Search Results (366)

Due to increasing demand for the exploration of marine resources, underwater optical wireless sensor networks (UOWSNs) have emerged as a promising solution by offering higher bandwidth and lower latency compared to traditional underwater acoustic wireless sensor networks (UAWSNs), with their existing routing protocols facing challenges in energy consumption and packet forwarding. To address these challenges, this paper proposes an energy-partitioned routing protocol based on an advancement function (EPAR) for UOWSNs. By dynamically classifying the nodes into high-energy and low-energy ones, the proposed EPAR algorithm employs an adaptive weighting strategy to prioritize the high-energy nodes in relay selection, thereby balancing network load and extending overall lifetime. In addition, a tunable advancement function is adopted by the proposed EPAR algorithm by comprehensively considering the Euclidean distance and steering angle toward the sink node. By adjusting a tunable parameter α, the function guides forwarding decisions to ensure energy-efficient and directionally optimal routing. Additionally, by employing a hop-by-hop neighbor discovery mechanism, the proposed algorithm enables each node to dynamically update its local neighbor set, thereby improving relay selection and mitigating the impact of void regions on the packet delivery ratio (PDR). Simulation results demonstrate that EPAR can obtain up to about a 10% improvement in PDR and up to about a 30% reduction in energy depletion, with a prolonged network lifetime when compared to the typical algorithms adopted in the simulations. Full article

Amyotrophic lateral sclerosis (ALS) is still a heterogeneous neurodegenerative disorder that can be identified clinically and biologically, without a strong set of biomarkers that can adequately measure its fast rate of progression and molecular heterogeneity. In this review, we intend to consolidate the most relevant and timely advances in ALS biomarker discovery, in order to begin to bring molecular, imaging, genetic, and digital areas together for potential integration into a precision medicine approach to ALS. Our goal is to begin to display how several biomarkers in development (e.g., neurofilament light chain (NfL), phosphorylated neurofilament heavy chain (pNfH), TDP-43 aggregates, mitochondrial stress markers, inflammatory markers, etc.) are changing our understanding of ALS and ALS dynamics. We will attempt to provide a framework for thinking about biomarkers in a systematic way where our candidates are not signals alone but part of a tethered pathophysiological cascade. We are particularly interested in the fast progressor phenotype, a devastating and under-characterized subset of ALS due to a rapid axonal degeneration, early respiratory failure, and very short life span. We will try to highlight the salient molecular features of this ALS subtype, including SOD1 A5V toxicity, C9orf72 repeats, FUS variants, mitochondrial collapse, and impaired autophagy mechanisms, and relate these features to measurable blood and CSF (biomarkers) and imaging platforms. We will elaborate on several interesting tools, for example, single-cell transcriptomics, CSF exosomal cargo analysis, MRI techniques, and wearable sensor outputs that are developing into high-resolution windows of disease progression and onset. Instead of providing a static catalog, we plan on providing a conceptual roadmap to integrate biomarker panels that will allow for earlier diagnosis, real-time disease monitoring, and adaptive therapeutic trial design. We hope this synthesis will make a meaningful contribution to the shift from observational neurology to proactive biologically informed clinical care in ALS. Although there are still considerable obstacles to overcome, the intersection of a precise molecular or genetic association approach, digital phenotyping, and systems-level understandings may ultimately redefine how we monitor, care for, and treat this challenging neurodegenerative disease. Full article

Asset discovery in critical infrastructures, and in particular within industrial control systems, constitutes a fundamental cybersecurity function. Ensuring accurate and comprehensive asset visibility while maintaining operational continuity represents an ongoing challenge. Existing methodologies rely on deterministic tools that apply fixed fingerprinting strategies and lack the capacity for contextual reasoning. Such approaches often fail to adapt to the heterogeneous architectures and dynamic configurations characteristic of modern critical infrastructures. This work introduces an architecture based on a Mixture of Experts model designed to overcome these limitations. The proposed framework combines multiple specialized modules to perform automated asset discovery, integrating passive and active software probes with physical sensors. This design enables the system to adapt to different operational scenarios and to classify discovered assets according to functional and security-relevant attributes. A proof-of-concept implementation is also presented, along with experimental results that demonstrate the feasibility of the proposed approach. The outcomes indicate that our LLM-based approach can support the development of non-intrusive asset management solutions, strengthening the cybersecurity posture of critical infrastructure systems. Full article

Encouraging discoveries and excellent advances in the fight against cancer have led to innovative therapies such as photothermal therapy (PTT), photodynamic therapy (PDT), drug targeting (DT), gene therapy (GT), immunotherapy (IT), and therapies that combine these treatments with conventional chemotherapy (CT). Furthermore, 2,041,910 new cancer cases and 618,120 cancer deaths have been estimated in the United States for the year 2025. The low survival rate (<50%) and poor prognosis of several cancers, despite aggressive treatments, are due to therapy-induced secondary tumorigenesis and the emergence of drug resistance. Moreover, serious adverse effects and/or great pain usually arise during treatments and/or in survivors, thus lowering the overall effectiveness of these cures. Although prevention is of paramount importance, novel anticancer approaches are urgently needed to address these issues. In the field of anticancer nanomedicine, carbon nanotubes (CNTs) could be of exceptional help due to their intrinsic, unprecedented features, easy functionalization, and large surface area, allowing excellent drug loading. CNTs can serve as drug carriers and as ingredients to engineer multifunctional platforms associated with diverse treatments for both anticancer therapy and diagnosis. The present review debates the most relevant advancements about the adjuvant role that CNTs could have in cancer diagnosis and therapy if associated with PTT, PDT, DT, GT, CT, and IT. Numerous sensing strategies utilising various CNT-based sensors for cancer diagnosis have been discussed in detail, never forgetting the still not fully clarified toxicological aspects that may derive from their extensive use. The unsolved challenges that still hamper the possible translation of CNT-based material in clinics, including regulatory hurdles, have been discussed to push scientists to focus on the development of advanced synthetic and purification work-up procedures, thus achieving more perfect CNTs for their safer real-life clinical use. Full article

We present a detailed dataset collected via a wireless IoT sensor network monitoring three industrial centrifugal pumps (units A, B, and C) at the Italian Aerospace Research Centre (CIRA), along with the methods for data collection and structuring. Background: Centrifugal pumps are critical in industrial plants, and monitoring their condition is essential to ensure reliability, safety, and efficiency. High-quality operational data under normal operating conditions are fundamental for developing effective maintenance strategies and diagnostic models. Methods: Data were gathered by means of smart sensors measuring motor and pump vibrations, temperatures, outlet fluid pressures, and environmental conditions. Data were transmitted over a WirelessHART mesh network and acquired through an IoT architecture. Results: The dataset consists of eight CSV files, each representing a specific pump during a distinct operational day. Each file includes timestamped measurements of displacement, peak vibration values, sensor temperatures, fluid pressure, ambient temperature, and atmospheric pressure. Conclusions: This dataset supports advanced methodologies in feature extraction, multivariate signal analysis, unsupervised pattern discovery, vibration analysis, and the development of digital twins and soft sensing models for predictive maintenance optimization. Full article

Structural symmetry significantly influences the fundamental characteristics of two-dimensional (2D) materials. In conventional transition metal dichalcogenides (TMDs), the absence of in-plane symmetry introduces distinct optoelectronic behaviors. To further enrich the functionality of such materials, recent efforts have focused on disrupting out-of-plane symmetry—often through the application of external electric fields—which leads to the generation of an intrinsic electric field within the lattice. This internal field alters the electronic band configuration, broadening the material’s applicability in fields like optoelectronics and spintronics. Among various engineered 2D systems, Janus transition metal dichalcogenides (JTMDs) have shown as a compelling class. Their intrinsic structural asymmetry, resulting from the replacement of chalcogen atoms on one side, naturally breaks out-of-plane symmetry and surpasses certain limitations of traditional TMDs. This unique arrangement imparts exceptional physical properties, such as vertical piezoelectric responses, pronounced Rashba spin splitting, and notable changes in Raman modes. These distinctive traits position JTMDs as promising candidates for use in sensors, spintronic devices, valleytronic applications, advanced optoelectronics, and catalytic processes. In this Review, we discuss the synthesis methods, structural features, properties, and potential applications of 2D JTMDs. We also highlight key challenges and propose future research directions. Compared with previous reviews, this work focusing on the latest scientific research breakthroughs and discoveries in recent years, not only provides an in-depth discussion of the out-of-plane asymmetry in JTMDs but also emphasizes recent advances in their synthesis techniques and the prospects for scalable industrial production. In addition, it highlights the rapid development of JTMD-based applications in recent years and explores their potential integration with machine learning and artificial intelligence for the development of next-generation intelligent devices. Full article

Spodumene-bearing pegmatites are geochemically anomalous among crystalline rocks and important critical mineral resources in the green energy transition. However, prospecting is challenging due to their small size and the fact that they are often covered by soil and vegetation. This study demonstrates that, rather than being a hindrance, soil cover can enhance geochemical exploration, at least at the prospect scale. This study examines the dispersion pathways of lithium (Li) and its pathfinder elements (Rb, B, Ga, and Sn) from pegmatites (<10 m thick) into metamorphic host rocks and further into overlying undisturbed soils in heavily forested, postglaciated terrain of northeastern Wisconsin, USA. Soil-sample traverses over the world-renowned, lepidolite-type Animikie Red Ace pegmatite and two nearby dikes reveal pronounced <20 m anomalies with up to 1400 ppm of Li, 450 ppm of Rb, 3100 ppm of B, 40 ppm of Ga, and 60 ppm of Sn, greatly exceeding the control soil concentrations from nonmineralized granite and pegmatites. Soils mirror both the magmatic fractionation and alteration of pegmatite bedrock and metasomatic halos in parent host rocks. Metasomatized amphibolite revealed the presence of a holmquistite-ferro-holmquistite mineral. This greenfield pilot exploration led to lithium-rich pegmatite discoveries within the district and demonstrates the applicability of proximal sensors for soil exploration in Wisconsin and beyond. Full article

Glutamine is an essential biomolecule that plays a pivotal role in many diseases, such as cancer, where it can serve as fuel for rapid proliferation. Treatments for these diseases can be monitored and optimized through the detection of glutamine, though standard glutamine detection procedures are costly and require complex instrumentation. Cell-free protein synthesis (CFPS) has recently enabled a paper-based, colorimetric glutamine sensor that carries the potential to increase test accessibility while dramatically reducing consumer cost to enable at-home, rapid treatment monitoring. Test sensitivity remained limited by residual assay background, thus motivating this work where CFPS reactions traditionally formulated with glutamate salts were compared to systems using alternative salts, including aspartate, acetate, citrate, and sulfate, to reduce the background generation of glutamine. This led to the discovery of a novel aspartate-based CFPS system that boasts a high signal strength and indetectable background noise over 225 min. Acetate-, citrate-, and sulfate-based systems also yielded zero background glutamine detection but at a lower signal response compared to the aspartate-based system. These findings mark crucial advancements in producing a cost-effective, simple glutamine monitor while simultaneously showcasing the adaptability of CFPS’s open reaction environment for solving complex challenges in next-generation biosensor development. Full article

This study explores the vibrational behavior of Thermoformed Magneto-Piezoelectrets (TMPs), multifunctional materials consisting of thermoformed piezoelectrets with open tubular channels integrated with an additional magnetic layer. The inverse piezoelectric effect was characterized using laser vibrometry analysis, measuring the mechanical response of TMPs subjected to electrical excitation over a frequency range of 0–20 kHz. Vibrational analysis was conducted at 144 spatial points, enabling the construction of detailed three-dimensional (3D) maps of the vibration operational modes and the spatial distribution of the piezoelectric coefficient ($d_{33}$). The results demonstrated significant frequency-dependent behavior, with open channels exhibiting pronounced resonance peaks, whereas valleys displayed smoother and more uniform responses due to enhanced damping effects. The observed heterogeneity in vibrational behavior is attributed to structural variations, material composition, and anisotropic coupling between the piezoelectric and magnetic properties. The findings presented in this research provide a comprehensive understanding of the development and utilization of TMPs, offering parameters for enhancing their application and supporting new discoveries in studies related to the fabrication of novel thermoformed piezoelectric sensors. Full article

The development of alternatives to animal models and traditional cell cultures has led to the emergence of organ-on-chip (OoC) systems, which replicate organ functions under both physiological and pathological conditions. These microfluidic platforms simulate key tissue interfaces—such as tissue–air, tissue–liquid, and tissue–tissue interactions—while incorporating biomechanical stimuli to closely resemble in vivo environments. This makes OoC systems particularly suitable for modeling biological barriers such as the skin, the placenta, and the blood–brain barrier, which play essential roles in maintaining homeostasis. This review explores various biological barrier models that can be replicated using the OoC technology, discussing the integration of induced pluripotent stem cells (iPSCs) to advance personalized medicine. Additionally, we examine the methods for assessing barrier formation, including real-time monitoring through integrated sensors, and discuss the advantages and challenges associated with these technologies. The potential of OoC systems in disease modeling, drug discovery, and personalized therapeutic strategies is also highlighted. Full article

This study introduces a novel framework for the inverse design of sustainable food packaging materials using generative adversarial networks (GANs) and the recently released OMat24 dataset containing 110 million DFT-calculated inorganic material structures. Our approach transforms traditional material discovery paradigms by enabling end-to-end design from desired performance metrics to material composition. We developed a GAN-driven inverse design architecture specifically optimized for food packaging applications, integrating sensor-derived data on critical constraints such as biodegradability and barrier properties directly into the generative process. This integration occurs at three levels: (1) sensor-measured properties define conditioning targets for the GAN, (2) sensor data train the property prediction network, and (3) sensor-based characterization validates generated materials. An enhanced EquiformerV2 graph neural network was employed to accurately predict the formation energy, stability, and sensor-measurable properties of candidate materials. The model achieved a mean absolute error of 12 meV/atom for formation energy on the OMat24 test set (25% improvement over baseline models), while predictions of sensor-measured functional properties reached $R^2$ values of 0.84–0.89 through the integration of experimental measurements and physics-based proxy models. The framework successfully generated over 100 theoretically viable candidate materials, with 20% exhibiting superior barrier properties and controlled degradation characteristics. Our computational approach demonstrated a 20–100× acceleration in screening efficiency compared to traditional DFT calculations while maintaining high accuracy. This work presents a significant advancement in computational materials discovery for sustainable packaging applications, offering a promising pathway to address the urgent global challenges of food waste and plastic pollution. Full article

Since the discovery of the unique auditory system of the Ormia ochracea fly, researchers have designed various directional acoustic sensors inspired by its principles. However, most of these sensors operate only within a single- or dual-frequency band and typically exhibit high eigenfrequencies, making them unsuitable for low-frequency applications. This paper proposes a low-frequency, multi-band piezoelectric MEMS acoustic sensor that incorporates an improved coupling structure within the inner diaphragm to enable low-frequency signal detection in a compact design. Additionally, an asymmetric wing and coupled structure are introduced in both the inner and outer diaphragms to achieve multi-band frequency response. Aluminum nitride (AlN), a material with low dielectric and acoustic losses, is selected as the piezoelectric material. The sensor operates in the d₃₃ mode and employs a branched comb-like interdigitated electrode design to enhance the signal-to-noise ratio (SNR). Simulation results demonstrate that the four eigenfrequencies of the sensor are evenly distributed below 2000 Hz, and at all eigenfrequencies, the sensor exhibits a consistent cosine response to variations in the incident elevation angle of the sound source. Full article

Causality, which involves distinguishing between cause and effect, is essential for understanding complex relationships in data. This paper provides a review of causality in two key areas: causal discovery and causal inference. Causal discovery transforms data into graphical structures that illustrate how variables influence one another, while causal inference quantifies the impact of these variables on a target outcome. The models are more robust and accurate with the integration of causal reasoning into machine learning, improving applications like prediction and classification. We present various methods used in detecting causal relationships and how these can be applied in selecting or extracting relevant features, particularly from sensor datasets. When causality is used in feature selection, it supports applications like fault detection, anomaly detection, and predictive maintenance applications critical to the maintenance of complex systems. Traditional correlation-based methods of feature selection often overlook significant causal links, leading to incomplete insights. Our research highlights how integrating causality can be integrated and lead to stronger, deeper feature selection and ultimately enable better decision making in machine learning tasks. Full article

Weed control is fundamental to modern agriculture, underpinning crop productivity, food security, and the economic sustainability of farming operations. Herbicides have long been the cornerstone of effective weed management, significantly enhancing agricultural yields over recent decades. However, the field now faces critical challenges, including stagnation in the discovery of new herbicide modes of action (MOAs) and the escalating prevalence of herbicide-resistant weed populations. High research and development costs, coupled with stringent regulatory hurdles, have impeded the introduction of novel herbicides, while the widespread reliance on glyphosate-based systems has accelerated resistance development. In response to these issues, advanced image-based plant phenotyping technologies have emerged as pivotal tools in addressing herbicide-related challenges in weed science. Utilizing sensor technologies such as hyperspectral, multispectral, RGB, fluorescence, and thermal imaging methods, plant phenotyping enables the precise monitoring of herbicide drift, analysis of resistance mechanisms, and development of new herbicides with innovative MOAs. The integration of machine learning algorithms with imaging data further enhances the ability to detect subtle phenotypic changes, predict herbicide resistance, and facilitate timely interventions. This review comprehensively examines the application of image phenotyping technologies in weed science, detailing various sensor types and deployment platforms, exploring modeling methods, and highlighting unique findings and innovative applications. Additionally, it addresses current limitations and proposes future research directions, emphasizing the significant contributions of phenotyping advancements to sustainable and effective weed management strategies. By leveraging these sophisticated technologies, the agricultural sector can overcome existing herbicide challenges, ensuring continued productivity and resilience in the face of evolving weed pressures. Full article

Over the past quarter-century, the enhanced availability of satellite imagery, characterized by improved temporal, spectral, radiometric, and spatial resolutions, has enabled valuable insights into the spatial soil variability of annual croplands and orchards. This study investigates the impact of spatial resolution on classifying three-year, multi-temporal vegetation indices derived from satellites with coarse (30 m, Landsat 8), medium (10 m, Sentinel-2), and fine spatial resolutions (3.7 m, PlanetScope). The classification was performed using the fuzzy c-means algorithm, with the fuzziness performance index (FPI) and normalized classification entropy (NCE), which were used to determine the optimal number of management zones (MZs). Our results revealed that the Landsat 8-based NDVI images produced the highest number of clusters (nine for annual cropland and six for orchards), while the finer resolutions from PlanetScope reduced this to three clusters for both cultivation types, more accurately capturing the intra-parcel variability. Except for Landsat 8, the NDVI means of MZs generated based on Sentinel-2 and PlanetScope using the fuzzy c-means algorithm showed statistically significant differences from each other, as determined by a one-way and Welch’s ANOVA (p < 0.05). The use of PlanetScope imagery demonstrated its superiority in generating zones that reflect inherent variability, offering farmers actionable insights at a reconnaissance scale. Multi-temporal satellite imagery has proved effective in monitoring plant growth responses to edaphological soil properties. In our study, the PlanetScope satellites, which offer the highest spatial resolution, consistently produced effective zones for orchard areas. These zones have the potential to enhance farmers’ discovery of knowledge at a reconnaissance scale. With the increasing spatial resolution and enhanced spectral resolution of newer satellite sensors, using cluster analysis with insights from soil scientists promise to help farmers better understand and manage the fertility of their fields in a cost-effective manner. Full article