Our Work

This data repository contains the species-level data used in reproducible analyses of changes in the conservation status of European butterflies between the 2010 and 2025 European Red Lists as well as…

This data repository contains the species-level data used in reproducible analyses of changes in the conservation status of European butterflies between the 2010 and 2025 European Red Lists as well as the results of these analyses. It combines Red List assessment data with ecological, biogeographical, and life-history trait data for European butterfly species. The input data consist of two CSV files: one containing species trait data and one containing the Red List assessment data. The output data consist of a CSV file for each trait with the results comparing the Red List Indices between 2025 and 2010 (see Technical info).

The Demonstration Test Catchments (DTC) programme was established in December 2009 to inform the mitigation of agricultural water pollution across farmed landscapes in England. We took a "learning by …

The Demonstration Test Catchments (DTC) programme was established in December 2009 to inform the mitigation of agricultural water pollution across farmed landscapes in England. We took a "learning by doing" approach, developing, testing, and demonstrating suites of measures at landscape scale that were practical within commercial farming systems. The river catchment was adopted as the spatial unit for understanding and managing water quality, balancing the demands of food production against environmental protection. Since agricultural pollution is diffuse, sporadic, and weather-driven, assessing the effectiveness of mitigation required long-term monitoring both before and after on-farm interventions. Delivering this at catchment scale required close interaction between stakeholders, regulators, and scientists. We monitored stream water quality and biology and examined catchment responses to storm events. Measurements included nitrogen, phosphorus, and sediment concentrations, together with the total volume of water leaving each catchment. Turbidity. Turbidity is the reduction in the transparency of a solution caused by suspended sediment particles, coloured organic matter, and the water itself, which scatter, reflect, and attenuate incident light. It is the most widely used surrogate for suspended sediment concentration. At the sub-stations, we used McVan Analite 390 Digital Series nephelometers, which comply with ISO 7027 and operate at depths of up to 100 m. These instruments measure light scattered at 90° to the incident beam, using a near-infrared source (860 nm; spectral bandwidth ≤ 60 nm) and a path length of less than 10 cm. They measure turbidity over 0–1000 NTU with a precision of ±1%. Readings were taken at 15-minute intervals. Automatic sampling. Each water quality monitoring station was equipped with an ISCO 3700 automatic sampler, comprising an intake, sample distributor, pump, bottle container unit, and activation system. Samples were drawn from the channel by suction, with volume determined by peristaltic pump speed and the number of pump rotations. These samplers are lightweight, affordable, and computer-controlled, and can be triggered remotely or automatically in response to rainfall or changes in river level.  Outlet stations: multiparameter sondes. Each outlet station was equipped with a YSI 6600 V2 sonde, which simultaneously measures: conductivity (0–100 mS cm⁻¹, ±0.5%) temperature (−5 to +50 °C, ±0.15 °C) pH (0–14, ±0.2 units) dissolved oxygen (0–500%, ±2%) turbidity (0–1000 NTU, ±2%) chlorophyll-a (0–400 µg L⁻¹) Newby and Pow outlet stations: nutrient sensors. These two stations carried additional in situ nutrient analysers: Ammonium: A Hach NH4D sc sensor used an ion-selective electrode (ISE) to measure ammonium (NH₄⁺) directly as NH₄-N, with a differential pH electrode as the reference for stability. Potassium (K⁺) is the main interferent, so an integrated potassium ISE was used to correct the ammonium reading. Operating range: 0.2–1000 mg L⁻¹ (±5%). Nitrate: A Hach Lange Nitratax sc sensor provided self-cleaning, immediate measurement of nitrate-N (NO₃-N), with turbidity compensation via a reference measurement. Operating range: 0.1–100 mg L⁻¹ (±3%). Phosphorus: A Phosphax sigma process photometer provided continuous measurement of total phosphorus and orthophosphate, based on the DIN-equivalent molybdenum blue method. A chemo-thermic digestion ensured complete breakdown within a few minutes. Operating range: 0.01–5.0 mg L⁻¹ for both total P and PO₄-P.  

What's Changed Security fixes: same-major dependency bumps for the HIGH/CRITICAL CVEs found in the 2026-09-23 Trivy scan, including Jackson, Spring, snakeyaml and ktor. Also migrated to Kotlin 2.3.20…

What's Changed Security fixes: same-major dependency bumps for the HIGH/CRITICAL CVEs found in the 2026-09-23 Trivy scan, including Jackson, Spring, snakeyaml and ktor. Also migrated to Kotlin 2.3.20 and radar-commons 1.2.7. The CVEs and how each was fixed are listed in the PR. By @pvannierop in https://github.com/RADAR-base/ManagementPortal/pull/1086 Fix login with the internal auth server when the public URL can't be reached from inside the pod by @pvannierop in https://github.com/RADAR-base/ManagementPortal/pull/1087 Include the audience when creating Hydra OAuth clients by @mpgxvii in https://github.com/RADAR-base/ManagementPortal/pull/1085, brought into dev by @pvannierop in https://github.com/RADAR-base/ManagementPortal/pull/1089 Widen source_type.description so the source-type import from the catalogue server doesn't fail on long descriptions by @pvannierop in https://github.com/RADAR-base/ManagementPortal/pull/1088 CI: replace the Snyk scans with Trivy by @pvannierop in https://github.com/RADAR-base/ManagementPortal/pull/1090 Important Notes No configuration changes are needed when upgrading from 3.0.0. The source_type.description column is widened by a Liquibase migration that runs automatically on startup. Full Changelog: https://github.com/RADAR-base/ManagementPortal/compare/v3.0.0...v3.0.1

Informal lending groups are often celebrated as locally embedded answers to financial exclusion, but their developmental value depends on whether borrowing remains productive and repayable. Objective:…

Informal lending groups are often celebrated as locally embedded answers to financial exclusion, but their developmental value depends on whether borrowing remains productive and repayable. Objective: This article examines optimism bias, over-indebtedness and default risk in Community Group Saving and Lending (CGSL) groups in rural South Sudan. The study uses a mixed-methods article conversion design based on 85 sampled respondents, 81 valid questionnaire returns and 17 qualitative interviews. Methods: Descriptive statistics, mean-score interpretation, chi-square tests, logistic regression and constructed behavioural-risk indices were used to interpret the relationship between credit access, investment optimism and repayment vulnerability. Results: Inferential findings further showed that CGSL participation was significantly associated with productivity indicators (chi-square = 15.92, p = 0.0001), while access to CGSL credit significantly increased the likelihood of investment in modern agricultural technologies (beta = 1.9459, p = 0.026; odds ratio approximately 7.00). These results confirm that credit matters, but the article argues that credit expansion without repayment realism can convert investment optimism into over-indebtedness. Conclusion: Respondents strongly agreed that rural finance makes a difference to productivity (overall mean = 4.58), that working capital scarcity constrains investment (mean = 4.68), that technology is capital intensive (mean = 4.29), and that credit is a prerequisite for technology adoption (mean = 4.02).

AI-enabled tools, such as generative and agentic AI, can decouple performance from becoming: learners can produce strong work without developing the capability, or the self, that producing it once req…

AI-enabled tools, such as generative and agentic AI, can decouple performance from becoming: learners can produce strong work without developing the capability, or the self, that producing it once required. Accounts of cognitive offloading and decremental research explain what learners stop doing when delegating to AI, but not what they stop becoming. I propose that learning has two outcomes: a capability and a domain self (what learners notice, care about, judge by and do when stuck), which only the work of acquisition builds. I introduce the rented self, the part of a learner's domain self whose capabilities reside in an AI-enabled tool and are available only on the provider's terms. Crossing where a capability resides with whom the learner credits for it gives four configurations and two costs: calibration costs, when learners misjudge where their capability resides, and self costs, which arise whenever a capability that builds the domain self is rented, even knowingly. I locate the mechanisms within Winne's models of self-regulated learning, trace how costs vary across the Model of Domain Learning, derive ten propositions and propose within-learner indices that pair decremental designs with an N=me approach. Implications follow for design, assessment and equity, along with unresolved tensions.

Release notes Prevent bad launch parameters and kernel definitions raising errors (#209) (@omlins) Improve shared memory handling and reduce launch overhead (#210) (@omlins) Precompute all memopt arg…

Release notes Prevent bad launch parameters and kernel definitions raising errors (#209) (@omlins) Improve shared memory handling and reduce launch overhead (#210) (@omlins) Precompute all memopt args (#211) (@omlins) 2D optimizations (#212) (@omlins) Fix launch parameter heuristic exceeding the maximum number of threads per block (#216) (@luraess) Update CUDA compatibility to v6.x.x (#218) (@albert-de-montserrat)

Background: Technology addiction has become an important concern among late adolescents due to the increasing use of smartphones, laptops, tablets, and other digital devices. Excessive use of technolo…

Background: Technology addiction has become an important concern among late adolescents due to the increasing use of smartphones, laptops, tablets, and other digital devices. Excessive use of technology may affect adolescents' physical, psychological, social, and academic well-being. Adequate knowledge regarding Technology Addiction is therefore essential for promoting healthy technology-use practices among late adolescents. Objectives: The objectives of the study were to assess the knowledge of late adolescents regarding Technology Addiction, to evaluate the effectiveness of the Structured Teaching Programme on their knowledge regarding Technology Addiction, and to determine the association between pre-test knowledge scores and selected socio-demographic variables. Methods A pre-experimental one-group pre-test–post-test research design was adopted for the study. The study was conducted at Rohilkhand College of Nursing, Bareilly, Uttar Pradesh. A total of 30 late adolescents were selected using a non-probability sampling technique. Data was collected using a structured knowledge questionnaire regarding Technology Addiction. The data collected was  analyzed using descriptive and inferential statistics. Results The findings revealed that 48% of participants were 18 years of age, 28% were 20 years, 14% were 19 years, and 10% were 21 years. Regarding gender, 56% were female and 44% were male. With respect to semester of study, 48% were studying in the I and II semesters, 32% in the III and IV semesters, and 20% in the V and VI semesters. Regarding family type, 48% belonged to nuclear families, 36% to extended families, and 16% to joint families. Most participants were Hindu (56%), followed by Christian (24%), Muslim (16%), and Sikh (4%). Fifty-six percent were from rural areas and 44% were from urban areas. Regarding daily technology use, 34% reported very high use of more than 6 hours, 24% reported high use of 4–6 hours, 22% reported moderate use of 2–4 hours, and 20% reported low use of less than 2 hours per day. Smartphones were the most frequently used devices (80%), followed by laptops (10%), tablets (6%), and multiple devices (4%). The mean post-test knowledge score (22.23 ± 2.12) was higher than the mean pre-test knowledge score (14.53 ± 2.28), with a mean difference of 7.70. The calculated ‘t’ value was 13.81, which was greater than the table value of *t*(29) = 2.05 at the 0.05 level of significance. This indicated a statistically significant improvement in knowledge following the administration of the Structured Teaching Programme. Conclusion: The findings of the study revealed that the Structured Teaching Programme was effective in improving the knowledge of late adolescents regarding Technology Addiction. Therefore, structured educational interventions may be incorporated into nursing education to enhance awareness and promote healthy and responsible technology-use practices among late adolescents.