Are your comments outdated? Towards automatically detecting code-comment consistency

In software development and maintenance, code comments can help developers understand source code, and improve communication among developers. However, developers sometimes neglect to update the corresponding comment when changing the code, resulting in outdated comments (i.e., inconsistent codes and comments). Outdated comments are dangerous and harmful and may mislead subsequent developers. More seriously, the outdated comments may lead to a fatal flaw sometime in the future. To automatically identify the outdated comments in source code, we proposed a learning-based method, called CoCC, to detect the consistency between code and comment. To efficiently identify outdated comments, we extract multiple features from both codes and comments before and after they change. Besides, we also consider the relation between code and comment in our model. Experiment results show that CoCC can effectively detect outdated comments with precision over 90%. In addition, we have identified the 15 most important factors that cause outdated comments, and verified the applicability of CoCC in different programming languages. We also used CoCC to find outdated comments in the latest commits of open source projects, which further proves the effectiveness of the proposed method

Ionic Liquid Design and Process Simulation for Decarbonization of Shale Gas

Ionic liquids (ILs) have been receiving increasing attention as a potential decarbonization solvent. However, the enormous number of potential ILs that can be synthesized makes it a challenging task to search for the best IL for CO₂ removal from methane. In this work, a method was proposed to screen suitable ILs based on the COSMO-RS (conductor-like screening model for real solvents) model, an absorption mechanism, and experimental data. Besides the Henry’s constant, the viscosity and toxicity of ILs should also be taken into consideration for an industrial decarbonization process. Furthermore, process simulation was performed to evaluate the new IL-based decarbonization technology. Considering CO₂ solubility, CO₂/CH₄ selectivity and toxicity and viscosity of ILs, [bmim]­[NTf₂] has been screened to be the potential solvent among 90 classes of ILs. Based on reliable experimental data, a rigorous thermodynamic model was established. The simulation results have been found to agree well with the available experimental results. Two process flow sheet options, use of two single-stage flash operations or a multistage flash operation following the absorber, have been simulated and assessed. Compared with the well-known MDEA (methyldiethanolamine) process for CO₂ capture, the single-stage and multistage process alternatives would reduce the total energy consumption by 42.8% and 66.04%, respectively

A simplified multi-model statistical approach for predicting the effects of forest management on land surface temperature in Fennoscandia

Forests interact with the local climate through a variety of biophysical mechanisms. Observational and modelling studies have investigated the effects of forested vs. non-forested areas, but the influence of forest management on surface temperature has received far less attention owing to the inherent challenges to adapt climate models to cope with forest dynamics. Further, climate models are complex and highly parameterized, and the time and resource intensity of their use limit applications. The availability of simple yet reliable statistical models based on high resolution maps of forest attributes representative of different development stages can link individual forest management practices to local temperature changes, and ultimately support the design of improved strategies. In this study, we investigate how forest management influences local surface temperature (LSTs) in Fennoscandia through a set of machine learning algorithms. We find that more developed forests are typically associated with higher LST than young or undeveloped forests. The mean multi-model estimates from our statistical system can accurately reproduce the observed LST. Relative to the present state of Fennoscandian forests, fully develop forests are found to induce an annual mean warming of 0.26 °C (0.03/0.69 °C as 5th/95th percentile), and an average cooling effect in the summer daytime from -0.85 to -0.23 °C (depending on the model). On the contrary, a scenario with undeveloped forests induces an annual average cooling of -0.29 °C (-0.61/-0.01 °C), but daytime warming in the summer that can be higher than 1 °C. A weak annual mean cooling of -0.01 °C is attributed to forest harvest from 2015 to 2018, with an increased daytime temperature in summer of about 0.04 °C. Overall, this approach is a flexible option to study effects of forest management on LST that can be applied at various scales and for alternative management scenarios, thereby helping to improve local management strategies with consideration of effects on local climate

Regional temperature response to different forest development stages in Fennoscandia explored with a regional climate model

Several studies investigated the regional temperature effects of afforestation or deforestation, but the impacts of different forest development stages or alternative forest management received limited attention. This is mainly due to challenges in representing area-limited forest dynamics in low-resolution climate models and the need for accurate forest parameters. This study investigates the impact of alternative forest development stages and composition on regional climate in Fennoscandia using a coupled regional climate model. By incorporating realistic and high-resolution forest maps, our modelling framework reduces biases in estimating surface temperature compared to default model runs. If today's forest composition of tree species is left to achieve a mature state (a proxy for the absence of harvesting), an annual mean reduction in 2 m air temperature is estimated, with a cooling peak in summer of -0.53 ± 0.20 °C (mean ± standard deviation) mainly induced by increased cloud cover. Conversely, undeveloped forests (a proxy for increased harvest) induce a contrasting seasonal response: a summer warming of 0.53 ± 0.15 °C (mainly caused by higher sensible heat fluxes), and a weak winter cooling of -0.14 ± 0.24 °C (mainly caused by a higher surface albedo). A transition from evergreen to deciduous forests shows a summer average cooling of -0.57 ± 0.28 °C, mainly attributed to changes in surface albedo. These temperature effects are equivalent to a relatively large fraction of the expected warming by 2050 in Fennoscandia (from 16 % to 70 %, depending on the specific scenario and season). Some modelling outputs appear inconsistent with observations and past modelling studies, such as the cooling effects in winter of more developed forests. Our results provide new insights into the complex relationships between forest dynamics and regional temperature, but modelling improvements are still needed to achieve a robust understanding of the regional climate effects of forest management

Expression and processing of fluorescent fusion proteins of amyloid precursor protein (APP)

AbstractProcessing of β-amyloid precursor protein (APP) by β- and γ-secretases in neurons produces amyloid-β (Aβ), whose excess accumulation leads to Alzheimer's disease (AD). Knowledge on subcellular trafficking pathways of APP and its fragments is important for the understanding of AD pathogenesis. We designed fusion proteins comprising a C-terminal fragment of APP (app) and fluorescent proteins GFP (G) and DsRed (D) to permit the tracking of the fusion proteins and fragments in cells. CAD cells expressing these proteins emitted colocalized green and red fluorescence and produce ectodomains, sGapp and sRapp, and Aβ, whose level was reduced by inhibitors of β- and γ-secretases. The presence of GappR in endosomes was observed via colocalization with Rab5. These observations indicated that the fusion proteins were membrane inserted, transported in vesicles and proteolytically processed by the same mechanism for APP. By attenuating fusion protein synthesis with cycloheximide, individual fluorescent colors from the C-terminus of the fusion proteins appeared in the cytosol which was strongly suppressed by β-secretase inhibitor, suggesting that the ectodomains exit the cell rapidly (t1/2 about 20min) while the C-terminal fragments were retained longer in cells. In live cells, we observed the fluorescence of the ectodomains located between parental fusion proteins and plasma membrane, suggesting that these ectodomain positions are part of their secretion pathway. Our results indicate that the native ectodomain does not play a decisive role for the key features of APP trafficking and processing and the new fusion proteins may lead to novel insights in intracellular activities of APP

Generative Software Engineering

The rapid development of deep learning techniques, improved computational power, and the availability of vast training data have led to significant advancements in pre-trained models and large language models (LLMs). Pre-trained models based on architectures such as BERT and Transformer, as well as LLMs like ChatGPT, have demonstrated remarkable language capabilities and found applications in Software engineering. Software engineering tasks can be divided into many categories, among which generative tasks are the most concern by researchers, where pre-trained models and LLMs possess powerful language representation and contextual awareness capabilities, enabling them to leverage diverse training data and adapt to generative tasks through fine-tuning, transfer learning, and prompt engineering. These advantages make them effective tools in generative tasks and have demonstrated excellent performance. In this paper, we present a comprehensive literature review of generative tasks in SE using pre-trained models and LLMs. We accurately categorize SE generative tasks based on software engineering methodologies and summarize the advanced pre-trained models and LLMs involved, as well as the datasets and evaluation metrics used. Additionally, we identify key strengths, weaknesses, and gaps in existing approaches, and propose potential research directions. This review aims to provide researchers and practitioners with an in-depth analysis and guidance on the application of pre-trained models and LLMs in generative tasks within SE

Spatially and taxonomically explicit characterisation factors for greenhouse gas emission impacts on biodiversity

In life-cycle impact assessment, currently available characterisation factors (CF) for climate change impacts on biodiversity are highly simplified and do not consider spatial and taxonomic differentiation of species or local climate variability. We develop the first spatially and taxonomically specific CFs for the impacts of 20 GHGs on biodiversity considering 26,648 species across terrestrial and marine ecosystems. Generally, CFs are higher in the tropics, and marine species are affected more severely than terrestrial ones. When global GHG emissions from 2020 are assessed in a scenario with a global temperature rise of 3 °C by 2100, an average of 0.25%, 0.15% and 0.03% of species are negatively affected in 2100 from CO2, CH4, and N2O emissions, respectively, across the globe. The new CFs can be used at different levels of spatial and taxonomic aggregation to quantify co-benefits for biodiversity of climate change mitigation in tools such as life-cycle assessment, input-output analyses, or integrated assessment models

Intrahousehold empowerment gaps and dietary diversity in China

ObjectiveThis article analyzes the relationship between intrahousehold empowerment gaps and food and nutrition security using quantitative data collected through a household survey organized by the Agricultural Information Institute, Chinese Academy of Agricultural Sciences (CAAS-AII), in 2023.MethodsBased on empowerment theory, this study measured the relative empowerment of spouses from the Abbreviated Women's Empowerment in Agriculture Index (A-WEAI).ResultsFrom the micro-level evidence of 468 rural households, this study found that intrahousehold empowerment gaps harm the diversity of household diets. In particular, reducing gender gaps in access to resources, leadership, and income can help diversify household diets. However, data on the impact of shortening the difference in working hours between wives and husbands for the benefit of food safety are yet to be conclusive. Additionally, gender gaps in the group of non-coresident mothers-in-law and non-migrants hurt household food security.ConclusionThe paper also provides further justification for policies and interventions that aim to improve women's bargaining position in the household