Objectives: To make a comparative study on the wear behavior, corrosion resistance, and bioactivity performance of conventional Co-Cr (Cobalt-Chromium) alloy and nTopology-designed Co-Cr alloy sintered by Direct Metal Laser for biomedical implant applications. Method: Comparative investigations were made on the CM-CoCr (Conventional Co-Cr) alloy and NT-DMLS-CoCr (nTopology-designed DMLS Co-Cr) alloy specimens. By using pin-on-disc tribometer wear tests were performed under 10, 30, and 50 N loads at a sliding velocity and a sliding distance of 1.25 m/s and 1000 m respectively. Corrosion behavior was also evaluated with the help of potentiodynamic polarization test in simulated body fluid (SBF) at 37ºC. Bioactivity behavior was assessed by immersing the specimens in SBF for a duration of up to 28 days and monitoring the apatite formation. Here CM-CoCr was chosen as the control material for comparison. Findings: The NT-DMLS-CoCr specimens exhibited better tribological, electrochemical, and bioactive performance when compared with CM-CoCr. At 50 N, the wear rate decreased from 6.24 × 10-5 mm3/Nm for CM-CoCr to 3.46 × 10-5 mm³/Nm for NT-DMLS-CoCr, representing a reduction of approximately 44.5%. The friction coefficient also gets decreased from 0.55 to 0.43. Potentiodynamic polarization studies concluded that the corrosion current density gets decreased from 1.76 μA/cm2 to 0.42 μA/cm2, while the corrosion rate gets reduced from 0.017 mm/year to 0.004 mm/year. Bioactivity evaluation revealed enhanced apatite formation, with NT-DMLS-CoCr achieving apatite coverage of 95% after 28 days compared to that of 68% for CM-CoCr. The enhanced performance is attributed in the refined DMLS microstructure and interconnected gyroid lattice architecture, which improved load distribution, passive film stability, and biomineralization behavior. Novelty: This study presents a comparative evaluation of conventional and nTopology-designed DMLS Co-Cr alloys, exhibiting the advantages of lattice-engineered additive manufacturing best suited for biomedical implant applications. Keywords: Co-Cr, nTopology, DMLS, Biomedical applications
Objectives: To examine whether acute stress can be recognised from smartphone typing behaviour and device motion without wearable sensors, and to identify the classifier family best suited to such data. Method: A browser-based application recorded keystroke events together with accelerometer and gyroscope readings (2 Hz) while 288 participants typed simple number series (calm condition) and mental-arithmetic sequences (stress condition), yielding 2,016 samples. Five keystroke timing features and the inertial features were combined, and ten supervised classifiers were trained and tested on the same stratified 80:20 split using accuracy, AUC and weighted and macro precision, recall and F1-score. Findings: Ensemble methods outperformed the linear, probabilistic, distance-based and neural models. XGBoost achieved the best result, with 89.30% accuracy and an AUC of 95.42%, and Random Forest was close behind (88.72%, 95.32%); Naïve Bayes was weakest (57.98%). Weighted and macro F1 scores of the leading models differed by less than 0.3 points, indicating balanced detection of both classes. The accuracy exceeds the 87.56% reported by the closest smartphone motion-sensor study on 46 participants, and the ranking of XGBoost ahead of Random Forest agrees with recent wearable-sensor studies. Unlike deep models applied to dense physiological signals, the multilayer perceptron performed poorly on these compact behavioural features, which indicates that tree ensembles suit this type of data better. Novelty: Keystroke dynamics and smartphone inertial sensing are combined in one installation-free, synchronised protocol and benchmarked with ten classifiers on a cohort six times larger than the closest smartphone study. Keywords: Acute Stress Detection, Keystroke Dynamics, Smartphone Sensing, Machine Learning, Behavioural Biometrics
Objectives: To evaluate perceived stress and multidimensional social support as predictors of suicidal ideation in postpartum women, and to develop an explainable, uncertainty-aware machine learning framework for individualized risk screening. Method: We analyzed data from 200 postpartum women, including perceived stress (Perceived Stress Scale, 10 items), multidimensional social support (emotional, positive interaction, affective, and instrumental subscales), and demographic and clinical variables. We conducted descriptive comparisons, univariate and multivariate logistic regression, and a machine learning pipeline comparing seven classifiers under stratified five-fold cross-validation with Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance (17% prevalence of suicidal ideation). The final Random Forest and Logistic Regression models were evaluated on a held-out test set, calibrated, and interpreted using SHAP and LIME. Split conformal prediction generated statistically valid, uncertainty-aware prediction sets, and counterfactual sensitivity analysis quantified actionable score changes associated with reduced predicted risk. Findings: Women reporting suicidal ideation had significantly higher perceived stress (mean 36.6 vs. 29.6, p<0.001) and lower total social support (mean 49.8 vs. 65.4, p<0.001) than women without ideation. In multivariate analysis, perceived stress remained independently associated with suicidal ideation (adjusted odds ratio 1.23, 95% CI 1.11-1.37, p<0.001). The Random Forest classifier achieved the best cross-validated discrimination (ROC-AUC 0.766) and test-set performance (ROC-AUC 0.804, accuracy 0.84). SHAP analysis identified perceived stress and social support subscales as the dominant predictors of risk. Conformal prediction achieved 98% empirical coverage at a 90% target, with 34% of test cases flagged as ambiguous rather than confidently classified. Novelty: To our knowledge, this is the first postpartum suicidal ideation risk model to combine SHAP and LIME explainability with split conformal prediction and counterfactual sensitivity analysis in a single framework, jointly quantifying which factors drive each prediction, how confident the model is in that prediction, and what modifiable change would most plausibly lower a given patient's predicted risk. Keywords: postpartum depression, suicidal ideation, perceived stress, social support, machine learning, explainable AI, conformal prediction
Objectives: To develop and evaluate a real-time, low-power multi-sensor fusion framework that performs localization and safe landing-zone detection together for an aerial vehicle operating over Martian terrain, where neither GPS nor prior maps are available. Method: A stereo camera (Intel RealSense D435i), a rotating 16-beam LiDAR and an inertial measurement unit (IMU) are fused on a Raspberry Pi 5 and Pixhawk flight controller. LiDAR-assisted visual-inertial SLAM (ORB-SLAM with an Extended Kalman Filter) provides localization, while a classical computer-vision pipeline in OpenCV (CLAHE normalization, Sobel flatness analysis, mask fusion and geometric filtering) selects landing zones. The system was tested in 50 AirSim descent trials from 12 m altitude over Martian-analogue terrain with craters, rocks and dust-covered plains. Findings: Localization error remained below 4 cm, with drift under 0.6 m over 100 m; visual-inertial fusion cut drift by 45% compared with vision-only tracking. Map coverage reached 95%. The LSDS classified no-fly zones with 93% accuracy, needed 18.5 ms per frame (about 5 times faster than the CNN-based HALO approach) and guided the vehicle to within 0.3 m of the target. Landing succeeded in 98% of trials (49/50; 95% CI 89.5–99.6%) at under 10 W, against more than 20 W for GPU-based VINS-Mono at comparable accuracy. Novelty: Earlier GPS-denied SLAM and planetary landing systems address either localization or hazard assessment or depend on GPU/LiDAR-heavy hardware. This work runs both functions concurrently on a single sub-10 W embedded platform without training data. Keywords: Mars exploration, Autonomous landing, SLAM, Sensor fusion, GPS-denied navigation
Support Price (MSP) forecasting under sparse and irregular agricultural time-series conditions. Method: District-level wheat production and price data from all districts of Rajasthan Region, for 2016–2025 were pre-processed and segmented into temporal windows. In this work, we propose a Masked Self-Supervised Temporal learning framework for MSP forecasting that emphasizes representation learning rather than direct label dependence using masked reconstructive temporal learning. Performance was compared with ARIMA and supervised LSTM using mean squared error (MSE) on log-transformed prices. Findings: ARIMA produced the MAE (164.97), while supervised long short-term memory (LSTM) achieved 60.58. The proposed Masked Self-Supervised Temporal Learning (MSSTL) framework achieved a MAE of 32.67 and showed stable convergence and improved data efficiency. MSSTL learned temporal representations capturing seasonal patterns, production–irrigation interactions, and longer-term market memory without relying exclusively on labeled price observations. The results indicate that Masked Self-Supervised Temporal learning can provide a useful balance between forecasting performance and robustness when agricultural datasets are limited. The future-trend analysis emphasizes directional stability rather than exact long-horizon price values. Novelty: The study applies two-stage Masked Self-Supervised Temporal representation learning to wheat MSP forecasting in a data-scarce district-level agricultural setting, reducing dependence on labeled price data. Keywords: Crop Price, Minimum Support Price, Deep Learning, Masked Self-Supervised Learning, Temporal Learning
