High Complexity Research Articles

Research on Urban Road Design Method in South China Based on Climate Zoning

The urban climate in South China is marked by high complexity and substantial precipitation, posing significant challenges to road performance. This study focuses on the importance of precise climate zoning for urban roads in South China and the application of performance grade (PG) asphalt grading technology to enhance pavement durability. Meteorological data from multiple stations across the region were analyzed to identify key climatic indicators. Using spatial interpolation methods and fuzzy c-means clustering, urban roads were classified into five distinct climate zones. Zone I has the highest temperature; Zone II experiences the lowest temperature, necessitating attention to low-temperature pavement cracking; Zone III exhibits greater temperature variability, requiring consideration of both low-temperature cracking and water stability; Zone IV demonstrates relatively stable climatic conditions; and Zone V receives the highest precipitation, demanding a focus on water stability in pavement design. Trend analysis indicates increasing precipitation across all zones except Zone II and a general rise in minimum temperatures, suggesting a diminishing influence of low-temperature conditions. By integrating the Strategic Highway Research Program temperature conversion method and PG classification technology, this study provides asphalt grade recommendations tailored to each climate zone, addressing diverse environmental challenges and optimizing pavement performance.

IMPORTANT ISSUES OF DATA PROCESSING AND PROTECTION IN DISTRIBUTED SYSTEMS

The purpose of this article is to explore the critical aspects of big data processing in distributed networks as a contemporary challenge in ensuring cybersecurity. Special attention is given to issues of confidentiality, integrity, and availability of data in multi-user environments with high interaction complexity. The methodology of the study involves a comprehensive approach, including the analysis of threats and vulnerabilities specific to distributed architectures such as cloud technologies, peer-to-peer networks, and decentralized infrastructures. Key issues such as asynchronous data access, limited computational resources, operation synchronization complexity, and risks of data interception during transmission are examined. The study evaluates modern cryptographic approaches, including homomorphic encryption, distributed key management mechanisms, and privacy-preserving protocols. Additionally, the application of machine learning and deep learning algorithms is explored for anomaly detection in system behavioral models in real-time. The scientific novelty of the research lies in the systematization of threats and vulnerabilities inherent to distributed data processing systems and the development of effective approaches to neutralize them. For the first time, a comprehensive approach to the use of blockchain technologies is proposed as a means of ensuring transparent auditing of events and tracking transactions in distributed environments. The conclusions provide practical recommendations for integrating modern cryptographic methods, distributed key management mechanisms, blockchain technologies, and machine learning algorithms to build resilient cybersecurity systems. The findings are of practical significance for ensuring the protection of big data in distributed networks and improving the effectiveness of responses to potential threats.

TOWARD ROBUST SPEECH EMOTION RECOGNITION AND CLASSIFICATION USING NATURAL LANGUAGE PROCESSING WITH DEEP LEARNING MODEL

Speech Emotion Recognition (SER) plays a significant role in human–machine interaction applications. Over the last decade, many SER systems have been anticipated. However, the performance of the SER system remains a challenge owing to the noise, high system complexity and ineffective feature discrimination. SER is challenging and vital, and feature extraction is critical in SER performance. Deep Learning (DL)-based techniques emerge as proficient solutions for SER due to their competence in learning unlabeled data, superior capability of feature representation, capability to handle larger datasets and ability to handle complex features. Different DL techniques, like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Deep Neural Network (DNN) and so on, are successfully presented for automated SER. The study proposes a Robust SER and Classification using the Natural Language Processing with DL (RSERC-NLPDL) model. The presented RSERC-NLPDL technique intends to identify the emotions in the speech signals. In the RSERC-NLPDL technique, pre-processing is initially performed to transform the input speech signal into a valid format. Besides, the RSERC-NLPDL technique extracts a set of features comprising Mel-Frequency Cepstral Coefficients (MFCCs), Zero-Crossing Rate (ZCR), Harmonic-to-Noise Rate (HNR) and Teager Energy Operator (TEO). Next, selecting features can be carried out using Fractal Seagull Optimization Algorithm (FSOA). The Temporal Convolutional Autoencoder (TCAE) model is applied to identify speech emotions, and its hyperparameters are selected using fractal Sand Cat Swarm Optimization (SCSO) algorithm. The simulation analysis of the RSERC-NLPDL method is tested using a speech database. The investigational analysis of the RSERC-NLPDL technique showed superior accuracy outcomes of 94.32% and 95.25% under EMODB and RAVDESS datasets over other models in distinct measures.

Coupling Mechanism and Compensation Control Method for a Frequency Bandwidth Expansion Compound Shaking Table

The frequency bandwidth expansion compound shaking table (FBECST) provides an effective solution for synchronously achieving large-load, long-stroke, and wide-band excitation. FBECST consists of an upper shaking table (UST) and a lower shaking table (LST) cooperating to realize the compound excitation, which results in a high system complexity compared to the traditional shaking table. However, the coupling mechanism and model-based compensation control method of FBECST have rarely been studied, which greatly limits the ability of FBECST to simulate earthquake excitation. In this paper, a closed-loop transfer function matrix theoretical model of FBECST is developed. Then, the coupling mechanism of FBECST is revealed by theoretical analysis, and the correctness of the mechanism analysis is verified by simulation. Finally, a closed-loop feedforward decoupling compensation control (C-FDCC) method based on the coupling mechanism of FBECST is proposed. A series of numerical analysis results show that even considering the influence of a high natural frequency specimen, the proposed C-FDCC method can achieve good control precision with a time-domain correlation coefficient near 0.98, whereas the three-variable control(TVC) provides insufficient control. This work provides strong support for large-load, long-stroke, and wide-band seismic excitation reproduction through the FBECST.

An Enhanced Local Optimization Algorithm for GNSS Shadow Matching in Mobile Phones

In the context of mobile phones, the local optimal global navigation satellite systems (GNSS) shadow matching algorithm, which is based on the urban three-dimensional model, can effectively reduce the error of GNSS pseudo-range single-point positioning. However, the positioning accuracy of this algorithm is susceptible to noise, and its continuous signal-to-noise ratio (SNR) scoring method does not fully exploit the probability density and probability distribution information contained in the SNR. Therefore, this paper proposes two improvements for the local optimal shadow matching algorithm: (1) utilizing low-pass filtering to filter SNR, thereby reducing the influence of noise on the algorithm and (2) introducing a probability-based SNR scoring method to fully leverage the probability density and probability distribution information of SNR. In dynamic single-point positioning, the improved algorithm attains an absolute positioning accuracy of up to 3 m, representing a decimeter-level enhancement over the original algorithm. Experiments confirm that using the SNR statistical information of non-line of sight (NLOS) and line-of-sight (LOS) as prior information results in better positioning accuracy compared to when this information is distorted by multipath effects. Additionally, to address the issue of high time complexity in the shadow matching algorithm, especially when considering local optima, this paper presents a scheme to simplify the algorithm’s flow, reducing its time complexity by approximately 75%.

Non-Surgical, In-Stent Membrane Bioelectronics for Long-Term Intracranial Pressure Monitoring.

Traditional intracranial pressure (ICP) monitoring methods, using intraventricular catheters, face significant limitations, including high invasiveness, discrete data, calibration complexities, and drift issues, which hinder long-term and stable monitoring. Here, a non-surgical, in-stent membrane bioelectronic system is presented for continuous and reliable ICP monitoring. This platform integrates a capacitive thin-film sensor with a stent, enabling precise real-time detection of pressure fluctuations directly within the dural venous sinus without requiring skull penetration or frequent recalibration. The sensor demonstrates a high sensitivity of 0.052%/mmHg and a broad, readable pressure range of 3-30mmHg while maintaining calibration-free and drift-free performance. A series of in vivo studies highlight the system's superior sensitivity, rapid sampling rate, and long-term stability compared to conventional microcatheters. Statistical analyses reveal a strong agreement between the device and clinical reference, underscoring its potential to revolutionize ICP monitoring. These advancements pave the way for broader clinical applications, minimizing complications and improving patient outcomes in neurocritical care.

Low-rank multi-view subspace clustering via adaptive weight

Multi-view subspace clustering, which aims to partition a set of multi-source data into a common space, has recently attracted wide attention in the field of data analysis and machine learning. Traditional algorithms may face the problem of high complexity in calculating the self-expression matrix and in turning parameter. This paper proposes a novel multi-view subspace clustering model termed as Low-rank Multi-view Subspace Clustering via Adaptive Weight (LMSCAW). LMSCAW decomposes the self-expression matrix into the product of two low-rank representation matrices and thus can fix the rank of the self-expression matrix of each view to increase the stability of the algorithm. In addition, in order to learn the common representation matrix better, LMSCAW fuses the self-expression matrices among multiple views and implicitly weights each view by the Frobenius norm without additional parameters. Extensive experimental results on multiple benchmark datasets are provided to show the effectiveness of the proposed algorithm and its superior performance over other state-of-the-art methods.

An Efficient Method for Amplitude–Phase Error Calibration in Direct Localization for Distributed Multi-Station Systems

Direct localization methods offer significant advantages in modern high-frequency (HF) radar systems for maritime target localization. However, the presence of amplitude–phase errors poses severe challenges to their accuracy and reliability. Traditional active calibration methods for direct localization often face high costs and operational complexities, while self-calibration techniques are limited to addressing only single or, at most, two types of error models. To address these issues, the Multi-Station Azimuth-based Position Error Calibration Algorithm (MAPEC) is proposed in this paper, which is an active calibration algorithm to solve mixed amplitude–phase errors in direct positioning. The proposed algorithm converts the estimated amplitude–phase errors on each angle of stations into the corresponding position on the localization search grid and adjusts the steering vectors based on the converted errors. By integrating MAPEC with direct localization algorithms, the method enables precise and robust localization under complex amplitude–phase error conditions involving multiple error sources. Simulation results demonstrate that this approach significantly improves localization accuracy across various error conditions, validating its applicability to direct localization applications.

Wearable Capacitive Pressure Sensors Based on Porous Sponge Dielectric Materials for Gait Analysis and Identity Recognition

AbstractGait often reveals valuable information about personal movements in daily life, and traditional gait monitoring often relies on inertial sensors, which are limited by high manufacturing costs, inconvenient operation, and complex analysis methods. This study proposes a simple and cost‐effective method to manufacture a wearable capacitive sensor, which can efficiently detect different gait signals. The sensor is made by a polyurethane sponge with deposited activated carbon (C@PU sponge) as the dielectric layer, and it has high sensitivity and pretty good stability. The sensor is further integrated into an insole for detecting and collecting signals of foot pressure during human movement. To overcome the limitations of traditional analysis methods, a convolutional neural network model based on residual networks is designed for detecting nine different human activities with a recognition accuracy of 98.15%. Furthermore, the residual network is optimized using a genetic algorithm, and the optimized model is able to effectively identify eight participants with a recognition accuracy of 98.75%. These results indicate that smart insoles based on wearable capacitive sensors show good application prospects in gait analysis and identity recognition, and are expected to be widely used in daily life in the future.

A Comprehensive CYP2D6 Drug-Drug-Gene Interaction Network for Application in Precision Dosing and Drug Development.

Conducting clinical studies on drug-drug-gene interactions (DDGIs) and extrapolating the findings into clinical dose recommendations is challenging due to the high complexity of these interactions. Here, physiologically-based pharmacokinetic (PBPK) modeling networks present a new avenue for exploring such complex scenarios, potentially informing clinical guidelines and handling patient-specific DDGIs at the bedside. Moreover, they provide an established framework for drug-drug interaction (DDI) submissions to regulatory agencies. The cytochrome P450 (CYP) 2D6 enzyme is particularly prone to DDGIs due to the high prevalence of genetic variation and common use of CYP2D6 inhibiting drugs. In this study, we present a comprehensive PBPK network covering CYP2D6 drug-gene interactions (DGIs), DDIs, and DDGIs. The network covers sensitive and moderate sensitive substrates, and strong and weak inhibitors of CYP2D6 according to the United States Food and Drug Administration (FDA) guidance. For the analyzed CYP2D6 substrates and inhibitors, DD(G)Is mediated by CYP3A4 and P-glycoprotein were included. Overall, the network comprises 23 compounds and was developed based on 30 DGI, 45 DDI, and seven DDGI studies, covering 32 unique drug combinations. Good predictive performance was demonstrated for all interaction types, as reflected in mean geometric mean fold errors of 1.40, 1.38, and 1.56 for the DD(G)I area under the curve ratios as well as 1.29, 1.43, and 1.60 for DD(G)I maximum plasma concentration ratios. Finally, the presented network was utilized to calculate dose adaptations for CYP2D6 substrates atomoxetine (sensitive) and metoprolol (moderate sensitive) for clinically untested DDGI scenarios, showcasing a potential clinical application of DDGI model networks in the field of model-informed precision dosing.

Co-Design as Institutional Innovation: Can We Build the Plane While Flying It?

ABSTRACT A revised co-design perspective can help address the specific work of reforms in-motion, or building the plane while flying it. The paper proposes co-design as a model for conditions of continuing high complexity. This involves more connected, user-responsive instruments, and ongoing consequent adjustments to rules and other institutional structures.

Unbiased Finite Element Mesh Delaunay Constrained Triangulation Applied to 2D Images with High Morphological Complexity Using Mathematical Morphology Tools Part 1: Binary Images

Unbiased Finite Element Mesh Delaunay Constrained Triangulation Applied to 2D Images with High Morphological Complexity Using Mathematical Morphology Tools Part 1: Binary Images

Efficient crowd simulation in complex environment using deep reinforcement learning

Simulating virtual crowds can bring significant economic benefits to various applications, such as film and television special effects, evacuation planning, and rescue operations. However, the key challenge in crowd simulation is ensuring efficient and reliable autonomous navigation for numerous agents within virtual environments. In recent years, deep reinforcement learning has been used to model agents’ steering strategies, including marching and obstacle avoidance. However, most studies have focused on simple, homogeneous scenarios (e.g., intersections, corridors with basic obstacles), making it difficult to generalize the results to more complex settings. In this study, we introduce a new crowd simulation approach that combines deep reinforcement learning with anisotropic fields. This method gives agents global prior knowledge of the high complexity of their environment, allowing them to achieve impressive motion navigation results in complex scenarios without the need to repeatedly compute global path information. Additionally, we propose a novel parameterized method for constructing crowd simulation environments and evaluating simulation performance. Through evaluations across three different scenario levels, our proposed method exhibits significantly enhanced efficiency and efficacy compared to the latest methodologies. Our code is available at https://github.com/tomblack2014/DRL_Crowd_Simulation.

American Academy of Orthopedic Surgery OrthoInfo provides more readable information regarding rotator cuff injury than ChatGPT.

With over 61% of Americans seeking health information online, the accuracy and readability of this content are critical. AI tools, like ChatGPT, have gained popularity in providing medical information, but concerns remain about their accessibility, especially for individuals with lower literacy levels. This study compares the readability and accuracy of ChatGPT-generated content with information from the American Academy of Orthopedic Surgery (AAOS) OrthoInfo website, focusing on rotator cuff injuries. We formulated seven frequently asked questions about rotator cuff injuries, based on the OrthoInfo website, and gathered responses from both ChatGPT-4 and OrthoInfo. Readability was assessed using multiple readability metrics (Flesch-Kincaid, Gunning Fog, Coleman-Liau, SMOG Readability Formula, FORCAST Readability Formula, Fry Graph, Raygor Readability Estimate), while accuracy was evaluated by three independent reviewers. Statistical analysis included t-tests and correlation analysis. ChatGPT responses required a higher education level to comprehend, with an average grade level of 14.7, compared to OrthoInfo's 11.9 (p < 0.01). The Flesch Reading Ease Index indicated that OrthoInfo's content (52.5) was more readable than ChatGPT's (25.9, p < 0.01). Both sources had high accuracy, with ChatGPT slightly lower in accuracy for the question about further damage to the rotator cuff (p < 0.05). ChatGPT shows promise in delivering accurate health information but may not be suitable for all patients due to its higher complexity. A combination of AI and expert-reviewed, accessible content may enhance patient understanding and health literacy. Future developments should focus on improving AI's adaptability to different literacy levels. IV.

Undercoordinated Two-Dimensional Pt Nanoring Stabilized by a Ring-on-Sheet Nanoheterostructure for Highly Efficient Alkaline Hydrogen Evolution Reaction.

Platinum (Pt) is a state-of-the-art electrocatalyst for green hydrogen production in alkaline electrolytes. The delicate design and fabrication of two-dimensional (2D) Pt nanocatalysts can significantly enhance atomic utilization efficiency, while further improving intrinsic catalytic performance by modulating the density of surface active sites. However, the high surface energy and morphology complexity of 2D nanostructures often result in poor structural stability under the working conditions. Here, we report the synthesis of a 2D ring-on-sheet nanoheterostructure featuring abundant low-coordination Pt sites in which a defect-rich Pt nanoring is stabilized by an ultrathin 2D rhodium (Rh) support. The Rh@Pt nanoring exhibits remarkably enhanced activity and stability in an electrocatalytic hydrogen evolution reaction in alkaline media compared to defect-free Rh@Pt core-shell nanoplates and commercial Pt/C. This work provides new insights for the design and synthesis of 2D nanoheterostructures with abundant surface active sites for efficient and durable electrocatalysis.

Early assessment of clinical complexity and home care in patients affected by trisomy 13 and 18

PurposeTrisomy 13 and 18 consist of a recurrent pattern of multiple congenital anomalies. The aim of this study was to analyze the clinical characteristics and disease trajectory of a cohort of children with trisomy 13 and 18 followed up by an Italian pediatric palliative care service.MethodsA single-center retrospective observational study was conducted examining the medical records of patients with trisomy 13 and 18 seen in the Pediatric Palliatives Care (PPC) center of the University Hospital of Padua from 2007 to 2022.ResultsSeventeen patients were included in the analysis. All were born alive; four children are still alive and only three (23%) died at home. All presented high care complexity, as estimated by ACCAPED index (median 86, range 38–129). The median time to receive care from PPC was 3 months (0–108). All patients’ parents shared an advance care plan with the PPC team: 13/17 patients (76%) accepted a do not resuscitate (DNR) order. Approximately 12% of patients received at least one surgery. The trend of survival compared with other cohorts reported in the literature does not appear to differ significantly after the initial stages.ConclusionsThe possible recognition of an early evolution toward medical complexity and the availability of home care resources and programs are crucial factors in the management of these children. These indices could become a driving factor in the definition of new outcomes that are more patient-oriented, in addition to mortality.What is known:• Trisomy 13 and 18 are serious genetic conditions with high mortality rates. In the last years medical interventions including surgery are being offered more frequently, though the appropriateness of these interventions is still debated.What is new:• The study emphasizes the crucial role of early referral to specialized pediatric palliative care teams and the coordination they provide enabling families to care for their children at home, even with complex medical needs.

Quantifying Unknown Multiqubit Entanglement Using Machine Learning.

Entanglement plays a pivotal role in numerous quantum applications, and as technology progresses, entanglement systems continue to expand. However, quantifying entanglement is a complex problem, particularly for multipartite quantum states. The currently available entanglement measures suffer from high computational complexity, and for unknown multipartite entangled states, complete information about the quantum state is often necessary, further complicating calculations. In this paper, we train neural networks to quantify unknown multipartite entanglement using input features based on squared entanglement (SE) and outcome statistics data produced by locally measuring target quantum states. By leveraging machine learning techniques to handle non-linear relations between outcome statistics and entanglement measurement SE, we achieve high-precision quantification of unknown multipartite entanglement states with a linear number of measurements, avoiding the need for global measurements and quantum state tomography. The proposed method exhibits robustness against noise and extends its applicability to pure and mixed states, effectively scaling to large-scale multipartite entanglement systems. The results of the experiment show that the predicted entanglement measures are very close to the actual values, which confirms the effectiveness of the proposed method.

Single-cell and spatial-resolved profiling reveals cancer-associated fibroblast heterogeneity in colorectal cancer metabolic subtypes

BackgroundColorectal cancer (CRC) presents significant treatment challenges due to its high heterogeneity and complex intercellular interactions. Further exploration of CRC subtypes and interactions among tumor-specific clusters will facilitate the development of personalized treatment strategies.MethodsSingle-cell RNA sequencing and bulk RNA sequencing datasets were integrated to determine CRC metabolic subtypes by hierarchical clustering. The analysis was further extended to cellchat, pseudotime, immune infiltration, and clinicopathological relevance to explore the characteristics of secreted frizzled related protein 2 (SFRP2) + cancer-associated fibroblast (CAF) clusters, and validated by spatial transcriptomics (ST), in vivo experiments, and multiple immunohistochemistry (mIHC).ResultsCRC samples were stably classified into three heterogeneous metabolic subtypes, each exhibiting different microenvironment and CAF heterogeneity, particularly in the distribution of SFRP2 + CAF, which was aligned with metabolic activity. SFRP2 + CAF exhibits high extracellular matrix (ECM) activity and is closely involved in cellular communication, not only promoting the malignant progression of cancer cells but also inducing the differentiation of Tregs. Compared to responders of chemotherapy, the proportion of SFRP2 + CAFs is significantly increased in non-responders. Importantly, mIHC and ST analyses confirm that cancer cells with low expression of agmatinase (AGMAT) can recruit SFRP2 + CAFs, and Treg infiltration surrounding SFRP2 + CAFs was observed. AGMAT combined with oxaliplatin showed the best efficacy in vivo, which may be associated with the inhibition of SFRP2 + CAF infiltration.ConclusionsOur study identified and described the potential protumor biological properties of SFRP2 + CAFs, and AGMAT may be a valuable target for disrupting their properties.

LFD-YOLO: a lightweight fall detection network with enhanced feature extraction and fusion

Falls are one of the significant safety hazards for the elderly. Current object detection models for fall detection often suffer from high computational complexity, limiting their deployment on resource-constrained edge devices. Although lightweight models can reduce computational requirements, they typically compromise detection accuracy. To address these challenges, and considering the more lightweight architecture of YOLOv5 compared to other YOLO series models such as YOLOv8, we propose a lightweight fall detection model based on YOLOv5, named Lightweight Fall Detection YOLO (LFD-YOLO). Our method introduces a novel lightweight feature extraction module, Cross Split RepGhost (CSRG), which reduces information loss during feature map transmission. We also integrate an Efficient Multi-scale Attention (EMA) to enhance focus on the human pose. Moreover, we propose a Weighted Fusion Pyramid Network (WFPN) and utilize Group Shuffle Convolutions (GSConv) to reduce the model’s computational complexity and improve the efficiency of multi-scale feature fusion. Additionally, we design an Inner Weighted Intersection over Union (Inner-WIoU) loss to accelerate model convergence and enhance generalization. We construct a Person Fall Detection Dataset (PFDD) dataset covering diverse scenarios. Experimental results on the PFDD and the publicly available Falling Posture Image Dataset (FPID) datasets show that, compared to YOLOv5s, LFD-YOLO improves mAP0.5 by 1.5% and 1.7%, respectively, while reducing the number of parameters and calculations by 19.2% and 21.3%. Furthermore, compared to YOLOv8s, LFD-YOLO reduces the number of parameters and calculations by 48.6% and 56.1%, respectively, while improving mAP0.5 by 0.3% and 0.5%. These results demonstrate that LFD-YOLO achieves higher detection accuracy and lower computational complexity, making it well-suited for fall detection tasks.

Comanagement of surgical patients between neurosurgeons and internal-medicine clinicians: observational cohort study.

The rising prevalence of chronic diseases have contributed to a population with high complexity of care. There has been an increasing need for a new organizational model based on the interaction in the same department between the specialist skills of surgical and medical disciplines. This study aims to describe the implementation of a hospitalist co-management program in a Neurosurgery Department (ND) and its impact on the incidence of medical complications, 30days readmission rate for medical causes, number of transfers to Intensive Care Units (ICU)/Neurosurgical Intensive Care Unit (NICU) or to medical wards (MW), length-of stay (LOS), mortality and satisfaction of health workers. We conducted an observational study comparing changes before and after the Internal medicine-Neurosurgical Comanagement (INC) intervention. We conducted a retrospective evaluation of patients enrolled before the INC intervention and a prospective evaluation of those enrolled after the INC intervention was implemented. We defined the pre-INC intervention group as 380 patients admitted to the ND for neurosurgical disease between January 2022 and April 2022 and the post-INC intervention group as 367 patients admitted to the ND between January 2023 and April 2023. INC intervention was associated with a significant decrease in medical complications during the hospital stay (OR 0.52; 95% CI; 0.39-0.70, p < 0.001), 30days in-hospital readmission for medical reasons (OR 0.95; 95% CI 0.93-0.97, p < 0.001) and numbers of transfers to ICU/NICU (OR 0.31; 95% CI; 0.17-0.55, p < 0.001) or MW (OR 0.51; 95% CI 0.33-0.77, p = 0.002). During the INC intervention period, we observed a high satisfaction rate in health workers, evaluated by standardized questionnaire. In our study, LOS, in-hospital mortality and 30-day mortality were not significantly associated with INC. Hospitalist co-management in Neurosurgical Departments was associated with a reduced incidence of medical complications, 30-days in-hospital readmission and numbers of transfers to ICU/NICU or MW with a high satisfaction rate among healthcare workers, but without a significant decrease in LOS and mortality rate.