Block Level Research Articles

Frictional Abrasion of Rubber: Transition from Sliding to Rolling

ABSTRACT To develop applicable friction and wear models on tire scale, reliable test data are required. Consequently, friction tests on block level are requested because the distribution of contact pressure as well as slip velocity is nearly homogeneous at the contact surface of the sliding rubber block. However, wear mechanism and energy intensity levels of sliding rubber blocks and rolling rubber wheels or tires differ significantly. Consequently, linking both sliding and rolling frictional abrasion is required; thus, a wear model for rubber material is introduced to consider both deformation slip and sliding. The model input for sliding friction and resulting wear rate is derived from linear friction test experiments using sliding rubber blocks at different loading. A unique and sophisticated re-mesh algorithm ensures proper mesh modification due to abrasion of the structure. A wear energy evolution approach is developed to consider low abrasion with small sliding distances to predict wear at rolling rubber wheels. The simulation framework of abrasion modeling is successfully validated using laboratory abrasion tests.

Exploring the Relationship Between Very-High-Resolution Satellite Imagery Data and Fruit Count for Predicting Mango Yield at Multiple Scales

Tree- and block-level prediction of mango yield is important for farm operations, but current manual methods are inefficient. Previous research has identified the accuracies of mango yield forecasting using very-high-resolution (VHR) satellite imagery and an ’18-tree’ stratified sampling method. However, this approach still requires infield sampling to calibrate canopy reflectance and the derived block-level algorithms are unable to translate to other orchards due to the influences of abiotic and biotic conditions. To better appreciate these influences, individual tree yields and corresponding canopy reflectance properties were collected from 2015 to 2021 for 1958 individual mango trees from 55 orchard blocks across 14 farms located in three mango growing regions of Australia. A linear regression analysis of the block-level data revealed the non-existence of a universal relationship between the 24 vegetation indices (VIs) derived from VHR satellite data and fruit count per tree, an outcome likely due to the influence of location, season, management and cultivar. The tree-level fruit count predicted using a random forest (RF) model trained on all calibration data produced a percentage root mean squared error (PRMSE) of 26.5% and a mean absolute error (MAE) of 48 fruits/tree. The lowest PRMSEs produced from RF-based models developed from location, season and cultivar subsets at the individual tree level ranged from 19.3% to 32.6%. At the block level, the PRMSE for the combined model was 10.1% and the lowest values for the location, seasonal and cultivar subset models varied between 7.2% and 10.0% upon validation. Generally, the block-level predictions outperformed the individual tree-level models. Maps were produced to provide mango growers with a visual representation of yield variability across orchards. This enables better identification and management of the influence of abiotic and biotic constraints on production. Future research could investigate the causes of spatial yield variability in mango orchards.

FusionGCN: Multi-focus image fusion using superpixel features generation GCN and pixel-level feature reconstruction CNN

In recent years, convolutional neural networks have demonstrated significant advancements in the domain of computer vision, effectively addressing numerous previously challenging issues. An increasing number of researchers are focusing their investigations on this field, proposing innovative network architectures. However, many existing networks necessitate intricate module designs and a substantial number of parameters to achieve satisfactory fusion outcomes, which poses challenges for lightweight devices with constrained computational resources. To mitigate this concern, the present study introduces a novel methodology that integrates block segmentation with pixel optimization. Specifically, we initially employ graph convolutional networks to execute flexible convolutions on large-scale, irregular regions generated through superpixel clustering, thereby achieving coarse segmentation at the block level. Subsequently, we utilize parallel lightweight convolutional networks to provide pixel-level guidance, ultimately resulting in a more accurate decision map. Furthermore, to leverage the strengths of both networks and facilitate the optimization of feature generation from the graph convolutional network for non-Euclidean data, we meticulously design a superpixel-based graph decoder alongside a pixel-based convolutional neural network extraction block to enhance feature acquisition and propagation. In comparison to numerous state-of-the-art methodologies, our approach demonstrates commendable competitiveness in both qualitative and quantitative analyses, as well as in efficiency evaluations. The code can be downloaded at https://github.com/ouyangbaicai/FusionGCN.

Block-level spatial integration of population density, social vulnerability, and heavy precipitation reveals intensified urban flooding risk

Under the context of global warming and rapid urbanization, cities worldwide confront the pressing problem of urban waterlogging, hindering progress towards Sustainable Development Goals. Effective planning and mitigation of urban flooding require a comprehensive understanding of the spatial and temporal patterns of rainfall and risk heterogeneity. However, evaluating urban water-logging risk is challenged by the need for city-scale hydrological simulation and generally lacks comprehensive metrics integrating fine-scale datasets. To address these gaps, we developed a simulation method for urban flood hazards by integrating hydrological models and Random Forest algorithms. We then took Shenzhen, a megacity in China, as a case study, and investigated the spatial patterns of urban flooding risk and its determinants at the block level based on the risk assessment framework represented by Hazards-Exposure-Vulnerability (H-E-V) dimensions. We found that socio-economic indicators exhibited spatial clustering, while hazard-related indicators displayed more dispersed patterns. High-risk areas exhibited a highly heterogeneous spatial pattern, predominantly influenced by vulnerability and exposure factors, as well as the spatial mismatch among the three dimensions. Our results emphasize the importance of integrating spatial heterogeneity of exposure and vulnerability into climate adaptation resource allocation, addressing both current and future demands for effective climate mitigation.

Trends in socioeconomic disparities in urban heat exposure and adaptation options in mid-size U.S. cities

There is ample evidence that Environmental Justice communities experience high levels of extreme heat. However, it is unknown how disparities in urban heat exposure and adaptation options change over time. This study investigates socioeconomic disparities over time in urban heat exposure and adaptation options in eight mid-sized Northeastern cities. We ask: How were socioeconomic factors associated with heat exposure and adaptation options over time? We analyzed disparities at the census block group level and census block level, respectively. At the census block group level, we ran spatial regression models between socioeconomic variables, including race, income, gender, and age, and heat exposure and adaptation variables, including land surface temperature, normalized different vegetation index (NDVI), tree cover, and air conditioning ownership rate. We found that: Low median household income is always associated with high LST and low NDVI from 1990 to 2020; Low percentage of females is always associated with high LST and low NDVI from 1990 to 2020. High percentage of POC is associated with high LST in 2010 and 2020, but not in 1990 and 2000; Low median household income and low percentage of elderly are associated with lower tree cover; High percentage of POC, low percentage of elderly, and low median household income are associated with lower AC rates. In analysis at the census block level by city, we found that disparities in urban heat exposure between predominantly POC and predominantly white communities increased in most cities during 1990-2020. Predominantly POC communities consistently have lower vegetation cover over time in most cities. Disparities in vegetation cover per unit area increased in most cities, whereas disparities in vegetation cover per capita decreased in most cities. Our findings of the trends in disparities in heat exposure and adaptation are useful for forecasting disparities in the future. These findings also suggest that interventions should prioritize cities with increasing disparities in heat exposure and adaptation.

A Multi-Scale Evaluation Model of Sustainable Development Goal 11.7 for Problem-Solving at Different Levels

Sustainable Development Goal 11.7 (SDG 11.7) aims to promote the improvement of urban public spaces. However, the localization process of SDG 11.7 mainly relies on a bottom-up problem-solving approach, which fails to fully encompass the connotation of SDG 11.7. Additionally, existing evaluations primarily focus on a single scale, neglecting the impact of scale issues. These limitations can lead to imbalanced development or misallocation of responsibilities when guiding governments at different levels in promoting the sustainable development of public spaces. Therefore, this article introduces a multi-scale assessment model of SDG 11.7. It employs a top-down problem-solving approach to construct a sustainable development indicator framework, setting appropriate sustainable development indicators for various levels of government based on the connotation of SDG 11.7, and generates city-scale results by integrating three scales: apartment complexes, street blocks, and counties. Testing this model in Xi’an, China, revealed that it adequately captures four key aspects of SDG 11.7—safety, inclusiveness, accessibility, and greenness—through 11 indicators. The evaluation outcomes at the apartment complex, street block, and county levels effectively guide future development directions for various levels of government. Ultimately, the synthesis of these scales reveals the spatial pattern of SDG 11.7 at the city scale and identifies focal areas for development. Overall, this exploratory model demonstrates high accuracy and robustness, providing a comprehensive understanding of the essence of SDG 11.7. It also alleviates challenges posed by scale issues, offering decision support for monitoring SDG 11.7 across different levels of government in Chinese cities and promoting the process of sustainable development.

Inequalities in emergency service accessibility: spatial analysis of urban infrastructure

PurposeThis study aims to investigate the relationship between emergency response times and crash severity in New York City, focusing on spatial disparities and their implications. It examines how these disparities impact disadvantaged neighborhoods, particularly regarding traffic safety and emergency service accessibility.Design/methodology/approachThe research uses comprehensive spatial analysis techniques, including hotspot mapping, network analysis for travel time modeling, local bivariate correlation analysis and service area calculations. It maps crash data alongside emergency facility locations, considering peak-hour traffic. The Area Deprivation Index (ADI) is integrated to evaluate socioeconomic factors influencing accessibility. This approach provides a nuanced understanding of how emergency response times correlate with crash severity at the census block level, accounting for socioeconomic disparities.FindingsThis study reveals significant disparities in emergency response times across New York City. In high-poverty, predominantly minority areas, response times are 2–3 min longer than average, correlating with a 15% increase in severe injury rates. Over 20% of neighborhoods show correlations between response times and crash severity, with positive linear (5.51%), negative linear (10.72%), concave (2.44%) and convex (2.80%) relationships. Blocks with positive linear relationships have a mean ADI rank of 3.918. During peak hours, 69.7% of Manhattan blocks show negative relationships, the highest among boroughs.Originality/valueThis research highlights spatial justice issues in urban emergency response systems, emphasizing the need for localized, data-driven planning and infrastructure adjustments. By integrating the ADI, the multifaceted approach reveals the complex dynamics of socioeconomic factors and emergency service accessibility that have not yet been investigated in diverse urban communities.

An Approach for Measuring Spatial Accessibility to Services/Facilities by Urban Community Aged Population

Abstract. Population aging is a global trend and a world-wide challenge to many governments. The high-quality life of the elderly cannot be separated from the support of basic services and facilities. Therefore, it is necessary to conduct analysis and research on the accessibility of urban community services and facilities. It is worth noting that the research in this article is based on master thesis (Bai, 2013). Drawing on the data, methods, and conclusions from the master's thesis, in this paper, an approach is designed for measuring spatial accessibility to services/facilities by a spatially dispersed ageing population in urban areas. The local government area (LGA) of Monash in the Melbourne metropolitan area has been selected the case study area. This study combines GIS-based spatial analytical procedures and two-step floating catchment area (2SFCA) method, in conjunction with mesh block level 2011 population data, service/facility data and transportation network data, to measure and map spatial variations in potential accessibility to services and facilities deemed essential to the aged population. Service facilities considered in the study include bus stops, train stations, bank facilities, shopping centres, post offices, churches, parks, public libraries, community centres, pharmacies, GP clinics and hospitals in Monash LGA. The research results can be used to optimize the spatial planning and layout of public facilities for ageing population.

Sensory assessment of intramuscular quadratus lumborum block at the L2 level in open inguinal hernia repair patients

BackgroundThe effectiveness of the quadratus lumborum block (QLB) for postoperative pain management depends on the injection pathway used. There is limited research on the block area produced by intramuscular injection of local anesthesia in the quadratus lumborum muscle. This study aimed to determine the cutaneous sensory blockade area produced by an intramuscular quadratus lumborum block (QLBi) at the L2 level.MethodsTwenty patients aged 18–60 years with ASA grade I-II and a BMI of 18–30 kg/m2 who were scheduled for open inguinal hernia repair with mesh underwent ultrasound-guided QLBi injection of 20 ml of 0.5% ropivacaine. The cutaneous sensory blockade area was measured by applying a cold stimulus 1 h after the block and then measured every hour after surgery until the sensation returned to normal. The duration of a blockade is defined as the time it takes for all affected areas to fully regain normal sensation following a blockade. Pain scores (numeric rating scale, NRS) were recorded at 2, 4, 8, 12, and 24 h after surgery. Adverse reactions to QLBi were recorded 24 h after surgery.ResultsAll 20 patients had reduced or lost cold sensation areas. The greatest extent of cold sensation reduction occurred at T7 (10%), and the least amount of cold sensation reduction occurred at L3 (10%). The block level covered T8 (20%), T9 (30%), T10 (45%), T11 (90%), T12 (95%), L1 (100%), and L2 (15%). Eighteen patients experienced areas of sensory loss, with the highest range at T11 and the lowest at L2. The duration of the blockade was 8.9 ± 3.8 h, with a maximum of 24 h and a minimum of 5 h. One patient experienced quadriceps weakness after surgery.ConclusionQuadratus lumborum block of intramuscular pathway can produce effective cutaneous sensory blockade, which can be used for postoperative analgesia of indirect inguinal hernia operation, and may also be beneficial to analgesia of other lower abdominal operations. However, the best method needs further confirmation to determine specific anesthesia methods for various operations.Chinese clinical trial registryJune 2, 2018; ChiCTR1800016457.

A bottom-up nationwide analysis of sectoral land use reveals spatial heterogeneity across the United States

The high degree of urbanization and economic development in the United States has intensified competition for land resources, leading to land use conflicts and environmental challenges. Addressing these issues first requires an exploration of high-resolution, sector-specific land use patterns. However, existing land classification studies rarely provide spatially explicit data on urban land use across all sectors at a national level. This study aims to fill this gap by analyzing land use areas across various economic sectors in the United States, particularly focusing on urban impervious surfaces. Using a bottom-up approach, we created a full-sector land use inventory, integrating Points of Interest (POI) data, road networks, building characteristics, and geographic information processing techniques. Our primary innovation lies in the development of a spatially explicit database that details sector-specific land use areas nationwide. On this robust foundation, we further explored the heterogeneity of sectoral land use eco-efficiency (SLUEE) among different sectors and states, providing supplementary insights into spatial disparities in urban land use. Our findings reveal significant spatial heterogeneity, with service sectors dominating land use and exhibiting higher SLUEE compared to goods-producing industries. Key sectors such as Professional, Scientific, and Technical Services (441064.58 ha), and Administrative and Support Services (420148.34 ha) are prominent across states according to land use area, reflecting the country's service-oriented economy. Additionally, regional disparities in SLUEE are evident, with jurisdictions like Washington D.C., New York, and California demonstrating more efficient land use. Compared with traditional land use evaluations, our model effectively offers higher spatial resolution, down to the census block level, to sectoral land use evaluation. Building on this robust database, our SLUEE analysis has uncovered notable heterogeneity between sectors and states, offering new insights into cross-sectoral and inter-state land use dynamics that can inform and promote sustainable urban planning.

Robo-Matter towards reconfigurable multifunctional smart materials

Maximizing materials utilization efficiency via enhancing their reconfigurability and multifunctionality offers a promising avenue in addressing the global challenges in sustainability. To this end, significant efforts have been made in developing reconfigurable multifunctional smart materials, which can exhibit remarkable behaviors such as morphing and self-healing. However, the difficulty in efficiently manipulating and controlling matter at the building block level with manageable cost and complexity, which is crucial to achieving superior responsiveness to environmental clues and stimuli, has significantly hindered the further development of such smart materials. Here we introduce a concept of Robo-Matter, which can be activated and controlled through external information exchange at the building block level, to enable a high-level of controllability, mutability and versatility for reconfigurable multifunctional smart materials. Using specially designed micro-robot building blocks with symmetry-breaking active motion modes, tunable anisotropic interactions, and interactive coupling with a programmable spatial-temporal dynamic light field, we demonstrate an emergent Robot-Matter duality, which enables a spectrum of desirable behaviors spanning from matter-like properties such as ultra-fast self-assembly and adaptivity, to robot-like properties including active force output, smart healing, smart morphing and infiltration. Our work demonstrates a promising direction for designing next-generation smart materials and large-scale robotic swarms.

The Prediction And Prevention Of Spınal Anaesthesıa-Induced Hypotensıon With Perfusion İndex And The Effect Of Crystalloid

Hypotension is a dangerous and common problem for patients under spinal anesthesia. Spinal anesthesia-induced hypotension(SIH) is often arisen from heart rate decrease and venous blood pooling under the spinal block level because of sympathetic blockage. Prediction and prevention of hypotension will be beneficial for the patients' health under spinal blockage. For prediction, many parameters like perfusion index(PI) have been using. This study aims to research the correlation between the patients' hemodynamic parameters, perfusion indices before spinal block and SIH, and the effectiveness of crystalloid preload before spinal block to the usage of ephedrine. Records from 101 patients between 18-100 years age, who underwent surgery with spinal anesthesia were studied. We enroll patients demografical characteristics, ASA-score, sensory-blockage level, opereation period, the patients' position for spinal block, local anethesic form-dosage, blockage level, patients' hemodynamic and PI parameters in the surgery, hypotension time, efedrin first usage time and dosage from records. These results analyzed with t-test, Pearson-Chi-Square, Wilcoxon-signed-rank and Mann-Whitney-U test. SIH were seen in 25,7% of patients. The incidence of hypotension increases with age and block level. Any corelation between SIH and PI degrees before spinal block was not found, but MAP degrees before spinal block were significantly different in hypotension group. 10ml/kg cristaloid-preload was used 37,6% of patients. SIH was not prevented by cristaloid-preload and cristaloid-preload didn’t affect efedrine dosage-timing. Cristaloid-preload changed significantly patients heart rate and PI. In this study, we found that PI was not predictive factor for the development of SIH. Besides cristaloid-preload don’t avoid development of SIH.

Exploring the Impact of Urban Amenities on Business Circle Vitality Using Multi-Source Big Data

Urban business circles are important locations for economic and social activities. Improving the vitality of urban business circles is conducive to stimulating the potential of the consumer market and promoting sustainable economic development. However, targeted research on the factors influencing business circle vitality is lacking. Therefore, in this study, we aimed to quantitatively examine the impact of the number and diversity of urban amenities on business circle vitality at the street block level using open-source geospatial big data from 32 Chinese metropolises. We found that the number of residential, transportation, educational, cultural, and recreational amenities and the diversity of catering and retail amenities had significant positive impacts on business circle vitality. Catering and retail diversity were the two most critical factors, followed by the number of transportation, cultural, and recreational amenities. However, the effect of urban amenities on business circle vitality varied considerably across different cities and business districts. The results of this study contribute to a holistic understanding of how to improve the vitality of business circles by optimizing urban amenities at the street block level.

Rainfall characteristics under changing climate in Sundergarh district of Odisha

Most pronounced signal of warming world is the increase in intensity and frequency of extremeevents worldwide. Many researchers have reported the nature of these events in coastal district of Odisha, but forSundergarh district which comes under North-western Plateau Agro-Climatic Zone, it still needed to be studied. Thepresent study was conducted with an objective to study rainfall characteristics - rainfall climatology, rainy days, trend -their deviation and resulting meteorological drought in Sundergarh district of Odisha at block level using 34 years ofdaily rainfall data (1988-2022). IMD classification for rainy days was adopted to determine the frequency of rainy days indifferent categories. SPI was computed using Climpact to determine the occurrence and severity of meteorologicaldrought and identify episodes of dry and wet events during the study period for each block. MK test and Sen’s slopeestimator was used for detecting trend in rainfall (annual and seasonal) and rainy days at 95% significance level. Longterm rainfall analysis reveals that the mean annual rainfall of the district is 1290 ± 314 mm, most of which is receivedduring SW monsoon (86%) followed by post-monsoon (6.2%), pre-monsoon (5.7%) and least during winter (2.1%).Rainfall received in the months of July and August together accounts for more than 50% of the total. Spatial distributionof rainfall indicated that mean annual rainfall varies from 1071 mm (Subdega) to 1578 mm (Bonai). Annually, rainfall isdependable in most of the blocks. However, during pre-monsoon, post-monsoon and winter, CV is very high, so therainfall is not dependable. Bulk of annual rainfall received in the district is contributed by rain events of light to moderatecategory. Direction and magnitude of the trend of rainfall amount and rainy days varies a lot in different blocks. SPI values calculated for the district as a whole shows that frequency of occurrence of these drought events were: mild (34% of months); moderate (7.6% of months); severe (3.3% of months) and extreme (3.2% of months). The present study provides pertinent information on the rainfall climatology and its trend under climate change over the Sundergarh district of Odisha. Finding of the study suggests that there is an urgent need to raise awareness about the climate resilient society, train personnels about sustainable development approaches and highlight the necessity of water harvesting techniques at community level.

Higher levels of neighborhood deprivation and risk for incident colorectal cancer and mortality outcomes: A systematic review and meta-analysis.

131 Background: Significant geographic and racial disparities exist for colorectal cancer (CRC) incidence and mortality in the United States (US), which may be caused by neighborhood-level deprivation and limited access to preventative cancer screening. We completed the first systematic review and meta-analysis of neighborhood-level deprivation in relation to CRC incidence, mortality, and screening among individuals from the US. Methods: A systematic literature review was completed to identify original research articles from PubMed, Scopus, and Web of Science. Eligible studies reported associations between deprivation at the census block or tract level using a validated deprivation index (e.g. the Area Deprivation Index or Yost Index) and CRC incidence, mortality, or screening outcomes in a US-based sample. A fixed-effects meta-analysis was completed using inverse-variance weighting to estimated hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) for CRC incidence, mortality, and screening outcomes, comparing the highest vs. lowest quantiles of neighborhood-level deprivation. All analyses were completed using the meta package in R. Results: In total, 19 studies were included in the meta-analysis. Living in a neighborhood in the highest quantile of deprivation was associated with significantly higher risk for incident CRC (HR 1.37, 95% CI 1.34-1.41, seven studies). In stratified analysis, positive associations with incident CRC were observed for White (HR 1.43, 95% CI 1.39-1.47, three studies), Black (HR 1.18, 95% CI 0.96-1.44, three studies), and Asian/Pacific Islander individuals (HR 1.18, 95% CI 1.08-1.28, two studies). For individuals diagnosed with CRC, higher deprivation was associated with elevated risk for overall mortality (HR 1.11, 95% CI 1.10-1.13, four studies) and CRC-specific mortality (HR 1.17, 95% CI 1.15-1.19, seven studies). Higher deprivation was associated with lower completion of recommended screening for CRC, including colonoscopy, sigmoidoscopy, or fecal occult blood testing (OR 0.76, 95% CI 0.74-0.79, four studies). Conclusions: Individuals who reside in neighborhoods with high levels of deprivation have significantly elevated risk for CRC incidence and mortality compared to individuals from low deprivation neighborhoods. Targeting healthcare resources to highly deprived neighborhoods to increase uptake of preventative screening may help to address racial and geographic disparities for CRC.

A successful combined spinal-epidural anesthesia for cesarean section in a patient with neurofibromatosis type 1-associated dural ectasia.

Dural ectasia is a common manifestation of neurofibromatosis type 1. Although there have been reports of unsuccessful spinal anesthesia due to dual ectasia in Marfan syndrome, reports describing similar unsuccessful spinal anesthesia in neurofibromatosis type 1 are lacking. A parturient with neurofibromatosis type 1 was scheduled for a repeat cesarean section. During a previous cesarean section, she had experienced a failed spinal anesthesia, which resulted in a conversion to general anesthesia. Preoperative lumbar magnetic resonance imaging revealed dural ectasia, which was speculated to be the cause of the previous spinal anesthesia failure. Therefore, combined spinal-epidural anesthesia was implemented. Because the block level of spinal anesthesia was insufficient as predicted, supplemental administration of epidural anesthesia successfully provided adequate analgesia for the surgery. Combined spinal-epidural anesthesia can be useful for the management of cesarean sections in patients with neurofibromatosis type 1-associated dural ectasia.

Scale effect on the relationship between urban landscape patterns and land surface temperature

Great efforts have been made to examine the linkages between land surface temperature (LST) and urban landscape patterns (ULPs), which, however, focus on a single spatial scale and linear relationships. This study aims to examine the influence range of four key ULP indicators on LST, namely sky view factor (SVF) and three indices of built-up area, greens, and blue spaces. The analyses are conducted at street block level in 38 big cities in China, and leverage a multiscalar approach to investigate change patterns of indicators within multiple buffer zones and identify the buffer distance that yields the greatest influence. Results reveal that (1) the greatest local impacts are produced within 0−150 m buffer zones in most cities for all key ULP metrics; (2) the maximum impacts of most indicators in summer are greater than other seasons; and (3) the influence magnitude of key ULP indicators increase after considering scale effect, and the influence of SVF varies significantly across cities. Results suggest that the consideration of maximum influence ranges of ULP indicators can better explain LST spatial variations. Our findings offer evidence on the local impact of ULPs on LST and contribute to urban design in urban heat mitigation.

Comparison of Low-dose Diluted Hyperbaric Bupivacaine and Standard Hyperbaric Bupivacaine in Subarachnoid Block for Ambulatory Endovenous Laser Ablation of Varicose Vein: A Randomized Controlled Trial

Abstract Background: Low-dose spinal anesthesia represents a highly coveted anesthetic technique for patients undergoing endovenous laser ablation of varicose veins in a day-care setting, given its prompt onset, reliable anesthesia, brief turnover time, and cost-effectiveness. Given that intense motor block is not required for this procedure, this trial compared the efficacy of low-dose diluted hyperbaric bupivacaine with standard hyperbaric bupivacaine in the subarachnoid block for endovenous laser ablation of varicose veins. Methodology: A prospective, randomized controlled trial at a tertiary care hospital, Tamil Nadu, South India. Sixty individuals with varicose veins requiring endovenous laser ablation surgery were split at random into two groups. Group A (n = 30) got 0.5% hyperbaric bupivacaine 10 mg (2 ml), Group B (n = 30) got diluted hyperbaric bupivacaine 5 mg (1 ml of 0.5% hyperbaric bupivacaine diluted with an equivalent volume of normal saline) intrathecally. This study evaluated the time to surgical readiness, peak sensory block level, motor block grade, sensory block level at the completion of surgery, duration of motor and sensory blocks, and timing of discharge. Results: The median peak sensory block level for Group A was T6 (T8–T6), whereas for Group B it was T8 (T10–T6), with a P = 0.03. At the conclusion of surgery, the median sensory block level was T6 (T6–T8) for Group A and T8 (T7–T10) for Group B. No patients in either group needed a rescue analgesic. The average timing of discharge for Group B was shorter (P = 0.00002). Conclusion: For patients receiving endovenous laser ablation therapy for varicose veins, subarachnoid blockade with low-dose diluted hyperbaric bupivacaine (5 mg) offers an effective anesthesia, the best recovery profile, and early hospital discharge.

Longitudinal Impacts of High Versus Low Greenness on Cardiovascular Disease Conditions.

Greenness-or vegetative presence-has been identified as a factor in chronic disease. The present study examines the longitudinal relationship between objective measures of greenness at the residential block level and incidence of 6 cardiovascular disease conditions. Analyses examined the impact of consistently high versus consistently low "precision" greenness at the Census block level on the 5-year incidence of cardiovascular disease conditions, including acute myocardial infarction, atrial fibrillation, heart failure, ischemic heart disease, stroke/transient ischemic attack, and hypertension, among 229 034 US Medicare beneficiaries in Miami-Dade County, Florida, USA. Zero-inflated Poisson regression was used to model the odds of developing any new cardiovascular disease and number of new cardiovascular disease conditions based on greenness tertiles computed across 2011 and 2016 Normalized Difference Vegetation Index values, adjusting for individual age, sex, race, ethnicity, baseline cardiovascular disease conditions, neighborhood income, and walkability in 2011 and 2016. When compared with individuals consistently in the low greenness tertile in 2011 and 2016, those consistently in the high greenness tertile in 2011 and 2016 had a 9% lower odds of having any new cardiovascular conditions (odds ratio [OR], 0.91 [95% CI, 0.84-0.99]; P=0.021). Over a 5-year period, consistently high greenness, when compared with consistently low greenness, was associated with lower odds of any new cardiovascular disease conditions. Identifying the role of greenness exposure in such a small geographic area, the Census block on which the older adult resides, allows for more precise, strategic decisions on where additional trees can be added-by selecting at-risk blocks rather than entire neighborhoods for tree-planting interventions.

Improving IIoT security: Unveiling threats through advanced side-channel analysis

The widespread deployment of IIoT edge devices makes them attractive victims for malicious activities. Consequently, how to implement trustworthy operations becomes a realistic topic in embedded systems. While most current physical systems for detecting malicious activities primarily focus on identifying known intrusion codes at the block level, they ignore that even an unnoticeable injected function can result in system-wide loss of security. In this paper, we propose a framework called CNDSW built on deep-learning side-channel analysis for function-level industrial control flow integrity monitoring. By collaboratively utilizing correlation analysis and deep-learning techniques, the dual window sliding monitoring mechanism in the proposed CNDSW framework demonstrates a real-time code intrusion tracking capacity on embedded controllers with a 99% detection accuracy on average. Instead of focusing on known block-level intrusions, we experimentally show that our model is feasible to detect function-level code intrusions without knowing the potential threat type. Besides, we further explore how different configurations of the CNDSW framework can help the monitoring process with different emphases and to which extent the model can concurrently detect multiple code intrusion activities. All our experiments are conducted on 32-bit ARM Cortex-M4 and 8-bit RISC MCUs across five different control flow programs, providing a comprehensive evaluation of the framework’s capabilities.