Local Freeway Ramp Metering using Self-Adjusted Fuzzy Controller

A self-adjusted fuzzy local ramp metering strategy is proposed to keep the mainline traffic state and the on-ramp queue length at reasonable levels. The fuzzy ramp metering strategy (FRMS) takes the following variables as inputs: error between desired density and measured density, change-in-error and on-ramp queue length. On-ramp metering flow is decided by these variables. It is difficult to construct fuzzy rules for a three-dimension inputs fuzzy controller based on expert knowledge, so the proposed FRMS generates fuzzy control rules by an analytic expression with correction factors. The correction factors reflect the weights upon linguistic variables of inputs and can be regulated according to actual traffic state of mainline and on-ramp. The proposed FRMS not only simplifies the process of rules definition for a multi-dimension fuzzy controller, but also has function of self-adjusted control rules. To examine the proposed FRMS, a freeway stretch in Los Angeles is simulated with distributed models. The proposed FRMS is also compared with an existing T-S FRMS and PI-ALINEA in the simulation experiments which cover different on-ramp inflow scenarios. Simulation results show the proposed FRMS provides improved adaptation to various scenarios and superiority in striking a balance between the mainline and on-ramp performances

Role of Protein Charge Density on Hepatitis B Virus Capsid Formation

The role of electrostatic interactions in the viral capsid assembly process was studied by comparing the assembly process of a truncated hepatitis B virus capsid protein Cp149 with its mutant protein D2N/D4N, which has the same conformational structure but four fewer charges per dimer. The capsid protein self-assembly was investigated under a wide range of protein surface charge densities by changing the protein concentration, buffer pH, and solution ionic strength. Lowering the protein charge density favored the capsid formation. However, lowering charge beyond a certain point resulted in capsid aggregation and precipitation. Interestingly, both the wild-type and D2N/D4N mutant displayed identical assembly profiles when their charge densities matched each other. These results indicated that the charge density was optimized by nature to ensure an efficient and effective capsid proliferation under the physiological pH and ionic strength

VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models

The ability to perceive how objects change over time is a crucial ingredient in human intelligence. However, current benchmarks cannot faithfully reflect the temporal understanding abilities of video-language models (VidLMs) due to the existence of static visual shortcuts. To remedy this issue, we present VITATECS, a diagnostic VIdeo-Text dAtaset for the evaluation of TEmporal Concept underStanding. Specifically, we first introduce a fine-grained taxonomy of temporal concepts in natural language in order to diagnose the capability of VidLMs to comprehend different temporal aspects. Furthermore, to disentangle the correlation between static and temporal information, we generate counterfactual video descriptions that differ from the original one only in the specified temporal aspect. We employ a semi-automatic data collection framework using large language models and human-in-the-loop annotation to obtain high-quality counterfactual descriptions efficiently. Evaluation of representative video-language understanding models confirms their deficiency in temporal understanding, revealing the need for greater emphasis on the temporal elements in video-language research.Comment: 23 pages, 6 figures, 18 tables, data is available at https://github.com/lscpku/VITATEC

Bioinformatics-based analysis of the roles of basement membrane-related gene AGRN in systemic lupus erythematosus and pan-cancer development

IntroductionSystemic lupus erythematosus (SLE) is an autoimmune disease involving many systems and organs, and individuals with SLE exhibit unique cancer risk characteristics. The significance of the basement membrane (BM) in the occurrence and progression of human autoimmune diseases and tumors has been established through research. However, the roles of BM-related genes and their protein expression mechanisms in the pathogenesis of SLE and pan-cancer development has not been elucidated.MethodsIn this study, we applied bioinformatics methods to perform differential expression analysis of BM-related genes in datasets from SLE patients. We utilized LASSO logistic regression, SVM-RFE, and RandomForest to screen for feature genes and construct a diagnosis model for SLE. In order to attain a comprehensive comprehension of the biological functionalities of the feature genes, we conducted GSEA analysis, ROC analysis, and computed levels of immune cell infiltration. Finally, we sourced pan-cancer expression profiles from the TCGA and GTEx databases and performed pan-cancer analysis.ResultsWe screened six feature genes (AGRN, PHF13, SPOCK2, TGFBI, COL4A3, and COLQ) to construct an SLE diagnostic model. Immune infiltration analysis showed a significant correlation between AGRN and immune cell functions such as parainflammation and type I IFN response. After further gene expression validation, we finally selected AGRN for pan-cancer analysis. The results showed that AGRN’s expression level varied according to distinct tumor types and was closely correlated with some tumor patients’ prognosis, immune cell infiltration, and other indicators.DiscussionIn conclusion, BM-related genes play a pivotal role in the pathogenesis of SLE, and AGRN shows immense promise as a target in SLE and the progression of multiple tumors

Comparative experimental study of fire resistance ratings of timber assemblies with different fire protection measures

This article presents the results of a comparative experimental study to investigate the effects of using three different fire protection measures to improve the fire endurance of timber assembly. The three fire protection measures were fire-retardant intumescent coating, gypsum plasterboard and filling the timber assembly void with mineral wool. The fire-resistance period obtained from the fire endurance tests was based on the integrity and the insulation criteria. Compared to the reference timber assembly without any fire protection (fire-resistance time = 37 min), the increases in fire resistance using the three different fire protection measures were 6 min (16%), 37 min (100%) and 142 min (384%) for using intumescent coating (specimen F2), 12-mm-thick gypsum plasterboard (specimen F4) and infill mineral wool (F3), respectively. The specific intumescent coating used in the test failed to expand. Therefore, this specimen (F2) behaved very similarly with the control specimen (F1) without any fire protection. Attaching an additional layer of gypsum plasterboard to the timber assembly on the fire-exposed side improved the fire-resistance rating by about 30 min, which is higher than that obtained from using the current design guidance such as Eurocode EN 1995-1-2. Among these three fire protection methods, filling the void between the top and bottom timber boards gave the best result because the mineral wool not only provided insulation but also stopped direct flame attack of the timber board on the unexposed side. </jats:p

Distinguishing the impact of tourism development on ecosystem service trade-offs in ecological functional zone

Tourism in ecological functional zones (EFZs) is rapidly becoming an increasing trend; however, its impact on ecosystem services remains poorly understood owing to the absence of a consistent quantification framework. This study uses the Taihang Mountains (THM), an EFZ in China, as an example to develop a framework for evaluating the direct and indirect impact pathways of scenic spots on the trade-offs between multiple ecosystem services by identifying the linkages between scenic spot development, socioeconomic change, land use transitions, and ecosystem services. The results show that the continued conversion of agricultural land, grassland, and forest to constructed land around scenic spots in 2000–2020 was accompanied by a decline in water yield (WY) and habitat quality (HQ); while food production (FP), carbon storage (CS), and soil retention (SR) increased. Land use and ecosystem service changes around scenic spots in the THM also exhibited significant spatial gradient effects. In particular, a 10-km buffer area was identified as a distinct “influence zone” where the ecosystem services trade-offs and land use changes were the most pronounced. In 2010, scenic spot revenue was the dominant factor that increased the trade-offs between SR with FP and CS via direct pathways. However, in 2020, the dominant factor was scenic spot level, which shifted the impact toward the relationship between CS and WY and HQ by intensifying the trade-offs to facilitating synergies. This was accomplished in an indirect manner, such as the facilitation of local population growth, industrial restructuring, and infrastructure development. This study reveals the varying effects of scenic spot development via different pathways, thereby providing useful insights for global EFZs to more precisely design policies that can adequately balance human activities with ecosystem services

Integrating tourism supply-demand and environmental sensitivity into the tourism network identification of ecological functional zone

One of the challenges facing ecological functional zones (EFZs) is achieving a balance between economic growth and environmental protection (management). Tourism presents an important avenue to tackle this challenge. However, research inadequately addresses the identification of tourism networks. Combining geo-referenced social media data analysis, the three-step floating catchment area method, and the minimum cumulative resistance model, this paper developed a multi-tiered mechanism for identifying tourism networks using scenic spots as nodes. This approach involved indicators like tourism potential (supply), tourists’ emotional appeal (demand), and ecological sensitivity. We employed the Taihang Mountains (THM), a representative EFZ, as an application case. Results indicate spatial heterogeneity in THM’s tourism potential, with higher tourism potential and relatively greater ecological sensitivity in the South and East THM. Furthermore, a substantial spatial mismatch in tourism demand and supply is evident, with South THM leading with a match of 0.29, while East THM recording the lowest match at 0.16. Based on this, this study identified a multi-level tourism development network having 34 tourism sources (9 primary sources, 13 secondary sources and 12 tertiary sources) and 51 corridors (11 primary corridors, 21 secondary corridors, and 19 tertiary corridors) consisted of a total length of 5,263 km, with an average length of 67 km. Our tourism networks have been tested to not only protect ecologically sensitive areas but also connect areas with economic advantages in tourism (i.e., South and East THM), which is conducive to achieving mutual benefits between tourism development and environmental protection. Our findings are conducive to improving the efficiency of tourism planning and management and provide a new path for coordinating EFZs’ conservation and development

A dosing strategy model of deep deterministic policy gradient algorithm for sepsis patients

Abstract Background A growing body of research suggests that the use of computerized decision support systems can better guide disease treatment and reduce the use of social and medical resources. Artificial intelligence (AI) technology is increasingly being used in medical decision-making systems to obtain optimal dosing combinations and improve the survival rate of sepsis patients. To meet the real-world requirements of medical applications and make the training model more robust, we replaced the core algorithm applied in an AI-based medical decision support system developed by research teams at the Massachusetts Institute of Technology (MIT) and IMPERIAL College London (ICL) with the deep deterministic policy gradient (DDPG) algorithm. The main objective of this study was to develop an AI-based medical decision-making system that makes decisions closer to those of professional human clinicians and effectively reduces the mortality rate of sepsis patients. Methods We used the same public intensive care unit (ICU) dataset applied by the research teams at MIT and ICL, i.e., the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) dataset, which contains information on the hospitalizations of 38,600 adult sepsis patients over the age of 15. We applied the DDPG algorithm as a strategy-based reinforcement learning approach to construct an AI-based medical decision-making system and analyzed the model results within a two-dimensional space to obtain the optimal dosing combination decision for sepsis patients. Results The results show that when the clinician administered the exact same dose as that recommended by the AI model, the mortality of the patients reached the lowest rate at 11.59%. At the same time, according to the database, the baseline mortality rate of the patients was calculated as 15.7%. This indicates that the patient mortality rate when difference between the doses administered by clinicians and those determined by the AI model was zero was approximately 4.2% lower than the baseline patient mortality rate found in the dataset. The results also illustrate that when a clinician administered a different dose than that recommended by the AI model, the patient mortality rate increased, and the greater the difference in dose, the higher the patient mortality rate. Furthermore, compared with the medical decision-making system based on the Deep-Q Learning Network (DQN) algorithm developed by the research teams at MIT and ICL, the optimal dosing combination recommended by our model is closer to that given by professional clinicians. Specifically, the number of patient samples administered by clinicians with the exact same dose recommended by our AI model increased by 142.3% compared with the model based on the DQN algorithm, with a reduction in the patient mortality rate of 2.58%. Conclusions The treatment plan generated by our medical decision-making system based on the DDPG algorithm is closer to that of a professional human clinician with a lower mortality rate in hospitalized sepsis patients, which can better help human clinicians deal with complex conditional changes in sepsis patients in an ICU. Our proposed AI-based medical decision-making system has the potential to provide the best reference dosing combinations for additional drugs