Search Results (13,883)

Implantable medical devices present several technological challenges, one of the most critical being how to provide power supply and communication capabilities to a device hermetically sealed within the body. Using a battery as a power source represents a potential harm for the individual’s health because of possible toxic chemical release or overheating, and it requires periodic surgery for replacement. This paper proposes a batteryless implantable device powered by an inductive link and equipped with bidirectional wireless communication channels. The device, designed in a 180 nm CMOS process, is based on two different pairs of mutually coupled inductors that provide, respectively, power and a low-bitrate bidirectional communication link and a separate, high-bitrate, one-directional upstream connection. The main link is based on a 13.56 MHz carrier and allows power transmission and a half-duplex two-way communication at 106 kbps (downlink) and 30 kbps (uplink). The secondary link is based on a 27 MHz carrier, which provides one-way communication at 2.25 Mbps only in uplink. The low-bitrate links are needed to send commands and monitor the implanted system, while the high-bitrate link is required to receive a continuous stream of information from the implanted sensing devices. The microchip acts as a hub for power and data wireless transmission capable of managing up to four different neural recording and stimulation front ends, making the device employable in a complex, distributed, bidirectional neural prosthetic system. Full article

Micro(nano)plastics are important emerging contaminants and a current research hotspot in the environmental field. Micro(nano)plastics widely exist in various organic wastes such as waste sludge, food waste (FW) and livestock manure and often enter into digesters along with anaerobic digestion (AD) treatment of these wastes, thereby exerting extensive and profound influences on anaerobic process performance. This study reviews sources of micro(nano)plastics and their pathways entering the anaerobic system and summarizes the quantities, sizes, shapes and micromorphology of various micro(nano)plastics in waste sludge, FW, livestock manure, yard waste and municipal solid waste. The current advances on the effects of multiple micro(nano)plastics mainly polyvinyl chloride (PVC), polystyrene (PS) and polyethylene (PE) with different sizes and quantities (or concentrations) on AD of organic wastes in terms of methane production, organic acid degradation and process stability are comprehensively overviewed and mechanisms of micro(nano)plastics affecting AD involved in microbial cells, key enzymes, microbial communities and antibiotic resistance genes are analyzed. Meanwhile, coupling effects of micro(nano)plastics with some typical pollutants such as antibiotics and heavy metals on AD are also reviewed. Due to the extreme complexity of the anaerobic system, current research still lacks full understanding concerning composite influences of different types, sizes and concentrations of micro(nano)plastics on AD under various operating modes. Future research should focus on elucidating mechanisms of micro(nano)plastics affecting organic metabolic pathways and the expression of specific functional genes of microorganisms, exploring the fate and transformation of micro(nano)plastics along waste streams including but not limited to AD, investigating the interaction between micro(nano)plastics and other emerging contaminants (such as perfluorooctanoic acid and perfluorooctane sulphonate) and their coupling effects on anaerobic systems, and developing accurate detection and quantification methods for micro(nano)plastics and technologies for eliminating the negative impacts of micro(nano)plastics on AD. Full article

Capacitive-coupling non-contact voltage sensors face a key challenge: their probe-conductor coupling capacitance varies, making it hard to accurately determine the division ratio. This capacitance is influenced by factors like the conductor’s insulation material, radius, and relative position. To address this challenge, this paper proposes a sensor gain self-calibration method based on switching capacitors. This method obtains multiple sets of real-time measurement outputs by connecting and switching different standard capacitors in parallel with the sensor’s structural capacitance, and then simultaneously solves for the coupling capacitance and the voltage under test, thereby achieving on-site autonomous calibration of the sensor gain. To effectively suppress interference from stray electric fields in the surrounding space, a shielded coaxial probe structure and corresponding back-end processing circuitry were designed, significantly enhancing the system’s anti-interference capability. Finally, an experimental platform incorporating insulated conductors of various diameters was built to validate the method’s effectiveness. Within the 100–300 V power-frequency range, the reconstructed voltage amplitude shows a maximum relative error of 1.06% and a maximum phase error of 0.76°, and harmonics are measurable up to the 50th order. Under inter-phase electric field interference, the maximum relative error of the reconstructed voltage amplitude is 1.34%, demonstrating significant shielding effectiveness. For conductors with diameters ranging from 6 mm² to 35 mm², the measurement error is controlled within 1.57%. These results confirm the method’s strong environmental adaptability and broad applicability across different conductor diameters. Full article

Cell motility is a process central to life and is undoubtedly influenced by mechanical and chemical signals. Even so, other stimuli are also involved in controlling cell migration in vivo and in vitro. Among these, electric fields have been shown to provide a powerful and programmable cue to manipulate cell migration. There is now a clear consensus that the electromigration of membrane components represents the first response to an external electric field, which subsequently activates downstream signals responsible for controlling cell migration. Here, we focus on a specific mode of electrotaxis: frictionless, amoeboid-like migration. We used the Finite Element Method to solve an active gel model coupled with a mathematical model of the electromigration of aquaporins and investigate the effect of electric fields on ameboid migration. We demonstrate that an electric field can polarize aquaporins in a cell and, consequently, that the electromigration of aquaporins can be exploited to regulate water flux across the cell membrane. Our findings indicate that controlling these fluxes allows modulation of cell migration velocity, thereby reducing the cell’s migratory capacity. Our work provides a mechanistic framework to further study the impact of electrotaxis and to add new insights into specific modes by which electric fields modify cell motility. Full article

The rapid growth of artificial intelligence (AI), data centers, and high-performance computing (HPC) has increased the demand for large bandwidth, high energy efficiency, and high-density optical interconnects. Co-packaged optics (CPO) technology offers a promising solution by integrating photonic integrated circuits (PICs) directly within or close to electronic integrated circuit (EIC) packages. This paper explores the evolution of CPO performance from various perspectives, including fan-out wafer level packaging (FOWLP), through-silicon via (TSV)-based packaging, through-glass via (TGV)-based packaging, femtosecond laser direct writing waveguides, ion-exchange glass waveguides, and optical coupling. Micro ring resonators (MRRs) are a high-density integration solution due to their compact size, excellent energy efficiency, and compatibility with CMOS processes. However, traditional thermal tuning methods face limitations such as high static power consumption and severe thermal crosstalk. To address these issues, non-volatile neuromorphic photonics has made breakthroughs using phase-change materials (PCMs). By combining the integrated storage and computing capabilities of photonic memory with the efficient optoelectronic interconnects of CPO, this deep integration is expected to work synergistically to overcome material, integration, and architectural challenges, driving the development of a new generation of computing hardware with high energy efficiency, low latency, and large bandwidth. Full article

Smart logistics systems generate massive amounts of data, such as images and videos, requiring real-time processing in edge clusters. However, the edge cluster systems face performance bottlenecks in reception and forwarding high-concurrency data streams from numerous smart terminals, resulting in degraded processing efficiency. To address this issue, a novel high-performance data stream model called CBPS-DPDK is proposed. CBPS-DPDK integrates the DPDK framework from Intel corporations with a content-based publish/subscribe model enhanced by semantic filtering. This model adopts a three-tier optimization architecture. First, the user-space data plane is restructured using DPDK to avoid kernel context switch overhead via zero-copy and polling. Second, semantic enhancement is introduced into the publish/subscribe model to reduce the coupling between data producers and consumers through subscription matching and priority queuing. Finally, a hierarchical load balancing strategy ensures reliable data transmission under high concurrency. Experimental results show that CBPS-DPDK significantly outperforms two baselines—OSKT (kernel-based data forwarding) and DPDK-only (DPDK). Relative to the OSKT baseline, DPDK-only achieves improvements of 37.5% in latency, 11.1% in throughput, and 9.1% in VMAF; CBPS-DPDK further increases these to 51.8%, 18.3%, and 11.2%, respectively. In addition, compared with the traditional publish–subscribe system NATS, CBPS-DPDK maintains lower delay, higher throughput, and more balanced CPU and memory utilization under saturated workloads, demonstrating its effectiveness for real-time, high-concurrency edge scenarios. Full article

Neuropeptides (NPs) are small molecular messengers synthesized in large dense core vesicles (LDCVs) and secreted to the extracellular space. In the central nervous system (CNS), NPs are secreted to the synaptic space, playing crucial roles in modulating neurons, astrocytes, microglia, oligodendrocytes, and other glial cells, through G-protein-coupled receptors, thereby influencing complex multicellular responses. During neuroinflammation, NPs regulate glial and neuronal reactions to inflammatory signals, promoting resolution and preventing chronic, non-resolving inflammation. For example, NPs inhibit apoptosis in neurons and oligodendrocytes while inducing anti-inflammatory effects in microglia and astrocytes, modulating cytokine secretion. Here, we present the notion that neuropeptides could participate in neuroinflammatory progression, altering glial responses, leading to excessive, non-resolutive inflammation when dysregulated. NP signaling—whether excessive or deficient—can disrupt specific cellular processes, leading to pathological inflammation, gliosis, and functional loss—hallmarks of neurodegenerative diseases. Despite their significance, the precise mechanisms underlying NP-mediated effects remain incompletely understood. This review synthesizes experimental and translational evidence highlighting the pivotal role of NPs in resolving neuroinflammation and explores how targeting NPs or their receptors could offer novel therapeutic strategies for neurodegenerative disorders. Further research is needed to elucidate the specific signaling pathways and receptor dynamics involved, which could pave the way for innovative treatments that address the root causes of these debilitating conditions. Full article

This article describes the implemented technological solution of utilizing waste heat by upgrading the potato protein drying line and using energy recuperation in the drying plant. In this article, the technological sequence of the potato starch and potato protein production plant was analyzed and the identification of possible solutions that lead to a reduction in energy demand was described. The method of analyzing the processing data is based on existing models describing the flow of mass and energy fluxes. The authors did not seek new mathematical descriptions of the physicochemical phenomena occurring during the drying processes, and only modification of the technological line based on the current state of knowledge in process engineering has been proposed. The full heat recovery of the production line was applied, and the exhaust air after drying and the heat from the decanter leachate after centrifugation of the coagulated potato protein, from two energy-coupled starch dryers, were used as the source of recovered heat energy. Temperature measurements were taken at key process nodes, and the energy effects were estimated after the process line upgrade. The solution proposed in the article fits with circular economy, bringing notable economic and environmental benefits consisting of utilizing waste heat from technological processes in the food industry. Full article

Surface-enhanced Raman Scattering (SERS) enables ultrasensitive detection but is often hindered by biocompatibility and sustainability concerns due to its reliance on noble metal substrates. To overcome these limitations, we develop a semiconductor-based SERS platform utilizing ultrathin tungsten trioxide (WO₃) nanofilms synthesized via a facile annealing process on fluorine-doped tin oxide (FTO). This system achieves an impressive Raman enhancement factor of 1.36 × 10⁶, enabling ultrasensitive detection of rhodamine 6G (R6G) and methylene blue (MB) at ultralow concentrations, surpassing conventional metal-based SERS platforms. It is further suggested that this is a substrate that can be easily coupled to other metals. An application for the detection of adenine molecules is realized through layered WO₃-Au NPs composites, where embedded gold nanoparticles act as plasma “hot spots” to amplify the sensitivity. Density functional theory (DFT) calculations and band structure analysis confirm that synergistic interface charge transfer and naturally formed oxygen vacancies enhance performance. By combining semiconductor compatibility with other metal amplification, this WO₃-based SERS platform offers a sustainable and high-performance alternative to conventional substrates, paving the way for environmentally friendly and scalable Raman sensing technologies. Full article

The partnership between government and market plays a crucial role in allocating green resources and fostering collaboration across organizations and departments. It integrates diverse knowledge types into the green innovation process and offers multifaceted insights into enterprises’ responses to green governance decisions. However, existing research predominantly examines the interplay among government green governance instruments, with insufficient exploration of the synergistic impacts of government and market in green governance. This study constructs a capacity coupling coefficient model to measure the synergy degree of “government–market” green governance (GMGG). Exploiting a balanced dynamic panel of 28,451 firm-year observations for 3807 Chinese listed companies from 2010 to 2020, we estimate the causal effect of GMGG synergy on corporate green innovation (CGI) and further dissect the underlying transmission mechanisms as well as the moderating channels through which the effect operates. Empirical results reveal that the effect of GMGG synergy on CGI is subject to diminishing marginal returns, with the effect being significantly more pronounced for substantive green innovation. Heterogeneity analysis indicates that non-state-owned firms, eastern-region firms, and those in non-heavy-polluting industries respond with markedly greater sensitivity. Mechanism analysis further demonstrates that the extent of marketization serves as a mediating channel, whereas an elevated level of digital-economy development mitigates the impact of GMGG synergy on CGI. This study delineates the effective boundary of GMCC synergy in stimulating CGI, providing empirical benchmarks for the synergistic implementation of effective government and efficient market actions in green governance. It further corroborates the positive roles of marketization and the digital economy as novel governance instruments, thereby offering critical policy insights for the coordinated advancement of the “dual-carbon” goals and high-quality economic development. Full article

This study investigates a UDETA-modified polyurethane–urea (PUU) self-healing coating for wind turbine blades, focusing on its ability to autonomously repair surface erosion damage under realistic environmental conditions. A multiphysics finite element model was developed to couple temperature, moisture, and stress effects on crack healing, and a Gaussian process regression (GPR) model was trained on 35 experimental data points to predict the mobile fraction and healing thresholds with high accuracy (R² = 0.79, MAE = 0.059). The diffusion coefficient of water in the PUU matrix was determined as 11.03 × 10⁻⁷ mm²/s, and stress-driven moisture accumulation at crack tips was shown to accelerate crack healing. Erichsen cupping test simulations were conducted to reproduce experimental crack patterns, demonstrating brittle behavior in dehydrated coatings with a Young’s modulus of 50 MPa and critical principal strains of 0.48. An exponential healing function was incorporated into the computational model and validated against experiments, predicting significant crack healing within 24 h of humidity exposure. These findings provide quantitative design criteria for self-healing coatings, enabling the selection of UDETA content, thickness, and curing strategies to extend wind turbine blade service life while reducing maintenance costs. Full article

(1) Background: Supernumerary teeth are developmental anomalies, and their pulp tissue may harbor unique cellular and molecular features. However, the biology of this rare tissue remains poorly understood. This study aimed to characterize the cellular diversity and regenerative potential of supernumerary pulp at single-nucleus resolution. (2) Methods: Human supernumerary tooth pulp samples were analyzed using single-nucleus RNA sequencing. Gene expression profiles were processed and reduced to their main patterns of variation using principal component analysis (PCA), supported by clustering, pathway analysis, and lineage-specific scoring. (3) Results: The analysis suggested two dominant biological programs: a vascular–immune/stress axis and an extracellular matrix (ECM)/contractile remodeling axis. Vascular lineages were closely linked to immune and stress responses, while mesenchymal and perivascular populations were enriched in ECM-related pathways. Neural and glial contributions were relatively minor. (4) Conclusions: These findings suggest that supernumerary pulp appears to preserve key regenerative features similar to normal pulp, but with potential reinforcement of vascular–immune coupling and ECM remodeling. This work represents the first single-nucleus transcriptomic reference for supernumerary pulp, offering a foundation for future studies on dental pulp regeneration. Full article

This paper proposes a second-order low-pass Butterworth multiple-feedback (MFB) filter with a reconfigurable bandwidth and gain, implemented in a 28 nm CMOS. The filter supports independent tuning of the bandwidth from 10 MHz to 100 MHz and the gain from 0 dB to 19 dB, effectively addressing the challenge of a tightly coupled gain and quality factor in traditional MFB designs. Notably, compared to the widely adopted Tow–Thomas structure, the proposed filter achieves second-order filtering and the same degree of flexibility using only a single operational amplifier (OPA), significantly reducing both the power consumption and area. Additionally, an RC tuning circuit is employed to reduce fluctuations in the RC time constant under process, voltage, and temperature (PVT) variations. To meet the requirements for high linearity and low power consumption in broadband applications, a three-stage push–pull OPA with current re-use feedforward and an RC Miller compensation technique is proposed. With the current re-use feedforward, the OPA’s loop gain at 100 MHz is significantly enhanced from 22.34 dB to 28.75 dB, achieving a 2.14 GHz unity-gain bandwidth. Using this OPA, the filter achieves a 48.2 dBm in-band (IB) OIP3, a 53.4 dBm out-of-band (OOB) OIP3, and a figure of merit (FoM) of 185.5 dBJ⁻¹ at a100 MHz bandwidth while consuming only 3.6 mW from a 1.8 V supply. Full article

In this study, we benchmark various machine learning techniques against a synthetic but physically based reference time series (model-simulated (ERA5-Land/FLake) bottom-temperature series) and assess whether decomposition methods (VMD and EMD) improve forecast accuracy using Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Random Forest (RF), Gaussian Process Regression (GPR), and Long Short-Term Memory (LSTM) with the monthly average data of Mjøsa, the largest lake in Norway, between 1950 and 2024 from the ERA5-Land FLake model. A total of 70% of the dataset was used for training and 30% was reserved for testing. To assess the performance several metrics, correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), Performance Index (PI), RMSE-based RSR, and Root Mean Square Error (RMSE) were used. The results revealed that without decomposition, the GPR-M03 combination outperforms other models (with scores r = 0.9662, NSE = 0.9186, KGE = 0.8786, PI = 0.0231, RSR = 0.2848, and RMSE = 0.2000). Considering decomposition cases, when VMD is applied, the SVM-VMD-M03 combination achieved better results compared to other models (with scores r = 0.9859, NSE = 0.9717, KGE = 0.9755, PI = 0.0135, RSR = 0.1679, and RMSE = 0.1179). Conversely, with decomposition cases, when EMD applied, LSTM-EMD-M03 is explored as the more effective combination than others (with scores r = 0.9562, NSE = 0.9008, KGE = 0.9315, PI = 0.0256, RSR = 0.2978, and RMSE = 0.3143). The results demonstrate that GPR and SVM, coupled with VMD, yield high correlation (e.g., r ≈ 0.986) and low RMSE (~0.12), indicating the ability to reproduce FLake dynamics rather than as accurate predictions of measured bottom temperature. Full article

The complex electricity consumption situation on the customer side and large-scale wind and solar power generation have gradually shifted the traditional “source-follow-load” model in the power system towards the “source-load interaction” model. At present, the voltage regulation methods require excessive computing resources to accurately predict the fluctuating load under the new energy structure. However, with the development of artificial intelligence and cloud computing, more methods for processing big data have emerged. This paper proposes a new method for electricity consumption analysis that combines traditional mathematical statistics with machine learning to overcome the limitations of non-intrusive load detection methods and develop a distributed optimization of cloud–edge–device distribution networks based on electricity consumption. Aiming at problems such as overfitting and the demand for accurate short-term renewable power generation prediction, it is proposed to use the long short-term memory method to process time series data, and an improved algorithm is developed in combination with error feedback correction. The R² value of the coupling algorithm reaches 0.991, while the values of RMSE, MAPE and MAE are 1347.2, 5.36 and 199.4, respectively. Power prediction cannot completely eliminate errors. It is necessary to combine the consistency algorithm to construct the regulation strategy. Under the regulation strategy, stability can be achieved after 25 iterations, and the optimal regulation is obtained. Finally, the cloud–edge–device distributed coevolution model of the power grid is obtained to achieve the economy of power grid voltage control. Full article