Time Graph Research Articles

Expected 1.x Makespan-Optimal Multi-Agent Path Finding on Grid Graphs in Low Polynomial Time

Multi-Agent Path Finding (MAPF) is NP-hard to solve optimally, even on graphs, suggesting no polynomial-time algorithms can compute exact optimal solutions for them. This raises a natural question: How optimal can polynomial-time algorithms reach? Whereas algorithms for computing constant-factor optimal solutions have been developed, the constant factor is generally very large, limiting their application potential. In this work, among other breakthroughs, we propose the first low-polynomial-time MAPF algorithms delivering 1-1.5 (resp., 1-1.67) asymptotic makespan optimality guarantees for 2D (resp., 3D) grids for random instances at a very high 1/3 agent density, with high probability. Moreover, when regularly distributed obstacles are introduced, our methods experience no performance degradation. These methods generalize to support 100% agent density. Regardless of the dimensionality and density, our high-quality methods are enabled by a unique hierarchical integration of two key building blocks. At the higher level, we apply the labeled Grid Rearrangement Algorithm (GRA), capable of performing efficient reconfiguration on grids through row/column shuffles. At the lower level, we devise novel methods that efficiently simulate row/column shuffles returned by GRA. Our implementations of GRA-based algorithms are highly effective in extensive numerical evaluations, demonstrating excellent scalability compared to other SOTA methods. For example, in 3D settings, GRA-based algorithms readily scale to grids with over 370,000 vertices and over 120,000 agents and consistently achieve conservative makespan optimality approaching 1.5, as predicted by our theoretical analysis.

Diagnostic yield of actionable arrhythmias in patients referred with syncope and collapse using ePatch extended holter

Abstract Background/Introduction Cardiac syncope commonly points to a potentially lethal disease process and carries one-year mortality up to 30%. Current guidelines on management of syncope suggest choosing ambulatory monitoring modality based upon the likelihood of symptoms during the monitoring period and is frequently undertaken with short duration (0-2 days) or extended wear Holter (EWH) monitoring (3-14 days). Purpose Determine the diagnostic yield (DY) of actionable arrhythmias (AA) in patients referred for syncope and collapse prescribed short or extended ambulatory monitoring. Methods A retrospective analysis was conducted for patients wearing the Philips ePatch between July 1st, 2022, and June 30th, 2023, with ICD-10 referral code R55 (Syncope and Collapse). Monitoring duration was grouped by 0-2, 3-7, and 8-14 days of wear time. Wear time was defined as number of hours with analysable ECG data. Arrhythmia detection was defined as either non-AA (did not meet urgent or emergent criteria) or AA (met urgent and/or emergent criteria). Urgent criteria were defined as Atrial Fibrillation/Flutter (>10 seconds), Ventricular Tachycardia (>100 BPM for >3 beats), Severe Tachycardia (>170 BPM for ≥30 seconds), Pause/Asystole (≥3 seconds), Severe Bradycardia (<35 BPM), and 2nd Degree or Higher AV Block. Emergent criteria were defined as VT (>100 BPM for ≥30 beats), Severe Tachycardia (≥220 BPM for ≥30 seconds), Pause/Asystole (≥10 seconds), and Severe Bradycardia (<20 BPM). Results The DY for any urgent and/or emergent AA was 6.8%, 11.0%, and 20.4% at 0-2, 3-7 and 8-14 days of wear time, respectively (p<0.00001 for all). For urgent AA, DY increased 1.6X at 3-7 vs 0-2 days, 1.9X at 8-14 vs 3-7 days and 3X at 8-14 vs 0-2 days of wear time (p<0.05 for all); for emergent AA, the DY increased 5X at 3-7 vs 0-2 days, 1.6X at 8-14 vs 3-7 days, and 8.2X at 8-14 vs 0-2 days of wear time (p<0.05 for all) (Graph 1). When examining tachyarrhythmias vs bradyarrhythmias, similar multiplicative yields in AA were noted between short, intermediate, and extended duration monitoring; statistical significance was noted for all arrhythmia subtypes (p<0.01), except for Severe Tachycardia and Bradycardia which were not significant between 0-2 and 3-7 days of wear time (Graph 2). Conclusion(s) In patients referred for ambulatory monitoring with syncope and collapse, urgent and emergent actionable arrhythmias increase significantly when extending from short to intermediate to long duration monitoring, with both tachyarrhythmias and bradyarrhythmias demonstrating similar increases in occurrence. Accordingly, EWH monitoring in patients referred with syncope and collapse has greater potential to identify arrhythmic aetiologies than use of standard duration Holter.

A recurrence for the surface area of (n, k)-arrangement graphs

An interesting property of an interconnected network is the number of nodes at distance i from an arbitrary processor, namely the node-centred surface area. This is an important property due to its applications in various fields of study. The ( n , k ) -arrangement graphs, denoted as A ( n , k ) are important class of graphs as they address the scalability issue in simpler topologies such as the hypercube and the star graph. Much work has been done to obtain a closed-form formula for the surface area of this class of graphs, but generally, it is not trivial to find an algorithm to compute the surface area of such graphs in polynomial time or to find an explicit formula with polynomially many terms in regard to the graph's parameters. In this paper, we present a simple recurrence that has linear computational complexity for A ( n , k ) for any arbitrary n and k in their defined range.

Spatial-temporal graph neural networks for groundwater data.

This paper introduces a novel application of spatial-temporal graph neural networks (ST-GNNs) to predict groundwater levels. Groundwater level prediction is inherently complex, influenced by various hydrological, meteorological, and anthropogenic factors. Traditional prediction models often struggle with the nonlinearity and non-stationary characteristics of groundwater data. Our study leverages the capabilities of ST-GNNs to address these challenges in the Overbetuwe area, Netherlands. We utilize a comprehensive dataset encompassing 395 groundwater level time series and auxiliary data such as precipitation, evaporation, river stages, and pumping well data. The graph-based framework of our ST-GNN model facilitates the integration of spatial interconnectivity and temporal dynamics, capturing the complex interactions within the groundwater system. Our modified Multivariate Time Graph Neural Network model shows significant improvements over traditional methods, particularly in handling missing data and forecasting future groundwater levels with minimal bias. The model's performance is rigorously evaluated when trained and applied with both synthetic and measured data, demonstrating superior accuracy and robustness in comparison to traditional numerical models in long-term forecasting. The study's findings highlight the potential of ST-GNNs in environmental modeling, offering a significant step forward in predictive modeling of groundwater levels.

Impact of Harmonic Voltage on the Partial Discharge Properties of Eco-Friendly Nanofluid

The Insulation is crucial in transformers to maintain electrical isolation between components and windings, ensuring safe and efficient operation. Mineral oil acts as both insulation and coolant in transformers. It is a substance that does not naturally degrade and can persist in the environment for an extended period if accidentally released. Therefore, Sunflower oil is used as a substitute for mineral oil. However, harmonic distortion is a common issue in power systems, leading to increased dielectric loss and partial discharge activity, ultimately causing insulation failure and equipment damage. Hence, it is vital to analyze the partial discharge performance of pure Sunflower oil under varying harmonic AC voltages to ensure the reliable operation of electrical devices. The study examines the partial discharge characteristics of a sunflower oil-based nanofluid under harmonic AC voltages, which is a blend of pure sunflower oil and SiO2 nanoparticles. Two nanofluid mixtures were created, one with a 0.01% mass ratio and the other with 0.05% sunflower oil. Various PD characteristics were evaluated, including Partial Discharge significance, starting voltage, duration of increase, maximum amplitude, Phase-Resolved PD sample, and frequency and time graphs of the Partial Discharge signal. All tests were carried out under IEC regulations. A comparison was made between the outcomes of Nano blended sunflower oil and natural sunflower edible oil. Experiments were conducted on partial discharges in Sunflower oil using specific harmonic superimposed AC Voltages, utilizing the third and fifth harmonics. The findings reveal that: a) PDIV value decreases with increasing harmonic order when using AC voltage with superimposed harmonics, b) PD characteristics are closely related to the voltage waveform, and the addition of Silica to sunflower oil results in reduced PD amplitude, pulse duration, and time duration compared to pure sunflower oil.

Maximum Cardinality \(f\) -Matching in Time \(O(n^{2/3}m)\)

We present an algorithm that finds a maximum cardinality \(f\) -matching of a simple graph in time \(O(n^{2/3}m)\) . Here \(f:V\to\mathbb{N}\) is a given function and an \(f\) -matching is a subgraph wherein each vertex \(v\in V\) has degree \(\leq f(v)\) . This result generalizes a string of algorithms that concentrate on simple bipartite graphs. The bipartite case is based on the notion of level graph, introduced by Dinic for network flow. In general graphs this notion breaks down: Vertices no longer have unique levels, and there are too many levels to analyze the corresponding level graph like bipartite graphs ( \(\Theta(n^{2})\) vs. \(n\) ). We use “natural” levels to prove properties of shortest augmenting trails (e.g., formulas for trail length). We use “shortened” levels to derive the algorithm's time bound. The algorithm, unmodified, is also efficient on multigraphs, achieving time \(O(\min\{\sqrt{f(V)},n\} m)\) for \(f(V)=\sum_{v}f(v)\) . The special case \(f\equiv 1\) shows the algorithm duplicates the classic time bound for maximum cardinality matching, \(O(\sqrt{n} m)\) .

Gegenbauer Graph Neural Networks for Time-Varying Signal Reconstruction.

Reconstructing time-varying graph signals (or graph time-series imputation) is a critical problem in machine learning and signal processing with broad applications, ranging from missing data imputation in sensor networks to time-series forecasting. Accurately capturing the spatio-temporal information inherent in these signals is crucial for effectively addressing these tasks. However, existing approaches relying on smoothness assumptions of temporal differences and simple convex optimization techniques that have inherent limitations. To address these challenges, we propose a novel approach that incorporates a learning module to enhance the accuracy of the downstream task. To this end, we introduce the Gegenbauer-based graph convolutional (GegenConv) operator, which is a generalization of the conventional Chebyshev graph convolution by leveraging the theory of Gegenbauer polynomials. By deviating from traditional convex problems, we expand the complexity of the model and offer a more accurate solution for recovering time-varying graph signals. Building upon GegenConv, we design the Gegenbauer-based time graph neural network (GegenGNN) architecture, which adopts an encoder-decoder structure. Likewise, our approach also uses a dedicated loss function that incorporates a mean squared error (MSE) component alongside Sobolev smoothness regularization. This combination enables GegenGNN to capture both the fidelity to ground truth and the underlying smoothness properties of the signals, enhancing the reconstruction performance. We conduct extensive experiments on real datasets to evaluate the effectiveness of our proposed approach. The experimental results demonstrate that GegenGNN outperforms state-of-the-art methods, showcasing its superior capability in recovering time-varying graph signals.

Energy Performance of Zeolite-Based Drying Bead Desiccants Used to Dry Paddy Rice.

This study utilizes differential scanning calorimetry and thermogravimetric analysis to assess the total energy required to regenerate saturated zeolite-based drying beads (DBs) used to dry paddy rice. We quantify the required heat energy for DB regeneration by calculating the area under the curve in a heat flow rate versus time graph, with the end of the regeneration process indicated by stabilization of the DB weight. Our findings suggest that at DB regeneration temperatures ranging from 120 to 350 °C, the process varied from 813 to 22 min, demonstrating that higher temperatures lead to faster regeneration speeds. The total energy used for regeneration showed similar values at 250 and 350 °C, averaging around 2032 and 2136 kJ per kg of dried DB, respectively. Additionally, the study showed that DBs can hold water between 28.7% and 54.4% higher than the manufacturer's specifications, suggesting a reduced quantity of DBs required for effective paddy rice drying. The overall required heat energy for the regeneration process was calculated at 4.86 MJ/kg, with a carbon intensity of approximately 275.61 g of CO2-eq per kg of water removed, resulting in lower values compared to conventional drying methods. The study underscores DB's possibility of lower total energy (thermal and electrical) consumption and greenhouse gas emissions, alongside its flexibility to regenerate with intermittent energy sources.

Max-plus Algebraic Description of Evolutions of Weighted Timed Event Graphs

Max-plus Algebraic Description of Evolutions of Weighted Timed Event Graphs

Chaotic Dynamics of the Fractional Order Predator-Prey Model Incorporating Gompertz Growth on Prey with Ivlev Functional Response

This paper examines dynamic behaviours of a two-species discrete fractional order predator-prey system with functional response form of Ivlev along with Gompertz growth of prey population. A discretization scheme is first applied to get Caputo fractional differential system for the prey-predator model. This study identifies certain conditions for the local asymptotic stability at the fixed points of the proposed prey-predator model. The existence and direction of the period-doubling bifurcation, Neimark-Sacker bifurcation, and Control Chaos are examined for the discrete-time domain. As the bifurcation parameter increases, the system displays chaotic behaviour. For various model parameters, bifurcation diagrams, phase portraits, and time graphs are obtained. Theoretical predictions and long-term chaotic behaviour are supported by numerical simulations across a wide variety of parameters. This article aims to offer an OGY and state feedback strategy that can stabilize chaotic orbits at a precarious equilibrium point.

Complex Dynamics of a Discretized Predator–Prey System with Prey Refuge Using a Piecewise Constant Argument Method

The objective of this work is to investigate the complex dynamics of a discrete predator–prey system using the method of piecewise constant argument for discretization. An analysis is conducted to examine the presence and stability of fixed points. Furthermore, the system is shown to undergo period-doubling (PD) and Neimark–Sacker (NS) bifurcations by the use of center manifold and bifurcation theories. The feedback and hybrid control strategies are used to regulate the system’s bifurcating and chaotic behaviors. Both strategies seem to be effective in managing bifurcation and chaos inside the system. Finally, the main results are validated by numerical evidence. Parameters of the system are varied to produce time graphs, phase portraits, bifurcation diagrams, and maximum Lyapunov exponent (MLE) graphs. The discrete model displays rich dynamics, as seen in the numerical simulations and graphs, indicating a complex and chaotic system.

Stress–strain characteristics of fire‐exposed recycled coarse aggregate concrete

AbstractConcrete sustainability and performance under extreme conditions are of growing interest in construction engineering. This study delves into the influence of recycled coarse aggregate (RCA) content and elevated temperatures on normal‐strength concrete containing RCA. Five different concrete compositions, featuring varying content of RCA (ranging from 0% to 100%), were examined. The heating and subsequent cooling followed the ISO‐834 temperature–time graph up to 800°C. The primary objective was to evaluate residual properties, including the stress–strain behavior, compressive and tensile strength, secant elastic modulus, peak strain, and bond strength of RCA concrete. The findings reveal a consistent decrease in both strength and stiffness parameters of RCA concrete with rising temperatures, while peak strain exhibits a rapid increase at elevated temperatures. Interestingly, RCA content had a negligible impact on the relative deterioration of high‐temperature exposed RCA concrete compared to that at ambient conditions. Moreover, the bond behavior closely resembled that of natural aggregate concrete when used in moderate proportions. Degradation models based on regression analysis of the data were used to quantify the bond strength reduction for RCA‐based concrete and the slip of rebar concerning various temperatures. Importantly, these models demonstrated consistency with those applicable to conventional concrete.

Proposal for a protection system of an industrial electrical network

An electrical protection system in an industry Works by detecting and acting against abnormal conditions in the electrical system with the objective of guaranteeing the safety of people, protecting equipment and ensuring the continuity of industrial process. Taking into account the importance of guaranteeing adequate electrical protection system in an industrial activity in this research a proposal for the protection system for an industrial electrical network is presented. As a previous step proposal, the methodology desingned for the coordination of protections for an industrial electrical network was proposed. The proposal was designed taking into account four operating scenarios required to calibrate the industry’s protection devices. The short circuit analysis maximum in each of the system bars for each of the scenarios allowed determining the maximum phase failure currents it is 44.44% for emergency 1 and 66.66% for emergency 2, while the maximum ground fault current was founding emergency scenario2. At the news to the four scenarios of the industrial network in the actuation times of the devices of protection, there is not considerable variation; this is justified by the current time graph because when there is a serious short circuit current, the action time should be shorter. On the contrary, when there is a small current time will be greater

Exploring the therapeutic potential of ethanol extract of Erythrina fusca Lour. roots as an analgesic, antipyretic, and anti-inflammatory agent in experimental animals

Erythrina fusca Lour., renowned in traditional medicine for its soothing, analgesic, anti-inflammatory, and neuroinhibitory properties, is the focus of our investigation. Expanding on its traditional applications, we assessed the analgesic, anti-inflammatory, and antipyretic characteristics of the ethanol extract from E. fusca root (REEF). Administered orally to mice at varying doses (50, 100, and 150 mg/kg for pain and fever reduction; 100, 150, and 200 mg/kg for anti-inflammatory experiments), REEF's effects were systematically evaluated through parameters such as reaction time, pain inhibition ratio, COX2 and PGE2 levels, rectal temperature, fever inhibition ratio, paw diameter, paw edema inhibition ratio, and inflammatory cytokines. Results reveal that REEF consistently reduced licking and biting time (p < 0.05) in response to formalin-induced pain in mice. Particularly noteworthy was the more pronounced reduction in COX2 and PGE2 during the late phase, with the most significant decrease observed at the 150 mg/kg REEF treatment (p < 0.05). The licking time graph substantiated the analgesic efficacy of REEF, illustrating simultaneous and sustained pain reduction over time. Moreover, REEF significantly alleviated fever (p < 0.05) in mice, with the most substantial effect observed three hours post-yeast injection. REEF not only efficiently reduced body temperature but also countered the fever-inducing effects of yeast at evaluated time points (p < 0.05). In the context of carrageenan-induced inflammation, paw swelling markedly diminished after 4 and 5 hours (p < 0.05). Notably, at the 5-hour mark, REEF treatment at 150 mg/kg dose exhibited significantly superior anti-inflammatory effects (p < 0.05). Additionally, mice treated with REEF (100, 150, and 200 mg/kg) exhibited a considerable decrease (p < 0.05) in serum concentrations of TNF-?, IL-1?, and IL-6 compared to the control treatment. The ethanol extract from Erythrina fusca roots demonstrates substantial potential for pain relief, fever reduction, and anti-inflammatory effects, positioning it as a promising herbal resource for the research and development of natural medicinal products with these therapeutic properties.

Algorithmic stock trading based on ensemble deep neural networks trained with time graph

Financial forecasting is generally implemented by analyzing the time series data related to the stock. This is accomplished widely with deep neural networks (DNNs) since DNNs can directly extract the related information that is otherwise hard to obtain. Time series is the core data representation of financial forecasting problem since it comes naturally. However recent studies show that even if time series representation is necessary, it still lacks certain aspects related to the problem. One of them is the relationship between the stocks of the market which can be captured through graph representation. Therefore, DNNs might solve the financial forecasting problem better when graph and time series representations are combined. In this study, we present different graph representations that can be used for this purpose. We also present an ensemble network that gives an investment strategy related to the stock market from stock predictions. Our proposed model returns an average of 20.09% annual profit on DOW30 dataset through daily buy–sell decisions based on close prices. Therefore, it can serve as a daily financial investment strategy, offering higher annual returns than conventional heuristic approaches.

The Recognition of Aerobics Movements Using Bone Data Combined with ST-GCN

To solve the aerobics action recognition and promote the gradual intelligence and standardization of aerobics teaching and evaluation, a network model based on spatial temporal graph convolution and combined with attention mechanism was proposed. This model improved the extraction efficiency of spatiotemporal features and channel features by introducing spatiotemporal graph and channel attention mechanisms, respectively, thereby improving the accuracy of action recognition. And a time extension module was introduced into each basic module, and additional features between adjacent vertices were extracted by extending the time graph between frames. These experiments confirmed that this model exhibited high accuracy in identifying aerobics movements. The recognition accuracy of basic actions was above 93.6%, and the recognition accuracy of two actions had reached 98.4% and 98.6% respectively. For advanced actions, the recognition accuracy of the model had slightly decreased, but the average value was still above 95%. The accuracy of difficult motion recognition had also achieved good results, reaching a maximum of 94.5%. These data indicate that this model can achieve high accuracy in handling action recognition tasks of different difficulty levels, and can identify aerobics movements of different difficulty levels.

Retrospective Correlation of the Circulation Time of Test Bolus Injections in MR Angiography and Cardiac Function

This retrospective study examines 248 test bolus examinations preceding contrast-enhanced magnetic resonance angiography (CE-MRA) to extract clinically relevant data for critical limb ischemia (CLI) management. The method involved a retrospective review of test bolus exams, analysing 60 graphs for time to peak (TTP), full-width half-maximum (FWHM) time, and time to continual rise in signal intensity. These values were correlated with heart function parameters (ejection fraction, ASA classification, Lee index, and MET score). The results indicate a mean TTP of 31.2 ± 7.3 s, showing a correlation between the ejection fraction and ASA classification. Patients with atrial fibrillation exhibited prolonged TTP compared to those without. Despite population heterogeneity, these findings facilitate risk stratification for limb-saving interventions in CLI. TTP emerges as a potential clinical cardiovascular parameter and a risk factor for vascular interventions. Given the variation in injection protocols across centres, this study underscores the importance of precise bolus arrival time documentation for future multicentre studies.

Implementation of Numerical Model for Prediction of Temperature Distribution for Metallic-Coated Firefighter Protective Clothing

The aim of this study is to predict the distribution of temperature at various positions on silver-coated firefighter protective clothing when subjected to external radiant heat flux. This will be helpful in the determination of thermal protective performance. Firefighter clothing consists of three layers, i.e., the outer shell, moisture barrier and thermal liner. The outer shell is the exposed surface, which was coated with silver particles through a physical vapor deposition process called magnetron sputtering. Afterwards, these uncoated and silver-coated samples were exposed to radiant heat transmission equipment at 10 kW/m2 as per the ISO 6942 standard. Silver-coated samples displayed better thermal protective performance as the rate of temperature rise in silver-coated samples slowed. Later, a numerical approach was employed, contemplating the impact of metallic coating on the exterior shell. The finite difference method was utilized for solving partial differential equations and the implicit method was employed to discretize the partial differential equations. The numerical model displayed a good prediction of the distribution of temperature at different nodes with respect to time. The comparison of time vs. temperature graphs at different nodes for uncoated and silver-coated samples acquired from numerical solutions showed similar patterns, as witnessed in the experimental results.

Counting kernels in directed graphs with arbitrary orientations

A kernel of a directed graph is a subset of vertices that is both independent and absorbing (every vertex not in the kernel has an out-neighbour in the kernel).Not all directed graphs contain kernels, and computing a kernel or deciding that none exist is NP-complete even on low-degree planar digraphs. The existing polynomial-time algorithms for this problem are all obtained by restricting both the undirected structure and the edge orientations of the input: for example, to chordal graphs without bidirectional edges (Pass-Lanneau, Igarashi and Meunier, Discrete Appl Math 2020) or to permutation graphs where each clique has a sink (Abbas and Saoula, 4OR 2005).By contrast, we count the kernels of a fuzzy circular interval graph in polynomial time, regardless of its edge orientations, and return a kernel when one exists. (Fuzzy circular interval graphs were introduced by Chudnovsky and Seymour in their structure theorem for claw-free graphs.)We also consider kernels on cographs, where we establish NP-hardness in general but linear-time solvability on the subclass of threshold graphs.

Chronic Microvascular Complication of Type 1 Diabetes Mellitus and Its Predictors Among Children with Type 1 Diabetes Mellitus in Ethiopia; A Single Center Experience: Ambi Directional Cohort Study.

Type 1 diabetes mellitus is the most common pediatric endocrine disorder. Poor glycemic control in diabetes mellitus can result in microvascular complications (retinopathy, neuropathy, and nephropathy). There is no study done in our setting either about prevalence of pediatric type 1 diabetes mellitus or chronic microvascular complication among these patients, which gap this study is expected to fill. This study aimed to assess the risk and predictors of chronic microvascular complication of type 1 diabetes mellitus among children with diabetes at Haramaya University Hiwot Fana Compressive Specialized Hospital from September 10, 2021 to January 30, 2023. A hospital-based Ambi directional cohort study was conducted. Survival data are described by follow-up time and Kaplan-Meier graph. To determine predictors associated with chronic microvascular complication we used a Poisson regression optimal model selected using the information criterion. All associations are tested at the 95% confidence level and a reported IRR P-value less than 0.05 is declared as a significant association between variables. A total of 124 children with type 1 diabetes mellitus were followed with total 407.5 years risk time. The overall incidence rate of chronic microvascular complication was 83 per 1000 population per year (95% CI: 59-116). The median time for detection of microvascular complication was 7 years after diagnosis. Being male with IRR 1.71 (95% CI: 0. 0.81-3.56), being at pubertal age IRR 1.91 (95% CI: 1.05-3.48), longer diabetes mellitus duration IRR 1.13 (95% CI: 1.07-1.28), and poor glycemic control IRR 1.50 (95% CI: 0.46-4.97) were found to be at higher risk for chronic microvascular complication. There was high incidence of chronic microvascular complication of diabetes mellitus. Being pubertal age group and more than 3 years duration after diagnosis had statistically significant association with complication.