Application of the 2-archive multi-objective cuckoo search algorithm for structure optimization.

The study suggests a better multi-objective optimization method called 2-Archive Multi-Objective Cuckoo Search (MOCS2arc). It is then used to improve eight classical truss structures and six ZDT test functions. The optimization aims to minimize both mass and compliance simultaneously.

Silver-coated PMMA nanoparticles-on-a-mirror substrates as high-performance SERS sensors for detecting infinitesimal molecules.

Surface-enhanced Raman scattering (SERS) technology has attracted more and more attention due to its high sensitivity, low water interference, and quick measurement. Constructing high-performance SERS substrates with high sensitivity, uniformity and reproducibility is of great importance to put the SERS technology into practical application. In this paper, we report a simple fabrication process to construct dense silver-coated PMMA nanoparticles-on-a-mirror SRES substrates.

Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework.

The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework.

Utilizing Artificial neural networks (ANN) to regulate Smart cities for sustainable Urban Development and Safeguarding Citizen rights.

The advent of smart cities has brought about a paradigm shift in urban management and citizen engagement. By leveraging technological advancements, cities are now able to collect and analyze extensive data to optimize service delivery, allocate resources efficiently, and enhance the overall well-being of residents. However, as cities become increasingly interconnected and data-dependent, concerns related to data privacy and security, as well as citizen participation and representation, have surfaced.

A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting.

Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, have become popular for modeling traffic sensor networks. However, traditional graph convolutional networks (GCNs) face limitations in capturing long-range spatial correlations, which can hinder accurate long-term predictions.

Human migration from the Levant and Arabia into Yemen since Last Glacial Maximum.

While a broad consensus about the first successful migration modern humans out of Africa seems established, the peopling of Arabia remains somewhat enigmatic. Identifying the ancestral populations that contributed to the gene pool of the current populations inhabiting Arabia and the impact of their contributions remains a challenging task. We investigate the genetic makeup of the current Yemeni population using 46 whole genomes and 169 genotype arrays derived from Yemeni individuals from all geographic regions across Yemen and 351 genotype arrays derived from neighboring populations providing regional context.

Graphene metamaterial solar absorber using Al-TiN-Fe for efficient solar thermal energy conversion and optimization using machine learning.

The contributed absorber design in graphene addition with the displacement of three materials for resonator design in Aluminum (Al), the middle substrate position with Titanium nitride (TiN), and the ground layer deposition by Iron (Fe) respectively. For the absorption validation highlight, the best four absorption wavelengths (µm) of 0.29, 0.

Impact of plasma discharge pressure on implant surface properties and osteoblast activities in vacuum-assisted plasma treatment.

Nonthermal plasma has been extensively utilized in various biomedical fields, including surface engineering of medical implants to enhance their biocompatibility and osseointegration. To ensure robustness and cost effectiveness for commercial viability, stable and effective plasma is required, which can be achieved by reducing gas pressure in a controlled volume. Here, we explored the impact of reduced gas pressure on plasma properties, surface characteristics of plasma-treated implants, and subsequent biological outcomes.

Electronic ferroelectricity in monolayer graphene moiré superlattices.

Extending ferroelectric materials to two-dimensional limit provides versatile applications for the development of next-generation nonvolatile devices. Conventional ferroelectricity requires materials consisting of at least two constituent elements associated with polar crystalline structures. Monolayer graphene as an elementary two-dimensional material unlikely exhibits ferroelectric order due to its highly centrosymmetric hexagonal lattices.

Dynamic arithmetic optimization algorithm control of distributed generations for demand balancing and enhancing power quality of unbalanced distribution systems.

Unbalanced power systems cause transformers and generators to overheat, system losses to climb, and protective devices to trigger. An optimization-based control technique for distributed generators (DG) balances demand and improves power quality in three imbalanced distribution systems with 10, 13, and 37 nodes. Each system phase has its own DG.

A machine learning based classifier for topological quantum materials.

Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new materials from ab initio calculations. In this work, an effective and robust deep learning-based model is proposed by incorporating persistent homology with graph neural network which offers an accuracy of and an F1 score of in classifying topological versus non-topological materials, outperforming the other state-of-the-art classifier models.

Dynamic ultrasound-based modeling predictive of response to neoadjuvant chemotherapy in patients with early breast cancer.

Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.

A deep transfer learning based convolution neural network framework for air temperature classification using human clothing images.

Weather recognition is crucial due to its significant impact on various aspects of daily life, such as weather prediction, environmental monitoring, tourism, and energy production. Several studies have already conducted research on image-based weather recognition. However, previous studies have addressed few types of weather phenomena recognition from images with insufficient accuracy.

Orientation-selective spin-polarized edge states in monolayer NiI.

Spin-polarized edge states in two-dimensional materials hold promise for spintronics and quantum computing applications. Constructing stable edge states by tailoring two-dimensional semiconductor materials with bulk-boundary correspondence is a feasible approach. Recently layered NiI is suggested as a two-dimensional type-II multiferroic semiconductor with intrinsic spiral spin ordering and chirality-induced electric polarization.

Unsupervised inter-domain transformation for virtually stained high-resolution mid-infrared photoacoustic microscopy using explainable deep learning.

Mid-infrared photoacoustic microscopy can capture biochemical information without staining. However, the long mid-infrared optical wavelengths make the spatial resolution of photoacoustic microscopy significantly poorer than that of conventional confocal fluorescence microscopy. Here, we demonstrate an explainable deep learning-based unsupervised inter-domain transformation of low-resolution unlabeled mid-infrared photoacoustic microscopy images into confocal-like virtually fluorescence-stained high-resolution images.

Wearable non-invasive neuroprosthesis for targeted sensory restoration in neuropathy.

Peripheral neuropathy (PN), the most common complication of diabetes, leads to sensory loss and associated health issues as pain and increased fall risk. However, present treatments do not counteract sensory loss, but only partially manage its consequences. Electrical neural stimulation holds promise to restore sensations, but its efficacy and benefits in PN damaged nerves are yet unknown.

Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers.

Evaluating the effectiveness of cancer treatments in relation to specific tumor mutations is essential for improving patient outcomes and advancing the field of precision medicine. Here we represent a comprehensive analysis of 78,287 U.S.

Dynamic control of 2D non-Hermitian photonic corner skin modes in synthetic dimensions.

Non-Hermitian models describe the physics of ubiquitous open systems with gain and loss. One intriguing aspect of non-Hermitian models is their inherent topology that can produce intriguing boundary phenomena like resilient higher-order topological insulators (HOTIs) and non-Hermitian skin effects (NHSE). Recently, time-multiplexed lattices in synthetic dimensions have emerged as a versatile platform for the investigation of these effects free of geometric restrictions.

All-polymer piezo-ionic-electric electronics.

Piezoelectric electronics possess great potential in flexible sensing and energy harvesting applications. However, they suffer from low electromechanical performance in all-organic piezoelectric systems due to the disordered and weakly-polarized interfaces. Here, we demonstrated an all-polymer piezo-ionic-electric electronics with PVDF/Nafion/PVDF (polyvinylidene difluoride) sandwich structure and regularized ion-electron interfaces.

Dual-role ion dynamics in ferroionic CuInPS: revealing the transition from ferroelectric to ionic switching mechanisms.

Due to its "ferroionic" nature, CuInPS combines switchable ferroelectric polarization with highly mobile Cu ions, allowing for multiple resistance states. Its conductive mechanism involves ferroelectric switching, ion migration, and corresponding intercoupling, which are highly sensitive to external electric field. Distinguishing the dominant contribution of either ferroelectric switching or ion migration to dynamic conductivity remains a challenge and the conductive mechanism is not clear yet.

Strained two-dimensional tungsten diselenide for mechanically tunable exciton transport.

Tightly bound electron-hole pairs (excitons) hosted in atomically-thin semiconductors have emerged as prospective elements in optoelectronic devices for ultrafast and secured information transfer. The controlled exciton transport in such excitonic devices requires manipulating potential energy gradient of charge-neutral excitons, while electrical gating or nanoscale straining have shown limited efficiency of exciton transport at room temperature. Here, we report strain gradient induced exciton transport in monolayer tungsten diselenide (WSe) across microns at room temperature via steady-state pump-probe measurement.

Peripheral contributions to resting state brain dynamics.

The correlational structure of brain activity dynamics in the absence of stimuli or behavior is often taken to reveal intrinsic properties of neural function. To test the limits of this assumption, we analyzed peripheral contributions to resting state activity measured by fMRI in unanesthetized, chemically immobilized male rats that emulate human neuroimaging conditions. We find that perturbation of somatosensory input channels modifies correlation strengths that relate somatosensory areas both to one another and to higher-order brain regions, despite the absence of ostensible stimuli or movements.

Cryogenic III-V and Nb electronics integrated on silicon for large-scale quantum computing platforms.

Quantum computers now encounter the significant challenge of scalability, similar to the issue that classical computing faced previously. Recent results in high-fidelity spin qubits manufactured with a Si CMOS technology, along with demonstrations that cryogenic CMOS-based control/readout electronics can be integrated into the same chip or die, opens up an opportunity to break out the challenges of qubit size, I/O, and integrability. However, the power consumption of cryogenic CMOS-based control/readout electronics cannot support thousands or millions of qubits.

Topological transformation of microbial proteins into iron single-atom sites for selective hydrogen peroxide electrosynthesis.

The emergence of single-atom catalysts offers exciting prospects for the green production of hydrogen peroxide; however, their optimal local structure and the underlying structure-activity relationships remain unclear. Here we show trace Fe, up to 278 mg/kg and derived from microbial protein, serve as precursors to synthesize a variety of Fe single-atom catalysts containing FeNO (1 ≤ x ≤ 4) moieties through controlled pyrolysis. These moieties resemble the structural features of nonheme Fe-dependent enzymes while being effectively confined on a microbe-derived, electrically conductive carbon support, enabling high-current density electrolysis.

Grain engineering for efficient near-infrared perovskite light-emitting diodes.

Metal halide perovskites show promise for next-generation light-emitting diodes, particularly in the near-infrared range, where they outperform organic and quantum-dot counterparts. However, they still fall short of costly III-V semiconductor devices, which achieve external quantum efficiencies above 30% with high brightness. Among several factors, controlling grain growth and nanoscale morphology is crucial for further enhancing device performance.