ratings on visually evident elements of reservoir impairment. To evaluate the ratings, we searched for correlations with factors known to have impacts on reservoir impairment. Canonical correlation analyses indicated that nine metrics used by experts to rate impairment levels on an ordinal scale were correlated with metrics descriptive of the physical qualities of reservoirs, land use in catchments, and prevailing local climate. We thus conclude that expert opinions may facilitate assessments of impairment levels over large geographical areas. Lastly, we define the circumstances under which it is permissible to rely on expert opinions and propose criteria that could improve the quality of the data collected.

Student entrepreneurs with triple bottom line objectives: capabilities in creating viable business models

PurposeThe objective of this instrumental case study is to investigate issues impacting capability development/transformation with respect to student entrepreneurs’ aspirations to create a viable, evolving business model for their start-ups, namely, that are underpinned by goals featuring the “triple bottom line”. That is, instead of simply profits as an economic performance metric, there is also a consideration of wider social issues within the notion of “responsible leadership”, involving people and the planet.Design/methodology/approachUnderpinned by a “capabilities lens”, the research setting focuses on the perceptions of student entrepreneurs together with other stakeholders in a Canadian university. The country context is pertinent, since to gain scalability, owners of start-up firms may need to internationalise to overcome limited domestic demand (despite the large geographic area); also, stakeholders’ support is sometimes needed to facilitate growth.FindingsNew insights demonstrate the need for appropriate stakeholders to facilitate effective transformative capability development amongst student entrepreneurs with triple bottom line objectives. Student entrepreneurs’ capability of validating facets of a viable evolving business model that address “all” and not “some” aspects of the triple bottom line is especially important. This is alongside the capability of being able to pivot product-market strategies where necessary, and this may feature the need to internationalise in the event there is limited domestic demand. A capability to develop soft skills is also likely to help student entrepreneurs communicate with stakeholders. In turn, such capabilities are likely to help move ideation, passed validation and through to commercialisation.Originality/valueThe utility of institutions and associated stakeholders offering education and training support to develop capabilities amongst students as aspiring entrepreneurs has featured in earlier studies. Likewise, issues associated with the notion of sustainability have also been previously considered. The originality of this instrumental case study is to offer a more nuanced investigation into salient issues associated with capability development amongst student entrepreneurs exhibiting triple bottom line objectives in their start-ups. That is, focusing on considerations related to validating their evolving business models and especially when facing limited domestic market demand.

Simplified site response analysis for regional seismic risk assessments

Seismic risk assessment on a regional scale requires an estimation of ground motion intensity and its spatial variation over large geographical areas. This task is particularly challenging in the presence of very soft soil deposits where significant amplification occurs at specific frequencies that are often referred to as local resonances. It is often not feasible to conduct a nonlinear site response analysis at thousands of sites of interest across a region such as an urban area due to the computational effort involved and the required detailed information on soil profiles at each site. Thus, in regional seismic risk analyses the hazard is typically represented by a field of response spectral ordinates estimated by ground motion models. This study proposes a simplified site response analysis procedure to improve the characterization of spectral ordinates to be used in regional seismic risk assessments. In the proposed procedure, reduced-order models are used to conduct site response analysis using very few parameters to transform response spectra at rock outcrop into response spectra at the surface of each of the sites. A particular emphasis is given to soft-soil sites characterized by low shear-wave velocities overlaid on rock or firm soils. A non-uniform continuous shear beam model with a parabolic variation of shear modulus along the depth and modal damping is used in combination with Random Vibration Theory. It is shown that the proposed approach provides better estimates than those of ground motion models or physics-based ground motion simulation models which often cannot capture the local resonances. Site-specific validation of the shear beam model and overall simplified site response analysis is performed at four soft-soil sites in San Francisco, California. Despite its simplicity, it is shown that the proposed procedure adequately captures the amplitudes and spectral shapes of the motions. It is also shown that in conjunction with ground motion models or physics-based models, the proposed simplified site response analysis procedure can be used to efficiently simulate the seismic hazard on a regional scale through an example of a regional seismic hazard analysis of 160 softer soil sites in Downtown San Francisco during the Loma Prieta earthquake.

ICS-LTU2022: A dataset for ICS vulnerabilities

Industrial control systems (ICS) are a collection of control systems and associated instrumentation for controlling and monitoring industrial processes. Critical infrastructure relies on supervisory control and data acquisition (SCADA), a subset of ICS specifically designed for monitoring and controlling industrial processes over large geographic areas. Cyberattacks like the Colonial Pipeline ransomware case have demonstrated how an adversary may compromise critical infrastructure. The Colonial Pipeline ransomware attack led to a week’s pipeline shutdown, causing a gas shortage in the United States. As existing vulnerability assessment tools cannot be used in the context of ICS systems, vulnerability datasets specified for ICSs are needed to evaluate the security weaknesses. Our secondary metadata, ICS-LTU2022, consists of multiple features that can be used for vulnerability assessment and risk evaluation in industrial control systems. A description of the dataset, its characteristics, and data analysis are also presented in this paper. Vulnerability analysis was conducted based on the top 10 vulnerabilities in terms of severity, frequency by year, impact, components of the ICS, and common weaknesses. The ICS-LTU2022 vulnerabilities dataset is updated biannually. Our proposed dataset provides security researchers with the most recent ICS critical vulnerabilities.

Geoparticipation as a tool for mapping calamities mosquito hotspots: A case study from Litovelské Pomoraví, Czechia

The coexistence of humans and nature has been a recurring theme throughout history, gaining particular importance in the context of climate change and the sustainability of our planet for future generations. However, the delicate balance between humans and nature is often disrupted, as exemplified by the Litovelské Pomoraví Protected Landscape Area in Czechia, where close interaction between humans and mosquitoes is a persistent issue. In this area, when environmental conditions are favorable for mosquito outbreaks, the local population living near the floodplain forests experiences significant discomfort for extended periods. The risk of mosquito overpopulation has intensified in recent years, largely attributed to climate change. Rising water temperatures in breeding habitats accelerate the developmental cycles of certain mosquito species, shortening the time required for maturation. This study aims to utilize modern geoinformation techniques to assess mosquito activity within the Litovelské Pomoraví region and evaluate the perceived impact on human populations. The methodological approach integrates the development of a representative population distribution layer and participatory mapping. These efforts culminate in the identification of active mosquito zones and the quantification of the at-risk population. While long-term data indicate that the situation is not deteriorating significantly, the study confirms that elevated mosquito activity continues to disrupt the daily lives of residents and visitors. Notably, summer mosquito outbreaks exert a broader impact than spring outbreaks, affecting both a larger geographic area and a greater proportion of the population.The study’s primary output, the spatial delineation of active mosquito zones and the identification of continuously populated areas at risk, holds significant value for the integrated management of the region. These findings can help mitigate the annual occurrence of mosquito outbreaks, improve the health of the floodplain forest ecosystem, and promote biodiversity conservation.

The impact of neighborhood socioeconomic deprivation on clinical outcomes for locally advanced and metastatic hepatocellular carcinoma: An incidence-based retrospective cohort study.

108 Background: Prior studies show that socioeconomic status impacts hepatocellular carcinoma (HCC) outcomes. However, these have used larger geographic areas such as zip codes or counties for assessment. Using a statewide cancer registry the Area Deprivation Index (ADI), a granular measure of socioeconomic deprivation (SED), was used to assess receipt of treatment and overall survival (OS) for locally advanced and metastatic HCC. Methods: The incidence-based Florida Cancer Data System was used to identify stage III/IV HCC patients diagnosed from 2007-2015. The ADI was used to measure SED and is a validated dataset that ranks neighborhoods (census block groups) from 1-100 (higher scores = higher deprivation) based on income, education, housing, and other socioeconomic factors. Demographic, tumor, and treatment variables were assessed using descriptive statistics, regression plots, and survival analysis. Chemotherapeutic treatment (CT) included receipt of chemotherapy and/or chemoembolization. Results: The study identified 2,534 eligible patients. Males comprised 80.6% of the cohort. The median age was 63 years. By SED quartile, there were significant differences by age, race, insurance, and receipt of chemotherapy (p<0.05) but no difference by gender, ethnicity, stage, or survival. Overall, 39.1% received chemotherapeutic treatment. However, 41.0% of the lowest-deprivation patients received CT compared to 32.1% in the highest-deprivation cohort (p<0.01). After adjustment for potential confounders, the lowest deprivation quartile had increased odds of CT compared to the highest deprivation quartile (OR 1.47; 95% CI,1.15-1.87). The median OS for the cohort was 4.0 months and was 4.6 and 3.3 months, respectively, for the lowest and highest deprivation quartiles (p<0.01). For patients who received and did not receive CT, the median OS was 8.0 and 2.2 months, respectively (p<0.01). However, there was no difference in median OS by SED for those who received CT (p=0.14). Conclusions: In an incidence-based, statewide cancer registry, the median OS for locally advanced and metastatic HCC remains poor and most patients do not receive CT. While there were differences in receipt of CT by SED, when patients did receive CT, median OS was similar across SED quartiles. These data suggest that SED may impact receipt of treatment and efforts are needed to understand and address treatment disparities. Lowest Deprivation Low Deprivation High Deprivation Highest Deprivation p-Value N=624 N=650 N=589 N=629 Chemotherapeutic treatment 0.001 No 347 (55.6%) 345 (53.1%) 339 (57.6%) 411 (65.3%) Yes 256 (41.0%) 283 (43.5%) 235 (39.9%) 202 (32.1%) Unknown 21 (3.4%) 22 (3.4%) 15 (2.5%) 15 (2.6%) Vital Status 0.93 Alive 41 (5.7%) 44 (5.9%) 43 (6.0%) 44 (6.5%) Dead 682 (94.3%) 695 (94.1%) 673 (94.0%) 632 (93.5%)

A systemic approach for assessing infrastructure component importance in hazard-prone communities

Investing in pre-event disaster mitigation interventions for physical infrastructure, such as structural retrofits and enhancements, can be costly due to limited resources. To prioritize investments, infrastructure components are ranked by their criticality within the overall system. To be effective in real-world deployment, this approach must account for the complex interactions between components, as failures can occur simultaneously across large geographical areas due to the hazard footprint. As a result, hazard-specific uncertainties and spatial correlations may lead to distinctive failure patterns. In this study, we propose a novel data-driven framework leveraging Monte Carlo simulation, that harnesses the individual realizations to capture and model realistic component damage patterns under a specified hazard scenario. This framework addresses a gap in literature by moving beyond traditional methods that often treat component failures as independent events. By capturing the interdependence between bridges, primarily through failure interactions, and system-wide effects, our method provides a more comprehensive criticality assessment. The simulation data provides a foundation for the framework, which applies to a wide variety of infrastructure networks and performance metrics. To demonstrate the method's effectiveness, a simplified transportation network from Shelby County, TN subjected to an earthquake event is analyzed. The proposed framework provides an effective approach for component ranking, suitable for decision-making where human intuition and simple methods are insufficient. Its broad applicability suggests a potential for large-scale and interdependent network problems.

The impact of neighborhood socioeconomic deprivation on treatment and survival for patients with metastatic gastric cancer: An incidence-based retrospective cohort study.

110 Background: Studies have shown that socioeconomic deprivation (SED) is associated with reduced treatment and overall survival (OS) for gastric cancer. However, these studies have typically used large geographic areas for assessment of SED and aggregated all stages. Using a statewide cancer registry, the Area Deprivation Index, a granular measure of SED, was used to assess receipt of treatment and OS for metastatic gastric cancer patients. Methods: Using the incidence-based Florida Cancer Data System, gastric adenocarcinoma patients diagnosed from 2007-2015were identified (n=8,304). The Area Deprivation Index (ADI) is a composite measure of SED and validated dataset that ranks neighborhoods (census block groups) from 1-100 (higher scores=higher deprivation). Demographic, tumor, and treatment variables were assessed using descriptive statistics, regression, and survival analysis. Results: Overall, 3,050 stage IV patients met inclusion criteria; 53.9% were male and the median age was 67. When assessed by SED quartile, there were significant differences by race, ethnicity, insurance, and receipt of chemotherapy (p<0.05). For the cohort, 55.3% of patients received chemotherapy. However, 61.3% of the lowest deprivation cohort received chemotherapy compared to 50.4% in the highest deprivation cohort (p<0.001). After adjustment for potential confounders, the lowest deprivation quartile had increased odds of chemotherapy compared to the highest deprivation quintile (OR 1.52; 95% CI,1.20-1.92). Of patients who received chemotherapy, the median time in months to initiation was 1.1 (interquartile range 0.6-1.7). Median OS was 5.8 months. The median OS for the lowest and highest SED quartiles was 7.5 and 5.1 months, respectively (p<0.001). For patients who received and did not receive chemotherapy, median OS was 10.0 and 1.9 months, respectively. The median OS of the lowest and highest SED quartiles for those who received chemotherapy was 11.3 and 8.8 months, respectively (p<0.001). Conclusions: In an incidence-based, statewide cancer registry examining metastatic gastric cancer, just over half of patients received chemotherapy and median OS was poor. Additionally, higher SED was associated with lower odds of chemotherapy and worse OS. These data suggest that SED impacts metastatic gastric cancer outcomes and future studies are needed to understand the multilevel factors driving disparities. Lowest Deprivation Low Deprivation High Deprivation Highest Deprivation p-value N=752 N=780 N=747 N=718 Chemotherapy 0.003 No 262 (34.8%) 315 (40.4%) 306 (41.0%) 314 (43.7%) Yes 461 (61.3%) 424 (54.4%) 410 (54.9%) 362 (50.4%) Unknown 29 (3.9%) 41 (5.3%) 31 (4.1%) 42 (5.8%)

Machine learning algorithms for the evaluation of risk by tick-borne pathogens in Europe

Background Tick-borne pathogens pose a major threat to human health worldwide. Understanding the epidemiology of tick-borne diseases to reduce their impact on human health requires models covering large geographic areas and considering both the abiotic traits that affect tick presence, as well as the vertebrates used as hosts, vegetation, and land use. Herein, we integrated the public information available for Europe regarding the variables that may affect habitat suitability for ticks and hosts and tested five machine learning algorithms (MLA) for predicting the distribution of four prominent tick species across Europe. Materials and methods A grid of cells 20 km in diameter was prepared to cover the entire territory, containing data on vegetation, points of water, habitat fragmentation, forest density, grass extension, or imperviousness, with information on temperature and water deficit. The distribution of the hosts (162 species) was modelled and included in the dataset. We used five MLA, namely, Random Forest, Neural Networks, Naive Bayes, Gradient Boosting, and AdaBoost, trained with reliable coordinates for Ixodes ricinus, Dermacentor reticulatus, Dermacentor marginatus, and Hyalomma marginatum in Europe. Results Both Random Forest and Gradient Boosting best predicted ticks and host environmental niches. Our results demonstrate that MLA can identify trait-matching combinations of environmental niches. The inclusion of land cover and land use variables has a superior capacity for predicting areas suitable for ticks, compared to classic methods based on the use of climate data alone. Conclusions Flexible MLA-driven models may offer several advantages over traditional models. We anticipate that these results may be extrapolated to other regions and combinations of tick-vertebrates. These results highlight the potential of MLA for inference in ecology and provide a background for the evolution of a completely automatized tool to calculate the seasonality of ticks for early warning systems aimed at preventing tick-borne diseases.

Frail Patients Referred to a Community Specialist Team- What Were Their Frailty Scores?

Abstract Background Frailty is synonymous with older patients, where multiple body systems slowly lose their reserves and lead patients to become more vulnerable. When developing the inclusion criteria for our integrated care programme for older persons (ICPOP), the limited resources and large geographical area played a role. In order to maximize the outcomes for patients we targeted those with a Rockwood Clinical Frailty Score (CFS) between 4 and 6. We perceived that these patients would benefit from multidisciplinary intervention. Methods All patients referred to ICPOP were reviewed from November 2022 to April 2024. Our electronic patient database records information on ICPOP patients. Using this database we searched for the word “frailty” in the reason for referral field. We also correlated this with any patients who had a CFS recorded. The level of frailty in older patients with the scores ranging from 1 (very fit) to 9 (terminally ill) was recorded. Results 182 patients were found during the database search. 55 patients were referred for frailty alone, 64 patients were referred for frailty with balance/mobility issues, 47 patients were referred for frailty with falls, 16 patients were referred for frailty with memory issues. CFS were available for 132/182(73%) patients. 119/132(90 %) patients with a CFS score were classified as scoring between 4 and 6 on the Rockwood scale. 3/132(2%) scored 2, 6/132(5%) scored 3 and 4/132(3%) scored 7 on the CFS. Conclusion The national ICPOP framework prioritise improving care of frail older patients. A measure of a persons’ frailty level is subjective and dependent on clinical judgement along with a good understanding of the term “frailty”. Overall, our findings highlight that the referrers to our service have a good knowledge of frailty as a term. Our ICPOP hub is also effectively targeting the appropriate cohort of patients as per our inclusion criteria.

Phylogeography and species distribution modeling unveil unnoticed Pliocene diversity: the case of a montane Iberian bush-cricket, Antaxius spinibrachius (Orthoptera: Tettigoniidae)

Abstract The geographic distribution and systematics of many groups of insects are still understudied across large geographic areas of the Iberian Peninsula. This lack of knowledge and the fact that many species have a complex evolutionary history due to the existence of “refugia within refugia” have hindered the taxonomic description of the true biodiversity in the Iberian Peninsula. Here, we discuss the evolutionary history of a flightless Iberian bush-cricket Antaxius spinibrachius (Fischer, 1853) using 1 nuclear and 2 mitochondrial markers. We applied species distribution modeling to design sampling strategies in climatically favorable areas and to detect missing isolated populations or unknown lineages. Following the species distribution modeled using all of the presence records available, we discovered 4 new isolated populations in Sierra Madrona, Serra de São Mamede, Sierra de Montánchez, and Sierra de la Demanda. Phylogenetic analyses recovered 2 major Pliocene lineages with a north–south geographic speciation pattern in the Iberian Peninsula. This north–south vicariant event split the common ancestor of these species on either side of the Tagus Valley, probably associated with the Pliocene climate change and the reorganization of paleobasins of the western Iberian Peninsula during the Miocene–Pliocene. We described the southern Iberian Plateau lineage of Antaxius as a new species of bush-cricket based on phylogenetic and morphological evidence, Antaxius oretanus sp. nov. We assessed the conservation status of the new species as “VU B2ab(iii,v)” under the IUCN criteria.

A study on meteorological characteristics and its consequences on human mortality in Rajkot City, Gujarat

Abstract Background: Climate change presents a crucial threat to human health. The health of human is affected by heat waves and extreme weather events. Hence, the present study was planned to explore weather conditions and their consequences on mortality of human being by accessing the meteorological and mortality data. Methodology: Meteorological data include maximum and minimum temperature, average wind speed, and total rainfall; relative humidity was obtained for January 1, 2017–December 31, 2021, from India Meteorological Department, Pune. Trend analysis of climatic variables was done. Human mortality data were obtained from January 1, 2017, to December 31, 2020, from Health Department, Rajkot Municipal Corporation, Rajkot. The impact of temperature and rainfall on mortality was observed. Results: Upward trend of maximum temperature, rainfall, and relative humidity while downward trend for minimum temperature and average wind speed was observed. Human mortality due to communicable and noncommunicable diseases was not affected by maximum temperature. The effect of rainfall on mortality due to vector-borne diseases was observed. Conclusion: The effect of rainfall on human mortality was observed. Upward and downward trend of climatic variables was not found to be statistically significant. Further study needs to be conducted by analyzing climate data over a longer duration and across a larger geographical area.

Preparing Future Special Education Faculty for Service in Rural Communities

In this program description, the authors explain how a doctoral-level embedded service-learning opportunity, part of Project PURPLE (Preparing Urban and Rural Personnel as Leaders in Education), a collaborative personnel preparation training program, taught future faculty to meet the needs of urban and rural schools in high-need settings. The collaboration between two large institutions in the same state offered scholars a unique opportunity to engage in teaching, research, and service across institutions. For the service-learning project, teams of cross-institution scholars partnered with consultants from a regional special education cooperative that provides technical assistance to a large rural geographical area. Eleven scholars completed seven diverse service projects, ranging from training education professionals to engaging caregivers. This description discusses these projects, along with the benefits to the scholars and the regional cooperative. It also explores implications for practice for future personnel preparation collaborations and possible avenues for future research.

Evaluation of bread wheat germplasm for adult plant resistance to stem rust using artificial inoculation

Wheat stem rust, caused by Puccinia graminis f.sp. tritici, is a major biotic threat to wheat (Triticum aestivum L.) in many wheat-producing countries worldwide, including Ethiopia. The pathogen is capable of continuously evolving new fungal races with high reproductive potential. It can be easily dispersed by wind over large geographic areas, allowing it to attack resistant varieties. Under optimal environmental conditions, it can cause epidemics leading to severe yield losses (up to 100%). A total of 91 germplasms, comprising 81 advanced bread wheat lines and 10 varieties, including the susceptible check Morocco, were planted using an augmented design. The experiment was conducted with artificial inoculation on spreaders to evaluate their slow rusting response against four virulent stem rust races viz. TTKTF, TKKTF, TTKTT, and TTTTF under field conditions at the Kulumsa Agricultural Research Center during the 2022-2023 offseason. Slow rusting resistance at the adult-plant stage was assessed by measuring the final rust severity (FRS) and the average coefficient of infection (ACI). Accordingly, 50 (61.7%) of the advanced lines and 6 (60%) of the released varieties namely Boru, Abay, Shaki, Kakaba, Deka, and Dursa exhibited low levels of FRS and ACI. In contrast, 31 (48.3%) of the advanced lines and 4 (40%) of the varieties showed high FRS and ACI values. The germplasms with low FRS and ACI values were considered to exhibit good slow rusting resistance and could be recommended for release into production and/or used in breeding programs aimed at achieving durable stem rust resistance in Ethiopia.

The Impact of MOOC Performance and Mediating Effect of Satisfaction with Instructor and Enjoyment of Interaction on Online Learning Self-Efficacy in China

ABSTRACT This study investigated the impact of MOOC performance (independent variable) on online learning self-efficacy (dependent variable). In this study, well-established scales with proven high reliability were selected to measure MOOC quality, satisfaction with instructor, enjoyment of interaction, and online learning self-efficacy. We used correlation analysis, regression analysis, and mediation effects test and the survey findings confirmed that all three aspects of MOOC performance (instruction quality 0.143**, interaction quality 0.262***, and system quality 0.593***) had a positive influence on online learning self-efficacy. Additionally, we delved into how MOOC performance affected online learning self-efficacy, considering the mediating roles of satisfaction with instructor and interaction enjoyment. The results indicated that both satisfaction with instructor and interaction enjoyment partially mediated the relationship between two dimensions of MOOC performance —system quality (0.041* and 0.050**) and interaction quality (0.075*** and 0.081***)— and online learning self-efficacy. Notably, the connection between instruction quality and online learning efficacy was entirely mediated by satisfaction with instructor (0.062**) and enjoyment of interaction (0.032**). This study’s observations underscored the crucial role of MOOC performance in enhancing online learning experiences and creating an environment characterized by enjoyable interactions and strong instructor acceptance. For limitations, the future research could increase the sample size and explored further over a larger geographic area.