Technology-driven energy revolution: the impact of digital technology on energy efficiency and its mechanism

Introduction: Improving energy efficiency is significant for achieving carbon emission reduction and promoting the transformation of green economic development. In the sustainable development framework set out in the 2030 Agenda for Sustainable Development, Goal 7.3 explicitly aims to double the global rate of energy efficiency improvement by 2030. The rapid development of digital technology, along with its universality and penetrative characteristics, has provide a feasible solution for improving energy efficiency and environmental conditions. However, the theoretical understanding of the impact and underlying logic of digital technology on energy efficiency remains unclear.Methods: Based on the panel data of 30 provinces in China from 2006 to 2021, this paper adopts econometric methods, including two-way fixed effect, instrumental variable method, and Driscoll-Kraay standard error. It investigates the influence of digital technology on energy efficiency and its internal mechanism from single factor and all factor levels.Result: The results show that Digital technology, represented by industrial robots, significantly improves energy efficiency, whether measured by the energy consumption intensity of GDP or the total-factor energy efficiency estimated using the SBM-GML model. The results still hold even after conducting endogeneity tests and robustness tests. Digital technology can improve energy efficiency by increasing virtual industrial agglomeration and promoting outward foreign direct investment.Discussion: In addition to promoting the theoretical understanding of the impact of digital technology on energy efficiency and exploring its mechanism, this paper also provides empirical evidence for policy makers and enterprises to formulate effective measures and strategies to improve energy efficiency under the background of digital economy

Impact of Natural Blind Spot Location on Perimetry.

We study the spatial distribution of natural blind spot location (NBSL) and its impact on perimetry. Pattern deviation (PD) values of 11,449 reliable visual fields (VFs) that are defined as clinically unaffected based on summary indices were extracted from 11,449 glaucoma patients. We modeled NBSL distribution using a two-dimensional non-linear regression approach and correlated NBSL with spherical equivalent (SE). Additionally, we compared PD values of groups with longer and shorter distances than median, and larger and smaller angles than median between NBSL and fixation. Mean and standard deviation of horizontal and vertical NBSL were 14.33° ± 1.37° and -2.06° ± 1.27°, respectively. SE decreased with increasing NBSL (correlation: r = -0.14, p \u3c 0.001). For NBSL distances longer than median distance (14.32°), average PD values decreased in the upper central (average difference for significant points (ADSP): -0.18 dB) and increased in the lower nasal VF region (ADSP: 0.14 dB). For angles in the direction of upper hemifield relative to the median angle (-8.13°), PD values decreased in lower nasal (ADSP: -0.11 dB) and increased in upper temporal VF areas (ADSP: 0.19 dB). In conclusion, we demonstrate that NBSL has a systematic effect on the spatial distribution of VF sensitivity

Research progress of MDSCs-targeted immunotherapy for pancreatic cancer

Pancreatic cancer (PC) is a highly malignant digestive system tumor with a poor survival rate and prognosis. Most patients with pancreatic cancer have no obvious clinical manifestations in the early stage of the disease, and are found to be in the middle and late stage of the disease when they seek treatment.A unique and complex tumor microenvironment (TME) is formed during its development and evolution. Due to the occult nature of pancreatic cancer, for patients with advanced pancreatic cancer, some traditional treatment methods such as surgical resection and chemotherapy are very limited, and there is a lack of effective treatment programs. Of course, this is also related to the immunosuppression of the TME of pancreatic cancer. Some immunosuppressive cells, such as myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs), play an important immunosuppressive role in helping tumor immune escape. Therefore, it is considered to be a major difficulty in the treatment of pancreatic cancer. In recent years, with the in-depth study of TME, immunotherapy has gradually become a new therapeutic strategy, and has made great progress in the treatment of various malignant tumors. The study found that targeted MDSCs therapy is a new and effective treatment for pancreatic cancer.In this paper, we introduce the role of MDSCs in TME and their progress as potential targets for immunotherapy of pancreatic cancer, hoping to provide new directions for the treatment of pancreatic cancer and other tumors

Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization

Fairness (also known as equity interchangeably) in machine learning is important for societal well-being, but limited public datasets hinder its progress. Currently, no dedicated public medical datasets with imaging data for fairness learning are available, though minority groups suffer from more health issues. To address this gap, we introduce Harvard Glaucoma Fairness (Harvard-GF), a retinal nerve disease dataset with both 2D and 3D imaging data and balanced racial groups for glaucoma detection. Glaucoma is the leading cause of irreversible blindness globally with Blacks having doubled glaucoma prevalence than other races. We also propose a fair identity normalization (FIN) approach to equalize the feature importance between different identity groups. Our FIN approach is compared with various the-state-of-the-art fairness learning methods with superior performance in the racial, gender, and ethnicity fairness tasks with 2D and 3D imaging data, which demonstrate the utilities of our dataset Harvard-GF for fairness learning. To facilitate fairness comparisons between different models, we propose an equity-scaled performance measure, which can be flexibly used to compare all kinds of performance metrics in the context of fairness. The dataset and code are publicly accessible via \url{https://ophai.hms.harvard.edu/datasets/harvard-glaucoma-fairness-3300-samples/}

An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis.

Purpose: To detect visual field (VF) progression by analyzing spatial pattern changes. Methods: We selected 12,217 eyes from 7360 patients with at least five reliable 24-2 VFs and 5 years of follow-up with an interval of at least 6 months. VFs were decomposed into 16 archetype patterns previously derived by artificial intelligence techniques. Linear regressions were applied to the 16 archetype weights of VF series over time. We defined progression as the decrease rate of the normal archetype or any increase rate of the 15 VF defect archetypes to be outside normal limits. The archetype method was compared with mean deviation (MD) slope, Advanced Glaucoma Intervention Study (AGIS) scoring, Collaborative Initial Glaucoma Treatment Study (CIGTS) scoring, and the permutation of pointwise linear regression (PoPLR), and was validated by a subset of VFs assessed by three glaucoma specialists. Results: In the method development cohort of 11,817 eyes, the archetype method agreed more with MD slope (kappa: 0.37) and PoPLR (0.33) than AGIS (0.12) and CIGTS (0.22). The most frequently progressed patterns included decreased normal pattern (63.7%), and increased nasal steps (16.4%), altitudinal loss (15.9%), superior-peripheral defect (12.1%), paracentral/central defects (10.5%), and near total loss (10.4%). In the clinical validation cohort of 397 eyes with 27.5% of confirmed progression, the agreement (kappa) and accuracy (mean of hit rate and correct rejection rate) of the archetype method (0.51 and 0.77) significantly (P \u3c 0.001 for all) outperformed AGIS (0.06 and 0.52), CIGTS (0.24 and 0.59), MD slope (0.21 and 0.59), and PoPLR (0.26 and 0.60). Conclusions: The archetype method can inform clinicians of VF progression patterns

Deep Sub–Wavelength Focusing Metalens at Terahertz Frequency

With the benefits of non–invasive and low radiation, terahertz radiation has shown great potential in biomedical imaging applications. However, the low spatial resolution of the imaging system significantly affects its application in these fields. Although immersion techniques and super–oscillation theory have achieved considerable success in improving the resolution of imaging systems, there are still problems with large focal spot sizes or large sidebands. Herein, a solid immersion lens based on super–oscillation is proposed to reduce the focal spot size when illuminated with circularly polarized light at a wavelength of 118.8 μm. The simulation results show that the lens can compress the full widths at half–maxima down to deep sub–wavelength scales, as small as 0.232 λ. At the same time, the maximum side–lobe ratio was 16.8%, which ensured that the device had a large field of view. The proposed method reveals new ideas in the field of super–resolution imaging

2019‒2020 Australian bushfire air particulate pollution and impact on the South Pacific Ocean

Abstract During late 2019 and early 2020, Australia experienced one of the most active bushfire seasons that advected large emissions over the adjacent ocean. Herein, we present a comprehensive research on mixed atmospheric aerosol particulate pollution emitted by wildfires in the atmosphere and the ocean. Based on a wide range of physical and biochemical data, including the Aerosol Robotic Network, multi-satellite observations, and Argo floats, we investigated the spatio-temporal variations and mixed compositions of aerosol particles, deposition in the coastal waters of eastern Australia and the South Pacific Ocean, and biogeochemical responses in the water column. Four types of wildfire-derived mixed particles were classified by using the optical properties of aerosols into four types, including the background aerosols, mineral dust, wildfire smoke particles, and residual smoke. The coarse particles accounted for more than 60% of the mineral dust on 22 November 2019 in the Tasman Sea; afterwards, during the wildfire smoke episode from December 2019 to January 2020, the particles affected large areas of the atmosphere such as eastern Australia, the South Pacific Ocean, and South America. The maximum value of the aerosol optical depth reached 2.74, and the proportion of fine particles accounted for 98.9% in the smoke episode. Mineral dust and smoke particles from the fire emissions changed the particle composition in the surface ocean. Particle deposition accounted for increases in chlorophyll-a concentration (Chla) standardized anomaly up to maximum of 23.3 with a lag time of less than 8 days. In the vertical direction, float observations showed the impact of exogenous particles on the water column could up to 64.7 m deep, resulting in Chla of 1.85 mg/m3. The high Chla lasted for a minimum period of two months until it returned to normal level