Search Results (4,265)

Smart cities (SCs) use the Internet of Things (IoT) to collect and process data to communicate with their infrastructure and assets in real time. A great deal of techniques, such as encryption protocols, Random Forest-based AI-driven threat detection, and blockchain architectures, have been developed to address cybersecurity challenges in smart cities (SCs). These techniques, however, have limitations such as their scalability, high computational expenses, and energy inefficiency. Therefore, in this study, to overcome these challenges, we propose a blockchain-based infrastructure called BlockSafeNet. This uses artificial intelligence, big data, and blockchain to enhance cybersecurity in SCs. The effectiveness of the proposed BlockSafeNet framework was evaluated using responsiveness, computational time, encryption quality score, detection rate, false positive rate, latency, throughput, and energy consumption as the primary cybersecurity performance metrics. These metrics were selected to assess communication efficiency, threat detection capability, privacy preservation, scalability, and overall security performance within smart-city IoT environments. To ensure secure data transactions, robust threat detection, and efficient communication. The system’s high calculation speed and detection rate show potential for managing sensitive maternal health data collected by IoT devices. The platform also shows how IoT may be used by healthcare services to monitor public health in real time, allowing hospitals, emergency services, and public health agencies to securely share data. This aids in resource optimization, improving service delivery, and preserving data privacy and trust in SCs. Data was obtained from the UCI Machine Learning Repository on Kaggle to validate the developed framework. By evaluating the effectiveness of BlockSafeNet in tackling cybersecurity challenges, we establish its practical relevance and usability in SCs. The proposed BlockSafeNet framework achieved a responsiveness of 24 s, an encryption quality score of 0.89, computational time of 85 s, and a detection rate of 91%, demonstrating significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures. shows that SC IoT security has significantly improved through the adoption of new data protection methods and better measures of security, providing a positive impact on the SC ecosystem. Full article

Bulk heterojunction (BHJ) active layers in organic photovoltaics (OPVs) are nanostructured composites in which electron-donating and electron-accepting semiconductors form interpenetrating phases for exciton dissociation and charge transport. The power conversion efficiency (PCE) of these organic-organic nanocomposites is governed by structural features spanning multiple length scales: molecular packing and energy-level alignment at donor/acceptor (D/A) interfaces, phase-separation morphology and crystallite connectivity, and thin-film optical and charge-transport characteristics. Rational design of high-performance OPV nanocomposites requires multiscale numerical modelling that bridges quantum chemistry, mesoscale morphology simulation, and device-scale optoelectronic modelling. This review surveys and critically compares recent advances in the structural design and numerical simulation of OPV BHJ nanocomposites. At the molecular scale, we examine density functional theory and non-adiabatic molecular dynamics approaches for resolving charge-separation driving forces, interfacial energy-level alignment, and exciton dynamics. At the mesoscale, we discuss molecular dynamics, kinetic Monte Carlo, and electronic coarse-graining methods for describing phase separation, crystallization kinetics, morphology evolution, and charge transport. At the device scale, we review exciton-diffusion, optical transfer-matrix, and drift-diffusion models that quantitatively link morphology to photovoltaic performance metrics. The review also evaluates how machine learning, high-throughput screening, surrogate models, and generative design accelerate donor–acceptor selection and morphology optimization, while distinguishing benchmark predictions from experimentally validated design rules. Across these scales, we compare the strengths, assumptions, and validation limits of the principal modelling approaches. Finally, we highlight emerging multiscale integration frameworks, including sequential parameter-passing pipelines and differentiable digital-twin concepts. By framing OPV BHJ layers as nanocomposites whose performance bottlenecks map onto composite-design challenges such as interface integrity, phase connectivity, multiscale charge transfer, and degradation-aware design, this review connects OPV modelling with broader structural-composites thinking for next-generation organic solar cells. Full article

Smart Factory programs increasingly connect shop-floor, quality, asset and planning data, but integrated data infrastructures do not necessarily align operational decisions. This paper reviews how smart manufacturing literature explains the transition from industrial data integration to closed-loop operations synchronization and value capture in high-throughput manufacturing contexts. Using the Systematic Search Flow method, 1949 records were screened and reduced to a final portfolio of 73 studies. The papers were coded by thematic cluster, dominant technology, research method, primary theme, value-stream coverage and operations-synchronization relevance. The coding shows that roadmaps, interoperability architectures, analytics applications and digital-twin models dominate the portfolio. Explicit operations-synchronization mechanisms are addressed in 16 of the 73 studies, mainly through planning-execution coupling and digital-twin-based decision support. Coverage across value streams is uneven, with stronger evidence for Strategy, Make and Plan than for Quality and Assets. Based on this evidence map, the paper proposes a Data-Driven Operations Synchronization Stack that links operational data capture, semantic and IT/OT interoperability, analytics-supported decision-making, closed-loop synchronization and operational or financial value capture. Full article

Photovoltaic-integrated smart grids require energy storage systems that mitigate net load fluctuations without imposing excessive operating burden on battery subsystems. This study formulates a stress-prioritized hierarchical MPC-based control allocation strategy for a lead-acid/LiFePO₄ hybrid energy storage system. Unlike grid-smoothing-oriented controllers, the proposed method explicitly incorporates lead-acid operating stress into the control objective and evaluation framework. The upper layer schedules the lead-acid battery using a low-frequency net load component while penalizing power magnitude, ramping, throughput, and state-of-charge deviation. The lower layer controls the LiFePO₄ battery to compensate residual net load variations and reduce the burden imposed on the lead-acid subsystem. The method was evaluated using 696 hourly samples of measured photovoltaic generation and load demand data from the Naju Sports Park smart-grid site. Compared with the lead-acid-only MPC case, the proposed strategy reduced lead-acid throughput and equivalent full cycles by 66.5%, ramp burden by 76.4%, high-power operation time by 78.8%, and high-power operation energy by 81.0%. Compared with rule-based hybrid control, it reduced lead-acid throughput and equivalent full cycles by 38.4% while accepting a 3.3% increase in grid power standard deviation. These results indicate that the proposed strategy provides a practical stress-prioritized operating framework for lead-acid/LiFePO₄ hybrid energy storage systems. Full article

Background/Objectives: PLGA/PLA microspheres are attractive candidates for depot delivery of protein therapeutics, but formulation development for antibodies remains challenging because protein encapsulation, burst release, stability, and polymer composition must be balanced across a large design space. This study evaluated an automated high-throughput formulation workflow for preparing and screening IgG-loaded pegylated PLGA/PLA microspheres, with emphasis on loading and early release behavior. Methods: Microspheres were prepared using an automated W/O/W emulsion workflow consisting of primary emulsion formation, microfluidic secondary emulsification, solvent removal, washing, and lyophilization. A formulation library was generated by varying polymer chemistry, polymer concentration, polymer blends, IgG concentration, and additives. Formulations were evaluated for IgG loading and in vitro release over the first 7 days using automated sampling and protein quantification. Statistical analyses were used to identify formulation variables associated with loading, burst release, day-7 release, and Weibull-derived kinetic descriptors. Results: Across 129 formulations, IgG loading was generally in the low-to-mid single-digit percentage range. Additive class and IgG concentration in the dispersed phase were the formulation variables most strongly associated with early release behavior. Additive-free systems were associated with lower burst release, whereas non-polymeric additives were associated with higher burst and faster early release. Day-7 release differences were less robust after correction for variance heterogeneity and resampling. Conclusions: The automated workflow enabled rapid preparation and comparative screening of IgG-loaded PLGA/PLA microspheres. The findings support the use of high-throughput automated screening to identify formulation variables associated with burst modulation and early release behavior. Because release was evaluated over 7 days and only one model IgG was used, the results should be interpreted as an early-stage formulation screening study rather than definitive evidence of long-term antibody depot performance. Full article

Ultrafast laser micro- and nanofabrication has emerged as a powerful platform for precision manufacturing due to the unique capability of femtosecond pulses to provide highly localized energy deposition and controlled laser–matter interactions. However, conventional scanning-based processing approaches remain limited by a fundamental trade-off between spatial resolution and fabrication throughput. Recent advances in ultrafast optical field engineering provide new strategies for overcoming these limitations through coordinated control of temporal, spatial, and spatiotemporal characteristics of ultrashort laser fields. This review presents recent developments in ultrafast optical field engineering for laser micro- and nanofabrication, covering programmable pulse shaping, spatiotemporal control, spatial light modulation, structured light approaches, holographic methods, and hybrid optical architectures. The operating principles of these technologies are discussed together with their influence on energy deposition, processing accuracy, scalability, and manufacturing efficiency. Particular attention is given to applications in high-throughput surface structuring, parallel microfabrication, three-dimensional processing, photonic device fabrication, and functional material modification. Different optical architectures are compared in terms of flexibility, optical efficiency, power-handling capability, and industrial applicability. The review highlights the transition from conventional single-spot processing toward adaptive, parallel, and programmable optical manufacturing systems, emphasizing integrated control of ultrafast optical fields as a key direction for future laser fabrication. Full article

Background/Objectives: Phenotypic drug tolerance, distinct from genetic resistance, allows Mycobacterium tuberculosis (Mtb) to survive prolonged antibiotic exposure and contributes to treatment failure and relapse. The trehalose recycling pathway, mediated by the LpqY-SugABC transporter, has been implicated as a metabolic “escape” mechanism that sustains Mtb viability under antibiotic and nutrient-limiting stress, making it an attractive target for adjunctive, tolerance-breaking therapeutics. Methods and Results: Here, we conducted a high-throughput phenotypic screen of 50,000 compounds from chemically diverse libraries, using a carbon source-restricted assay that forces Mtb to rely on trehalose uptake for growth, to identify small-molecule inhibitors of this pathway. This approach yielded 23 confirmed hits in Mtb, spanning several chemical scaffolds, including thioureas, propanamides, benzamides, and carboxamides. Using an isogenic set of Mtb wild-type, LpqY-SugABC transposon knockout, and complemented strains, we confirmed that the genetic loss of transporter loss reproduces accelerated killing by isoniazid, rifampicin, and bedaquiline, but not moxifloxacin, and that loss of trehalose recycling sensitizes mycobacteria to oxidative stress. Using orthogonal functional assays, fluorescent trehalose probe (FITC-tre) uptake inhibition and H₂O₂ hypersensitization, thiourea-containing compounds emerged as the candidates most consistent with transporter-specific activity, phenocopying the effects of genetic LpqY-SugABC loss, while biochemical assays against recombinant trehalase (Rv2402) excluded downstream enzymatic inhibition as their mechanism of action. In addition, several hits potentiated rifampicin-mediated killing of intracellular Mtb in THP-1 macrophages, in some cases reducing bacterial burden below levels achieved by monotherapy. Conclusions: These findings indicate that the trehalose recycling pathway is functionally druggable by small molecules identified through unbiased phenotypic screening and nominate thiourea- and propanamide-based scaffolds as priority candidates for further mechanistic characterization, including direct target-engagement studies, and optimization as adjunctive anti-tuberculosis agents targeting drug-tolerant Mtb populations. Full article

Background: Rice breeding requires faster development of high-yielding, climate-resilient, resource-efficient, and high-quality cultivars for production systems exposed to environmental variability and increasing input constraints. Genomic selection offers an opportunity to predict breeding value before extensive field evaluation, although its effectiveness depends on the integration of genomic, phenotypic, and environmental information. Methods: This narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding. Results: Genome-wide marker data can support early ranking of breeding materials for grain yield, grain quality, disease resistance, drought tolerance, salinity tolerance, and nutrient-use efficiency. Prediction performance is influenced by trait architecture, marker density, training-population size, genetic relatedness between training and candidate populations, phenotypic data quality, and genotype-by-environment interaction. Red-green-blue, multispectral, hyperspectral, thermal, and light detection and ranging platforms can generate temporal traits associated with plant architecture, biomass, water status, nutrient status, and stress responses, which may improve prediction under suitable population and validation designs. Speed-breeding systems shorten generation intervals and facilitate rapid advancement, recurrent selection, and recycling of superior parental lines. Conclusions: Integrated breeding pipelines that combine genomic prediction, high-throughput phenotyping, environmental data, and speed breeding can improve selection efficiency and shorten rice improvement cycles. Wider adoption will require affordable technology platforms, standardized data systems, multi-environment validation, breeder capacity development, and collaborative data-sharing frameworks for smart and greener agriculture. Full article

(1) Objective: To characterize structural and functional alterations of gut microbiota in patients with newly diagnosed active pulmonary tuberculosis (ATB), screen differential bacterial taxa associated with Mycobacterium tuberculosis (MTB) infection, and explore tuberculosis-related microbial metabolic alterations via predictive functional profiling. (2) Methods: Fresh morning fecal samples were collected from 33 treatment-naive patients with newly diagnosed ATB (ATB group) and 30 healthy controls (HC group). 16S rRNA gene high-throughput sequencing was performed to compare intergroup differences in gut microbial α/β diversity, taxonomic composition, and predicted KEGG functional profiles. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the internal discriminative ability of candidate differential genera in this single small cohort. (3) Results: The ATB group showed significantly lower gut microbial α-diversity than healthy controls (all P < 0.05). Principal coordinate analysis (PCoA) based on Bray-Curtis distances combined with permutational multivariate analysis of variance (PERMANOVA) revealed significant overall dissimilarity of gut microbial community structure between the two groups (R² = 0.31, p = 0.005). At the phylum level, the relative abundances of Firmicutes and Bacteroidetes were higher, while Proteobacteria was less abundant in ATB patients relative to HC. At the genus level, Streptococcus, R. gnavus and Parabacteroides were significantly enriched in ATB patients, whereas Bifidobacterium, Pseudomonas, Megamonas and Faecalibacterium were depleted. Linear discriminant analysis effect size (LEfSe) analysis uncovered group-specific signature taxa, with pro-inflammatory genera Streptococcus and R. gnavus markedly enriched in the ATB group. ROC analysis yielded area under the curve (AUC) values of 0.836 for Streptococcus and 0.805 for R. gnavus. Predictive KEGG functional analysis demonstrated obvious intergroup differences in microbial metabolism, with purine and pyrimidine nucleotide metabolism pathways significantly upregulated in the ATB group. (4) Conclusions: Treatment-naive patients with ATB exhibited reduced gut microbial diversity in this small cohort. The enrichment of Streptococcus and R. gnavus, as well as the upregulation of purine and pyrimidine metabolic pathways, are all associated with active MTB infection. Full article

Background/Objectives: Prostate cancer (PC) exhibits marked disparities in incidence and mortality across ethnicities, with men of Sub-Saharan African (SSA) ancestry experiencing 1.7 and 2.0 times higher values, respectively, than European men (EUR). However, SSA preclinical models remain scarce (just one commercial cell line). In this study, we established and characterized a novel panel of PC cell lines derived from SSA patients using conditional reprogramming (CR), a method that enables efficient propagation of primary cells while maintaining their genotypic and phenotypic features. Methods: CR was applied to five SSA-PC samples, and successfully propagated samples were authenticated by STR and ~1 million SNP profiling, and extensively characterized for proliferative capacity, migratory behaviour, karyotyping and epithelial and prostate tumour lineage markers. To explore drug response profiles, a high-throughput screen (HTS) of 1280 clinically annotated compounds was conducted. Results: Three SSA-PC cell lines were successfully established and authenticated, and five potential drug hits were validated. A new finding was the reduced sensitivity of SSA-derived models (9.0 times difference compared to commercial EUR PC cell lines) to camptothecin, a TOP1 inhibitor, while being equally sensitive to epirubicin hydrochloride, a TOP2 inhibitor. The cardiac glycoside digoxin, anthelmintic pyrvinium pamoate and antirheumatic agent auranofin were also efficient drugs in the in vitro testing. Conclusions: These results support the relevance of SSA-derived PC models for preclinical drug screening and highlight the value of including ancestry-diverse models in oncobiology research. Full article

Lipopolysaccharide (LPS), as the core active component of endotoxins from Gram-negative bacteria, serves as a mandatory quality control indicator in the biological safety evaluation of blood- and body fluid-contacting medical devices. Its rapid and accurate detection is of paramount importance for ensuring clinical patient safety and meeting global regulatory compliance requirements. Traditional endotoxin detection methods have inherent limitations, including poor anti-interference capability against medical device-specific matrices, limited automation capacity, and narrow regulatory applicability, which hinder their utility in meeting the practical demands of the modern medical device industry for high-throughput online quality control and on-site rapid screening. This review focuses on medical device-specific regulatory requirements, characteristics of extract matrices, and full-process quality control scenarios, and systematically summarizes the research progress in novel LPS-targeted biosensing detection technologies. By constructing a three-layer technical framework (biological recognition, interface engineering, and system integration), we provide a comparative assessment of four mainstream technologies—TLR4 biomimetic sensing, genetically engineered elements, EIS, and microfluidics—based on their matrix compatibility, automation potential, and regulatory compliance. It deeply analyzes the core role of microfluidic integration technology in driving the upgrading of detection systems towards automation, miniaturization and compliance, and objectively evaluates the applicability and limitations of various technologies in medical device scenarios. Unlike general reviews on LPS detection, this work specifically addresses endotoxin testing within the medical device quality control context. We systematically examine device-specific matrix interferences and regulatory landscapes, synthesizing cutting-edge research with critical analysis of industrialization bottlenecks. This review provides a theoretical foundation and technical roadmap for developing next-generation, pharmacopeial-compliant rapid automated endotoxin detection platforms—accelerating the advancement of medical device safety standards. Full article

Food products containing plant ingredients are particularly vulnerable to economically motivated adulteration (EMA), which poses risks to consumer trust and regulatory compliance. While traditional methods—such as microscopy, chemical profiling or targeted PCR—struggle to detect adulterants in processed food products or complex mixes, DNA metabarcoding offers a non-targeted, high-throughput alternative. This study presents a nanopore sequencing-based technique that is easy to implement, cost-effective and sufficiently sensitive to detect substitutions, with a focus on spices and herbal teas as model matrices. The method was evaluated using eight single-species reference samples and five commercial multi-ingredient products. It reliably detected undeclared contaminants (e.g., mint in oregano) and species substitutions. Compared to single-barcode approaches, the combination of ITS2 + matK + trnH-psbA markers achieved higher sensitivity. The proposed workflow requires minimal infrastructure and a 2–4-day turnaround time. However, factors such as DNA degradation in highly processed foods, database gaps, and biological diversity limited detection in some cases. These findings demonstrate the workflow’s potential as a first-line screening tool for food authenticity testing, aligning with requirements such as EU regulation 1169/2011 on food labelling or the FDA’s Economically Motivated Adulteration (EMA) program. Future work should validate the method against regulatory thresholds and expand testing to a broader variety of species and matrices. Full article

A multiplex liquid array assay based on Luminex xTAG technology was developed for the first time for the simultaneous detection of five respiratory bacterial pathogens in Specific Pathogen-Free (SPF) chickens—Mycoplasma gallisepticum (MG), Mycoplasma synoviae (MS), Pasteurella multocida (Pm), Avibacterium paragallinarum (Apg), and Mycobacterium avium (Mav)—filling the gap that these five pathogens could not be simultaneously detected previously. Specific primers were designed targeting conserved genes of each pathogen, with a TAG sequence attached to the 5′ end of each forward primer and biotin to the 5′ end of each reverse primer. After multiplex PCR amplification, the products were hybridized with MagPlex-TAG microspheres and analyzed using the Luminex system. The method was validated for sensitivity, specificity, and repeatability, and tested with simulated positive samples (a mixture of genomic DNA from the five target pathogens) and SPF chicken samples. The LOD was 10 copies/μL for Pm and 100 copies/μL for the other four pathogens. No cross-reactivity was observed with 18 non-target bacterial species. Intra- and Inter-assay coefficients of variation (CV) ranged from 0.8% to 12.2% and 2.3% to 14.4%, respectively, both below 15%. All simulated mixed positive samples yielded results consistent with expectations, and none of the 114 nasopharyngeal swab samples from SPF chickens tested positive for any of the five targets. The developed assay offers high throughput, high sensitivity, high specificity, and good repeatability, providing a robust tool for routine quality control in SPF chickens. Full article

This study evaluates the potential of UV–Vis absorption and fluorescence detection (FLD), combined with chemometrics for the characterization of red wines and the preliminary screening of their Protected Designation of Origin (PDO) and grape variety. More than 100 samples were analyzed under both acidic and basic conditions using flow injection analysis (FIA), as it offers advantages in terms of automation, speed, and online coupling multichannel UV–Vis and FLD detectors, enabling the acquisition of complementary datasets. Spectral data generated were analyzed using exploratory and supervised chemometric methods. Results showed comparable performance across different data types. Although the discriminant information was limited, data exploration by Principal Component Analysis (PCA) revealed meaningful differences related to PDO and grape variety. Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogies (SIMCA) provided remarkable classification results, with good overall performance, supporting the potential of this approach as a fast-screening tool for wine authentication and quality control, particularly in high-throughput analytical settings. Full article

The escalating accumulation of microplastics (MPs) in marine ecosystems presents a critical environmental crisis. However, current monitoring efforts rely heavily on labor-intensive, contamination-prone, and time-consuming laboratory analyses. While these conventional off-chip methods provide high accuracy, they inherently lack the throughput and autonomy required for continuous, real-time oceanic surveillance. To bridge this technological gap, microfluidic technologies (Lab-on-a-Chip) provide a viable route towards miniaturized, reagent-free in situ detection with reduced sample volumes and continuous operation capability. This review examines the transition from benchtop to field-deployable platforms and organizes the available microfluidic approaches for MP analysis into a structured overview. We examine on-chip sample manipulation and complementary separation techniques, such as acoustophoresis, dielectrophoresis, and optical tweezers, which are essential for isolating target particles from complex environmental matrices and overcoming intrinsic microfluidic challenges. Following sample preparation, we provide a comprehensive evaluation of state-of-the-art optical and spectroscopic identification methods optimized for continuous flow detection. Finally, we address current analytical limitations and discuss how the integration of machine learning with dynamic spectral libraries could enable autonomous, field-deployed monitoring networks for long-term MP surveillance. Full article