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Critically ill patients are at high risk for hospital acquired infections, which can significantly increase the mortality rate and treatment costs for these patients. Therefore, in the process of treating the primary disease, strict prevention and control of new hospital infections is an essential component of the treatment for critically ill patients. The treatment of critically ill patients involves multiple steps and requires a concerted effort from various aspects such as theory, management, education, standards, and supervision to achieve effective prevention and control of hospital infections. However, there is currently a lack of unified understanding and standards for hospital infection prevention and control. To address this, in March 2024, a group of experts in critical care medicine, infectious diseases, and hospital infection from China discussed the current situation and issues of hospital infection control in the intensive care unit together. Based on a review of the latest evidence-based medical evidence from both domestic and international sources, 2024 Expert Consensus on Hospital Acquired Infection Control Principles in the Department of Critical Care Medicine was formed, aiming to provide a basis for the development of hospital infection prevention and control strategies in the field of critical care medicine.

The penetration rate of photovoltaic power generation keeps increasing. To address the issues of poor cluster partitioning and lengthy processing times for photovoltaic power station clusters， the paper proposes a method for rapid cluster partitioning method for photovoltaic （PV） plants based on prototype extraction and clustering. Firstly， photovoltaic data is preprocessed to eliminate differences in magnitude and dimensionality among data sets. Subsequently， influential factors on photovoltaic output power are identified using the Pearson correlation coefficient method. Random sampling， k-means++， and an improved spectral clustering method are then employed for sampling， prototype extraction， and prototype clustering of PV plants， respectively. Building upon an enumeration approach and hierarchical optimization， optimal hyperparameters for the aforementioned processes are determined. Finally， various scenarios are set up for case study comparisons， calculating both intra-cluster and inter-cluster indicators as well as clustering time metrics. Through comprehensive analysis， the effectiveness of the proposed method in addressing the rapid clustering for large-scale PV plants is validated.

Stroke has become the leading cause of death in China, and rational diet is an important primary prevention strategy, but the optimal dietary pattern remains to be studied. Based on this, this paper reviews the progress of epidemiological studies on the correlation of Mediterranean Diet (MD) , Dietary Approaches to Stop Hypertension (DASH) pattern, Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet, Japanese dietary pattern and Chinese diet pattern with cerebrovascular diseases, and suggests that MD pattern, DASH pattern and Japanese dietary pattern have protective effects on cerebrovascular disease; however, the relationship between MIND pattern and cerebrovascular disease remains uncertain. There is a lack of large-scale randomized controlled trials to examine the effect of traditional Chinese dietary pattern on cerebrovascular diseases. In the future, active efforts in exploring the healthy dietary patterns in line with local dietary habits are needed for providing a Chinese strategy for primary stroke prevention.

Accurate prediction of photovoltaic （PV） power is of great significance to the stable operation of power grid. Therefore， a PV power prediction method is proposed based on frequent changes of meteorological features to improve the prediction accuracy. Firstly， the multivariate time series of PV power prediction is constructed based on Person correlation analysis. Secondly， multivariable phase space reconstruction （MPSR） is performed for the time series of PV power prediction by C-C method to further investigate the coupling between PV power and meteorological characteristics. Finally， support vector regression （SVR） is used for non-linear fitting and predicting the phase space after PV power and meteorological feature reconstruction. To verify MPSR can improve the prediction effect， the paper compares MPSR that combines with back propagation neural network （BPNN） and radial basis function neural network （RBFNN）. Example analysis shows that MPSR can further explore the feature information contained in the regions with frequent changes of meteorological features. The prediction model that combines with MPSR improves the prediction accuracy of PV power.

The output of renewable power generation and its resources and environment are characterized by complex correlation in respect to time and space. The paper analyzes the spatiotemporal correlation of renewable energy under different resources and environmental conditions and builds differentiated output models of renewable energy power generation. Firstly， principal component analysis （PCA） is used to determine the principal components of the renewable energy sources in new energy stations. It combines with the natural output characteristic indexes that characterize the operating characteristics of new energy stations to generate their characteristics data. The improved K-means algorithm clusters the new energy stations to obtain the new energy generation clusters with the spatiotemporal correlation of resources. Secondly， based on the historical output data of each new energy station in the cluster， a new energy differential output model for different environmental conditions is constructed by cluster analysis. Finally， a simulation analysis is conducted using wind farms' actual environmental and output monitoring data in southeast China. The results demonstrate that the proposed method has improved the correlation between the output characteristics of new energy generation clusters and the error level of the constructed differentiated output model is higher than that of the typical daily method.

In order to improve the prediction accuracy of photovoltaic output power， this paper proposes a prediction method using AFSA （artificial fish swarm algorithm） to optimize BP （back-propagation） neural network. Based on the cleaned data， the paper takes highly correlative meteorological data as input， and photovoltaic output power data as output. It uses the global optimization capabilities and inherent parallel computing capabilities of AFSA to optimize the weights and thresholds of the BP neural network. The photovoltaic output power prediction model based on the AFSA-BP neural network is obtained after training. The simulation analysis of a photovoltaic power station shows that compared with using BP neural networks, genetic algorithm optimized BP neural network， and PSO-BP network， the prediction results of this method are more accurate, the degree of fitting to the original data curve is better， the corresponding error evaluation index is lower, and the training is less time-consuming; the method can rapidly and accurately predict the photovoltaic output power.

Neurocritical care is an important branch of critical care medicine. The mechanism of critical neurological damage is complex and diverse, and the pathophysiology changes rapidly. Different pathophysiological changes determine different degrees of brain injury. In a special plateau environment, the incidence of critical neurological disease is higher, the age of onset is younger, the disease progress is faster, and the degree of damage is more severe. In order to standardize the diagnosis and treatment, enhance monitoring and management, provide timely and precise treatment, prevent irreversible brain injury, and improve the prognosis of patients with critical neurological illness at high altitudes, the Research Group of Calm Treatment of China, Research Group of Critical Care Ultrasound of China, and the Quality Control Center of Critical Care Medicine in Tibet formulated the Expert Consensus on Monitoring and Management of Patients with Critical Neurological Illness at High Altitudes on the basis of full discussion and communication of relevant critical medical experts and neurosurgery experts according to domestic and foreign literature and years of experience in clinical application and promotion. The main contents of the consensus are as follows.(1) According to the pathophysiological mechanism of neurological involvement in critical illness, scenarios of neurocritical care at high altitudes can be divided into cerebral hemorrhage at high altitudes, severe traumatic brain injuries, ischemic stroke, cerebral edema at high altitudes, and septic encephalopathy (8.4 points).(2) It is recommended to use cerebral blood flow, brain function monitoring and cerebral oxygen saturation as a 'triad' monitoring core in management of neurocritical care at high altitude, to as well as cerebrospinal fluid dynamics monitoring and brain structure surveillance (9.0 points).(3) It is recommended to grade patients quickly, and the '5-avoids' approach based on 'brain protection' theory were adhered to avoid fever, seizures, anxiety, agitation or pain, shivering, stimulation and nociception, according to different levels. Especially in the 'super critical' stage, with the protection of '446'targets, choose the window for analgesia and sedation (8.4 points).(4) It is recommended to monitor systemic and cerebral hemodynamic continuously and dynamically in order to improve systemic perfusion and optimize cerebral perfusion simultaneously (8.4 points).(5) It is recommended to choose the method of direct measurement of intracranial pressure by intraventricular catheter or optic nerve sheath diameter under ultrasound to estimate intracranial pressure, and choose the appropriate target mean arterial pressure to ensure optimal brain perfusion (8.8 points).(6) It is recommended to use transcranial Doppler ultrasound to evaluate the blood flow velocity and blood flow waveform of the bilateral cerebral arteries. It is recommended to target the blood flow velocity of M1 at 40 cm/s in the 'super critical' period (8.2 points).(7) In the 'super critical' period, we recommend to routinely monitor BIS and maintain the BIS value around 40 as the goal to guide the depth of sedation; those with conditions can be monitored by quantitative electroencephalography to assist determining whether there are non-convulsive seizures, and perform diagnostic evaluation of the prognosis (8.6 points).(8) It is recommended to monitor brain oxygen levels routinely, starting early in the ICU admission of patients with critical neurological conditions at high altitudes, which can assist in the assessment of brain damage (8.6 points).(9) It is recommended to evaluate the cerebral blood flow self-regulation ability routinely to achieve the optimal cerebral perfusion pressure in time and timely adjust the intensity and scheme of treatment (8.2 points).(10) It is recommended to emphasize the importance of target arterial partial pressure of carbon dioxide in the artery in critical illness and neurocritical care at high altitudes (8.0 points).(11) It is recommended to devote attention to the importance of targeted temperature management in in critical illness and neurocritical care at high altitudes (8.6 points).(12) It is recommended that multidisciplinary consultation and multi-professional cooperation could improve the management in critical neurological illness at high altitudes (8.8 points).(13) It is recommended that the constitution of improvement in brain structure imaging, pressure normalization of cerebrospinal fluid and restoration of cerebral blood autoregulation could be as the de-escalation triad (8.0 points).(14) It is recommended to be cautious of paroxysmal sympathetic hyperreactivity patients in neurocritical and critical illness at high altitude (8.0 points).(15) It is recommended to be cautious about the management of agitation (delirium) and cognitive function of patients in TBI at high altitudes(8.0 points).(16) It is recommended to assess the itinerary of the rehabilitation in a timely manner for critically sick patients at high altitudes (8.2 points).(17) It is recommended to be cautious of post-traumatic hydrocephalus and related neuroendocrine abnormalities in patients with critical neurological illness at high altitudes (7.6 points).

The gravimetric forward method is crucial in geophysical applications for a gravimetric interpretation of the Earth's inner structure. In this study, we present the gravimetric forward modelling open‐source software that incorporates a graphical user interface. This software allows data preparation, manipulation and result interpretation both spatially and spectrally. For spatial domain modelling, it uses prism and tesseroid elements, whereas in the spectral domain, it extends Parker's formulas within specified boundaries. The software's utility is demonstrated through synthetic models and real‐world applications, including calculating corrections for topography, sediments and consolidated crust using ETOPO1 and CRUST1.0 models. Performance comparisons show that Parker's method delivers computation speed superior to that of the prism, tesseroid and Terrain gravity forward (TGF) software, with variances ranging within ±12 mGal for Gz${{G}_z}$ and ±0.3 E for Gzz${{G}_{zz}}$ across different geological scenarios. [ABSTRACT FROM AUTHOR]