<p class="abstract"><strong>Background:</strong> Supra-condylar and inter-condylar fractures of the distal femur account for 7% of all femoral fractures and have always been difficult to treat and regaining full knee function is often difficult. The purpose of this study is to evaluate the functional outcome, fracture healing, complications of distal femoral intercondylar fractures managed by locking compression plate.</p><p class="abstract"><strong>Methods:</strong> Total 72 patients of intercondylar femur fracture were operated by ORIF with distal femur-locking compression plate via the standard swashbuckler approach.<strong> </strong>The functional outcomes were analyzed using modified hospital for special surgery scoring system.<strong></strong></p><p class="abstract"><strong>Results:</strong> Muller type C2 fracture was the most common fracture type with 50 out of 72 patients. The average range of motion achieved was about 99.03°±24.73° (Closed fractures =105.83°±19.41°and open fractures = 89.50°±28.36°). There was also a significant difference in the duration of operative time, 84.28±18.32 minutes for closed fractures and 98.46±22.47 minutes for open fractures. The average duration for radiological union was 14.52±2.21 weeks for closed and 17.20±2.44 weeks for open fractures. The average knee score was 80.13±13.38 using modified Hospital for Special Surgery score.</p><p class="abstract"><strong>Conclusions:</strong> Closed fractures have a higher range of motion and a better knee score compared to open fractures, supporting the fact that soft tissue compromise also affects range of motion and post-op rehabilitation of the limb. The outcome seems to correlate with the nature of injury i.e. high vs low velocity, type of fracture, anatomic reduction, associated injuries, time elapsed since injury to fixation and the stability of fixation.</p>

PurposeDepression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.Design/methodology/approach(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.Findings1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.Originality/valueA novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

Steel Melting Shop-I, Rourkela Steel Plant, Rourkela, produces 0.5 MT of various special steels through BOF-VAR/VOR-LF-CC route. One of the most serious problems in BOF operations was lance skulling, hood jamming, and build-up of metal in the mouth and cone of the furnace due to spitting and slopping. As spitting occurring during blowing increases, these particles of metal are deposited inside the mouth and cone and affect badly BOF productivity. In the present work, control of spitting has been established by addition of sinter during the period of spitting. It also helped to improve the slag formation and fluidity of slag through increase of FeO content during peak decarburisation period.