Forecasting Mental Stress Using Machine Learning Algorithms

Depression is a crippling affliction and affects millions of individuals around the world. In general, the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts, which results in lower costs and improved patient outcomes. However, this strategy can necessitate a lot of buy-in from a large number of people, as well as additional training and logistical considerations. Thus, utilizing the machine learning algorithms, patients with depression based on information generally present in a medical file were analyzed and predicted. The methodology of this proposed study is divided into six parts: Proposed Research Architecture (PRA), Data Pre-processing Approach (DPA), Research Hypothesis Testing (RHT), Concentrated Algorithm Pipeline (CAP), Loss Optimization Stratagem (LOS), and Model Deployment Architecture (MDA). The Null Hypothesis and Alternative Hypothesis are applied to test the RHT. In addition, Ensemble Learning Approach (ELA) and Frequent Model Retraining (FMR) have been utilized for optimizing the loss function. Besides, the Features Importance Interpretation is also delineated in this research. These forecasts could help individuals connect with expert mental health specialists more quickly and easily. According to the findings, 71% of people with depression and 80% of those who do not have depression can be appropriately diagnosed. This study obtained 91% and 92% accuracy through the Random Forest (RF) and Extra Tree Classifier. But after applying the Receiver operating characteristic (ROC) curve, 79% accuracy was found on top of RF, 81% found on Extra Tree, and 82% recorded for the eXtreme Gradient Boosting (XGBoost) algorithm. Besides, several factors are identified in terms of predicting depression through statistical data analysis. Though the additional effort is needed to develop a more accurate model, this model can be adjustable in the healthcare sector for diagnosing depression.     

Microarchitected Stretching‐Dominated Mechanical Metamaterials with Minimal Surface Topologies

Historically, the creation of lightweight, yet mechanically robust, materials have been the most sought‐after engineering pursuit. For that purpose, research efforts are dedicated to finding pathways to emulate and mimic the resilience offered by natural biological systems (i.e., bone and wood). These natural systems evolved over time to provide the most attainable structural efficiency through their architectural characteristics that can span over multiple length scales. Nature‐inspired man‐made cellular metamaterials have effective properties that depend largely on their topology rather than composition and are hence remarkable candidates for a wide range of application. Despite their geometrical complexity, the fabrication of such metamaterials is made possible by the emergence of advanced fabrication techniques that permit the fabrication of complex architectures down to the nanometer scale. In this work, we report the fabrication and mechanical testing of nature‐inspired, mathematically created, micro‐architected, cellular metamaterials with topologies based on triply periodic minimal surfaces (TPMS) with cubic symmetries fabricated through direct laser writing two‐photon lithography. These TPMS‐based microlattices are sheet/shell‐ and strut‐based metamaterials with high geometrical complexity. Interestingly, results show that TPMS sheet‐based microlattices follow a stretching‐dominated mode of deformation, and further illustrate their mechanical superiority over the traditional and well‐known strut‐based microlattices and microlattice composites. The TPMS sheet‐based polymeric microlattices exhibited mechanical properties superior to other micrloattices comprising metal‐ and ceramic‐coated polymeric substrates and, interestingly, are less affected by the change in density, which opens the door for fabricating ultralightweight materials without much sacrificing mechanical properties.

     

Ultrathin Transition Metal Dichalcogenide/3d Metal Hydroxide Hybridized Nanosheets to Enhance Hydrogen Evolution Activity

The vast majority of the reported hydrogen evolution reaction (HER) electrocatalysts perform poorly under alkaline conditions due to the sluggish water dissociation kinetics. Herein, a hybridization catalyst construction concept is presented to dramatically enhance the alkaline HER activities of catalysts based on 2D transition metal dichalcogenides (TMDs) (MoS₂ and WS₂). A series of ultrathin 2D‐hybrids are synthesized via facile controllable growth of 3d metal (Ni, Co, Fe, Mn) hydroxides on the monolayer 2D‐TMD nanosheets. The resultant Ni(OH)₂ and Co(OH)₂ hybridized ultrathin MoS₂ and WS₂ nanosheet catalysts exhibit significantly enhanced alkaline HER activity and stability compared to their bare counterparts. The 2D‐MoS₂/Co(OH)₂ hybrid achieves an extremely low overpotential of ≈128 mV at 10 mA cm^?2 in 1 m KOH. The combined theoretical and experimental studies confirm that the formation of the heterostructured boundaries by suitable hybridization of the TMD and 3d metal hydroxides is responsible for the improved alkaline HER activities because of the enhanced water dissociation step and lowers the corresponding kinetic energy barrier by the hybridized 3d metal hydroxides.     

Protein‐Corona‐by‐Design in 2D: A Reliable Platform to Decode Bio–Nano Interactions for the Next‐Generation Quality‐by‐Design Nanomedicines

Hard corona (HC) protein, i.e., the environmental proteins of the biological medium that are bound to a nanosurface, is known to affect the biological fate of a nanomedicine. Due to the size, curvature, and specific surface area (SSA) 3‐factor interactions inherited in the traditional 3D nanoparticle, HC‐dependent bio–nano interactions are often poorly probed and interpreted. Here, the first HC‐by‐design case study in 2D is demonstrated that sequentially and linearly changes the HC quantity using functionalized graphene oxide (GO) nanosheets. The HC quantity and HC quality are analyzed using NanoDrop and label‐free liquid chromatography–mass spectrometry (LC‐MS) followed by principal component analysis (PCA). Cellular responses (uptake and cytotoxicity in J774 cell model) are compared using imaging cytometry and the modified lactate dehydrogenase assays, respectively. Cellular uptake linearly and solely correlates with HC quantity (R² = 0.99634). The nanotoxicity, analyzed by retrospective design of experiment (DoE), is found to be dependent on the nanomaterial uptake (primary), HC composition (secondary), and nanomaterial exposure dose (tertiary). This unique 2D design eliminates the size–curvature–SSA multifactor interactions and can serve as a reliable screening platform to uncover HC‐dependent bio–nano interactions to enable the next‐generation quality‐by‐design (QbD) nanomedicines for better clinical translation.     

Polymer Nanosheet Containing Star‐Like Copolymers: A Novel Scalable Controlled Release System

Poly(ε‐caprolactone) (PCL)‐based nanomaterials, such as nanoparticles and liposomes, have exhibited great potential as controlled release systems, but the difficulties in large‐scale fabrication limit their practical applications. Among the various methods being developed to fabricate polymer nanosheets (PNSs) for different applications, such as Langmuir–Blodgett technique and layer‐by‐layer assembly, are very effort consuming, and only a few PNSs can be obtained. In this paper, poly(ε‐caprolactone)‐based PNSs with adjustable thickness are obtained in large quantity by simple water exposure of multilayer polymer films, which are fabricated via a layer multiplying coextrusion method. The PNS is also demonstrated as a novel controlled guest release system, in which release kinetics are adjustable by the nanosheet thickness, pH values of the media, and the presence of protecting layers. Theoretical simulations, including Korsmeyer–Peppas model and Finite‐element analysis, are also employed to discern the observed guest‐release mechanisms.     

The influence of partial surface shot peening on fatigue crack growth behaviour of a high‐strength ferritic steel

The effects of partial surface shot peening on the fatigue crack growth behaviour of a ferritic steel have been experimentally investigated in this paper. Dog‐bone specimens fabricated from Optim700QL were tested under tension‐tension fatigue loads. Three distinct extents of partial shot peening, with respect to the crack tip and specimen symmetry line, were tested. The fatigue crack growth results from these experiments have been compared with those obtained from the same specimen geometry but with no peening. The results show that the residual stress fields formed ahead of the initial notch tip due to the partial peening process play a significant role in the fatigue crack growth behaviour of the material and effectively result in accelerated crack propagation at the midwidth of the specimens. It has been shown in this study that partial peening can lead to a fatigue crack growth rate around twice as fast as that of the unpeened specimen.     

Network Quality Assessment in Heterogeneous Wireless Settings: An Optimization Approach

The identification of an effective network which can efficiently meet the service requirements of the target, while maintaining ultimate performance at an increased level is significant and challenging in a fully interconnected wireless medium. The wrong selection can contribute to unwanted situations like frustrated users, slow service, traffic congestion issues, missed and/or interrupted calls, and wastefulness of precious network components. Conventional schemes estimate the handoff need and cause the network screening process by a single metric. The strategies are not effective enough because traffic characteristics, user expectations, network terminology and other essential device metrics are not taken into account. This article describes an intelligent computing technique based on Multiple-Criteria Decision-Making (MCDM) approach developed based on integrated Fuzzy AHP-TOPSIS which ensures flexible usability and maximizes the experience of end-users in miscellaneous wireless settings. In different components the handover need is assessed and the desired network is chosen. Further, fuzzy sets provide effective solutions to address decision making problems where experts counter uncertainty to make a decision. The proposed research endeavor will support designers and developers to identify, select and prioritize best attributes for ensuring flexible usability in miscellaneous wireless settings. The results of this research endeavor depict that this proposed computational procedure would be the most conversant mechanism for determining the usability and experience of end-users.     

Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

Deep learning techniques, particularly convolutional neural networks (CNNs), have exhibited remarkable performance in solving vision-related problems, especially in unpredictable, dynamic, and challenging environments. In autonomous vehicles, imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of CNNs. In this regard, globally, researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best results. Literature has proven the superiority of metaheuristic algorithms over the manual-tuning of CNNs. However, to the best of our knowledge, these techniques are yet to be applied to address the problem of imitation-learning-based steering angle prediction. Thus, in this study, we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters, which are employed to solve the steering angle prediction problem. To validate the performance of each hyperparameters’ set and architectural parameters’ set, we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set: optimizer, Adagrad; learning rate, 0.0052; and nonlinear activation function, exponential linear unit. As per our findings, we determined that the deep learning models show better results but require more training epochs and time as compared to shallower ones. Results show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning approach. Infield testing was also performed using the model trained with the optimal architecture, which we developed using our approach.     

Selective excitation of LP01 mode in multimode fiber using solid-core photonic crystal fiber

To address the overwhelming bandwidth increase in premise backbones, an attractive alternative for selective mode excitation in multimode fiber (MMF) using solid-core photonic crystal fiber (PCF) is presented. The power coupling efficiency, differential mode delay, and bit-error rate performance of several structural designs of solid-core PCF waveguides are investigated for the selective excitation of mode LP01 in a MMF. The achieved coupling efficiency into mode LP01 is above 90% for PCF profiles with seven rings.