High Success Ratio Research Articles

Statistical and machine learning models for location-specific crop yield prediction using weather indices.

Crop yield prediction gains growing importance for all stakeholders in agriculture. Since the growth and development of crops are fully connected with many weather factors, it is inevitable to incorporate meteorological information into yield prediction mechanism. The changes in climate-yield relationship are more pronounced at a local level than across relatively large regions. Hence, district or sub-region-level modeling may be an appropriate approach. To obtain a location- and crop-specific model, different models with different functional forms have to be explored. This systematic review aims to discuss research papers related to statistical and machine-learning models commonly used to predict crop yield using weather factors. It was found that Artificial Neural Network (ANN) and Multiple Linear Regression were the most applied models. Support Vector Regression (SVR) model has a high success ratio as it performed well in most of the cases. The optimization options in ANN and SVR models allow us to tune models to specific patterns of association between weather conditions of a location and crop yield. ANN model can be trained using different activation functions with optimized learning rate and number of hidden layer neurons. Similarly, the SVR model can be trained with different kernel functions and various combinations of hyperparameters. Penalized regression models namely, LASSO and Elastic Net are better alternatives to simple linear regression. The nonlinear machine learning models namely, SVR and ANN were found to perform better in most of the cases which indicates there exists a nonlinear complex association between crop yield and weather factors.

Fast and accurate distal locking of interlocked intramedullary nails using computer-vision and a 3D printed device

IntroductionDistal locking is a challenging and time-consuming step in interlocked intramedullary nailing of long bone fractures. Current methods have limitations in terms of simplicity, universality, accuracy, speed, and safety. We propose a novel device and software for distal locking using computer vision.Methods and materialsThe device consists of an universal ancillary clamp, a telescopic arm, a viewfinder clamp, and a radio-opaque cross. The software uses a camera photo from the C-arm intensifier and adjusts for geometric projection deformities. The software employs edge detection, Hough transform, perspective interpolation, and vector calculation algorithms to locate the distal hole center. The device and software were designed, manufactured, and tested using 3D CAD, FEM, DRR, and performance testing on phantom bones.ResultsThe device and software showed high accuracy and precision of 98.7% and 99.2% respectively in locating the distal hole center and calculating the correctional vector. The device and software also showed high success ratio in drilling the hole and inserting the screw. The device and software reduced the radiation exposure for the surgeon and the patient. The success ratio of the device and software was validated by the physical testing, which simulated the real clinical scenario of distal locking. The radiation exposure was as low as 5 s with a radiation dose of 0.2mSv, drastically reducing radiation exposure during distal locking.DiscussionOur device and software have several advantages over other distal locking methods, such as simplicity, universality, accuracy, speed, and safety. Our device and software also have some disadvantages, such as reliability and legislation. Our device and software can be compared with other distal locking methods based on these criteria. Our device and software have some limitations and challenges that need to be addressed in the future, such as clinical validation, and regulatory approval.ConclusionThe device showed promising results in terms of low-cost, reusability, low radiation exposure, high accuracy, fast distal locking, high stiffness, and adaptability. The device has several advantages over other distal locking techniques, such as free-hand technique, mechanical aiming devices, electromagnetic navigation systems, and computer-assisted systems. We believe that our device and software have the potential to revolutionize the distal locking technique and to improve the outcomes and quality of life of the patients with long bone fractures.

Export Potential Index for Textile Industry (EPIT) model proposal with structural equation modelling and application

PurposeThe aim of this study is to develop a new multidimensional index to measure export potential of textile firms by using firm-level data.Design/methodology/approachAfter a conceptual model, a structural equation model is developed with five dimensions and 27 observed variables based on resource-based view theory. The measurement model is solved by Linear Structural Relations (LISREL) with maximum likelihood algorithm by using data collected from 454 textile firms in Türkiye.FindingsIn this study, a new multidimensional index that measures export potential of textile firms is developed. With the proposed model, the export potential of textile firms can be calculated numerically with the five dimensions: Resources, Dynamism, Knowledge, Innovation and Sustainability. The comparison of the output of the proposed model with the control variable, firm’s actual export values, shows a significantly high success ratio of 90.76%.Research limitations/implicationsThe model is applicable for textile firms at different export levels, regions and sub-sectors. The Export Potential Index for Textile Industry model is verified by using Turkish textile industry data. The robustness of the model may be increased by verifying the model by using some other countries data. This model can be implemented to other industrial sectors with some modification of the dimensions and variables.Practical implicationsThe proposed model will contribute to the firms by calculating their export potential in five dimensions with their own variables numerically. The model will help firms to develop strategies to increase their export potential and to the governmental and industrial organizations to develop incentives policies.Originality/valueThis paper fills the gap in the literature by proposing a multidimensional index that determines a firm’s export potential numerically by using firm-level data.

Adaptive Mobility-Based IoT LoRa Clustering Communication Scheme

Long Range (LoRa) as a low-power wide-area technology is distinguished by its robust long-distance communications tailored for Internet of Things (IoT) networks. Because LoRa was primarily designed for stationary devices, when applied to mobile devices, they become susceptible to frequent channel attenuation. Such a condition can result in packet loss, higher energy consumption, and extended transmission times. To address these inherent challenges posed by mobility, we propose an adaptive mobility-based IoT LoRa clustering communication (AMILCC) scheme, which employs the 2D random waypoint mobility model, strategically partitions the network into optimal spreading factor (SF) regions, and incorporates an adaptive clustering approach. The AMILCC scheme is bolstered by a hybrid adaptive data rate (HADR) mechanism categorized into two approaches, namely intra-SF and inter-SF region HADRs, derived from the standard network-based ADR mechanism for stationary devices, to ensure efficient resource allocation for mobile IoT LoRa devices. Evaluation results show that, based on simulations at low mobility speeds of up to 5 m/s, AMILCC successfully maximizes the packet success ratio to the gateway (GW) by over 70%, reduces energy consumption by an average of 55.5%, and minimizes the end-to-end delay by 47.62%, outperforming stationary schemes. Consequently, AMILCC stands as a prime solution for mobile IoT LoRa networks by balancing the high packet success ratio (PSR) with reliability with energy efficiency.

Attributes and Impacts of Learning from Prior Lenders in Peer-to-peer Lending

Learning from others is a facet of human nature. In peer-to-peer lending, potential lenders who are interested in the loan, but have not yet funded it, observe and learn from the behavior of prior lenders who have funded the loan. It is unclear, however, whether potential lenders learn from the prior lenders’ attributes other than the observed bidding behavior. Using data from PPDai.com, the study finds that potential lenders consider the prior lender’s risk preference, investment experience, and historical investment performance when making investment decisions. Specifically, potential lenders are more likely to fund loans that have more female or older prior lenders. The potential lenders’ decisions are positively affected by the proportion of prior lenders with long account duration, high investment success ratio, low bad debt ratio, and high money-weighted rate of return.

Adaptive Gradient-based Word Saliency for adversarial text attacks

Natural Language Processing (NLP) models are known vulnerable to adversarial text attacks. Various word-level attacks have been proposed to modify input words by a pre-calculated word saliency order or the heuristic optimization algorithm. However, the predefined fixed order usually leads to a low attack success ratio, and the heuristic algorithm is often inefficient due to many model queries. In this paper, an Adaptive Gradient-based Word Saliency (AGWS) is proposed to improve the query-efficiency and simultaneously preserve a high attack success ratio for the word-level adversarial text attack. Firstly, the AGWS calculates the word saliency with a one-time gradient to all words instead of iterative queries, so the query-efficiency is enhanced. Secondly, the AGWS adaptively updates the word saliency via the variable neighborhood search algorithm to avoid the fixed modification order, which is significant in improving the attack success ratio. Additionally, the AGWS collect substitutes with a hybrid sememe and synonym strategy to enlarge adversary options. Extensive experiments and ablation studies manifest that the AGWS achieves the highest or second highest attack success ratio with lowest word perturbation percentage and improves the query-efficiency compared with baselines. Besides, the AGWS also shows superiorities in adversarial training and transferability.

Pairs trading in cryptocurrency markets: A comparative study of statistical methods

ABSTRACT This study aims to identify a secure and efficient trading approach for investors in highly volatile cryptocurrency markets. While pairs trading is a promising strategy, the available literature on this topic in cryptocurrency markets is limited and primarily based on traditional methods. This study compares six statistical approaches for selecting trading pairs: Cointegration, Correlation, Distance, Fluctuation Behaviour, Hurst Exponent, and Stochastic Differential Residual. We utilise intraday data from 30 cryptocurrencies on the Binance exchange during a three-month period from 1 January 2022 to 31 March 2022. The trading results are measured by 11 criteria covering return, risk, and trade aspects. The findings show that Distance performs well at all three frequencies of 1 minute, 5 minutes, and 60 minutes, with a total return of 208.12%, 236.31%, and 210.36%, respectively. At the 60-minute frequency, this method exhibits low risk and performs impressively even when all other techniques generate negative returns, as well as displays the lowest number of expired counts and the highest success ratio among the methods. At 1-minute and 5-minute frequencies, Cointegration and Hurst Exponent produce results comparable to those of Distance. The Student t-test conducted confirms these conclusions.

Efficient distributed broadcasting algorithms for cognitive radio networks-enabled smart agriculture

Smart agriculture has a broad prospect with the fast development of information technologies, such as Internet of Things, big data, edge computing and artificial intelligence. As more intelligent devices are deployed for agricultural scenarios, such as quality monitoring and information aggregation, the communication channels among these devices will be crowded and the performance can be greatly affected. Cognitive Radio Networks (CRNs) are promising in promoting better spectrum utilization, where unlicensed users can opportunistically use the vacant spectrum assigned to licensed users. On the basis of CRNs, many agricultural scenarios can be empowered with efficient communication capabilities. However, the broadcasting problem, which handles the information dissemination, has not been thoroughly studied in CRNs. Existing works are either centralized solutions performing broadcast on single/multiple channels, or distributed algorithms without performance guarantee for general networks. In this paper, we propose efficient distributed algorithms with short broadcast delay and high success ratio. The difficulties lie in the non-uniform channel availability, which entails broadcasting on multiple channels even for single-hop neighbors, and the distributed behaviors, where the users are only aware of the local information. Our contributions are threefold. First, we propose an efficient distributed rendezvous algorithm which spurs the neighbors to find the common channels in a short time. Second, we handle the single-hop broadcast by presenting two distributed algorithms and they guarantee a successful broadcast in O(Δ2loglogN) and Δ time slots respectively, where N is the number of channels and Δ is the maximum number of neighbors. Finally, we extend these algorithms to multi-hop broadcast and the broadcast delay is only up to D times the delay of single-hop, where D is the network diameter. We also conduct extensive simulations and the results corroborate our theoretical analyses.

Prediction of transition state structures of gas-phase chemical reactions via machine learning

The elucidation of transition state (TS) structures is essential for understanding the mechanisms of chemical reactions and exploring reaction networks. Despite significant advances in computational approaches, TS searching remains a challenging problem owing to the difficulty of constructing an initial structure and heavy computational costs. In this paper, a machine learning (ML) model for predicting the TS structures of general organic reactions is proposed. The proposed model derives the interatomic distances of a TS structure from atomic pair features reflecting reactant, product, and linearly interpolated structures. The model exhibits excellent accuracy, particularly for atomic pairs in which bond formation or breakage occurs. The predicted TS structures yield a high success ratio (93.8%) for quantum chemical saddle point optimizations, and 88.8% of the optimization results have energy errors of less than 0.1 kcal mol−1. Additionally, as a proof of concept, the exploration of multiple reaction paths of an organic reaction is demonstrated based on ML inferences. I envision that the proposed approach will aid in the construction of initial geometries for TS optimization and reaction path exploration.

Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence.

This study provides the profiles and success predictions of students considering data before, during, and after the COVID-19 pandemic. Using a field experiment of 396 students and more than 7400 instances, we have analyzed students' performance considering the temporal distribution of autonomous learning during courses from 2016/2017 to 2020/2021. After applying unsupervised learning, results show 3 main profiles from the clusters obtained in the simulations: students who work continuously, those who do it in the last-minute, and those with a low performance in the whole autonomous learning. We have found that the highest success ratio is related to students that work in a continuous basis. However, last-minute working is not necessarily linked to failure. We have also found that students' marks can be predicted successfully taking into account the whole data sets. However, predictions are worse when removing data from the month before the final exam. These predictions are useful to prevent students' wrong learning strategies, and to detect malpractices such as copying. We have done all these analyses taking into account the effect of the COVID-19 pandemic, founding that students worked in a more continuous basis in the confinement. This effect was still present one year after. Finally, We have also included an analysis of the techniques that could be more effective to keep in a future non-pandemic scenario the good habits that were detected in the confinement.

Cancer Segmentation by Entropic Analysis of Ordered Gene Expression Profiles.

The availability of massive gene expression data has been challenging in terms of how to cure, process, and extract useful information. Here, we describe the use of entropic measures as discriminating criteria in cancer using the whole data set of gene expression levels. These methods were applied in classifying samples between tumor and normal type for 13 types of tumors with a high success ratio. Using gene expression, ordered by pathways, results in complexity-entropy diagrams. The map allows the clustering of the tumor and normal types samples, with a high success rate for nine of the thirteen, studied cancer types. Further analysis using information distance also shows good discriminating behavior, but, more importantly, allows for discriminating between cancer types. Together, our results allow the classification of tissues without the need to identify relevant genes or impose a particular cancer model. The used procedure can be extended to classification problems beyond the reported results.

Evaluating GFS and ECMWF Ensemble Forecasts of Integrated Water Vapor Transport along the U.S. West Coast

Abstract Atmospheric rivers (ARs) are long and narrow regions in the atmosphere of enhanced integrated water vapor transport (IVT) and can produce extreme precipitation and high societal impacts. Reliable and skillful forecasts of landfalling ARs in the western United States are critical to hazard preparation and aid in decision support activities, such as Forecast-Informed Reservoir Operations (FIRO). The purpose of this study is to compare the cool-season water year skill of the NCEP Global Ensemble Forecast System (GEFS) and ECMWF Ensemble Prediction System (EPS) forecasts of IVT along the U.S. West Coast for 2017–20. The skill is analyzed using probability-over-threshold forecasts of IVT magnitudes ≥ 250 kg m−1 s−1 (P250) using contingency table skill metrics in coastal Northern California and along the west coast of North America. Analysis of P250 with lead time (dProg/dt) found that the EPS provided ∼1 day of additional lead time for situational awareness over the GEFS at lead times of 6–10 days. Forecast skill analysis highlights that the EPS leads over the GEFS with success ratios 0.10–0.15 higher at lead times > 6 days for P250 thresholds of ≥25% and ≥50%, while event-based skill analysis using the probability of detection (POD) found that both models were largely similar with minor latitudinal variations favoring higher POD for each model in different locations along the coast. The relative skill of the EPS over the GEFS is largely attributed to overforecasting by the GEFS at longer lead times and an increase in the false alarm ratio. Significance Statement The purpose of this study is to evaluate the efficacy of the NCEP Global Ensemble Forecast System (GEFS) and the ECMWF Ensemble Prediction System (EPS) in forecasting enhanced water vapor transport along the U.S. West Coast commonly associated with landfalling atmospheric rivers and heavy precipitation. The ensemble models allow us to calculate the probability that enhanced water vapor transport will occur, thereby providing situational awareness for decision-making, such as in hazard mitigation and water resource management. The results of this study indicate that the EPS model is on average more skillful than the GEFS model at lead times of ∼6–10 days with a higher success ratio and lower false alarm ratio.

Mix-and-match: an improved, fast and accessible protocol for hypocotyl micrografting of Arabidopsis seedlings with systemic ACC responses as a case study

BackgroundGrafting is a technique widely used in horticulture that also has been applied in agriculture. In plant physiology, grafting facilitates the elucidation of mechanisms underlying growth and developmental processes, through the construction of chimeric plants with organs of different genotypes. Despite its small size, the model species Arabidopsis thaliana is very amenable for grafting, which can be useful to investigate transport of nutrients, amino acids or secondary metabolites between different tissues, or to investigate developmental processes depending on root-to-shoot communication, such as shoot branching, root and shoot plasticity upon shade avoidance, or disease resistance. Nevertheless, grafting protocols are usually technically challenging and training is required to achieve a reasonable success rate. Additionally, specialized tools and equipment are often needed, such as chips to accommodate the grafted plantlets or collars to maintain the contact between root and shoot.ResultsIn this methodology paper, we provide a fast, easy, accessible, and specialized equipment-free protocol that enables high success ratios. Critical steps and notes are detailed, easing the implementation of the procedure for non-trained researchers. An example of the protocol application by three independent non-trained researchers shows that this method allows to achieve a 90–100% of grafting efficiency after 6 days post-grafting recovery. In addition, the grafting of Col-0 with the acs8x mutant, depleted in 1-aminocyclopropane-1-carboxylic acid (ACC), the biosynthetic precursor of the phytohormone ethylene, provides an example of the application of this optimized protocol, showing the suitability of the process to study long-distance transport processes.ConclusionsWe present an optimized protocol for hypocotyl grafting of 4-day-old Arabidopsis thaliana seedlings. The combination of conditions yields a grafting success of 90–100% and provides an easy and accessible methodology, reducing the time frame, and without the necessity of acquiring specialized equipment. The presented protocol is simple, fast and highly efficient, easing the inclusion of hypocotyl grafting assays in any research project. In addition, the description of the protocol is detailed to a level ensuring that even non-trained researchers, are sufficiently prepared to adopt the grafting methodology.

Smart Mina: LoRaWAN Technology for Smart Fire Detection Application for Hajj Pilgrimage

The Long-Range Wide Area Network (LoRaWAN) is one of the used communication systems that serve and enables the deployment of the Internet of Things (IoT), which occasionally transmit small size data. As part of the Low Power Wide Area Network (LPWAN), LoRaWAN is characterized by its ability for low power consumption. In addition, it is built to provide more extended coverage and higher capacity with minimum cost. In this paper, we investigate the feasibility and scalability of LoRaWAN for the Mina area using a realistic network model. Mina, known as the world’s largest tent city, is a valley located in the east of Makkah city and surrounded by mountains. It accommodates up to 3 million pilgrims annually and contains more than 100000 tents. The performance was evaluated based on a fire detection-like application. Extensive simulations were conducted using the OMNeT++ simulator and Flora model to determine the delivery ratio, collision, and SF distribution for the simulated scenario with a network consisting of up to ten thousand end devices. The conducted simulations show a promising result for LoRaWAN technology for Mina city. It showed a consistent performance for LoRaWAN in most simulated scenarios when a high success ratio was achieved.

TTGN: Two-Tier Geographical Networking for Industrial Internet of Things With Edge-Based Cognitive Computing

Industrial Internet of Things (IIoT) is based on data acquisition and data analytics technologies. A variety of and a large amount of data is collected at management nodes with computing and storage capacities. Recently, the computing ability, denoted by the edge, has been located closer to the service fields to achieve faster and more reliable data-driven service provisioning. Edge computing is a useful resource to facilitate smart manufacturing based on IIoT. Since the current Industrial Wireless Sensor Networks (IWSNs) technologies for IIoT do not perfectly cover all the demands of industries with smart manufacturing such as agile flexibility with asset movement. A major future demand for IWSNs should be to support the mobility of assets in a wireless environment. This paper investigates the shortages of current technologies such as WirelessHART and proposes a novel wireless networking scheme based on edge-based cognitive computing in order to support reliable and low latency communication of mobile assets, which is involved in the smart manufacturing processes. We devise a two-tier geographical networking (TTGN) system that supports position-based mobility detection and networking. Also, resource allocation for the reliable and real-time has been obtained based on the game theory. Evaluation results demonstrate that TTGN can guarantee a high data transmission success ratio, as well as a fast delivery ratio for link path establishment.

DETERMINATION OF FIBER ORIENTATION IN HIGH ANGULAR RESOLUTION DIFFUSION IMAGING USING SPHERICAL HARMONICS AND PARTICLE SWARM OPTIMIZATION

The fiber directions in High Angular Resolution Diffusion Imaging (HARDI) with low fractional anisotropy or low Signal to Noise Ratio (SNR) cannot be estimated accurately. In this paper, the fiber directions are estimated using Particle Swarm Optimization and Spherical Deconvolution (PSO-SD). Fiber orientation is modeled as a Dirac delta function in [Formula: see text]. The Spherical Harmonic Coefficients (SHC) of the Dirac delta function in the [Formula: see text] direction are obtained using the rotational harmonic matrix and the SHC of the Dirac delta function in the [Formula: see text]-axis. The PSO-SD method is used to determine ([Formula: see text]). We generated noise-free synthetic data for isotropic regions (FA varied from 0.1 to 0.8) and synthetic data with two crossing fibers for anisotropic regions with SNRs of 20, 15, 10 and 5 (FA [Formula: see text] 0.78). In the noise-free signal (FA [Formula: see text] 0.3), the Success Ratio (SR) and Mean Difference Angle (MDA) of the PSO-SD method were 1∘ and 9.48∘, respectively. In the noisy signal (FA [Formula: see text] 0.78, SNR [Formula: see text] 10, crossing angle [Formula: see text] 40), the SR and MDA of PSO-SD (with [Formula: see text]) were 0.46∘ and 12.3∘, respectively. The PSO-SD method can estimate fiber directions in HARDI with low fractional anisotropy and low SNR. Moreover, it has a higher SR and lower MDA in comparison with those of the super-CSD method.

Unbiased fuzzy global optimization of Morse clusters with short-range potential for N ≤ 400

Global optimization of Morse clusters with shortrange potential is a great challenge. Here, we apply our recently developed unbiased fuzzy global optimization method to systematically study Morse clusters with the potential range ρ = 14 and the number of atoms N up to 400. All the putative global minima reported in the literature have been successfully reproduced with relatively high success ratios. Compared to the available results for N ≤ 240 and several larger Morse clusters, new global minima (and local minima) with lower energies have been found out for N = 164, 175, 188, 193, 194, 197, 239, 246, 260, 318, and 389. Clusters with magic numbers are figured out through fitting the size-dependent global minimum energies. The cluster structures tend to be close-packed for short-range potential with large N.

CAPACITANCE RESISTANCE MODEL (CRM) APPLICATION TO RAPID EVALUATE AND OPTIMIZE PRODUCTION IN PERIPHERAL WATERFLOOD FIELD, PANDHAWA FIELD CASE STUDY

Waterflooding is one of the most effective methods to improve oil recovery in mature fields because of its high success ratio, easy in application and cost efficiency. Development until now has shown that Capacitance Resistance Model (CRM) can be used as alternative from reservoir model and simulation studies. CRM can be used as model to predict reservoir characterization and reservoir performance quickly and accurately with only require historical production and injection data. CRM characterizes the reservoir by calculating the connectivity value and the response delay between the injections well and the production well as unknown parameters. Pandhawa Field is a heterogeneous carbonate reservoir with an average permeability of 65 mD with peripheral waterflood since 20 years ago. By knowing the injection efficiency, the optimization process can be carried out by increasing the water injection rate in injection wells that have high efficiency and vice versa. In this study, the performance of waterflood is analyzed using the Capacitance-Resistance Injection-Production Model (CRM-IP) to determine the connectivity of each injection and production well. This study also discuss CRM-IP implementation on MATLAB programming language and optimization of injection rate allocation for the most optimum cumulative oil production. Result of this study indicate total additional oil 505 MBO will be obtained during 120 months period by conduct redistribution water injection management for each injector. By using CRMIP methodology, waterflood management in this field can be done much faster, therefore decision taken for this field will be more effective.

Augmenting zero trust architecture to endpoints using blockchain: A state‐of‐the‐art review

AbstractWith the purpose of defending against lateral movement in today's borderless networks, zero trust architecture (ZTA) adoption is gaining momentum. With a full‐scale ZTA implementation, it is unlikely that adversaries will be able to spread through the network starting from a compromised endpoint. However, the already authenticated and authorized session of a compromised endpoint can be leveraged to carry out limited, though malicious, activities ultimately rendering the endpoints the Achilles heel of ZTA. To effectively detect such attacks, distributed collaborative intrusion detection systems with an attack scenario‐based approach have been developed. Nonetheless, advanced persistent threats have demonstrated their ability to bypass this approach with a high success ratio. As a result, adversaries can pass undetected or potentially alter the detection logging mechanisms to achieve a stealthy presence. Recently, blockchain technology has demonstrated solid use cases in the cyber security domain. In this paper, motivated by the convergence of ZTA and blockchain‐based intrusion detection and prevention, we examine how ZTA can be augmented onto endpoints. Namely, we perform a state‐of‐the‐art review of ZTA models, real‐world architectures with a focus on endpoints, and blockchain‐based intrusion detection systems. We discuss the potential of blockchain's immutability fortifying the detection process and identify open challenges as well as potential solutions and future directions.

Outcomes of Ilizarov Ring Fixator in Infected Nonunion of Tibia

Objective: This study was designed to evaluate the effectiveness of Ilizarov. We aimed to explore the infection rate, bony union, and functional outcomes of Ilizarov fixators.
 Methodology: This retrospective study was conducted in Orthopedic department of Bolan Medical Complex Hospital Quetta Pakistan from June 2020 to June 2021. In this timeframe total of fifty-five patients of infected nonunion tibia were enrolled for Ilizarov technique treatment. For surgical intervention, patients were placed in a supine position on a radiolucent table. Ilizarov fixator was prepared on the behalf of patient's limb length, infection site, and ankle and knee functional status. We applied assembled Ilizarov fixator at the tibial shaft while keeping in mind that the rings were positioned in on the proximal and distal fragments. The ring was placed parallel to the joints whereas pins were inserted perpendicular to the tibial mechanical axis.
 Results: A total of 55 patients were recruited for this study. The mean age of the selected participants was 45.65±16.69 years. The overall successful bone results of the ASAMI score were observed as 80% whereas 88% functional outcomes were achieved. In bone results, we observed 28 (50.9%) cases with excellent results, 16 (29%) with good, 7 (12.7%) with fair, and 3 (5.4%) with poor outcomes. On the other hand, 25 (45.4%) cases observed excellent functional results, 25 (45.4%) with good, 4 (7.6%) with fair, and 2 (3.6%) with poor outcomes.
 Conclusion: Our results show a high success ratio therefore we recommend Ilizarov external fixators for infected nonunion tibial fracture. This method helps to recover limbs without any amputations. However, the discomfort of patients is one of the main problems with this method of treatment.