Study of stereopsis using a depth sensation detection platform equipped with computer vision technology (DALE3D) / Estudio de estereopsis mediante una plataforma de detección de sensaciones de profundidad equipada con tecnología de visión por ordenador (DALE3D)

Background and objectives: The invention described herein is a prototype based on computer vision technology that measures depth perception and is intended for the early examination of stereopsis. Materials and methods: The prototype (software and hardware) is a depth perception measurement system that consists on: (a) a screen showing stereoscopic models with a guide point that the subject must point to; (b) a camera capturing the distance between the screen and the subject's finger; and (c) a unit for recording, processing and storing the captured measurements. For test validation, the reproducibility and reliability of the platform were calculated by comparing results with standard stereoscopic tests. A demographic study of depth perception by subgroup analysis is shown. Subjective comparison of the different tests was carried out by means of a satisfaction survey. Results: We included 94 subjects, 25 children and 69 adults, with a mean age of 34.2 ± 18.9 years; 36.2 % were men and 63.8 % were women. The DALE3D platform obtained good repeatability with an interclass correlation coefficient (ICC) between 0.94 and 0.87, and coefficient of variation (CV) between 0.1 and 0.26. Threshold determining optimal and suboptimal results was calculated for Randot and DALE3D test. Spearman's correlation coefficient, between thresholds was not statistically significant (p value > 0.05). The test was considered more visually appealing and easier to use by the participants (90 % maximum score). Conclusions: The DALE3D platform is a potentially useful tool for measuring depth perception with optimal reproducibility rates. Its innovative design makes it a more intuitive tool for children than current stereoscopic tests. Nevertheless, further studies will be needed to assess whether the depth perception measured by the DALE3D platform is a sufficiently reliable parameter to assess stereopsis.(AU)

National survey on perioperative cognitive dysfunction.

BACKGROUND: Perioperative cognitive dysfunction (PCD) is a very prevalent clinical syndrome due to the progressive aging of the surgical population.The aim of our study is to evaluate the clinical practice of Spanish anesthesiologists surveyed regarding this entity. MATERIAL AND METHODS: Prospective online survey conducted by the Neurosciences Section and distributed by SEDAR. RESULTS: 544 responses were obtained, with a participation rate of 17%. 54.4% of respondents never make a preoperative assessment of cognitive impairment, only 7.5% always do it. 79.6% lack an intraoperative management protocol for the patient at risk of PCD. In the anesthetic planning, only 23.3% of the patients was kept in mind. Eighty-nine percent considered regional anesthesia with or without sedation preferable to general anesthesia for the prevention of PCD. 88.8% considered benzodiazepines to present a high risk of PCD. 71.7% considered that anesthetic depth monitoring could prevent postoperative cognitive deficit. Routine evaluation of postoperative delirium is low, only 14%. More than 80% recognize that PCD is underdiagnosed. CONCLUSIONS: Among Spanish anesthesiologists surveyed, PCD is still a little known and underappreciated entity. It is necessary to raise awareness of the need to detect risk factors for PCD, as well as postoperative assessment and diagnosis. Therefore, the development of guidelines and protocols and the implementation of continuing education programs in which anesthesiologists should be key members of multidisciplinary teams in charge of perioperative care are suggested.

Source Identification of Urban dust Heavy Metals Using in Situ and Satellite Data in Three Mega Cities of Iran.

The aim of this study was an integrative assessment of heavy metals associated with urban dust data in Iran (Ahvaz, Isfahan, and Shiraz). Samples of urban dust from four sites (traffic, industrial, residential, and Greenland) were collected, and ten heavy metal concentrations were determined using ICP_MS in each sample. The highest average concentrations of metals were at the traffic site for the Mn, Zn, and Cr metals. The PMF model indicates a higher percentage of Pb participation, which shows the importance of traffic resources. The highest non-carcinogenic risk (HI) was for the Cr and the carcinogenic risk was tolerable. To evaluate aerosol and its effects on urban dust, Aerosol Optical Depth (AOD) data were used during 2003-2023. According to the Mankendall test, the trend of AOD has been increasing in Esfahan (p_value < 0.05) and Shiraz. Although Ahvaz's AOD is about two times greater than other cities, the aerosol trend in Ahvaz is decreasing.

Enhancing Rose Bengal penetration in ex vivo human corneas using iontophoresis.

Aim: Rose Bengal photodynamic antimicrobial therapy (RB-PDAT) has poor corneal penetration, limiting its efficacy against acanthamoeba keratitis (AK). Iontophoresis enhances corneal permeation of charged molecules, piquing interest in its effects on RB in ex vivo human corneas. Methods: Five donor whole globes each underwent iontophoresis with RB, soaking in RB, or were soaked in normal saline (controls). RB penetration and corneal thickness was assessed using confocal microscopy. Results: Iontophoresis increased RB penetration compared with soaking (177 ± 9.5 µm vs. 100 ± 5.7 µm, p < 0.001), with no significant differences in corneal thickness between groups (460 ± 87 µm vs. 407 ± 69 µm, p = 0.432). Conclusion: Iontophoresis significantly improves RB penetration and its use in PDAT could offer a novel therapy for acanthamoeba keratitis. Further studies are needed to validate clinical efficacy.

The study aimed to improve a new treatment for eye infections known as photodynamic antimicrobial therapy. It investigated whether the use of electricity through a technique called iontophoresis could help a chemical called Rose Bengal go deeper into the eye in order to target more severe infections. The iontophoresis machine was custom built, with patient-contacting components 3D printed. The experiments were performed using donated human eye tissue and found that iontophoresis significantly improved the penetration depth of Rose Bengal as compared with the current technique of only soaking the eye in Rose Bengal.

The direct and indirect drivers shaping RNA viral communities in grassland soils.

Recent studies have revealed diverse RNA viral communities in soils. Yet, how environmental factors influence soil RNA viruses remains largely unknown. Here, we recovered RNA viral communities from bulk metatranscriptomes sequenced from grassland soils managed for 5 years under multiple environmental conditions including water content, plant presence, cultivar type, and soil depth. More than half of the unique RNA viral contigs (64.6%) were assigned with putative hosts. About 74.7% of these classified RNA viral contigs are known as eukaryotic RNA viruses suggesting eukaryotic RNA viruses may outnumber prokaryotic RNA viruses by nearly three times in this grassland. Of the identified eukaryotic RNA viruses and the associated eukaryotic species, the most dominant taxa were Mitoviridae with an average relative abundance of 72.4%, and their natural hosts, Fungi with an average relative abundance of 56.6%. Network analysis and structural equation modeling support that soil water content, plant presence, and type of cultivar individually demonstrate a significant positive impact on eukaryotic RNA viral richness directly as well as indirectly on eukaryotic RNA viral abundance via influencing the co-existing eukaryotic members. A significant negative influence of soil depth on soil eukaryotic richness and abundance indirectly impacts soil eukaryotic RNA viral communities. These results provide new insights into the collective influence of multiple environmental and community factors that shape soil RNA viral communities and offer a structured perspective of how RNA virus diversity and ecology respond to environmental changes. IMPORTANCE: Climate change has been reshaping the soil environment as well as the residing microbiome. This study provides field-relevant information on how environmental and community factors collectively shape soil RNA communities and contribute to ecological understanding of RNA viral survival under various environmental conditions and virus-host interactions in soil. This knowledge is critical for predicting the viral responses to climate change and the potential emergence of biothreats.

Modeling of scour hole characteristics under turbulent wall jets using machine learning.

The novelty of the present study is to investigate the parameters that depict the scour hole characteristics caused by turbulent wall jets and develop new mathematical relationships for them. Four significant parameters i.e., depth of scouring, location of scour depth, height of the dune and location of dune crest are identified to represent a complete phenomenon of scour hole formation. From the gamma test, densimetric Froude number, apron length, tailwater level, and median sediment size are found to be the key parameters that affect these four dependent parameters. Utilizing the previous data sets, Multi Regression Analysis (linear and non-linear) has been performed to establish the relationships between the dependent parameters and influencing independent parameters. Further, artificial neural network-particle swarm optimisation (ANN-PSO) and gene expression programming (GEP) based models are developed using the available data. In addition, results obtained from these models are compared with proposed regression equations and the best models are identified employing statistical performance parameters. The performance of the ANN-PSO model (RMSE = 1.512, R2 = 0.605), (RMSE = 6.644, R2 = 0.681), (RMSE = 6.386, R2 = 0.727) and (RMSE = 1.754, R2 = 0.636) for predicting four significant parameters are more satisfactory than that of regression and other soft computing techniques. Overall, by analysing all the statistical parameters, uncertainty analysis and reliability index, ANN-PSO model shows good accuracy and predicts well as compared to other presented models.

Comparing Patient Comfort During Bronchoscopy Under Conscious Sedation and Monitored Anesthesia Care: A Prospective, Observational, Controlled Study.

BACKGROUND: Flexible bronchoscopy (FB) often involves sedation, with the choice left to the bronchoscopist's discretion. Prior research on sedation in gastroscopic endoscopies yields conflicting information regarding the preferred method for FB. This study compares patient comfort levels during bronchoscopy with mindful sedation using fentanyl, nalbuphine, and midazolam versus monitored anesthesia care (MAC) using propofol, midazolam, and ketamine. METHODS: This prospective observational study assessed 83 patients undergoing bronchoscopy under either conscious sedation (CS) (n=40) or MAC (n=43). Patient comfort, sedation levels, emotional state, recovery time, safety, and the impact of smoking history and comorbidities were evaluated. Data collection included direct patient questioning and observation using the Modified Observed Assessment of Alertness and Sedation (MOAA/S) form. RESULTS: Comfort levels were similar between groups, with mean scores of 3.6±0.89 for CS and 3.3±0.54 for MAC. MAC induced deeper sedation (mean scores: 4.37±0.66 vs. 3.8±0.98). Recovery time and complications were comparable. Emotional states and medical history did not significantly differ between groups. CONCLUSION: CS is not inferior to MAC for bronchoscopy, providing comparable comfort and safety with less intense sedation and lower cost. These findings support the use of CS for bronchoscopy procedures, offering a cost-effective alternative without compromising patient comfort or safety.

Relationship between the polymerization distance of monowave and polywave light-curing units and the irradiance and physical properties of dental resin-based composites.

PURPOSE: To evaluate the influence of the polymerization distance of monowave and polywave light curing units (LCUs) on the measured irradiance relative to the value reported by the manufacturer in relation to the physical properties of resin-based composites (RBCs). METHODS: Four LCUs were used: one monowave and three polywave. The irradiance was measured with a digital radiometer. Depth of cure (DC) and flexural strength (FS) tests were performed according to ISO 4049:2019 at polymerization distances of 0 mm and 5 mm. RESULTS: The irradiance of all LCUs was higher than that reported by the manufacturer (>25-64%). The irradiance of the four LCUs was reduced when polymerization was performed at between 0 to 5 mm (paired t-test, P < 0.001). The DC at 0 mm was similar in all groups but was significantly decreased at 5 mm distance (ANOVA P < 0.001). FS showed differences among the LCUs at 0 mm (ANOVA P < 0.001) and was affected by the polymerization distance. The elastic modulus was unaffected by the LCU used or the distance (ANOVA P > 0.001). CONCLUSIONS: The LCU must be positioned as near as possible to RBCs during the polymerization process, as increased distance negatively affects the depth of cure and flexural strength.

Fast Three-Dimensional Profilometry with Large Depth of Field.

By applying a high projection rate, the binary defocusing technique can dramatically increase 3D imaging speed. However, existing methods are sensitive to the varied defocusing degree, and have limited depth of field (DoF). To this end, a time-domain Gaussian fitting method is proposed in this paper. The concept of a time-domain Gaussian curve is firstly put forward, and the procedure of determining projector coordinates with a time-domain Gaussian curve is illustrated in detail. The neural network technique is applied to rapidly compute peak positions of time-domain Gaussian curves. Relying on the computing power of the neural network, the proposed method can reduce the computing time greatly. The binary defocusing technique can be combined with the neural network, and fast 3D profilometry with a large depth of field is achieved. Moreover, because the time-domain Gaussian curve is extracted from individual image pixel, it will not deform according to a complex surface, so the proposed method is also suitable for measuring a complex surface. It is demonstrated by the experiment results that our proposed method can extends the system DoF by five times, and both the data acquisition time and computing time can be reduced to less than 35 ms.

Monocular Depth Estimation via Self-Supervised Self-Distillation.

Self-supervised monocular depth estimation can exhibit excellent performance in static environments due to the multi-view consistency assumption during the training process. However, it is hard to maintain depth consistency in dynamic scenes when considering the occlusion problem caused by moving objects. For this reason, we propose a method of self-supervised self-distillation for monocular depth estimation (SS-MDE) in dynamic scenes, where a deep network with a multi-scale decoder and a lightweight pose network are designed to predict depth in a self-supervised manner via the disparity, motion information, and the association between two adjacent frames in the image sequence. Meanwhile, in order to improve the depth estimation accuracy of static areas, the pseudo-depth images generated by the LeReS network are used to provide the pseudo-supervision information, enhancing the effect of depth refinement in static areas. Furthermore, a forgetting factor is leveraged to alleviate the dependency on the pseudo-supervision. In addition, a teacher model is introduced to generate depth prior information, and a multi-view mask filter module is designed to implement feature extraction and noise filtering. This can enable the student model to better learn the deep structure of dynamic scenes, enhancing the generalization and robustness of the entire model in a self-distillation manner. Finally, on four public data datasets, the performance of the proposed SS-MDE method outperformed several state-of-the-art monocular depth estimation techniques, achieving an accuracy (δ1) of 89% while minimizing the error (AbsRel) by 0.102 in NYU-Depth V2 and achieving an accuracy (δ1) of 87% while minimizing the error (AbsRel) by 0.111 in KITTI.

Vision-Based UAV Detection and Localization to Indoor Positioning System.

In recent years, the technological landscape has undergone a profound metamorphosis catalyzed by the widespread integration of drones across diverse sectors. Essential to the drone manufacturing process is comprehensive testing, typically conducted in controlled laboratory settings to uphold safety and privacy standards. However, a formidable challenge emerges due to the inherent limitations of GPS signals within indoor environments, posing a threat to the accuracy of drone positioning. This limitation not only jeopardizes testing validity but also introduces instability and inaccuracies, compromising the assessment of drone performance. Given the pivotal role of precise GPS-derived data in drone autopilots, addressing this indoor-based GPS constraint is imperative to ensure the reliability and resilience of unmanned aerial vehicles (UAVs). This paper delves into the implementation of an Indoor Positioning System (IPS) leveraging computer vision. The proposed system endeavors to detect and localize UAVs within indoor environments through an enhanced vision-based triangulation approach. A comparative analysis with alternative positioning methodologies is undertaken to ascertain the efficacy of the proposed system. The results obtained showcase the efficiency and precision of the designed system in detecting and localizing various types of UAVs, underscoring its potential to advance the field of indoor drone navigation and testing.

A Robust Monocular and Binocular Visual Ranging Fusion Method Based on an Adaptive UKF.

Visual ranging technology holds great promise in various fields such as unmanned driving and robot navigation. However, complex dynamic environments pose significant challenges to its accuracy and robustness. Existing monocular visual ranging methods are susceptible to scale uncertainty, while binocular visual ranging is sensitive to changes in lighting and texture. To overcome the limitations of single visual ranging, this paper proposes a fusion method for monocular and binocular visual ranging based on an adaptive Unscented Kalman Filter (AUKF). The proposed method first utilizes a monocular camera to estimate the initial distance based on the pixel size, and then employs the triangulation principle with a binocular camera to obtain accurate depth. Building upon this foundation, a probabilistic fusion framework is constructed to dynamically fuse monocular and binocular ranging using the AUKF. The AUKF employs nonlinear recursive filtering to estimate the optimal distance and its uncertainty, and introduces an adaptive noise-adjustment mechanism to dynamically update the observation noise based on fusion residuals, thus suppressing outlier interference. Additionally, an adaptive fusion strategy based on depth hypothesis propagation is designed to autonomously adjust the noise prior of the AUKF by combining current environmental features and historical measurement information, further enhancing the algorithm's adaptability to complex scenes. To validate the effectiveness of the proposed method, comprehensive evaluations were conducted on large-scale public datasets such as KITTI and complex scene data collected in real-world scenarios. The quantitative results demonstrate that the fusion method significantly improves the overall accuracy and stability of visual ranging, reducing the average relative error within an 8 m range by 43.1% and 40.9% compared to monocular and binocular ranging, respectively. Compared to traditional methods, the proposed method significantly enhances ranging accuracy and exhibits stronger robustness against factors such as lighting changes and dynamic targets. The sensitivity analysis further confirmed the effectiveness of the AUKF framework and adaptive noise strategy. In summary, the proposed fusion method effectively combines the advantages of monocular and binocular vision, significantly expanding the application range of visual ranging technology in intelligent driving, robotics, and other fields while ensuring accuracy, robustness, and real-time performance.

Synthetic Data Enhancement and Network Compression Technology of Monocular Depth Estimation for Real-Time Autonomous Driving System.

Accurate 3D image recognition, critical for autonomous driving safety, is shifting from the LIDAR-based point cloud to camera-based depth estimation technologies driven by cost considerations and the point cloud's limitations in detecting distant small objects. This research aims to enhance MDE (Monocular Depth Estimation) using a single camera, offering extreme cost-effectiveness in acquiring 3D environmental data. In particular, this paper focuses on novel data augmentation methods designed to enhance the accuracy of MDE. Our research addresses the challenge of limited MDE data quantities by proposing the use of synthetic-based augmentation techniques: Mask, Mask-Scale, and CutFlip. The implementation of these synthetic-based data augmentation strategies has demonstrably enhanced the accuracy of MDE models by 4.0% compared to the original dataset. Furthermore, this study introduces the RMS (Real-time Monocular Depth Estimation configuration considering Resolution, Efficiency, and Latency) algorithm, designed for the optimization of neural networks to augment the performance of contemporary monocular depth estimation technologies through a three-step process. Initially, it selects a model based on minimum latency and REL criteria, followed by refining the model's accuracy using various data augmentation techniques and loss functions. Finally, the refined model is compressed using quantization and pruning techniques to minimize its size for efficient on-device real-time applications. Experimental results from implementing the RMS algorithm indicated that, within the required latency and size constraints, the IEBins model exhibited the most accurate REL (absolute RELative error) performance, achieving a 0.0480 REL. Furthermore, the data augmentation combination of the original dataset with Flip, Mask, and CutFlip, alongside the SigLoss loss function, displayed the best REL performance, with a score of 0.0461. The network compression technique using FP16 was analyzed as the most effective, reducing the model size by 83.4% compared to the original while maintaining the least impact on REL performance and latency. Finally, the performance of the RMS algorithm was validated on the on-device autonomous driving platform, NVIDIA Jetson AGX Orin, through which optimal deployment strategies were derived for various applications and scenarios requiring autonomous driving technologies.

Redefining Accuracy: Underwater Depth Estimation for Irregular Illumination Scenes.

Acquiring underwater depth maps is essential as they provide indispensable three-dimensional spatial information for visualizing the underwater environment. These depth maps serve various purposes, including underwater navigation, environmental monitoring, and resource exploration. While most of the current depth estimation methods can work well in ideal underwater environments with homogeneous illumination, few consider the risk caused by irregular illumination, which is common in practical underwater environments. On the one hand, underwater environments with low-light conditions can reduce image contrast. The reduction brings challenges to depth estimation models in accurately differentiating among objects. On the other hand, overexposure caused by reflection or artificial illumination can degrade the textures of underwater objects, which is crucial to geometric constraints between frames. To address the above issues, we propose an underwater self-supervised monocular depth estimation network integrating image enhancement and auxiliary depth information. We use the Monte Carlo image enhancement module (MC-IEM) to tackle the inherent uncertainty in low-light underwater images through probabilistic estimation. When pixel values are enhanced, object recognition becomes more accessible, allowing for a more precise acquisition of distance information and thus resulting in more accurate depth estimation. Next, we extract additional geometric features through transfer learning, infusing prior knowledge from a supervised large-scale model into a self-supervised depth estimation network to refine loss functions and a depth network to address the overexposure issue. We conduct experiments with two public datasets, which exhibited superior performance compared to existing approaches in underwater depth estimation.

Refining Risk Criteria May Substantially Reduce Unnecessary Additional Surgeries after Local Resection of T1 Colorectal Cancer.

BACKGROUND: The low positive predictive value for lymph node metastases (LNM) of common practice risk criteria (CPRC) in T1 colorectal carcinoma (CRC) leads to manyunnecessary additional surgeries following local resection. This study aimed to identify criteria that may improve on the CPRC. METHODS: Logistic regression analysis was performed to determine the association of diverse variables with LNM or 'poor outcome' (LNM and/or distant metastases and/or recurrence) in a single center T1 CRC cohort. The diagnostic capacity of the set of variables obtained was compared with that of the CPRC. RESULTS: The study comprised 161 cases. Poorly differentiated clusters (PDC) and tumor budding grade > 1 (TB > 1) were the only independent variables associated with LNM. The area under the curve (AUC) for these criteria was 0.808 (CI 95% 0.717-0.880) compared to 0.582 (CI 95% 0.479-0.680) for CPRC. TB > 1 and lymphovascular invasion (LVI) were independently associated with 'poor outcome', with an AUC of 0.801 (CI 95% 0.731-0.859), while the AUC for CPRC was 0.691 (CI 95% 0.603-0.752). TB > 1, combined either with PDC or LVI, would reduce false positives between 41.5% and 45% without significantly increasing false negatives. CONCLUSIONS: Indicating additional surgery in T1 CRC only when either TB > 1, PDC, or LVI are present could reduce unnecessary surgeries significantly.

Heart rate variability as a predictor of intraoperative autonomic nervous system homeostasis.

The aim of the proof-of-concept study is to investigate the level of concordance between the heart rate variability (HRV), the EEG-based Narcotrend Index as a surrogate marker for the depth of hypnosis, and the minimal alveolar concentration (MAC) of the inhalation anesthetic sevoflurane across the entire course of a surgical procedure. This non-blinded cross-sectional study recorded intraoperative HRV, Narcotrend Index, and MAC in 31 male patients during radical prostatectomy using the Da-Vinci robotic-assisted surgical system at Mannheim University Medical Center. The degree of concordance was calculated using repeated measures correlation with the R package (rmcorr) and presented using the rmcorr coefficient (rrm). The Narcotrend Index correlates significantly across all measures with the time-dependent parameter of HRV, the standard deviation of the means of RR intervals (SDNN) (rrm = 0.2; p < 0.001), the frequency-dependent parameters low frequency (LF) (rrm = 0.09; p = 0.04) and the low frequency/high frequency ratio (LF/HF ratio) (rrm = 0.11; p = 0.002). MAC correlated significantly negatively with the time-dependent parameter of heart rate variability, SDNN (rrm = -0.28; p < 0.001), the frequency-dependent parameter LF (rrm = -0.06; p < 0.001) and the LF/HF ratio (rrm = -0.18; p < 0.001) and the Narcotrend Index (rrm = -0.49; p < 0.001) across all measures. HRV mirrors the trend of the Narcotrend Index used to monitor depth of hypnosis and the inhibitory influence of the anesthetic sevoflurane on the autonomic nervous system. Therefore, HRV can provide essential information about the homeostasis of the autonomic nervous system during general anesthesia. DRKS00024696, March 9th, 2021.

Bacterial community composition and function vary with farmland type and soil depth around a mining area.

Heavy metal pollution can have adverse impacts on microorganisms, plants and even human health. To date, the impact of heavy metals on bacteria in farmland has yielded poor attention, and there is a paucity of knowledge on the impact of land type on bacteria in mining area with heavy metal pollution. Around a metal-contaminated mining area, two soil depths in three types of farmlands were selected to explore the composition and function of bacteria and their correlations with the types and contents of heavy metals. The compositions and functions of bacterial communities at the three different agricultural sites were disparate to a certain extent. Some metabolic functions of bacterial community in the paddy field were up-regulated compared with those at other site. These results observed around mining area were different from those previously reported in conventional farmlands. In addition, bacterial community composition in the top soils was relatively complex, while in the deep soils it became more unitary and extracellular functional genes got enriched. Meanwhile, heavy metal pollution may stimulate the enrichment of certain bacteria to protect plants from damage. This finding may aid in understanding the indirect effect of metal contamination on plants and thus putting forward feasible strategies for the remediation of metal-contaminated sites. MAIN FINDINGS OF THE WORK: This was the first study to comprehensively explore the influence of heavy metal pollution on the soil bacterial communities and metabolic potentials in different agricultural land types and soil depths around a mining area.

[To assess Resilience of Russian cities under international sanctions and the consequences of COVID-19: a sociological study].

The paper presents the results of in-depth interviews and a questionnaire survey of the Russian cites administration representatives about cities resilience under the sanctions pressure and COVID-19. The survey was conducted by the Center for Territorial Changes and Urban Development of IPEI RANEPA in March-May 2023, it was attended by representatives of the administration of more than 50 cities of the Russian Federation. We found overall situation as stable: social programs are being implemented in full, unemployment is decreasing, construction of municipal facilities continues, problems with failures in the supply of spare parts, equipment and components are being solved. At the same time, the sanctions have affected the urban economy in completely different ways: while in some cities show significant negative effect, in others the impact of sanctions is insignificant. Cites face number of new challenges: disruption of supply chains, refusal to supply paid equipment, inability to find analogues of imported equipment with the necessary characteristics, rising prices for spare parts. components and construction materials, the rupture of established sales channels to unfriendly countries, a drop in municipal budget revenues, etc. The heads of the city administration work overtime to solve emerging problems, organize interaction between enterprises, establish and deepen business contacts with friendly countries, put forward proposals to improve the situation at the federal level. New tasks successfully solved, although it requires serious efforts. To respond to new challenges, we need a new, more decentralized and local-oriented style of public administration, a process of well-established feedback.

Study on the Nodal Composite Bearing Performance of Nontruncated PHC Pipe Pile and Bearing Platform.

In this paper, low circumferential reciprocating load foot-scale tests were performed on two nontruncated PHC B 600 130 tubular piles with bearing nodes to characterize the damage process and morphology of the specimens and to investigate the load-carrying performance of the members. The test results reveal that under the action of tensile-bending-shear loading, the bearing concrete in the node area buckles and is damaged, the anchored reinforcement in the node area yields, the constraint is weakened, an articulation point is formed, and the node rotational capacity increases. When the embedment depth increases from 200 mm to 300 mm, the ultimate bearing capacities of the positive and negative nodes increase by 31.04% and 36.16%, respectively. A numerical simulation is used to verify the test results. Considering the four types of piles without truncated nodes, the numerical simulation is used to analyze the node-bearing capacity at different embedment depths. Finally, a preferred node type is proposed as follows: a terminal plate welded anchor bar and pipe pile core-filled longitudinal reinforcement anchored into the bearing node, with a preferred embedment depth of 250 mm.

Resistance Analysis of Crack Propagation in Concrete Subjected to Hydraulic Pressure.

The KR resistance curve for hydraulic crack propagation in a concrete beam was determined and discussed. A semi-analytical method was introduced to calculate the hydraulic crack propagation in concrete. A series of concrete beams with various hydraulic pressures and initial crack depths were tested, and the hydraulic crack propagation in these beams was calculated. The calculated P-CMOD curves were first verified, and then the calculated KR resistance curve for hydraulic crack propagation was determined. Based on the test results and calculation results, the following conclusions can be drawn: The proposed analysis method can accurately predict the hydraulic crack propagation process in concrete. The KR resistance to hydraulic crack propagation in concrete decreases with the increase in hydraulic pressure but is less influenced by the initial crack depth of the test beams. In addition, the concrete beams collapse immediately under hydraulic fracturing once the KIw curve reaches the KR resistance curve. This indicates that the failure of concrete structures under hydraulic fracturing occurs immediately once the driving force of crack propagation, dominated by the hydraulic pressure in the crack, becomes significant.