Masquelet technique using an allogeneic cortical bone graft for a large bone defect:A case report

BACKGROUND The induced-membrane technique was initially described by Masquelet as an effective treatment for large bone defects,especially those caused by infection.Here,we report a case of chronic osteomyelitis of the radius associated with a 9 cm bone defect,which was filled with a large allogeneic cortical bone graft from a bone bank.Complete bony union was achieved after 14 months of follow-up.Previous studies have used autogenous bone as the primary bone source for the Masquelet technique;in our case,the exclusive use of allografts is as successful as the use of autologous bone grafts.With the advent of bone banks,it is possible to obtain an unlimited amount of allograft,and the Masquelet technique may be further improved based on this new way of bone grafting.CASE SUMMARY In this study,we reported a case of repair of a long bone defect in a 40-year-old male patient,which was characterized by the utilization of allograft cortical bone combined with the Masquelet technique for the treatment of the patient's long bone defect in the forearm.The patient's results of functional recovery of the forearm were surprising,which further deepens the scope of application of Masquelet technique and helps to strengthen the efficacy of Masquelet technique in the treatment of long bones indeed.CONCLUSION Allograft cortical bone combined with the Masquelet technique provides a new method of treatment to large bone defect.

An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.

Advancements and challenges in neuroimaging for the diagnosis of intracranial aneurysms:Addressing false positive diagnoses and emerging techniques

Despite advancements in neuroimaging,false positive diagnoses of intracranial aneurysms remain a significant concern.This article examines the causes,prevalence,and implications of such false-positive diagnoses.We discuss how conditions like arterial occlusion with vascular stump formation and infundibular widening can mimic aneurysms,particularly in the anterior circulation.The article compares various imaging modalities,including computer tomography angiogram,magnetic resonance imaging/angiography,and digital subtraction angiogram,highlighting their strengths and limitations.We emphasize the im-portance of accurate differentiation to avoid unnecessary surgical interventions.The potential of emerging technologies,such as high-resolution vessel wall ima-ging and deep neural networks for automated detection,is explored as promising avenues for improving diagnostic accuracy.This manuscript underscores the need for continued research and clinical vigilance in the diagnosis of intracranial aneurysms.

Software Defect Prediction Using Hybrid Machine Learning Techniques: A Comparative Study

When a customer uses the software, then it is possible to occur defects that can be removed in the updated versions of the software. Hence, in the present work, a robust examination of cross-project software defect prediction is elaborated through an innovative hybrid machine learning framework. The proposed technique combines an advanced deep neural network architecture with ensemble models such as Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The study evaluates the performance by considering multiple software projects like CM1, JM1, KC1, and PC1 using datasets from the PROMISE Software Engineering Repository. The three hybrid models that are compared are Hybrid Model-1 (SVM, RandomForest, XGBoost, Neural Network), Hybrid Model-2 (GradientBoosting, DecisionTree, LogisticRegression, Neural Network), and Hybrid Model-3 (KNeighbors, GaussianNB, Support Vector Classification (SVC), Neural Network), and the Hybrid Model 3 surpasses the others in terms of recall, F1-score, accuracy, ROC AUC, and precision. The presented work offers valuable insights into the effectiveness of hybrid techniques for cross-project defect prediction, providing a comparative perspective on early defect identification and mitigation strategies. .

Detection of Knowledge on Social Media Using Data Mining Techniques

In light of the rapid growth and development of social media, it has become the focus of interest in many different scientific fields. They seek to extract useful information from it, and this is called (knowledge), such as extracting information related to people’s behaviors and interactions to analyze feelings or understand the behavior of users or groups, and many others. This extracted knowledge has a very important role in decision-making, creating and improving marketing objectives and competitive advantage, monitoring events, whether political or economic, and development in all fields. Therefore, to extract this knowledge, we need to analyze the vast amount of data found within social media using the most popular data mining techniques and applications related to social media sites.

Research on Image Recognition Using Deep Learning Techniques

This study delves into the applications,challenges,and future directions of deep learning techniques in the field of image recognition.Deep learning,particularly Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),and Generative Adversarial Networks(GANs),has become key to enhancing the precision and efficiency of image recognition.These models are capable of processing complex visual data,facilitating efficient feature extraction and image classification.However,acquiring and annotating high-quality,diverse datasets,addressing imbalances in datasets,and model training and optimization remain significant challenges in this domain.The paper proposes strategies for improving data augmentation,optimizing model architectures,and employing automated model optimization tools to address these challenges,while also emphasizing the importance of considering ethical issues in technological advancements.As technology continues to evolve,the application of deep learning in image recognition will further demonstrate its potent capability to solve complex problems,driving society towards more inclusive and diverse development.

Change Analysis of Land Use and Urban Growth in the Municipalities of Culiacan and Navolato, Sinaloa, Mexico Using Statistical Techniques and GIS

Previous analysis of land use and land cover changes help us to understand the range, importance and effects that this dynamic has in the environment and its relation with the human’s activities. This work consists in analyzing the land use/cover for the Municipalities of Culiacan and Navolato, Mexico, through statistical techniques and Geographic Information Systems. The methodology is allowed to determine the changes, gains, losses and transitions in the different categories in the period studied. The results show significant changes in the denominate categories, agriculture and forest. However, the greatest change is the increase of the urban areas. The knowledge in the studied area and its dynamics are carried out and this work serves as a reference to study, manage and plan for our territory.

Land Use/Land Cover Changes of Ago-Owu Forest Reserve, Osun State, Nigeria Using Remote Sensing Techniques

Remote sensing (RS) and GIS are important methods for land use assessment and land cover transition. In this study, land use/land cover changes in the Ago-Owu Forest Reserve, Osun State, Nigeria have been assessed. Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI were acquired for 1986, 2002 and 2017 respectively. The three scenes corresponded to path 190 and row 055 of WRS-2 (Worldwide Reference System). The processing of the imagery was preceded by the clipping of the study area from the satellite image. The boundary of the reserve was carefully digitized and used to clip the imagery to produce an image map of the forest reserve. Using the supervised image classification procedure, training sites were used to produce land use/land cover maps. The same classification scheme was used for the 1986, 2002 and 2017 images to facilitate the detection of change. The differences in the area covered by the different polygons between the three sets of images were measured in km2. The results show that during 1986 and 2017, there is a dramatic increase of build-up areas with a change of 55.65 km2 and sparse vegetation (farmland and grassland) with a change of 53.97 km2, while a dramatic decrease of dense vegetation (forest areas) with a change of 109.61 km2. The consequence of these results is that over the years, the population of people living in the forest reserve has increased and many of them are engaged in farming, leading to an increase in farmland. In addition, logging activities continued unabated in the forest reserve, as demonstrated by a sharp increase in the deforested area within the reserve. The maps produced in this study will serve as a planning tool for the Osun State Forestry Department to plan reforestation activities for the forest reserve.

Classification of Multi-User Chirp Modulation Signals Using Wavelet Higher-Order-Statistics Features and Artificial Intelligence Techniques

Higher order statistical features have been recently proved to be very efficient in the classification of wideband communications and radar signals with great accuracy. On the other hand, the denoising properties of the wavelet transform make WT an efficient signal processing tool in noisy environments. A novel technique for the classification of multi-user chirp modulation signals is presented in this paper. A combination of the higher order moments and cumulants of the wavelet coefficients as well as the peaks of the bispectrum and its bi-frequencies are proposed as effective features. Different types of artificial intelligence based classifiers and clustering techniques are used to identify the chirp signals of the different users. In particular, neural networks (NN), maximum likelihood (ML), k-nearest neighbor (KNN) and support vector machine (SVMs) classifiers as well as fuzzy c-means (FCM) and fuzzy k-means (FKM) clustering techniques are tested. The Simulation results show that the proposed technique is able to efficiently classify the different chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy. It is shown that the NN classifier outperforms other classifiers. Also, the simulations prove that the classification based on features extracted from wavelet transform results in more accurate results than that using features directly extracted from the chirp signals, especially at low values of signal-to-noise ratios.

Evaluation of Geological Hazards for Landuse Planning in Nabq Protectorate, Southeastern Sinai Using GIS Techniques

Nabq protectorate is one of wonderful natural places in Egypt. It is characterized by diversity of bio-lives such as mangrove forests, coral colonies, wild life plants and migratory birds. Ongoing growth of tourism industries at Sharm El Sheikh northward into the Nabq protectorate causes severe hazards on its natural resources. The aim of the present study is to assess the present geo-environmental hazards in the Nabq protectorate. Assessment includes the analysis of satellite images, topographical, geological and other ancillary geological data using GIS technology. GIS data analyses indicate that the area is under threat from some of geo-hazards. Rough topography and mass wasting with high probability of flash flooding threaten different constructions in this area. The mobilization of coastal sand dunes, wave action and tidal currents are natural impacts on Nabq ecosystems, where moved dunes leave clay soils that are removed in some places by tropical storms increasing sea water turbidity that threaten the corals and other living organisms in the tidal flat region. The seismic activity hazard in the study area is usually active on lineaments extending parallel to the trend of the Gulf of Aqaba-Dead Sea transform fault where the Nabq protectorate occupies its southern segment. Unwise planning activities destroy the natural environmental resources in Nabq area by construction of new resorts on mangrove forests, coral colonies and raised beaches. Hazard assessment identifies land suitability and land use maps that are clearly exhibit models of traditional dams and buffer strips on coastal zone and highways as well as around the Bedouin communities which are worked on tourism and fishing. These maps are urgent in need of an assessment and rehabilitation program to mitigate geo-hazard.

Determination of Leafy Vegetables Need in Water andNitrogen Use Efficiency by Using the Diviner 2000 andthe Isotopes Techniques: Case of Amaranth(Amaranthus cruentus)

A study was conducted to determine how the nitrogen(N)in the fertilisers can be quantified and what amounts of fertilizers should be given to leafy vegetables to achieve their requirements.This study also aimed to determine the efficient use of water by the plant.The experiment was laid out in a randomized complete block with three replicates and three levels of urea(T0=0 kg/ha,T1=43.5 kg/ha,T2=65 kg/ha).Estimation of growth parameters and biomass yield revealed that the treatments produced statistically identical values.But numerically,T1(43.5 kg of urea/ha)gave the highest yields and T2(65 kg of urea/ha)produced the lowest.It was the same for the determination of the water use efficiency(WUE)by the plant where T1 produced the highest values compared to T2.The yield curve as a function of the applied urea dose allowed the identification of the urea dose that corresponds to optimal yield in amaranth.From the dose of 65 kg of urea/ha,any increase becomes harmful to the plant.This results in a decrease in yield in the amaranth plant.

Effects of Different Tillage Techniques on Yield and Water Use Efficiency in Broomcorn Millet

[Objective] The effects of different tillage techniques on dry matter accu- mulation, soil water content, water use efficiency and yield of broomcom millet were studied. [Method] With Jinsu 9 as an experiment material, the effects of deep tillage, traditional tillage and no tillage and rotary tillage on dry matter accumulation, soil water content, water use efficiency and yield of broomcom millet were investi- gated. [Result] Dry matter accumulation rate and accumulated amount were signifi- cantly higher in the deep tillage, no tillage and rotary tillage treatments than in the conventional tillage treatment, and the highest in the deep tillage treatment. The soil water content of the deep tillage treatment at 0-100 cm was higher than those of other tillage techniques, deep tillage also exhibited the highest soil water storage, and water use efficiency values were in order of deep tillage〉rotary tillage〉no tillage〉conventional tillage. The deep tillage treatment also showed the highest grain weight per spike, 1 000-grain weight and yield, while conventional tillage exhibited the lowest values, indicating that deep tillage is most beneficial to improvement of yield and water use efficiency of broomcom millet. [Conclusion] This study provides a scientific basis for water use efficiency of broomcorh millet in its main producing areas.

Assessment of Land Use Land Cover Change Detection Using Geospatial Techniques in Southeast Rajasthan

Change analysis acquires effective information in the form of maps and statistical data which becomes the central component in spatial planning, monitoring environmental changes, management and utilization of land. The present study makes an attempt to assess the changes in land use land cover using multi-temporal satellite data in south-east Rajasthan. These maps were derived from geocoded dia-positive False Color Composites (FCC’s) of IRS 1991, 2001, 2010 & 2018 using Arc GIS platform. The present study demonstrates the extension, approach and result of change analysis which might be helpful for decision making and sustainable growth. The landscape has been divided into 12 categories. Mining and its associated features were increased whereas forest and open scrub cover shows decreasing trend during the study period. The former increased by 23.82 km2 while the later shrunk by 26.08 km2. Most significant changes are also witnessed in settlement and industrial areas which shows increment by 8.8 km2 and 1.33 km2. Stone quarrying has destroyed arable land, natural vegetation cover, topsoil, subsoil and consequently the soil profile of the area. On the other hand cultivated land is increasing due to the conversion of uncultivated land and scrub cover with facilitation of irrigation and modern agricultural activities under different government schemes. The study shows that the area of 184.88 km2 has undergone significant spatial and temporal changes during the study period.

Assessing the Impact of Land Use and Land Cover Change on Air Quality in East Baton Rouge—Louisiana Using Earth Observation Techniques

There has been significant research in recent decades on Land use Land cover (LULC) changes and their influence on biodiversity but little to no research on its impact on air quality. This research seeks to demonstrate how geospatial technologies such as geographic information system (GIS) and remote sensing can be used to assess the effects of LULC changes on particulate matter emissions and their impact on air quality in the East Baton Rouge area. In pursuit of these objectives, this study uses LANDSAT imageries from the past 30 years specifically Landsat Thematic Mapper (TM C2L2) and Landsat 8 Operational Land Imager/Thermal Infrared (OLI/TIRS C2L2) covering 1991, 2001, 2011 and 2021 were collected, processed, and analyzed for the LULC change analysis using QGIS software. Additionally, Sentinel 5P and the Air quality index from the U.S. Environmental Protection Agency (EPA) were used to assess the air quality trend over the years to establish the correlation between LULC and air quality. Results showed an increasing trend in air quality over the past 3 decades with concentrations of CO, NO2, and PM2.5 abruptly falling however, urbanization and the population expanded throughout the time. The paper concludes by outlining a policy recommendation in the form of encouraging Louisiana residents to use alternative renewable energies rather than the over-dependence on coal-fired electric generating plants that have an impact on the environment.

Clinical diagnostic advances in intestinal anastomotic techniques:Hand suturing,stapling,and compression devices

The development of intestinal anastomosis techniques,including hand suturing,stapling,and compression anastomoses,has been a significant advancement in surgical practice.These methods aim to prevent leakage and minimize tissue fibrosis,which can lead to stricture formation.The healing process involves various phases:hemostasis and inflammation,proliferation,and remodeling.Mechanical staplers and sutures can cause inflammation and fibrosis due to the release of profibrotic chemokines.Compression anastomosis devices,including those made of nickel-titanium alloy,offer a minimally invasive option for various surgical challenges and have shown safety and efficacy.However,despite advancements,anastomotic techniques are evaluated based on leakage risk,with complications being a primary concern.Newer devices like Magnamosis use magnetic rings for compression anastomosis,demonstrating greater strength and patency compared to stapling.Magnetic technology is also being explored for other medical treatments.While there are promising results,particularly in animal models,the realworld application in humans is limited,and further research is needed to assess their safety and practicality.

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.

Research on Measure Techniques Using CAD-Vector Method for Heteromorphism Leaf Area of Garden Trees

Heteromorphism leaf area of garden trees was measured by CAD-vector method.It was showed that all the coefficients of variation were below 1% when measurement accuracy was relatively high.This method was fit for accuracy area measurement of abnormity leaf,pests and disease leaf,big leaf,small leaf and so on.

Exploring battery material failure mechanisms through synchrotron X-ray characterization techniques

Rechargeable battery cycling performance and related safety have been persistent concerns.It is crucial to decipher the capacity fading induced by electrode material failure via a range of techniques.Among these,synchrotron-based X-ray techniques with high flux and brightness play a key role in understanding degradation mechanisms.In this comprehensive review,we summarize recent advancements in degra-dation modes and mechanisms that were revealed by synchrotron X-ray methodologies.Subsequently,an overview of X-ray absorption spectroscopy and X-ray scattering techniques is introduced for charac-terizing failure phenomena at local coordination atomic environment and long-range order crystal struc-ture scale,respectively.At last,we envision the future of exploring material failure mechanism.

Delineation of groundwater potential zones in Theni district,Tamil Nadu,using remote sensing,GIS and MIF techniques

Integration of remote sensing data and the geographical information system （GIS） for the exploration of groundwater resources has become a breakthrough in the field of groundwater research, which assists in assessing, monitoring, and conserving groundwater resources. In the present paper, various groundwater potential zones for the assessment of groundwater availability in Theni district have been delineated using remote sensing and GIS techniques. Survey of India toposheets and IRS-IC satel- lite imageries are used to prepare various thematic layers viz. lithology, slope, land-use, lineament, drainage, soil, and rainfall were transformed to raster data using feature to raster converter tool in ArcGIS. The raster maps of these factors are allocated a fixed score and weight computed from multi influencing factor （MIF） technique. Moreover, each weighted thematic layer is statistically computed to get the groundwater potential zones. The groundwater potential zones thus obtained were divided into four categories, viz., very poor, poor, good, and very good zones. The result depicts the groundwater potential zones in the study area and found to be helpful in better planning and management of ground- water resonrces.

Repairing peripheral nerve injury using tissue engineering techniques

Each year approximately 360,000 people in the United States suffer a peripheral nerve injury （PNI）, which is a leading source of lifelong disability （Kelsey et al., 1997; Noble et al., 1998）. The most frequent cause of PNIs is motor vehicle accidents, while gunshot wounds, stabbings, and birth trauma are also common factors. Patients suffering from disabilities as a result of their PNIs are also burdensome to the healthcare system, with aver- age hospital stays of 28 days each year （Kelsey et al., 1997; Noble et al., 1998）.