Search Results (800)

This paper proposes an enhanced cooling method for multi-chip power modules (e.g., in MMC inverters) with uneven power loss in all-electric propulsion ships based on sintered heat pipe parameter optimization and special-shaped design. First, five key parameters of straight sintered heat pipes were optimized: placement directly under hotspot chips, 10 mm in diameter, quantity matching the number of hotspot chips, length equal to the heatsink side length, and direction perpendicular to heatsink fins. Then, a C-shaped heat pipe was designed using the parallel thermal resistance principle, which forms two parallel low-thermal-resistance paths and outperforms conventional U-shaped ones. Finite element simulations showed that the hotspot temperature of the conventional heatsink was 91.26 °C, while it dropped to 87.35 °C with optimized straight heat pipes and further to 80.85 °C with C-shaped ones. Experiments verified an 11.65% temperature reduction (from 86.7 °C of conventional heatsinks to 76.6 °C of C-shaped heat pipe heatsinks). This method effectively lowers hotspot temperatures, reduces device failure rates, improves the thermal reliability of power modules, and provides a generalized design methodology for heatsinks of various power electronic converters. Full article

Research on in vitro skin models has advanced remarkably, driven by a better understanding of the skin and the search for more ethical and efficient methods. The development of these models was initially motivated by the need for reduced animal testing and a faster and more ethical approach for the safety evaluation of cosmetic and pharmaceutical products. Stricter regulations and growing ethical awareness have driven further evolution, resulting in more refined and reliable methods. Diversity of cell types is crucial to replicating the complexity of human skin, including epithelial, dendritic, endothelial, and adipose cells, providing environments that closely mimic the physiological skin environment. This allows for more precise studies on skin interactions with cosmetic, dermatological, and pharmaceutical products. In vitro skin models have applications in toxicity testing, dermatological product evaluation, skin ageing studies, and drug research, reducing dependence on animal testing. This review presents a look at the different types of in vitro skin models developed for various applications, with a brief look at their strengths and drawbacks. Models developed for disease-specific applications are also covered. Techniques such as bioprinting and organ-on-a-chip have revolutionised the manufacturing of these models. Challenges persist, such as the need to improve vascularisation and faithfully replicate skin architecture. The promising future of these models points to an exciting path forward for dermatological research and the cosmetic industry. This review addresses the history and regulations of skin models, explores various skin models, and highlights the most recent advances, outlining future perspectives and offering a comprehensive overview. Full article

We present a large-scale neuromodulation system-on-chip (SoC) that integrates a 128-channel neural recording and 32-channel stimulation ASIC designed for a wide range of neuroscientific applications. Each recording channel achieves low-noise performance (~4 μV_rms) with a configurable bandwidth of 0.05 Hz–7.5 kHz and supports 16-bit digitization with scalable sampling rates up to 30 kS/s. To enhance signal quality, the ASIC includes an adjustable digital high-pass filter and a fast-settling function for rapid recovery from stimulation artifacts. SoC also incorporates on-chip electrode-impedance measurements as a built-in safety feature by reusing the recording channels. The stimulation subsystem generates current-controlled monopolar biphasic pulses with a high compliance voltage of ±6 V using standard low-voltage (1.8 V/3.3 V) CMOS devices. Each of the 32 stimulation channels provides arbitrary 9-bit programmable waveforms and dual current modes (4 μA/bit and 8 μA/bit), supporting both fine-resolution microstimulation and high-current applications such as spinal-cord and deep-brain stimulation. On-chip charge-balancing switches in each channel further ensure safe and reliable stimulation delivery. The SoC supports digital communication via a standard SPI with both 3.3 V CMOS and low-voltage differential signaling options and integrates all required analog references and low-dropout regulators. The prototype was fabricated in a standard 180 nm CMOS process, occupying 31.92 mm² (equivalently, 0.2 mm² per recording-and-stimulation channel), and was fully validated through benchtop measurements and in vitro experiments. Full article

As an emerging type of non-volatile memory, magneto-resistive random access memory (MRAM) stands out for its exceptional reliability and rapid read–write speeds, thereby garnering considerable attention within the industry. The memory cell architecture of MRAM is centered around the magnetic tunnel junction (MTJ), which, however, is prone to interference from external magnetic fields—a limitation that restricts its application in demanding environments. To address this challenge, we propose an innovative open magnetic shielding structure. This design demonstrates remarkable shielding efficacy against both in-plane and perpendicular magnetic fields, effectively catering to the magnetic shielding demands of both spin-transfer torque (STT) and spin–orbit torque (SOT) MRAM. Finite element magnetic simulations reveal that when subjected to an in-plane magnetic field of 40 mT, the magnetic field intensity at the chip level is reduced to nearly 1‰ of its original value. Similarly, under a perpendicular magnetic field of 40 mT, the magnetic field at the chip is reduced to 2‰ of its initial strength. Such reductions significantly enhance the anti-magnetic capabilities of MRAM. Moreover, the magnetic shielding performance remains unaffected by the height of the packaging structure, ensuring compatibility with various chip stack packaging requirements across different layers. The research presented in this paper holds immense significance for the realization of highly reliable magnetic shielding packaging solutions for MRAM. Full article

Investment decision-making is often characterized by uncertainty and the subjective weighting of criteria. This study aims to develop a more robust decision support framework by integrating the Generalized Interval-Valued Hesitant Intuitionistic Fuzzy Soft Set (GIVHIFSS) with the Analytic Hierarchy Process (AHP) to objectively weight criteria and handle multi-evaluator hesitancy. In the proposed GIVHIFSS-AHP model, the AHP is employed to derive mathematically consistent criterion weights, which are subsequently embedded into the GIVHIFSS structure to accommodate interval-valued and hesitant evaluations from multiple decision-makers. The model is applied to a numerical case study evaluating five investment alternatives. Its performance is assessed through a comparative analysis with standard GIVHIFSS and GIFSS models, as well as a sensitivity analysis. The results indicate that the model produces financially rational rankings, identifying blue-chip technology stocks as the optimal choice (score: +2.4). The comparative analysis confirms its superiority over existing models, which yielded less-stable rankings. Moreover, the sensitivity analysis demonstrates the robustness of the results against minor perturbations in criterion weights. This research introduces a novel and synergistic integration of the AHP and GIVHIFSS. The key advantage of this approach lies in its ability to address the long-standing issue of arbitrary criterion weighting in Fuzzy Soft Set models by embedding the AHP as a foundational mechanism for ensuring validation and objectivity. This integration results in mathematically derived, consistent weights, thereby yielding empirically validated, more reliable, and defensible decision outcomes compared with existing models. Full article

Microphysiological systems (MPSs) have emerged as alternatives to animal testing in drug development, following the FDA Modernization Act 2.0. Double-layer channel-type MPS chips with porous membranes are widely used for modeling various organs, including the intestines, blood–brain barrier, renal tubules, and lungs. However, these chips faced challenges owing to optical interference caused by light scattering from the porous membrane, which hinders cell observation. Trans-epithelial electrical resistance (TEER) measurement offers a non-invasive method for assessing barrier integrity in these chips. However, existing electrode-integrated MPS chips for TEER measurement have non-uniform current densities, leading to compromised measurement accuracy. Additionally, chips made from polydimethylsiloxane have been associated with drug absorption issues. This study developed an electrode-integrated MPS chip for TEER measurement with a uniform current distribution and minimal drug absorption. Through a finite element method simulation, electrode patterns were optimized and incorporated into a polyethylene terephthalate (PET)-based chip. The device was fabricated by laminating PET films, porous membranes, and patterned gold electrodes. The chip’s performance was evaluated using a perfused Caco-2 intestinal model. TEER levels increased and peaked on day 5 when cells formed a monolayer, and then they decreased with the development of villi-like structures. Concurrently, capacitance increased, indicating microvilli formation. Exposure to staurosporine resulted in a dose-dependent reduction in TEER, which was validated by immunostaining, indicating a disruption of the tight junction. This study presents a TEER measurement MPS platform with a uniform current density and reduced drug absorption, thereby enhancing TEER measurement reliability. This system effectively monitors barrier integrity and drug responses, demonstrating its potential for non-animal drug-testing applications. Full article

One of the key measures of cutting tool efficiency in machining processes is tool wear. In recent decades, numerical modeling of this phenomenon—primarily through finite element cutting models—has gained increasing importance. A crucial requirement for the reliable application of such models is the selection of an appropriate friction model, which strongly affects the accuracy of wear predictions. However, choosing the friction model type and its parameters remains a nontrivial challenge. This paper examines the effect of different friction model types and their parameters on the Archard and Usui wear model indicators, as well as on the main cutting process characteristics: cutting force components, temperature in the primary cutting zone, contact length between the tool rake face and the chip, shear angle, and chip compression ratio. To evaluate their impact on predicted tool wear—expressed qualitatively through the wear indicators of the aforementioned models—several widely used friction models implemented in commercial FEM software were applied: the shear friction model, Coulomb friction model, hybrid friction model, and constant tau model. The simulated values of these cutting process characteristics were then compared with experimental results. Full article

Gallium nitride (GaN) power devices have attracted extensive attention due to their superior performance in high-frequency and high-power applications. However, the reliability and lifetime prediction of these devices under various operating conditions remain critical challenges. In this study, a hybrid approach combining finite element simulation and deep learning is proposed to predict the lifetime of GaN power devices. COMSOL Multiphysics (V6.3) is employed to simulate the thermal and mechanical stress behavior of GaN devices under different power and frequency conditions, while capturing key degradation indicators such as temperature cycles and stress concentrations. The variation in temperature over time can reflect the degradation of the device and also reveal the fatigue damage caused by the long-term accumulation of thermal stress on the chip. LSTM performs exceptionally well in extracting features from time series data, effectively capturing the long-term and short-term dependencies within the time series. By using simulation data to establish a connection between the chip temperature and its service life, the temperature data and the lifespan data are combined into a dataset, and the LSTM neural network is used to explore the impact of temperature changes over time on the lifespan. The method mentioned in this paper can make preliminary predictions of the results when sufficient experimental data cannot be obtained in a short period of time. The prediction results have a certain degree of reliability. Full article

Progress in clinical diagnosis increasingly relies on innovative technologies and advanced disease biomarker detection methods. While cell labeling remains a well-established technique, label-free approaches offer significant advantages, including reduced workload, minimal sample damage, cost-effectiveness, and simplified chip integration. These approaches focus on the morpho-biophysical properties of cells, eliminating the need for labeling and thus reducing false results while enhancing data reliability and reproducibility. Current label-free methods span conventional and advanced technologies, including phase-contrast microscopy, holographic microscopy, varied cytometries, microfluidics, dynamic light scattering, atomic force microscopy, and electrical impedance spectroscopy. Their integration with artificial intelligence further enhances their utility, enabling rapid, non-invasive cell identification, dynamic cellular interaction monitoring, and electro-mechanical and morphological cue analysis, making them particularly valuable for cancer diagnostics, monitoring, and prognosis. This review compiles recent label-free cancer cell detection developments within clinical and biotechnological laboratory contexts, emphasizing biophysical alterations pertinent to liquid biopsy applications. It highlights interdisciplinary innovations that allow the characterization and potential identification of cancer cells without labeling. Furthermore, a comparative analysis addresses throughput, resolution, and detection capabilities, thereby guiding their effective deployment in biomedical research and clinical oncology settings. Full article

Respiratory infectious diseases, such as COVID-19, influenza, and tuberculosis, continue to impose a significant global health burden, underscoring the urgent demand for rapid, sensitive, and cost-effective diagnostic technologies. Integrated microfluidic platforms offer compelling advantages through miniaturization, automation, and high-throughput processing, enabling “sample-in, answer-out” workflows suitable for point-of-care applications. However, their clinical deployment faces challenges, including the complexity of sample matrices, low-abundance target detection, and the need for reliable multiplexing. The convergence of artificial intelligence (AI) with microfluidic systems has emerged as a transformative paradigm, addressing these limitations by optimizing chip design, automating sample pre-processing, enhancing signal interpretation, and enabling real-time feedback control. This critical review surveys AI-enabled strategies across each functional layer of respiratory pathogen diagnostics: from chip architecture and fluidic control to amplification analysis, signal prediction, and smartphone/IoT-linked decision support. We highlight key areas where AI offers measurable benefits over conventional methods. To transition from research prototypes to clinical tools, future systems must become more adaptive, data-efficient, and clinically insightful. Advances such as sensor-integrated chips, privacy-preserving machine learning, and multimodal data fusion will be essential to ensure robust performance and meaningful outputs across diverse scenarios. This review outlines recent progress, current limitations, and future directions. The rapid development of AI and microfluidics presents exciting opportunities for next-generation pathogen diagnostics, and we hope this work contributes to the advancement of intelligent, point-of-care testing (POCT) solutions. Full article

Semantic communication (SemCom), as a revolutionary paradigm for next-generation networks, shifts the focus from traditional bit-level transmission to the delivery of meaning and purpose. Grounded in the Data, Information, Knowledge, Wisdom, Purpose (DIKWP) model and its mapping framework, together with the relativity of understanding theory, the discussion systematically reviews advances in semantic-aware communication and personalized semantic security. By innovatively introducing the “Purpose” dimension atop the classical DIKW hierarchy and establishing interlayer feedback mechanisms, the DIKWP model enables purpose-driven, dynamic semantic processing, providing a theoretical foundation for both SemCom and personalized semantic security based on cognitive differences. A comparative analysis of existing SemCom architectures, personalized artificial intelligence (AI) systems, and secure communication mechanisms highlights the unique value of the DIKWP model. An integrated cognitive–conceptual–semantic network, combined with the principle of semantic relativity, supports the development of explainable, cognitively adaptive, and trustworthy communication systems. Practical implementation paths are explored, including DIKWP-based semantic chip design, white-box AI evaluation standards, and dynamic semantic protection frameworks, establishing theoretical links with emerging trends such as task-oriented communication and personalized foundation models. Embedding knowledge representation and cognitive context into communication protocols is shown to enhance efficiency, reliability, and security significantly. In addition, key research challenges in semantic alignment, cross-domain knowledge sharing, and formal semantic metrics are identified, while future research directions are outlined to guide the evolution of intelligent communication networks and provide a systematic reference for the advancement of the field. Full article

Laser Powder Bed Fusion (L-PBF) parts combine geometric freedom with process-induced rough surfaces that challenge residual-stress metrology. We evaluated the accuracy of the incremental hole-drilling (IHD) method with electronic speckle pattern interferometry (ESPI) by applying defined stresses via four-point bending to stress-relieved AlSi10Mg coupons, rather than measuring unknown process stresses. Flat specimens (2 mm, thin per ASTM E837) were analyzed on up-skin, side-skin, and CNC-milled surfaces; thin-specimen calibration coefficients were used. After a preliminary inter-specimen check (three specimens per surface; spread < 8 MPa), one representative specimen per surface was tested with three drill sites to assess intra-specimen uniformity. Measured IHD–ESPI stresses agreed best at 70 MPa: deviations were ~4.1% (up-skin), 6.0% (side-skin), and 6.24% (CNC-milled). At 10 MPa the relative errors increased (23.6%, 18.4%, and 1.40%), consistent with reduced ESPI signal-to-noise and fixture compliance in the low-stress regime. At 140 MPa, deviations rose again (21.1%, 14.3%, and 13.1%), reflecting operation near the ~60% Rp0.2 elastic limit of hole-drilling and potential local plasticity. Surface-dependent artifacts also mattered as follows: the side-skin required no coating and performed comparably to CNC-milled, whereas the up-skin’s roughness plus matting spray introduced fringe distortions and chip/coating debris near the hole. This controlled study indicates that IHD–ESPI can provide reliable results on L-PBF AlSi10Mg in the mid-stress range when surface preparation, coating, and rig compliance are carefully managed. Limitations include excluding down-skin surfaces and testing only one specimen per condition; thus, results should be generalized cautiously. Full article

This paper presents a temperature-robust current-mode vertical-cavity surface-emitting laser (VCSEL) driver (or CMVD) fabricated in a standard 180 nm CMOS process. While prior art relies on conventional current-mirror circuits for bias generation, the proposed CMVD integrates a bandgap-based biasing architecture to achieve high thermal stability and process insensitivity. The bandgap core yields a temperature-compensated reference voltage and is then converted into both stable bias and modulation currents through a cascode current-mirror and switching logic. Post-layout simulations of the proposed CMVD show that the reference voltage variation remains within ±2%, and the bias current deviation is under 10% across full PVT conditions. Furthermore, the output current variation is limited to 7.4%, even under the worst-case corners (SS, 125 °C), demonstrating the reliability of the proposed architecture. The implemented chip occupies a compact core area of 0.0623 mm² and consumes an average power of 18 mW from a single 3.3 V supply, suggesting that the bandgap-stabilized CMVD is a promising candidate for compact, power-sensitive optical systems requiring reliable and temperature-stable performance. Full article

With the rapid evolution of 5G wireless communications, millimeter-wave (mmWave) technology has become a crucial enabler for high-speed, low-latency, and large-scale connectivity. As the critical interface for signal transmission, mmWave antennas directly affect system performance, reliability, and application scope. This paper reviews the current state of mmWave antenna technologies in 5G systems, focusing on antenna types, design considerations, and integration strategies. We discuss how the multiple-input multiple-output (MIMO) architectures and advanced beamforming techniques enhance system capacity and link robustness. State-of-the-art integration methods, such as antenna-in-package (AiP) and chip-level integration, are examined for their importance in achieving compact and high-performance mmWave systems. Material selection and fabrication technologies—including low-loss substrates like polytetrafluoroethylene (PTFE), hydrocarbon-based materials, liquid crystal polymer (LCP), and microwave dielectric ceramics, as well as emerging processes such as low-temperature co-fired ceramics (LTCC), 3D printing, and micro-electro-mechanical systems (MEMS)—are also analyzed. Key challenges include propagation path limitations, power consumption and thermal management in highly integrated systems, cost–performance trade-offs for mass production, and interoperability standardization across vendors. Finally, we outline future research directions, including intelligent beam management, reconfigurable antennas, AI-driven designs, and hybrid mmWave–sub-6 GHz systems, highlighting the vital role of mmWave antennas in shaping next-generation wireless networks. Full article

Recent progress in microfluidic technologies has led to the development of compact and highly efficient electrochemical platforms, including lab-on-a-chip (LoC) systems, that integrate multiple testing functions into a single, portable device. Combined with smartphone-based electrochemical devices, these systems enable rapid and accurate on-site detection of food contaminants, including pesticides, heavy metals, pathogens, and chemical additives at farms, markets, and processing facilities, significantly reducing the need for traditional laboratories. Smartphones improve the performance of these platforms by providing computational power, wireless connectivity, and high-resolution imaging, making them ideal for in-field food safety testing with minimal sample and reagent requirements. At the core of these systems are electrochemical biosensors, which convert specific biochemical reactions into electrical signals, ensuring highly sensitive and selective detection. Advanced nanomaterials and integration with Internet of Things (IoT) technologies have further improved performance, delivering cost-effective, user-friendly food monitoring solutions that meet regulatory safety and quality standards. Analytical techniques such as voltammetry, amperometry, and impedance spectroscopy increase accuracy even in complex food samples. Moreover, low-cost engineering, artificial intelligence (AI), and nanotechnology enhance the sensitivity, affordability, and data analysis capabilities of smartphone-integrated electrochemical devices, facilitating their deployment for on-site monitoring of food and agricultural contaminants. This review explains how these technologies address global food safety challenges through rapid, reliable, and portable detection, supporting food quality, sustainability, and public health. Full article