Congestion Rate Research Articles

Autonomous vehicles congestion model: A transparent LSTM-based prediction model corporate with Explainable Artificial Intelligence (EAI)

Urban traffic congestion presents a range of vital difficulties requiring precise prediction models in order to facilitate traffic management for Autonomous Vehicles. This work introduces a novel framework that regulates a Long Short-Term Memory (LSTM) system with methods provided by Explainable Artificial Intelligence (XAI) to explain traffic congestion behavioural modes. For enhanced accuracy and transparency, the integration of EAI methodologies with LSTM based models is addressed as a novel approach towards congestion prediction, while significant research has been done previously using Machine Learning that compared previous proposed based model congestion monitoring improvement through Federated Learning Waqas et al. [18]. This wok proposes the enhances ML focused on Long Short-Term Memory with EAI (LSTM-EAI) model for Smart City environments that require accurate traffic congestion rate forecast to improve the urban mobility. The proposed model provides better interpretability that help stakeholders to understand how the input plays an important role in the condition of traffic jams. The results show that the LSTM-EAI model is 5 % better than previous methods for both the accuracy and reliability of congestion prediction, and may become a practical and effective solution for the urban traffic problem.

Validating the porcine model for microsurgical perforator training: Can surgeons trained on pig perforator dissection successfully perform human DIEP flap procedures? A pilot study

Autologous breast reconstruction, especially using the deep inferior epigastric artery perforator (DIEP) flap, is increasingly seen as a reliable, safe, and long-term alternative to implant-based reconstruction. Despite the recognized advantages of the DIEP flap for breast reconstruction, successful realization demands excellent anatomical knowledge, a thorough understanding of autologous breast reconstruction concepts and advanced microsurgical skills. Given that the porcine model is widely employed in microsurgical training, our study aims to assess this model using validated outcomes, with the objective of evaluating the enhancement in a surgeon's learning curve following training with this model. Forty DIEP flaps were harvested on 20 swines by a single surgeon in "Pius Branzeu Center" (Timisoara, RO) and "Drazan Institute" (University of veterinary of Brno, CZ) laboratories for microsurgical training in 6months (January 2015-June 2015). Then we analyzed data from 40 DIEP flaps harvested by the same surgeon on first 20 consecutive patients undergoing DIEP flap breast reconstruction. Perforator dissection time, surgeon-determined dissection difficulty score (DDS) and venous congestion rate were collected for each flap in porcine model and in patients, then compared and analyzed. The mean of DDS score analysis in first and second swines group dissection resulted as statistically significant (P-value 0.0001), while it was not statistically significant between those analyzed in the second group of swines dissected and patients (P-value 0.8037). Reduction in perforator dissection time between the two swines' groups and in venous congestion rates from the first swines groups to the second to the human group resulted statistically significant too (P-value respectively 0.0001 and 0.0079). The porcine model has been used for a long time together with other animal models for microsurgical training. Our study confirms and objective by validated scores that it is a valid and reliable model, comparable to the human one and which mimics the dissection of human perforating vessels.

Dynamic resilience analysis of the liner shipping network: From structure to cooperative mechanism

Liner shipping networks play a vital role in global and regional trade. However, they are susceptible to damage from unexpected interruptions, which can trigger dynamic cascading failures and undermine the system’s resilience. To address this challenge, we propose a novel cascading failure model for liner shipping networks that considers the characteristics of the network structure and port functions. First, we design two load redistribution methods that rely on network topology and employ a cooperative mechanism for coordination. This cooperative mechanism aims to balance the benefits for carriers and shippers by effectively controlling losses. Subsequently, we develop three metrics—network congestion rate, failure rate, and shipper loss—to assess the resilience of the network during cascading failures. To verify the impact of the cooperative mechanism, we apply the proposed methods to the China-Europe liner shipping network. Through simulations involving various port failures and resistance levels, we analyze the effectiveness of the cooperative mechanism. The results demonstrate that redistributing the load to downstream ports within the network effectively mitigates deep cascading failures. Additionally, the implementation of a port cooperative mechanism enhances resilience in the face of uncertainties by safeguarding crucial ports within the network and significantly reducing shipper losses. When port resistance is low, the cooperative mechanism reduces shipper losses by nearly half and lowers the average congestion rate. Although port reserve capacity can resist cascading failures, it falls short in the face of severe disruptions. In such cases, the cooperative mechanism compensates for capacity shortages, enhancing port resilience at a low cost. This study contributes to combating and minimizing cascading congestion in liner shipping networks, offering valuable insights for risk prevention and management strategies for ports and shipping companies. It also has implications for yield management and policy decisions from a network perspective.

Performance evaluation of the congestion severity aware rate regulation (CSRR) algorithm in wireless body area networks

SummaryWireless body area network (WBAN) is a potential low‐cost technology for privacy‐sensitive telemedicine and e‐health monitoring and services. However, it faces limited protocol and physical resource support challenges, which can result in packet transfer difficulties. In particular, WBAN requires an emergency‐aware technology that ensures a promising quality of service (QoS). One significant issue affecting QoS and energy efficiency in WBAN is congestion. Effective congestion control techniques are essential for achieving proper load balancing. To address these challenges, we propose a congestion severity aware rate control (CSRR) algorithm that enhances packet transmission rate by reducing packet losses and retransmissions. The CSRR algorithm incorporates a fuzzy controller to predict congestion rates based on runtime metrics. To regulate congestion window growth in different algorithm phases, we introduce sequences such as the Fibonacci retracement sequence, knight's move sequence, and the binary logarithm of the primorial sequence to regulate congestion window growth in the different phases of the proposed algorithm. We mathematically analyze the proposed CSRR algorithm using a Markov model. The simulation results demonstrate the superiority of our algorithm compared to existing approaches. Specifically, our algorithm achieves significant optimizations in terms of throughput (52.92%), packet loss (38.11%), delay (37.23%), and remaining energy (36.86%) when compared to existing algorithms.

Congestion aware clustered WSN based on an improved ant colony algorithm

Conventional works carried out in Wireless Sensor Networks (WSN) mostly focussed on energy oriented services and very less significant measures given to delay oriented and congestion aware services. Hence the proposed mechanism specially focuses on network structural design by placing rendezvous location for each cluster as well as route segmentation for controlling the congestion occurrence and unwanted delay. Here Congestion Aware Clustering with Improved Ant Colony Algorithm (CAC_IACA) is proposed. This mechanism involves two steps (i) identifying the best route by following the Ant Colony Optimization (ACO) algorithm and (ii) data segmentation using rendezvous mobile nodes. The Rendezvous nodes are present in each cluster to reduce the congestion rate on receiver side during data transmission. This proposed methodology mainly concentrates on reducing coverage cost for 3D environmental monitoring. Simulation results are analysed and the efficiency of the proposed scheme proves 26.54 % better than the conventional method.

Analysis of Vascular Anatomy in Deep Inferior Epigastric Perforator Flap

Abstract Objectives The deep inferior epigastric artery perforator (DIEP) flap is considered the gold standard in autologous breast reconstruction. One of the existing challenges of the procedure is maintaining a pedicle of suitable length and diameter for flap survival. In this study, we analyzed the vascular anatomy of bi-pedicle DIEP flap cases in terms of the vessel diameter, the rate of secondary venae comitantes, and pedicle length to determine the efficacy of microvascular anastomosis and venous drainage. Methods We retrospectively evaluated 108 patients who underwent immediate breast reconstruction using free bi-pedicle DIEP flaps between 2012 and 2019. The patient characteristics, diameters of the deep inferior epigastric artery (DIEA) and accompanying veins (DIEVs), DIEA pedicle length, vessel re-anastomosis rate, flap failure rate, and fat necrosis were recorded. Results Comparison of the right side and left side diameters and pedicle lengths obtained showed no significant differences (p > 0.05). A total of 148 sides (68.52%) of flaps had double venous drainage, whereas 68 sides (31.48%) of flaps had one. Vein congestion occurred in 5 cases, and all involved with just one DIEV anastomosis. No arterial occlusions were observed. Conclusions This is an analysis of extensive clinical data in terms of DIEA, DIEV, and pedicle length. The low vein congestion rate observed reveals adequate venous drainage was achieved in the DIEV system for DIEP reconstruction. Adequate deep inferior epigastric vessel pedicle length and vessel size, as well as greater than 60% of flaps with secondary DIEV, may allow comfortable microsurgery with reduced complications.

Research on smart city public health detection system and improvement technology based on intelligent multi-objective.

With the increase of urban population density, urban sanitation becomes more severe; urban sanitation has important influence on public health. Therefore, in order to realize the detection of public health in smart cities, the research will use cutting-edge scientific and technological methods to improve urban environmental health, so as to promote the realization of public health achievements. This study introduces public health detection and optimizationtechnologies for smart cities. Firstly, a data detection system for urban public health environment was established using sensors and intelligent multi-objective technology to evaluate the water quality, air quality, and noise level of the city. Then, an intelligent garbage management system based on Tensor-flow was constructed to achieve efficient garbage collection and treatment. Finally, an intelligent traffic management system was developed to monitor and regulate urban traffic flow. The results of the simulation experiment demonstrated that the life data detection system was operationally stable, with a high success rate of 98%. Furthermore, its accuracy in detecting residents' living environment data was above 95%, the maximum relative error was only 0.0465, making it a reliable and efficient tool. The waste recycling system achieved a minimum accuracy of 83.6%, the highest accuracy rate was 95.3%, making it capable of sorting and recycling urban waste effectively. Additionally, the smart traffic management system led to a 20% reduction in traffic congestion rates, 20 tonnes less tailpipe emissions and an improvement in public health and well-being. In summary, the plan proposed in this study aims to create a more comfortable, safe, and healthy urban public health environment, while providing theoretical support for environmental health management in smart cities.

VALIDATION OF KEY PERFORMANCE INDICATORS OF MOBILE TELECOMMUNICATION OPERATORS USING ENSEMBLE MODELS AND ARTIFICIAL NEURAL NETWORKS WITH NIGERIAN COMMUNICATION COMMISSION’S THRESHOLDS

This paper presents the comparative studies among the predictive models used for the validation of the key parameter indicators of mobile network operators with the Nigerian Communication Commission’s threshold. Four Key Performance Indicators were predicted using artificial neural networks and ensemble models which include bagging and LSBoost models. The Key Performance Indicators and weather parameters for six locations in Southwestern Nigeria were employed. MATLAB R2020a was employed to develop the three models. Microsoft Excel was used in the analysis of the dataset. The bagging model gave the best average compliance of 94% and 100% for CSSR and TCH Congestion Rate respectively while the ANN model yielded the best average compliance of 76.7% and 85.2% for DCR and SDCCH Congestion Rate respectively..

Correlations between Urban Morphological Indicators and PM2.5 Pollution at Street-Level: Implications on Urban Spatial Optimization

During rapid urbanization, microclimate environment deterioration through events such as haze pollution and heat waves has continuously occurred in cities, which greatly affects the living environment, production activities, and health of urban residents. Therefore, it is particularly necessary to explore methods for controlling and optimizing the urban microclimate environment. In this paper, based on the mechanism of the effect of urban spatial structure at street-level on the distribution of atmospheric particulate matter, an indicator system that can be employed to comprehensively describe and quantify urban morphological structure at street-level was constructed from eight aspects: the spatial morphology of street-valleys, intensity of land use and development, geometric structure of buildings, inhomogeneity of buildings, roughness of the underlying surface, distribution of ecological landscapes, 3D architectural landscape morphology, and ventilation potential. Furthermore, using satellite remote sensing images and vector thematic maps of Shanghai, indicator factors were quantified by applying GIS technique. The intrinsic mechanism of the influence of the urban morphology on the diffusion and transport of atmospheric particulate matter was comprehensively analyzed by combining statistical methods and data mining algorithm, and eight key dominant factors were identified that can be considered to improve the urban ventilation conditions and help control urban air pollution, namely, the land use intensity, urban canopy resistance, vegetation cover, spatial congestion rate, comprehensive porosity, height-to-gross floor area ratio, building density, and average building volume ratio. As such, according to the quantitative analysis results for various combinations of the dominant factors, a spatial optimization strategy at street-level that can help improve the urban air quality was proposed in terms of identifying the pathways through which urban spatial elements affect the distribution of particulate matter, i.e., controlling the source–flow diversion–flow convergence process.

Congestion control method for overloaded service of smart substation under high concurrent users

Overload service in the communication network of smart substation will cause congestion, resulting in low overload service throughput, high congestion rate and long congestion control time in the average smart substation. A congestion control method for overloaded services in smart substations with high concurrent users is proposed. According to the characteristics of overload service request of smart substation, the mathematical model of the algorithm is defined by describing the overload service request of smart substation on the basis of network topology model. Combined with the wavelength rotation strategy, the congestion rate of overloaded services in smart substations is reduced, and the throughput rate of overloaded services in smart substations is improved. Considering the factors of high concurrent users, by judging and feeding back the congestion of the overloaded services of smart substations, the congestion control of overloaded services of smart substations under high concurrent users is realized. The experimental results show that the proposed method has better effect and scalability in the congestion control of the overloaded service of the smart substation, and can effectively shorten the congestion control time of the overloaded service of the smart substation.

Venous anatomy of the superficial circumflex iliac artery perforator flap: A cadaveric and clinical study

Reconstructive surgeons frequently face challenges with free tissue transfer when transplanted flaps are thicker than intended. This is especially pronounced in lower limb cases, where the soft tissue below the knee is thin. The supra-fascial superficial circumflex iliac perforator (SCIP) flap overcomes this problem, but venous congestion remains a concern. We aim to examine the venous anatomy of the SCIP flap through cadaveric dissections and clinical data analyses to enhance the understanding of the venous anatomy and reduce venous congestion in future procedures. Eight cadaveric groins underwent venous dye injection and dissection to identify the superficial circumflex iliac vein (SCIV) and venae comitantes (VC) vascular networks. The venous anatomy was studied for dominant drainage. From April 2015 to December 2019, we conducted 102 SCIP flap reconstructions, mainly using the superficial circumflex iliac artery's superficial branch. Clinical data were analyzed and correlated with cadaveric dissections. SCIP flaps were categorized into three groups: dual drainage (VC and SCIV) in group I, SCIV-only in group II, and VC-only in group III. Correlations between clinical cases and cadaveric dissections revealed the SCIV as an independent drainage system with oscillating links to the VC. The SCIV is approximately twice the diameter of the VC, and the area of each vascular network suggests codominance. No significant difference in flap loss or venous congestion rates was noted when SCIV was the sole drainage, compared with VC or dual drainage. The SCIP flap with one artery and SCIV anastomosis, exhibits low venous congestion rates. Surgeons should consider SCIV for safe SCIP flap reconstruction.

SHM-NDN: A seamless hybrid mobility management scheme for named data mobile ad hoc networks

Named Data Networking (NDN), a counterpart of address-centric networking resolves consumer mobility issues to some extent, however, it confronts excessive bandwidth consumption and network congestion problems in Mobile Adhoc Networks (MANET) due to an inefficient default request re-transmission mechanism. The producer mobility in NDN-based MANET, on the other hand, poses even worse challenges owing to the continuous flooding of location advertisement messages in the network. This paper aims to address the aforementioned producer and consumer mobility issues in MANET and proposes SHM-NDN, a seamless hybrid mobility management scheme for NDN-based MANETs. The proposed scheme features new packet types and extends the functionality of wireless mobile FIB data structure to support efficient mobility management. Moreover, the SHM-NDN proposes a path recovery mechanism to deal with the network partitioning problem caused by a low signal-to-noise ratio and high congestion rate. Through an extensive simulation study, we demonstrate that SHM-NDN achieves on average 12.2% less bandwidth and energy consumption compared to CBILEM. Similarly, SHM-NDN also attains on average 25% less bandwidth and energy consumption compared to EEIF.

Enhancing QoS with LSTM-Based Prediction for Congestion-Aware Aggregation Scheduling in Edge Federated Learning

The advancement of the sensing capabilities of end devices drives a variety of data-intensive insights, yielding valuable information for modelling intelligent industrial applications. To apply intelligent models in 5G and beyond, edge intelligence integrates edge computing systems and deep learning solutions, which enables distributed model training and inference. Edge federated learning (EFL) offers collaborative edge intelligence learning with distributed aggregation capabilities, promoting resource efficiency, participant inclusivity, and privacy preservation. However, the quality of service (QoS) faces challenges due to congestion problems that arise from the diverse models and data in practical architectures. In this paper, we develop a modified long short-term memory (LSTM)-based congestion-aware EFL (MLSTM-CEFL) approach that aims to enhance QoS in the final model convergence between end devices, edge aggregators, and the global server. Given the diversity of service types, MLSTM-CEFL proactively detects the congestion rates, adequately schedules the edge aggregations, and effectively prioritizes high mission-critical serving resources. The proposed system is formulated to handle time series analysis from local/edge model parameter loading, weighing the configuration of resource pooling properties at specific congestion intervals. The MLSTM-CEFL policy orchestrates the establishment of long-term paths for participant-aggregator scheduling and follows the expected QoS metrics after final averaging in multiple industrial application classes.

A cellular automata model for car–truck heterogeneous traffic flow considering drivers’ risky driving behaviors

Driving behavior is the core of traffic simulation modeling. Despite the sizable efforts to investigate the influence of risky driving behavior on traffic flow, very little is known about its influence under car–truck heterogeneous traffic conditions. Therefore, this study aims to analyze the influence of several common risky driving behaviors on car–truck heterogeneous traffic flow. A two-lane cellular automata (CA) model for heterogeneous traffic flow consisting of cars and trucks was proposed, the conditions for the occurrence of dangerous situations (DSs) and traffic congestion were defined. Then the vehicle performance under the influence of risky driving behaviors prevalent in car–truck heterogeneous traffic was obtained and incorporated into the proposed CA model to measure their influence on crash probabilities and congestion rates. The simulation results indicate that risky driving behaviors have a significant negative influence on traffic safety and efficiency, increasing the frequencies of DSs, reducing the traffic flow and aggravating the traffic congestion. In addition, the combinations of risky driving behaviors in traffic flow further deteriorate the traffic safety and efficiency. For example, when two risky driving behaviors, such as distracted driving and aggressive lane-changing, affect the traffic flow simultaneously, the frequencies of DSs increase dramatically and in some density ranges, they are even higher than the sum of the frequencies caused by two single risky driving behaviors. These findings may have notable practical implications in developing policies and regulations that enable truck operations without compromising safety and efficiency.

Application of a Nonlinear Deformation Model to the Analysis of Complete Moment-Curvature Diagrams of Reinforced Concrete Elements with Reinforcement A500

This article suggests a calculation procedure for the parameters of complete moment-curvature deformation diagrams with a down leg that can be used for the calculations of the multistorey monolithic building frameworks taking into account the specifics of reinforced concrete. To construct complete deformation diagrams for bending elements, a non-linear deformation model is used that is based on using the physical diagrams of the concrete and reinforcements to calculate the inner moments of cross-sections at all loading stages via balance and strain compatibility equations. Using this model, the authors researched complete moment-curvature deformation diagrams for the elements with different percentages of longitudinal tensile reinforcement that changes the bending properties of the structure. We used grade А500 steel as the reinforcement. The parameters of the tensile reinforcement diagram were determined in the tension testing of samples with constant deformation rates. The performed computer tests to calculate the moment-curvature diagram parameters for reinforced concrete beams reinforced with grade А500 efficiency rods and conventional grade A400 reinforcement rods with different longitudinal reinforcement congestion rates helped the authors assess the bending properties of the elements under conditions approaching their destruction and determine their efficient use in the calculations of building frameworks taking into account the redistribution of forces.

PO-04-108 THE EFFICACY AND SAFETY OF ETRIPAMIL NASAL SPRAY FOR THE TREATMENT OF ACUTE PAROXYSMAL SUPRAVENTRICULAR TACHYCARDIA: A META-ANALYSIS OF RANDOMIZED CONTROLLED TRIALS

Paroxysmal Supraventricular Tachycardia (PSVT) treatment requires medically supervised intervention. This is derived from the lack of effective and safe self-administrated treatment for PSVT. Etripamil is a novel short-acting calcium channel blocker with a rapid intranasal spray and shows promising potential for the unsupervised treatment of PSVT, especially in the dosage of 70 mg. We aim to evaluate the efficacy and safety of Etripamil for the acute conversion of PSVT. We conducted a systematic review and meta-analysis synthesizing randomized controlled trials (RCTs), which were retrieved by systematically searching: PubMed, EMBASE, Web of Science, SCOPUS, and Cochrane through December 1st, 2022. RevMan version 5.4 software was used to pool dichotomous outcomes using risk ratio (RR) presented with the corresponding confidence interval (CI) while using the fixed-effects model. Three RCTs with a total of 496 patients (296 in the etripamil group and 200 in the placebo group) were included in our analysis. Etripamil was effective for PSVT conversion at 15 minutes (RR: 1.84 with 95% CI [1.37, 2.48]), 30 minutes (RR: 1.86 with 95% CI [1.42, 2.44]), and at 60 minutes after the drug administration (RR: 1.25 with 95% CI [1.05, 1.50]); decreasing medical intervention seeking (RR: 0.58 with 95% CI [0.37, 0.90]); and decreasing emergency (ER) visits (RR: 0.61 with 95% CI [0.38, 0.97]). However, there was no difference at 300 minutes (RR: 1.10 with 95% CI [0.97, 1.25]). Also, Etripamil was associated with higher rates of the incidence of any adverse events (AEs) (RR: 3.17 with 95% CI [2.15, 4.69]), nasal discomfort (RR: 3.82 with 95% CI [2.01, 7.24]), nasal congestion (RR: 5.89 with 95% CI [1.93, 17.97]), epistaxis (RR: 4.74 with 95% CI [1.21, 18.50]), and rhinorrhea (RR: 3.53 with 95% CI [1.22, 10.26]) with no difference regarding any serious adverse events (SAEs) incidence (RR: 0.30 with 95% CI [0.01, 7.21]). Etripamil was effective in inducing PSVT termination for up to 60 minutes and reduced medical treatment seeking and ER visits. Also, Etripamil was associated with higher rates of AEs, including epistaxis, nasal congestion, and rhinorrhea; however, it was not associated with SAEs. Therefore, Etripamil constitutes a promising strategy for PSVT self-termination without medical supervision; however, further RCTs are required before endorsement in clinical practice.

Cross Layer based Energy Aware and Packet Scheduling Algorithm for Wireless Multimedia Sensor Network

Video transmission using sensor networks plays a most significant role in industrial and surveillance applications. Multimedia transmission is also a challenging task in case of guaranteeing quality of service in conditions like limited bandwidth, high congestion, multi-hop routing, etc. Cross layer approach is carried out to handle multimedia transmission over sensor networks for improving network adaptivity. Cross layer based energy aware and packet scheduling algorithm is proposed here to reduce congestion ratio and to improve link quality between the routing nodes. Link quality estimation among nodes is done using Semi-Markov process. Node congestion rate is determined for identifying node’s data channel rate. Packet scheduling process determines the highly prioritized packets by using queue scheduler component thereby the active nodes are selected through link quality process and the packets are transmitted to sink based on prioritize level. Simulation analysis is carried out and the efficiency of the proposed mechanism is proved to be better while comparing with the conventional schemes.

The effect of intraoperative nerve identification technique on distally-based dorsal fasciocutaneous flap for thumb and fingertip reconstruction

IntroductionThumb and fingertip resurfacing continues to be a challenge for hand surgeons, as the treatment varies widely. Being donor nerves in the flap for sensory restoration, the dorsal cutaneous nerves in hand have been widely described in the literature. However, their applications in providing anatomical information to sufficiently harvest the longitudinal axis of a robust flap are usually underestimated. This study reports reconstruction of thumb and fingertip defects with the distally based dorsal fasciocutaneous (DDF) flap and explores the effect of intraoperative nerve identification technique on clinical outcomes. HypothesisThe nerve identification technique can contribute considerably to the improvement of flap circulation, with less risk of poor venous return. Materials and methodsA retrospective study was conducted in 89 patients who had thumb or fingertip defects treated with the DDF flap from February 2014 to June 2020. Patients were divided into two groups based on whether intraoperative nerve identification technique was used. Flap survival was evaluated. The follow-up was conducted by using smartphone or by face-to-face visit. Time for complete wound healing, time to return to work, hand performance, the total active motion of the joints and static two-point discrimination of flaps were analyzed. ResultsThe 46 flaps (group A) that underwent intraoperative nerve identification technique had an 8.7% venous congestion rate versus a 27.9% venous congestion rate in the 43 flaps (group B) without using nerve identification technique (p=0.018). The mean follow-up was 11.08 (range 6–26) months. Time for complete wound healing and time to return to work were significantly shorter in group A (p=0.026 and p=0.012). Michigan Hand Outcomes Questionnaire (MHQ) scores were similar between groups. In the face-to-face follow-up, there was no significant difference in the total active motion of the injured fingers and static two-point discrimination between the two groups (p=0.657 and p=0.182). DiscussionThe use of nerve identification technique could decrease the odds of postoperative venous congestion in DDF flap for thumb and fingertip reconstruction, and result in improved outcomes in terms of time for complete wound healing and time to return to work. Level of evidenceIII; retrospective comparative study.

Incidence of Post-Operative Pulmonary Complications in Patients Undergoing Cardiac Surgery with Low Tidal Volume Ventilation

Objective: This study sought to determine if low-tidal ventilation during CPB was superior to a resting-lung strategy with no ventilation in terms of pulmonary complications following surgery. Study Design: This was a retrospective study. Place and Duration: This study was done at the Punjab Institute of cardiology, Lahore from 1st May 2021 to 1st Nov, 2022. Methodology: A total of 1348 patients were enrolled in this study following inclusion criteria. They were divided into two groups equally. SPSS version 24 was used to enter and analyzed collected data. For qualitative variables was presented in frequency(%). For quantitative variables i.e., age, height, weight, preoperative FEV1/FCV, tidal volume, PEEP, PaO2 and PaCO2 mean ±SD was calculated. Chi-square was applied to compare post-operative outcomes between the groups. P-value of <0.05 was taken as significant. Results: The mean age of the cases in Group A was 55 ± 9.08 while in Group B was 56 ± 9.19. There were 75% male and 25% females in Group A while 84% male and 16% females in Group B. In our data there were 67% diabetic and 59% smokers in Group A however 62% diabetic and 58% smokers were enrolled in Group B. Mean tidal volume given to Group A was 8.5 ± 1.8 while in Group B was 7.5 ± 1.3. Peak respiratory pressure in Group A was 18 and in Group B was 16. Mean perfusion time, cross clamp time and anesthesia duration was significant between the groups p-value 0.01. Mean tidal volume given to Group A was 8.5 ± 1.8 while in Group B was 7.5 ± 1.3. Peak respiratory pressure in Group A was 18 and in Group B was 16. Mean perfusion time, cross clamp time and anesthesia duration was significant between the groups p-value 0.01. Conclusion: This study found that continuing low tidal volume ventilation was not superior to no ventilation during CPB in terms of the rate of reintubation, pleural effusion, and pulmonary congestion, although there was a significant difference between pneumonia, atelectasis, and prolong ventilation. Keywords: PEEP, CPB, FEV1/FCV ,Pulmonary Complications, Ventilation, Tidal Volume

Cross-domain cooperative route planning for edge computing-enabled multi-connected vehicles

The safety and effectiveness of transportation systems could be improved significantly through edge computing which can be relied on to assist intelligent and connected vehicles (ICVs). However, with the rapid increase in the number of ICVs, traffic congestion worsens as a result of demand exceeding the capacity of a specific roads. To solve the issues, a cooperative route planning transportation system is proposed for edge computing-enabled multi-connected vehicles. This system is intended to achieve the cross-domain load balancing of connected vehicles and traffic flow domains. Furthermore, an iterative interior point is proposed to minimize the overall probability of traffic congestion by directing the routes of multi-connected vehicles in a coordinated way. According to the simulation results, the suggested scheme reduces the average travel time by 4.85%, 15.62%, 35.54% and 63.11% compared with others. Meanwhile, there is a significant improvement of performance in terms of traffic congestion rate and traffic load.