The Age-Period-Cohort Model is used in this paper to examine how farmers’ confidence has changed in response to various measures for reducing poverty, based on data from 13,559 household tracking surveys, with a view to inform rural poverty reduction policies within Targeted Poverty Reduction Strategy (TPRS). The findings indicate that: (1) Farmers who get monetary grants have significantly lower levels of confidence than farmers who do not. The difference between the ages of 18 and 70, where this issue is more noticeable, grew between 2013 and 2018. (2) Between 2010 and 2018, transfer employment was more likely than monetary handouts to increase farmers’ confidence, and this difference was particularly obvious among young people (18–45 years old) and elderly individuals (65+). (3) The confidence gap between farmers with and without medical insurance has widened over time. Farmers with medical insurance have significantly higher confidence than farmers without it. Lessons for TPRS suggest that to reduce poverty among poor groups in a way that is both stable and sustainable, poverty alleviation strategies should take psychological factors into account when evaluating their efficacy. They should also concentrate on how employment boosts self-confidence.

The precise identification of energy poor households is a critical step towards achieving the United Nations Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy) and SDG 1 (No Poverty), while also intersecting with climate action (SDG 13). As the world’s largest developing country, China faces unique energy poverty challenges characterized by significant regional disparities and uneven access to modern energy services. To support targeted interventions and equitable policy-making, this study proposes an explainable artificial intelligence (XAI) framework for predicting and interpreting energy poverty. Utilizing nationally representative data from the China Family Panel Studies (CFPS) from 2014 to 2020, we developed a predictive model that integrates a Convolutional Neural Network with SHapley Additive exPlanations (SHAP). Our model, EPPE-FCS, demonstrated exceptional predictive performance, achieving an average accuracy of 98.23%, outperforming several mainstream benchmarks. Crucially, the SHAP interpretability analysis revealed that annual per capita household expenditure is the most influential driver, while the contribution of energy burden indicators (electricity and gas expenses) exhibited a significant decreasing trend. This trend likely reflects the positive impact of China’s national policies, such as the “Clean Heating Initiative” and “Targeted Poverty Alleviation,” on improving energy infrastructure and affordability. The findings underscore the necessity of a dual-track policy that combines immediate energy cost subsidies with long-term strategies for income enhancement and clean energy transition. This research provides policymakers with a robust tool to alleviate energy poverty, thereby advancing a just, sustainable, and climate-resilient energy future in China and other developing regions.

With the latest Q20 technology, the base error rate in ONT long-read sequencing has been down to 1%. However, such rates of sequencing errors (base insertions, deletions or substitutions) still lag behind the 0.1% base error rate in NGS short reads, resulting in many complicated variation regions in the full alignment data of deeply sequenced long reads and posing a big challenge to germline variant calling. For example, current deep learning methods could misidentify 20,000 to 30,000 variants from the ONT long reads basecalled by Q20 on a single chromosome, or could misidentify more than 30,000 at the complicated variation regions when the reads basecalled by Guppy v5.0.14. We proposed a spatiotemporal attention deep learning method (Attdeepcaller) to boost the performance of variation calling on these complicated variation regions. The novel use of spatiotemporal attention is to modulate the confusion between genuine sequencing errors and the true germline variations in the alignment data so that the identification by the algorithms becomes clear at most cases. As tested on the complicated regions in the alignment data basecalled by Q20 on chr1 of HG002, Attdeepcaller made only 22,739 misidentifications, reduced by 12.69% from current 26,043 misidentifications. Similarly, the misidentification number is reduced by 16.49% on HG003 and by 23.58% on HG004 compared with the current best. When tested on the Guppy 5 alignment data, Attdeepcaller improved the precision by 3 percent and the recall by 1 percent on the complicated variation regions. We also conducted comparative analysis of these methods on old versions of guppy data. Specifically on the Guppy v3.4.5 datasets, Attdeepcaller boosted the precision by a jump of 16 percent and improved the recall by 10 percent. This result suggests that Attdeepcaller can still work and can work substantially better when the reads alignment data becomes more complicated (the older the version of basecalling, the higher the base error rate of the sequencing data, and the more complicated the alignment data is).