Livestock production, greenhouse gas emissions, air pollution, and grassland conservation: Quasi-natural experimental evidence

Serious climate challenges and environmental concerns have led to calls to mitigate greenhouse effects and pollution by controlling livestock production. In this study, we performed a cross-boundary quasi-natural experimental analysis of the Mongolian Plateau to examine the causal effects of livestock reduction on greenhouse gas (GHG) emissions and air pollutants. Aimed at grassland conservation by controlling overgrazing, China’s grassland ecological compensation policy (GECP) unintendedly offered the opportunity to estimate the causal effects of livestock reduction. To this end, we used official statistical data, remote sensing data, reanalysis data, and household survey data. Empirical findings based on the synthetic difference-in-differences (SDID) approach showed that with the implementation of the GECP, livestock reduction reduced atmospheric GHG and air pollutant concentrations and increased grassland quality and carbon sequestration in grasslands. We extended the basic SDID to the dynamic SDID and used it to estimate the causal effects in each policy year, which presented that the policy effects were more pronounced after several years of continuous implementation. The pathway analysis revealed that atmospheric CH4 concentrations decreased with the reduction in animal CH4 emissions and that the PM2.5 and PM10 concentrations decreased with grassland restoration. These findings provided empirical references for reforming the global food system to ensure both food security and environmental protection

Twofold Symmetry Observed in Bi2_{2}Te3_{3}/FeTe Interfacial Superconductor

Superconducting pairing symmetry are crucial in understanding the microscopic superconducting mechanism of a superconductor. Here we report the observation of a twofold superconducting gap symmetry in an interfacial superconductor Bi$_{2}$Te$_{3}$/FeTe, by employing quasiparticle interference (QPI) technique in scanning tunneling microscopy and macroscopic magnetoresistance measurements. The QPI patterns corresponding to energies inside and outside the gap reveal a clear anisotropic superconducting gap. Furthermore, both the in-plane angle-dependent magnetoresistance and in-plane upper critical field exhibit a clear twofold symmetry. This twofold symmetry align with the Te-Te direction in FeTe, which weakens the possible generation by bi-collinear antiferromagnetism order. Our finding provides key information in further understanding of the topological properties in Bi$_{2}$Te$_{3}$/FeTe superconducting system and propels further theoretical interests in the paring mechanism in the system

Mitigating the Alignment Tax of RLHF

LLMs acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting, which is also known as the alignment tax. To empirically verify this hypothesis, we conducted experiments with existing RLHF algorithms using OpenLLaMA-3B, which revealed a pronounced alignment tax in NLP tasks. On the other hand, despite various techniques to mitigate forgetting, they are often at odds with the RLHF performance, leading to a trade-off between reward maximization and forgetting mitigation. In light of the above pressing issue in aligning LLMs, in this paper we explore model averaging, which interpolates between pre and post RLHF model weights, to achieve a more efficient reward-tax Pareto front. To understand its effectiveness, We offer theoretical insights into model averaging, revealing that it enhances performance Pareto front by increasing feature diversity on the layers where tasks share overlapped feature spaces. Empirical evidence corroborates our analysis by showing the benefits of averaging low-level transformer layers. Building on the analysis and the observation that averaging different layers of the transformer leads to significantly different reward-tax trade-offs, we propose Adaptive Model Averaging (AMA) to adaptively find various combination ratios of model layers. AMA seeks to maximize the alignment reward while incurring minimal alignment tax. Moreover, we validate AMA's performance across a range of RLHF algorithms over OpenLLaMA-3B and further extend our findings to Mistral-7B.Comment: 28 Page

Challenges and support needs in psychological and physical health among pilots: a qualitative study

IntroductionPhysical and mental health problems among pilots affect their working state and impact flight safety. Although pilots’ physical and mental health problems have become increasingly prominent, their health has not been taken seriously. This study aimed to clarify challenges and support needs related to psychological and physical health among pilots to inform development of a more scientific and comprehensive physical and mental health system for civil aviation pilots.MethodsThis qualitative study recruited pilots from nine civil aviation companies. Focus group interviews via an online conference platform were conducted in August 2022. Colaizzi analysis was used to derive themes from the data and explore pilots’ experiences, challenges, and support needs.ResultsThe main sub-themes capturing pilots’ psychological and physical health challenges were: (1) imbalance between family life and work; (2) pressure from assessment and physical examination eligibility requirements; (3) pressure from worries about being infected with COVID-19; (4) nutrition deficiency during working hours; (5) changes in eating habits because of the COVID-19 pandemic; (6) sleep deprivation; (7) occupational diseases; (8) lack of support from the company in coping with stress; (9) pilots’ yearly examination standards; (10) support with sports equipment; (11) respecting planned rest time; and (12) isolation periods.DiscussionThe interviewed pilots experienced major psychological pressure from various sources, and their physical health condition was concerning. We offer several suggestions that could be addressed to improve pilots’ physical and mental health. However, more research is needed to compare standard health measures for pilots around the world in order to improve their physical and mental health and contribute to overall aviation safety

Extraction of Medication and Temporal Relation from Clinical Text using Neural Language Models

Clinical texts, represented in electronic medical records (EMRs), contain rich medical information and are essential for disease prediction, personalised information recommendation, clinical decision support, and medication pattern mining and measurement. Relation extractions between medication mentions and temporal information can further help clinicians better understand the patients' treatment history. To evaluate the performances of deep learning (DL) and large language models (LLMs) in medication extraction and temporal relations classification, we carry out an empirical investigation of MEDTEM project using several advanced learning structures including BiLSTM-CRF and CNN-BiLSTM for a clinical domain named entity recognition (NER), and BERT-CNN for temporal relation extraction (RE), in addition to the exploration of different word embedding techniques. Furthermore, we also designed a set of post-processing roles to generate structured output on medications and the temporal relation. Our experiments show that CNN-BiLSTM slightly wins the BiLSTM-CRF model on the i2b2-2009 clinical NER task yielding 75.67, 77.83, and 78.17 for precision, recall, and F1 scores using Macro Average. BERT-CNN model also produced reasonable evaluation scores 64.48, 67.17, and 65.03 for P/R/F1 using Macro Avg on the temporal relation extraction test set from i2b2-2012 challenges. Code and Tools from MEDTEM will be hosted at https://github.com/HECTA-UoM/MedTem</p

SiPM-based dual-ended-readout DOI-TOF PET module based on mean-time method

Positron emission tomography (PET) with high resolution and high sensitivity is desirable for detecting cancers and neurological diseases. In this work, a depth-of-interaction (DOI)-time-of-flight (TOF) PET has been attempted to achieve both high spatial and high timing resolution. Dual-ended readout is a simple technique that can provide excellent timing and DOI resolutions and consistent signal arrival times, regardless of the DOI position along the scintillation crystal, as a mean-time method is used. A dual-ended readout DOI-TOF PET module consisting of a 6 × 6 array of 2 × 2 × 20 mm3 saw-cut cerium-doped lutetium-yttrium oxyorthosilicate (LYSO) crystals is constructed. Both ends of the LYSO crystal array are optically coupled to a multi-pixel photon counter (MPPC) with 4 × 4 channels. The sixteen MPPC outputs are reduced to four position signals using a charge division circuit (CDC) board, and the timing signal is extracted from the common cathode of the MPPC. The four position signals from the MPPC are digitized by a DRS4-based high-speed waveform digitizer with a sampling rate of 5 GSa/s. A 22Na source is placed in front of a reference detector and at the side of the dual-ended readout DOI-TOF PET module in five steps of 2 mm, 6 mm, 10 mm, 14 mm, and 18 mm to measure the DOI, coincidence timing resolutions (CTRs), and mean-times. The full-width-half-maximums (FWHMs) of DOI resolutions and CTRs varied from 3.0 mm to 3.8 mm, with an average of 3.5 mm, and from 333 ps to 367 ps, with an average of 349 ps, respectively. The average of the slopes of the mean-time versus DOI position, for the 36 crystals, was −0.60 ± 1.68 ps/nm, which was consistent with the null value. The dual-ended readout DOI-TOF PET module based on the mean-time method produced both good DOI and CTRs, and consistent signal arrival times. The found solution would be the most advantageous in the small aperture PET systems, such as those for brain and breast imaging