Search Results (2,413)

Cell-free biosensors harness the selectivity of cellular machinery without living cells’ constraints, offering advantages in environmental monitoring, medical diagnostics, and biotechnological applications. This review examines recent advances in cell-free biosensor development, highlighting their ability to detect diverse analytes including heavy metals, organic pollutants, pathogens, and clinical biomarkers with high sensitivity and specificity. We analyze technological innovations in cell-free protein synthesis optimization, preservation strategies, and field deployment methods that have enhanced sensitivity, and practical applicability. The integration of synthetic biology approaches has enabled complex signal processing, multiplexed detection, and novel sensor designs including riboswitches, split reporter systems, and metabolic sensing modules. Emerging materials such as supported lipid bilayers, hydrogels, and artificial cells are expanding biosensor capabilities through microcompartmentalization and electronic integration. Despite significant progress, challenges remain in standardization, sample interference mitigation, and cost reduction. Future opportunities include smartphone integration, enhanced preservation methods, and hybrid sensing platforms. Cell-free biosensors hold particular promise for point-of-care diagnostics in resource-limited settings, environmental monitoring applications, and food safety testing, representing essential tools for addressing global challenges in healthcare, environmental protection, and biosecurity. Full article

Monitoring the activity index of animals is crucial for assessing their welfare and behavior patterns. However, traditional methods for calculating the activity index, such as pixel intensity differencing of entire frames, are found to suffer from significant interference and noise, leading to inaccurate results. These classical approaches also do not support group or individual tracking in a user-friendly way, and no open-access platform exists for non-technical researchers. This study introduces an open-source web-based platform that allows researchers to calculate the activity index from top-view videos by selecting individual or group animals. It integrates Segment Anything Model2 (SAM2), a promptable deep learning segmentation model, to track animals without additional training or annotation. The platform accurately tracked Cobb 500 male broilers from weeks 1 to 7 with a 100% success rate, IoU of 92.21% ± 0.012, precision of 93.87% ± 0.019, recall of 98.15% ± 0.011, and F1 score of 95.94% ± 0.006, based on 1157 chickens. Statistical analysis showed that tracking 80% of birds in week 1, 60% in week 4, and 40% in week 7 was sufficient (r ≥ 0.90; p ≤ 0.048) to represent the group activity in respective ages. This platform offers a practical, accessible solution for activity tracking, supporting animal behavior analytics with minimal effort. Full article

pH is a critical parameter requiring precise monitoring across scientific, industrial, and biological domains. Fluorescent pH probes offer a powerful alternative to traditional methods (e.g., electrodes, indicators), overcoming limitations in miniaturization, long-term stability, and electromagnetic interference. By utilizing photophysical mechanisms—including intramolecular charge transfer (ICT), photoinduced electron transfer (PET), and fluorescence resonance energy transfer (FRET)—these probes enable high-sensitivity, reusable, and biocompatible sensing. This review systematically details recent advances, categorizing probes by operational pH range: strongly acidic (0–3), weakly acidic (3–7), strongly alkaline (>12), weakly alkaline (7–11), near-neutral (6–8), and wide-dynamic range. Innovations such as ratiometric detection, organelle-specific targeting (lysosomes, mitochondria), smartphone colorimetry, and dual-analyte response (e.g., pH + Al³⁺/CN⁻) are highlighted. Applications span real-time cellular imaging (HeLa cells, zebrafish, mice), food quality assessment, environmental monitoring, and industrial diagnostics (e.g., concrete pH). Persistent challenges include extreme-pH sensing (notably alkalinity), photobleaching, dye leakage, and environmental resilience. Future research should prioritize broadening functional pH ranges, enhancing probe stability, and developing wide-range sensing strategies to advance deployment in commercial and industrial online monitoring platforms. Full article

The force ripple of a permanent magnet synchronous linear motor (PMSLM) caused by multi-source disturbances in practical applications seriously restricts its high-precision motion control performance. The traditional single-mechanism model has difficulty fully characterizing the nonlinear disturbance factors, while the data-driven method has real-time limitations. Therefore, this paper proposes a hybrid modeling framework that integrates the physical mechanism and measured data and realizes the dynamic compensation of the force ripple by constructing a collaborative suppression algorithm. At the mechanistic level, based on electromagnetic field theory and the virtual displacement principle, an analytical model of the core disturbance terms such as the cogging effect and the end effect is established. At the data level, the acceleration sensor is used to collect the dynamic response signal in real time, and the data-driven ripple residual model is constructed by combining frequency domain analysis and parameter fitting. In order to verify the effectiveness of the algorithm, a hardware and software experimental platform including a multi-core processor, high-precision current loop controller, real-time data acquisition module, and motion control unit is built to realize the online calculation and closed-loop injection of the hybrid compensation current. Experiments show that the hybrid framework effectively compensates the unmodeled disturbance through the data model while maintaining the physical interpretability of the mechanistic model, which provides a new idea for motor performance optimization under complex working conditions. Full article

This study presents a cross-cultural comparison of two fortified residences—Palazzo Farnese in Italy and Dong’s Fortified Compound in China—through a triadic analytical framework encompassing architectural form, symbolic ornamentation, and public perception. By combining field observation, iconographic interpretation, and digital ethnography, the research investigates how heritage meaning is constructed, encoded, and reinterpreted across distinct sociocultural contexts. Empirical materials include architectural documentation, decorative analysis, and a curated dataset of 4947 user-generated images and 1467 textual comments collected from Chinese and international platforms between 2020 and 2024. Methods such as CLIP-based visual clustering and BERTopic-enabled sentiment modelling were applied to extract patterns of perception and symbolic emphasis. The findings reveal contrasting representational logics: Palazzo Farnese encodes dynastic authority and Renaissance cosmology through geometric order and immersive frescoes, while Dong’s Compound conveys Confucian ethics and frontier identity via nested courtyards and traditional ornamentation. Digital responses diverge accordingly: international users highlight formal aesthetics and photogenic elements; Chinese users engage with symbolic motifs, family memory, and ritual significance. This study illustrates how historically fortified residences are reinterpreted through culturally specific digital practices, offering an interdisciplinary approach that bridges architectural history, symbolic analysis, and digital heritage studies. Full article

Since the initial use of biological ion channels to detect single-stranded genomic base pair differences, label-free and highly sensitive resistive pulse sensing (RPS) with nanopores has made remarkable progress in single-molecule analysis. By monitoring transient ionic current disruptions caused by molecules translocating through a nanopore, this technology offers detailed insights into the structure, charge, and dynamics of the analytes. In this work, the RPS platforms based on biological, solid-state, and other sensing pores, detailing their latest research progress and applications, are reviewed. Their core capability is the high-precision characterization of tiny particles, ions, and nucleotides, which are widely used in biomedicine, clinical diagnosis, and environmental monitoring. However, current RPS methods involve bottlenecks, including limited sensitivity (weak signals from sub-nanometer targets with low SNR), complex sample interference (high false positives from ionic strength, etc.), and field consistency (solid-state channel drift, short-lived bio-pores failing POCT needs). To overcome this, bio-solid-state fusion channels, in-well reactors, deep learning models, and transfer learning provide various options. Evolving into an intelligent sensing ecosystem, RPS is expected to become a universal platform linking basic research, precision medicine, and on-site rapid detection. Full article

This study explores the determinants of startup intention within the context of e-commerce platform-based startups in South Korea. We employ an extended technology acceptance model (TAM) that integrates individual, social, and entrepreneurial characteristics. A two-step analytical approach is applied, combining variable extraction through data mining and hypothesis testing using structural equation modeling. The results indicate that personal and social factors—such as entrepreneurial mindset and social influence—positively affect perceived usefulness, while job relevance and exposure to successful startup models enhance perceived ease of use. In contrast, security concerns and technological barriers negatively impact these relationships, posing critical obstacles to platform-based startups. This study extends the TAM framework to the platform-based startup context, offering theoretical contributions and proposing policy implications, including promoting digital literacy, developing entrepreneurial networks, and addressing security and regulatory issues. These insights offer a deeper understanding of how platform environments shape entrepreneurial behavior, providing practical guidance for startup founders, developers, and policymakers. Full article

This study synthesizes the rapidly expanding evidence on how digital technologies reshape international trade, with a particular focus on small and medium-sized enterprises (SMEs). Guided by two research questions—(RQ1) How do digital tools influence the volume and composition of cross-border trade? and (RQ2) How do these effects vary by countries’ development level and firm size?—we conducted a PRISMA-compliant systematic literature review covering 2010–2024. Searches across eight major databases yielded 1857 records; after duplicate removal, title/abstract screening, full-text assessment, and Mixed Methods Appraisal Tool (MMAT 2018) quality checks, 86 peer-reviewed English-language studies were retained. Findings reveal three dominant technology clusters: (1) e-commerce platforms and cloud services, (2) IoT-enabled supply chain solutions, and (3) emerging AI analytics. E-commerce and cloud adoption consistently raise export intensity—doubling it for digitally mature SMEs—while AI applications are the fastest-growing research strand, particularly in East Asia and Northern Europe. However, benefits are uneven: firms in low-infrastructure settings face higher fixed digital costs, and cybersecurity and regulatory fragmentation remain pervasive obstacles. By integrating trade economics with development and SME internationalization studies, this review offers the first holistic framework that links national digital infrastructure and policy support to firm-level export performance. It shows that the trade-enhancing effects of digitalization are contingent on robust broadband penetration, affordable cloud access, and harmonized data-governance regimes. Policymakers should, therefore, prioritize inclusive digital-readiness programs, while business leaders should invest in complementary capabilities—data analytics, cyber-risk management, and cross-border e-logistics—to fully capture digital trade gains. This balanced perspective advances theory and practice on building resilient, equitable digital trade ecosystems. Full article

The Korteweg–de Vries (KdV) equation is a nonlinear evolution equation that reflects a wide variety of dispersive wave occurrences with limited amplitude. It has also been used to describe a range of major physical phenomena, such as shallow water waves that interact weakly and nonlinearly, acoustic waves on a crystal lattice, lengthy internal waves in density-graded oceans, and ion acoustic waves in plasma. The KdV equation is one of the most well-known soliton models, and it provides a good platform for further research into other equations. The KdV equation has several forms. The aim of this study is to introduce and investigate a (2+1)-dimensional generalized fifth-order KdV equation with power law nonlinearity (gFKdVp). The research methodology employed is the Lie group analysis. Using the point symmetries of the gFKdVp equation, we transform this equation into several nonlinear ordinary differential equations (ODEs), which we solve by employing different strategies that include Kudryashov’s method, the $(G^{\prime }/G)$ expansion method, and the power series expansion method. To demonstrate the physical behavior of the equation, 3D, density, and 2D graphs of the obtained solutions are presented. Finally, utilizing the multiplier technique and Ibragimov’s method, we derive conserved vectors of the gFKdVp equation. These include the conservation of energy and momentum. Thus, the major conclusion of the study is that analytic solutions and conservation laws of the gFKdVp equation are determined. Full article

Accurate estimation of biomass higher heating value (HHV) is crucial for designing efficient bioenergy systems. In this study, we developed a Backpropagation artificial neural network (ANN) that predicts HHV from routine proximate/ultimate composition data. The network (9-6-6-1 architecture, trained for 15,000 epochs with learning rate 0.3 and momentum 0.4) was calibrated on 99 diverse Spanish biomass samples (inputs: moisture, ash, volatile matter, fixed carbon, C, H, O, N, S). The optimized ANN achieved strong predictive accuracy (validation R² ≈ 0.81; mean squared error ≈ 1.33 MJ/kg; MAE ≈ 0.77 MJ/kg), representing a substantial improvement over 54 analytical models despite the known complexity and variability of biomass composition. Importantly, in direct comparisons it significantly outperformed 54 published analytical HHV correlations—the ANN achieved substantially higher R² and lower prediction error than any fixed-form formula in the literature. A sensitivity analysis confirmed chemically intuitive trends (higher C/H/FC increase HHV; higher moisture/ash/O reduce it), indicating the model learned meaningful fuel-property relationships. The ANN thus provided a computationally efficient and robust tool for rapid, accurate HHV estimation from compositional data. Future work will expand the dataset, incorporate thermal pretreatment effects, and integrate the model into a user-friendly decision-support platform for bioenergy applications. Full article

The unique molecular fingerprint spectral characteristics in the terahertz (THz) band provide distinct advantages for non-destructive and rapid biomolecular detection. However, conventional THz metasurface biosensors still face significant challenges in achieving highly sensitive and precise detection. This study proposes a sensing platform based on quasi-bound states in the continuum (Quasi-BIC), which enhances molecular fingerprint recognition through resonance amplification. We designed a symmetric graphene double-split square ring metasurface structure. By modulating the Fermi level of graphene, this system generated continuously tunable Quasi-BIC resonance peaks across a broad THz spectral range, achieving precise spectral overlap with the characteristic absorption lines of lactose (1.19 THz and 1.37 THz) and tyrosine (0.958 THz). The results demonstrated a remarkable 763-fold enhancement in absorption peak intensity through envelope analysis for analytes with 0.1 μm thickness, compared to conventional bare substrate detection. This terahertz BIC metasurface sensor demonstrates high detection sensitivity, holding significant application value in fields such as biomedical diagnosis, food safety, and pharmaceutical testing. Full article

Aiming at the multifunctional requirements of the limited space in high-end vehicle side-door latches, a double single-loop RRUPRR mechanism driven by a single motor for both electric releasing and cinching is proposed based on the POC set. The kinematical equations of the RRURR mechanism possess 2 × 2 analytical solutions. In order to apply the current motion/force transmission performance index of the parallel mechanisms to the transmission performance analysis of the serial mechanisms, matching methods for chain-driving transference and the moving/fixed platform inversion are proposed. The solution of the performance index of a single-degree-of-freedom single-loop mechanism is equivalent to the solution of the input motion/force transmission performance index of a parallel mechanism. The overall motion/force transmission performance index of a single-loop mechanism is constructed, and the corresponding calculation procedure is defined. Chain-driving transference can be obtained through forward and inverse solutions of the RRURR mechanism. In response to the extremely high requirements for motion/force transmission performance of electric release mechanisms, the proposed overall motion/force transmission performance index is used to calculate for the input motion screw and corresponding transmission-force screw of the single-loop RRURR mechanism and obtain the overall motion/force transmission performance of the mechanism. The performance atlas of the mechanism shows that it has excellent motion/force transmission characteristics within the workspace. Using ADAMS simulation software, the driving torque required for electric releasing and cinching of a vehicle side-door latch mechanism with a single motor is analyzed. The overall motion/force transmission performance index of a single-loop mechanism can be applied to single-loop overconstrained mechanisms and non-overconstrained mechanisms. Full article

Accurate peak detection in the frequency domain is fundamental to reliable range estimation in Frequency-Modulated Continuous-Wave (FMCW) radar systems, particularly in challenging conditions characterized by a low signal-to-noise ratio (SNR) and phase noise impairments. This paper presents a comprehensive comparative analysis of five peak detection algorithms: FFT thresholding, Cell-Averaging Constant False Alarm Rate (CA-CFAR), a simplified Matrix Pencil Method (MPM), SVD-based detection, and a novel Learned Thresholded Subspace Projection (LTSP) approach. The proposed LTSP method leverages singular value decomposition (SVD) to extract the dominant signal subspace, followed by signal reconstruction and spectral peak analysis, enabling robust detection in noisy and spectrally distorted environments. Each technique was analytically modeled and extensively evaluated through Monte Carlo simulations across a wide range of SNRs and oscillator phase noise levels, from $-100$ dBc/Hz to $-70$ dBc/Hz. Additionally, real-world validation was performed using a custom-built harmonic FMCW radar prototype operating in the 2.4–2.5 GHz transmission band and 4.8–5.0 GHz harmonic reception band. Results show that CA-CFAR offers the highest resilience to phase noise, while the proposed LTSP method delivers competitive detection performance with improved robustness over conventional FFT and MPM techniques. Furthermore, the hardware feasibility of each algorithm is assessed for implementation on a Xilinx FPGA platform, highlighting practical trade-offs between detection performance, computational complexity, and resource utilization. These findings provide valuable guidance for the design of real-time, embedded FMCW radar systems operating under adverse conditions. Full article

Co-infections pose significant challenges not only clinically, but also in terms of simultaneous diagnoses. The development of sensitive, multiplexed analytical platforms is critical for accurately detecting viral co-infections, particularly in complex biological environments. In this study, we present a mass spectrometry (MS)-based detection strategy employing a target-triggered hybridization chain reaction (HCR) to amplify signals and in situ photocleavable mass tags (PMTs) for the simultaneous detection of multiple targets. Hairpin DNAs modified with PMTs and immobilized loop structures on magnetic particles (Loop@MPs) were engineered for each target, and their hybridization and amplification efficiency was validated using native polyacrylamide gel electrophoresis (PAGE) and laser desorption/ionization MS (LDI-MS), with silica@gold core–shell hybrid (SiAu) nanoparticles being employed as an internal standard to ensure quantitative reliability. The system exhibited excellent sensitivity, with a detection limit of 415.12 amol for the hepatitis B virus (HBV) target and a dynamic range spanning from 1 fmol to 100 pmol. Quantitative analysis in fetal bovine serum confirmed high accuracy and precision, even under low-abundance conditions. Moreover, the system successfully and simultaneously detected multiple targets, i.e., HBV, human immunodeficiency virus (HIV), and hepatitis C virus (HCV), mixed in various ratios, demonstrating clear PMT signals for each. These findings establish our approach as a robust and reliable platform for ultrasensitive multiplexed detection, with strong potential for clinical and biomedical research. Full article

Background: Cell-free DNA (cfDNA) has become a cornerstone of liquid biopsy applications, offering promise for early disease detection and monitoring. However, its widespread clinical adoption is limited by variability in pre-analytical processing, especially during isolation. Current extraction methods face challenges in yield, purity, and reproducibility. Methods: We developed and optimized SafeCAP 2.0, a novel magnetic bead-based cfDNA extraction kit, focusing on efficient recovery, minimal genomic DNA contamination, and PCR compatibility. Optimization involved systematic evaluation of magnetic bead chemistry, buffer composition, and reagent volumes. Performance was benchmarked against a commercial reference kit (Apostle MiniMax) using spiked oligonucleotides and plasma from patients with stage IV NSCLC. Results: The optimized protocol demonstrated superior recovery with a limit of detection (LoD) as low as 0.3 pg/µL and a limit of quantification (LoQ) of 1 pg/μL with no detectable PCR inhibition. In comparative studies, SafeCAP 2.0 showed equivalent or improved performance over the commercial kit. Clinical validation using 47 patient plasma samples confirmed robust cfDNA recovery and fragment integrity. Conclusions: SafeCAP 2.0 offers a cost-effective, high-performance solution for cfDNA extraction in both research and clinical workflows. Its design and validation address key pre-analytical barriers, supporting integration into routine diagnostics and precision medicine platforms. Full article