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Snitch! offers a vivid account of how some citizens actively assist state surveillance by \"informing\" on others.

Background Harmful alcohol use is a modifiable risk factor contributing to the increasing burden of non-communicable diseases and deaths and the implementation of policies focused on primary prevention is pivotal to address this challenge. Policies with actions targeting the harmful use of alcohol have been developed in Nigeria. This study is an in-depth analysis of alcohol-related policies in Nigeria and the utilization of WHO Best Buy interventions (BBIs) and multi-sectoral action (MSA) in the formulation of these policies. Methods A descriptive case study design and the Walt and Gilson framework of policy analysis was utilized for the research. Components of the study included a scoping review consisting of electronic search of Google and three online databases (Google Scholar, Science Direct and PubMed) to identify articles and policy documents with no language and date restrictions. Government institution provided documents which were not online. Thirteen policy documents, reports or articles relevant to the policy formulation process were identified. Other components of the study included interviews with 44 key informants ( Bureaucrats and Policy Makers ) using a pretested guide. The qualitative data were coded and analyzed using thematic analysis. Results Findings revealed that policy actions to address harmful alcohol use are proposed in the 2007 Federal Road Safety Act, the Non-communicable Diseases Prevention and Control Policy and the Strategic Plan of Action. Only one of the best buy interventions, (restricted access to alcohol) is proposed in these policies. Multi-sectoral action for the formulation of alcohol-related policy was low and several relevant sectors with critical roles in policy implementation were not involved in the formulation process. Overall, alcohol currently has no holistic, health-sector led policy document to regulate the marketing, promotion of alcohol and accessibility. A major barrier is the low government budgetary allocation to support the process. Conclusions Nigeria has few alcohol-related policies with weak multi-sectoral action. Funding constraint remains a major threat to the implementation and enforcement of proposed policy actions.

With the widespread use of distributed generation and electric vehicles, the uncertainty of distribution network operation is increased, challenging risk assessment. This paper proposes a generalized load modeling and risk assessment method based on GNG–Informer–WOA. GNG adaptively clusters load curves to identify typical patterns and noise; WOA optimizes Informer’s hyperparameters for high-precision prediction. An index system covering voltage out-of-limit, regulation capacity, and new energy consumption risks is established, with weights determined by fusing AHP and PCA via game theory. Case studies on the improved IEEE 33-bus system show the method effectively characterizes generalized load characteristics and accurately evaluates risks under different scenarios, supporting safe operation.

Wind turbine main bearings are critical drivetrain components whose operating status directly affects the stability and safety of the entire unit. However, traditional unsupervised health monitoring methods suffer from difficulty in capturing early weak faults, low anomaly detection sensitivity, and inability to fully exploit axial–radial vibration coupling characteristics. To address these issues, this paper proposes an Axial–Radial Coupling-aware Informer (ARC-Informer) for unsupervised main bearing health monitoring. First, 20 time-frequency domain features are extracted from each of the axial and radial vibration signals and concatenated into a 40-dimensional coupled health feature vector. A cross-attention-based coupling enhancement module with residual fusion explicitly models the dynamic interaction between the two directions. Second, a self-attention channel-gating mechanism adaptively reweights the feature channels, and an Informer backbone captures long-range temporal dependencies for multistep prediction of the coupled features. At last, a health index (HI) is constructed from the prediction residuals, with a 99.7% quantile threshold and a six-step consecutive exceedance criterion for anomaly alarm triggering. Experimental results on real wind turbine data show that the proposed ARC-Informer achieves MSE of 0.180–0.257 across prediction horizons 1–16, with its advantage over TPE-optimized baselines (GRU, LSTM, RNN, TCN) growing from negligible at short horizons to 8.1% MSE reduction at H = 16, validating the effectiveness of the coupling enhancement for long-range forecasting. A cross-turbine case study on 10 healthy segments from five wind turbines confirms zero false alarms, and a simulated fault experiment successfully triggers an early warning, demonstrating practical unsupervised health monitoring capability.

Background Despite malaria prevention initiatives, malaria remains a major health problem in Malawi, especially for pregnant mothers and children under the age of five. To reduce the malaria burden, Malawi established its first National Malaria Control Programme in 1984. Implementation of evidence-based policies contributed to malaria prevalence dropping from 43% in 2010 to 22% in 2017. In this study, we explored challenges to implementing malaria policies in Malawi from the perspective of key stakeholders in the country. Methods In this qualitative study, we conducted in-depth interviews with 27 key informants from April to July 2015. We stopped sampling new participants when themes became saturated. Purposive and snowballing sampling techniques were used to identify key informants including malaria researchers that were policy advisors, policy makers, programme managers, and other key stakeholders. Interviews were conducted in English, recorded and transcribed, and imported into QSR Nvivo 11 for coding and analysis. Data were analysed using the qualitative content analysis approach. Results Participants identified three main categories of challenges to the implementation of malaria policies. First structural challenges include inadequate resources, unavailability of trained staff, poor supervision and mentorship of staff, and personnel turnover in government. The second challenge is unilateral implementation of policies. The third category is the inadequately informed policy development and includes lack of platforms to engage with communities, top-down approach in policy formulation and lack of understanding of socio-cultural factors affecting policy uptake by communities. Conclusions Policy makers should recognize that inadequate support of policy objectives leads to an implementation gap. Therefore, policy development and implementation should not be viewed as distinct, but rather as interactive processes shaping each other. Support for health policy and systems research should be mobilized to strengthen the health system. Detailed assessment of implementation challenges to specific malaria policies should also be conducted to address these challenges and support the shift from the paradigm of malaria prevention and control to elimination in Malawi.

Background The One Health concept promotes the enhancement of human, animal and ecosystem health through multi-sectorial governance support and policies to combat health security threats. In Vietnam, antimicrobial resistance (AMR) in animal and human health settings poses a significant threat, but one that could be minimised by adopting a One Health approach to AMR surveillance. To advance understanding of the willingness and abilities of the human and animal health sectors to undertake investigations of AMR with a One Health approach, we explored the perceptions and experiences of those tasked with investigating AMR in Vietnam, and the benefits a multi-sectorial approach offers. Methods This study used qualitative methodology to provide key informants’ perspectives from the animal and human health sectors. Two scenarios of food-borne AMR bacteria found within the pork value chain were used as case studies to investigate challenges and opportunities for improving collaboration across different stakeholders and to understand benefits offered by a One Health approach surveillance system. Fifteen semi-structured interviews with 11 participants from the animal and six from the human health sectors at the central level in Hanoi and the provincial level in Thai Nguyen were conducted. Results Eight themes emerged from the transcripts of the interviews. From the participants perspectives on the benefits of a One Health approach: (1) Communication and multi-sectorial collaboration; (2) Building comprehensive knowledge; (3) Improving likelihood of success. Five themes emerged from participants views of the challenges to investigate AMR: (4) Diagnostic capacity; (5) Availability and access to antibiotics (6) Tracing ability within the Vietnamese food chain; (7) Personal benefits and (8) Managing the system. Conclusion The findings of this study suggest that there is potential to strengthen multi-sectorial collaboration between the animal and human health sectors by building upon existing informal networks. Based on these results, we recommend an inclusive approach to multi-sectorial communication supported by government network activities to facilitate partnerships and create cross-disciplinary awareness and participation. The themes relating to diagnostic capacity show that both sectors are facing challenges to undertake investigations in AMR. Our results indicate that the need to strengthen the animal health sector is more pronounced.

Reservoir impoundment significantly impacts the hydrogeological conditions of reservoir bank slopes, and bank slope deformation or destruction occurs frequently under cyclic impoundment conditions. Ground deformation prediction is crucial to the early warning system for slow-moving landslides. Deep learning methods have developed rapidly in recent years, but only a few studies are on combining deep learning and landslide warning. This paper proposes a slow-moving landslide displacement prediction method based on the Informer deep learning model. Firstly, the Sentinel-1 (S1) data are processed to obtain the cumulative displacement time-series image of the bank slope by the Small-BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) method. Then, combining data on rainfall, humidity, and horizontal and vertical distances of pixel points from the water table line, this study created a dataset with landslide displacement as the target feature. After that, this paper improves the Informer model to make it applicable to our dataset. This study chose the Dawanzi landslide in the Baihetan reservoir area, China, for validation. After training with 50-time series deformation data points, the model can predict the displacement results of 12-time series deformation data points using 12-time series multi-feature data, and compared with the monitoring values, its Mean Square Error (MSE) was 11.614. The results show that the multivariate dataset is better than the deformation univariate data in predicting the displacement in the large deformation zone of bank slopes, and our model has better complexity and prediction performance than other deep learning models. The prediction results show that among zones I–IV, where the Dawanzi Tunnel is located, significant deformation with the maximum deformation rate detected exceeding –100mm/year occurs in Zones I and III. In these two zones, the initiation of deformation relates to the drop in water level after water storage, with the deformation rate of Zone III exhibiting a stronger correlation with the change in water level. It is expected that deformation in Zone III will either remain slow or stop, while deformation in Zone I will continue at the same or a decreased rate. Our proposed method for slow-moving landslide displacement forecasting offers fast, intuitive, and economically feasible advantages. It can provide a feasible research idea for future deep learning and landslide warning research.

Background Hygiene promotion is a cornerstone of humanitarian response during infectious disease outbreaks. Despite this, we know little about how humanitarian organisations design, deliver or monitor hygiene programmes, or about what works to change hygiene behaviours in outbreak settings. This study describes humanitarian perspectives on changing behaviours in crises, through a case study of hygiene promotion during the 2014–2016 Liberian Ebola outbreak. Our aim was to aid better understanding of decision making in high-stress situations where there is little precedent or evidence, and to prompt reflection within the sector around how to improve and support this. Methods We conducted in-depth, semi-structured interviews with fourteen purposively-sampled individuals (key informants) from international organisations involved in hygiene behaviour change during the outbreak. Through thematic analysis we identified the decisions that were made and processes that were followed to design, deliver and monitor interventions. We compared our findings with theory-driven processes used to design behaviour change interventions in non-outbreak situations. Results Humanitarians predominantly focussed on providing hygiene products (e.g. buckets, soap, gloves) and delivering messages through posters, radio and community meetings. They faced challenges in defining which hygiene behaviours to promote. Assessments focused on understanding infrastructural needs, but omitted systematic assessments of hygiene behaviours or their determinants. Humanitarians assumed that fear and disease awareness would be the most powerful motivators for behaviour change. They thought that behaviour change techniques used in non-emergency settings were too ‘experimental’, and were beyond the skillset of most humanitarian actors. Monitoring focussed on inputs and outputs rather than behavioural impact. Conclusions The experiences of humanitarians allowed us to identify areas that could be strengthened when designing hygiene programmes in future outbreaks. Specifically, we identified a need for rapid research methods to explore behavioural determinants; increased skills training for frontline staff, and increased operational research to explore behaviour change strategies that are suited to outbreak situations.