Search Results (1,765)

Wastewater-derived microplastics (WW-MPs) are increasingly recognised as reactive vectors for aromatic organic contaminants (AOCs), yet their role in contaminant fate remains insufficiently constrained. This review synthesises current knowledge on the transformation of microplastics in wastewater treatment plants, including fragmentation, oxidative ageing, additive leaching, and biofilm formation, and links these processes to changes in sorption capacity toward phenols, PAHs and their derivatives, and organochlorine pesticides (OCPs). We summarise the dominant adsorption mechanisms—hydrophobic partitioning, π–π interactions, hydrogen bonding, and electrostatic and, in some cases, halogen bonding—and critically evaluate how wastewater-relevant parameters (pH, ionic strength, dissolved organic matter, temperature, and biofilms) can modulate these interactions. Evidence in the literature consistently shows that ageing and biofouling enhance WW-MP affinity for many AOCs, reinforcing their function as mobile carriers. However, major gaps persist, including limited data on real wastewater-aged MPs, lack of methodological standardisation, and incomplete representation of ageing, competitive sorption, and non-equilibrium diffusion in existing isotherm and kinetic models. We propose key descriptors that should be incorporated into future sorption and fate frameworks and discuss how WW-MP-AOC interactions may influence ecological exposure, bioavailability, and risk assessment. This critical analysis supports more realistic predictions of AOC behaviour in wastewater environments. Full article

In this work we demonstrate the laser-driven reconfiguration of stripe domains in a thick bismuth-substituted iron garnet film with the (210) crystallographic orientation exhibiting strong in-plane anisotropy. Under a weak in-plane external magnetic field (H_‖), laser irradiation leads to local “twisting” of the magnetic domains; domains with opposite magnetization rotate in different directions. The twisting angle increases linearly with the in-plane magnetic field (H_‖) (above a threshold of approximately 6 Oe) and also changes linearly with the average laser intensity, being fully reversible after the irradiation process. The magnitude of the domain rotation effect does not depend on the light polarization state or its orientation. After optical irradiation, the magnetization distribution in the sample returns to its initial state. It is also observed that moving the focused beam spot along the surface can lead to irreversible modifications in the domain topology in several ways: there is a shift in the dislocations in stripe domain structure (domain “heads”) across the beam transfer direction, expanding the area with a specific magnetization vector orientation, and the stabilization of domain wall positions by their pinning on crystallographic defects. The proposed analytical model based on a local reducing of the effective anisotropy fully describes the rotation type and angle of domains and domain walls, defining their possible trajectories and certain values of the area heating or local anisotropy modulation and the rotation angles. The experimental results and the theoretical model demonstrate a thermal origin of the laser-induced effect in this type of magnetic domain structure. Full article

We present the cloning and heterologous expression of the α-amylase gene amyBL159 from a thermophilic strain Bacillus licheniformis MGMM159, which was isolated from wastewater sediments self-heated to 70 °C. The gene was successfully cloned into the pET22b and pHT01 vectors, expressed and AmyBL159_Ec and AmyBL159_Bs recombinant α-amylases were purified from Escherichia coli BL21(DE3)pLys and Bacillus subtilis 168 strains, respectively. The AmyBL159_Ec enzyme was most active in the range of 75–95 °C, while AmyBL159_Bs showed maximum activity at temperatures from 45 to 75 °C. AmyBL159_Bs was shown to be more thermostable. Both enzymes were active over a broad pH range of 4.0–12.0. It was shown that Mn²⁺ ions enhanced the activity of both enzymes (up to 163% for AmyBL159_Ec and 142% for AmyBL159_Bs). These results highlight the importance of choosing an expression system for modulating the functional characteristics of recombinant α-amylase. The obtained AmyBL159_Ec and AmyBL159_Bs enzymes are promising for biotechnological applications under extreme conditions. The structure of the α-amylase was generated using the AlphaFold 3 web service. A structure–function analysis of this enzyme and previously studied α-amylases from B. licheniformis identified significant amino acid substitutions at positions 134(133) and 210(209) of the amino acid chain which may contribute to enhanced enzyme thermostability. Full article

In an increasingly interconnected world, cybersecurity professionals play a pivotal role in safeguarding organizations from cyber threats. To secure their cyberspace, organizations are forced to adopt a cybersecurity framework such as the NIST National Initiative for Cybersecurity Education Workforce Framework for Cybersecurity (NICE Framework). Although these frameworks are a good starting point for businesses and offer critical information to identify, prevent, and respond to cyber incidents, they can be difficult to navigate and implement, particularly for small-medium businesses (SMBs). To help overcome this issue, this paper identifies the most frequent attack vectors to SMBs (Objective 1) and proposes a practical model of both technical and non-technical tasks, knowledge, skills, abilities (TKSA) from the NICE Framework for those attacks (Objective 2). This research develops a scenario-based curriculum. By immersing learners in realistic cyber threat scenarios, their practical understanding and preparedness in responding to cybersecurity incidents is enhanced (Objective 3). Finally, this work integrates practical experience and real-life skill development into the curriculum (Objective 4). SMBs can use the model as a guide to evaluate, equip their existing workforce, or assist in hiring new employees. In addition, educational institutions can use the model to develop scenario-based learning modules to adequately equip the emerging cybersecurity workforce for SMBs. Trainees will have the opportunity to practice both technical and legal issues in a simulated environment, thereby strengthening their ability to identify, mitigate, and respond to cyber threats effectively. We piloted these learning modules as a semester-long course titled “Hack Lab” for both Computer Science (CS) and Law students at CSU during Spring 2024 and Spring 2025. According to the self-assessment survey by the end of the semester, students demonstrated substantial gains in confidence across four key competencies (identifying vulnerabilities and using tools, applying cybersecurity laws, recognizing steps in incident response, and explaining organizational response preparation) with an average improvement of +2.8 on a 1–5 scale. Separately, overall course evaluations averaged 4.4 for CS students and 4.0 for Law students, respectively, on a 1–5 scale (college average is 4.21 and 4.19, respectively). Law students reported that hands-on labs were difficult, although they were the most impactful experience. They demonstrated a notable improvement in identifying vulnerabilities and understanding response processes. Full article

The pervasive environmental contamination by microplastics (MPs) and per- and polyfluoroalkyl substances (PFAS) represents a critical challenge of the Anthropocene. While historically studied in isolation, a growing body of evidence confirms that these pollutants interact to form a complex and dynamic MP-PFAS Nexus. This review synthesizes current knowledge to elucidate the multifaceted mechanisms of this interaction, where MPs act as vectors, concentrators, and secondary sources for PFAS. We detail how environmental aging and water chemistry modulate adsorption and transport, fundamentally altering the fate of both contaminants. Crucially, the review consolidates evidence demonstrating that co-exposure often leads to synergistic toxicity, disrupting physiological processes from photosynthesis in algae to lipid metabolism and neurogenesis in animals, with significant implications for trophic transfer. The nexus also presents formidable challenges for water treatment and soil remediation, while simultaneously offering opportunities for targeted destructive technologies like pyrolysis. Furthermore, we explore the emerging threats of this complex to human health via seafood and water, and the amplifying feedback of climate change. Finally, we argue that current regulatory frameworks, which assess pollutants individually, are inadequate and must evolve to account for combined effects. This review underscores the imperative to reframe MPs and PFAS as an interconnected pollutant system, necessitating integrated research and policy for effective environmental risk assessment and management. Full article

Review-aware recommendation systems aim to enhance recommendation performance by leveraging user reviews and their associated attributes to model user preferences. However, most existing methods fail to address two critical challenges introduced by user reviews: polarity bias and temporal dynamics. Polarity bias refers to inconsistencies between a user’s numerical ratings and the sentiment expressed in their reviews—for example, a user might give a restaurant a high rating while writing a negative review. In addition, user preferences may evolve over time, as individuals can review the same item on multiple occasions. To address these issues, we propose RARPT, a review-aware recommendation framework that jointly models polarity and temporality. Specifically, we process positive and negative reviews separately and employ a sequential model to capture the temporal evolution of user preferences. We also introduce a polarity balance module, which uses a cross-attention mechanism to generate supplementary collaborative vectors from reviews of the opposite polarity, thereby mitigating both quantitative and relational imbalances. We conduct extensive experiments on two real-world datasets from Amazon and Yelp. The results show that our proposed model significantly outperforms several state-of-the-art baselines. Moreover, our model offers enhanced interpretability, helping deliver more effective personalized recommendations. Full article

Chimeric antigen receptor (CAR) T cell therapy for B cell malignancies is often limited by T cell exhaustion, which is frequently driven by the PD-1/PD-L1 immune checkpoint axis. To overcome this, we developed an “armored” CAR-T cell strategy using a novel bidirectional promoter system. We engineered a single vector to co-express a CD19-specific CAR alongside a secreted anti-PD1 molecule, in either a full-length antibody or a single-chain variable fragment (scFv) format, using the Sleeping Beauty (SB) transposon system. The sequences for the anti-PD1 modules were derived from the clinical antibody nivolumab. Both armored constructs demonstrated robust CAR expression, comparable to or higher than conventional CAR-T cells, and proliferated significantly more than untransfected controls. The engineered cells successfully secreted their anti-PD1 payloads, with the full-length antibody showing more sustained secretion than the scFv. This autocrine blockade resulted in significantly reduced surface PD1 expression on the armored CAR-T cells. Functionally, the anti-PD1-secreting cells exhibited superior cytotoxicity against PD-L1-positive Raji target cells, particularly at low effector-to-target ratios. Critically, in a serial rechallenge assay designed to simulate chronic antigen exposure, both armored CAR-T cell groups showed markedly enhanced proliferation and persistence compared to conventional CAR-T cells, which failed to expand after repeated stimulation. Our findings validate the bidirectional EF1 promoter as an efficient system for generating multi-functional T cells and demonstrate that armoring CAR-T cells with secreted anti-PD1 antibodies is a potent strategy to enhance their persistence and anti-tumor efficacy. Full article

Background: Diabetic foot ulcers (DFUs) are chronic wounds with high morbidity and mortality. Secretory proteins coordinate intercellular communication and may regulate inflammation, tissue repair and regeneration, but their contributions to DFU pathogenesis remain unclear. Aim: To discover and validate secretory protein–linked biomarkers that distinguish non-healing DFUs and to explore their potential utility for diagnosis and therapy. Methods: We integrated bulk RNA-sequencing datasets (GSE199939 training set; GSE80178 and GSE143735 validation sets) and a single-cell RNA-sequencing dataset (GSE223964). Differentially expressed genes, secretory protein lists, and weighted gene co-expression networks were intersected to select candidates. Functional enrichment, protein interaction networks and support vector machine–recursive feature elimination identified key markers. We visualized their cell-type distribution at single-cell resolution and validated their expression in external cohorts. Pathway enrichment, gene co-expression networks, ceRNA regulatory analysis and qRT-PCR in patient samples were used for further characterization. Results: Among 4803 differentially expressed genes, 743 overlapped with known secretory proteins. WGCNA highlighted modules strongly associated with DFUs, yielding 386 candidates. SVM-RFE combined with protein interaction analysis pinpointed four secretory proteins—LYZ, MPO, SLCO2B1 and TREM2—as putative biomarkers. Single-cell analyses showed that MPO, LYZ, SLCO2B1 and TREM2 transcripts are detectable in multiple skin-resident and immune cell populations, and that the DFU-associated upregulation of MPO and LYZ is most pronounced within keratinocyte clusters, while MPO and TREM2 remained consistently dysregulated in independent bulk cohorts. MPO-associated genes were enriched for immune and inflammatory pathways, whereas TREM2-linked genes implicated cell cycle and cytoskeletal regulation. GeneMANIA and ceRNA analyses revealed extensive interaction networks. qRT-PCR confirmed differential expression of MPO and TREM2 in clinical DFU tissues. Conclusions: Integrated multi-modal analysis identifies secretory proteins, particularly MPO and TREM2, as central determinants of impaired healing in DFUs. These molecules and their regulatory networks represent promising biomarkers and therapeutic targets for precision management of diabetic wounds. Full article

Accurate and efficient multi-agent trajectory prediction remains a core challenge for autonomous driving, particularly in modeling complex interactions while maintaining physical plausibility and computational efficiency. Many existing methods- especially those based on large transformer architectures- tend to overlook physical constraints, leading to unrealistic predictions and high deployment costs. In this work, we propose a lightweight trajectory prediction framework that integrates physical information to enhance interaction modeling and runtime performance. Our method introduces two physically inspired strategies: (1) a constraint-guided mechanism is used to filter irrelevant or distracting neighbors, and (2) a physics-aware attention module is applied to steer attention weights toward physically plausible interactions. The overall architecture adopts a modular and vectorized design, effectively reducing model complexity and inference latency. Experiments on the Argoverse V1 dataset, comparing against multiple existing methods, demonstrate that our approach achieves a favorable balance among accuracy, physical feasibility, and efficiency, running in real time on a commodity desktop GPU. Future work will focus on validating its performance on embedded hardware. Full article

Frozen storage modulates the progression of key oxidative and nitrogenous reactions within fish muscle. We therefore identify the drivers of quality degradation in filleted whiting (Merlangius merlangus) and Atlantic bonito (Sarda sarda) during 10-month frozen storage at −12, −18, and −24 °C, and to integrate state-of-the-art machine learning architectures to predict deterioration kinetics and shelf-life trajectories. To this end, following blast freezing at −30 °C for 6 h, samples were periodically (0, 2, 4, 6, 8, and 10 months) assessed for biochemical indices—total volatile base nitrogen (TVB-N), trimethylamine nitrogen (TMA-N), thiobarbituric acid (TBA), and free fatty acids (FFA)—in which proximate composition and pH were determined solely on the same day (Day 0). Whiting displayed progressive increases in all indices, yet values at −24 °C remained within regulatory acceptability, supporting a safe storage period of up to nine months. By contrast, Atlantic bonito retained TVB-N and TMA-N values below regulatory thresholds across storage, but TBA exceeded acceptability limits from the second month onward, and FFA rose after month four. Complementing these findings, machine learning (ML) approaches, including Naïve Bayes, Support Vector Machine, Decision Tree, Multilayer Perceptron, and Extreme Gradient Boosting, were implemented to classify species and predict spoilage kinetics, with Extreme Gradient Boosting achieving the highest accuracy (98.9%, κ = 0.978) and Random Forest providing superior regression performance (R² = 0.986, RMSE = 0.392). ML models consistently identified TVB-N as the dominant predictor for whiting and TBA for Atlantic bonito, correctly capturing the critical time points of 9 months and 2 months, respectively, and highlighting −24 °C as the most reliable condition for preserving quality. These results underscore the potential of ML as a transformative tool for accurate shelf-life prediction and smarter cold-chain management in frozen fish products. Full article

This paper employs a four-leg inverter topology to mitigate the high cost and zero-sequence current suppression challenges associated with dual-inverter open-winding permanent magnet synchronous motor (OW-PMSM) systems. Building on this topology, an improved current hysteresis control strategy incorporating a switching-state lookup table is proposed to suppress switching frequency fluctuations and current ripple. The developed system maintains high DC-link utilization and low cost while addressing the modulation complexity of conventional vector control and the switching frequency instability inherent in traditional hysteresis control. The study establishes a mathematical model of the OW-PMSM, analyzes the voltage vector distribution of the four-leg inverter, and designs an enhanced hysteresis control algorithm. By utilizing a predefined switching table to regulate switching logic in real time, the strategy achieves fixed switching frequency and effective harmonic suppression while preserving the fast-response characteristics of conventional hysteresis control. The experimental results demonstrate that the proposed control strategy achieves superior performance, effectively suppressing current ripple and providing ample stability margin, thereby validating its feasibility and effectiveness for practical engineering applications. Full article

This paper presents a fault-tolerant control strategy that dynamically reconfigures the proposed system, and the inverter leg with a fault is isolated through a MOSFET-based clamping branch. With the use of a modified Vector Control (VC) and Pulse-Width Modulation (PWM) technique, the remaining two phases can continue operating. MATLAB/Simulink is used to create a thorough simulation model that examines various fault scenarios and evaluates how well the control process adjusts to each one. The obtained findings demonstrate that, in the event of a fault, the system can maintain accurate speed regulation, maintain a tolerable current balance, and deliver steady torque. The obtained findings demonstrate that, in the event of a fault, the system can maintain accurate speed regulation, maintain a reasonable current balance, and deliver steady torque. In contrast to traditional methods that rely on hardware redundancy, this software-driven technique maintains the electric vehicle’s functionality even when a malfunction arises. In just a few milliseconds, normal operation is restored without the need for more sensors or additional expenses. Because of these characteristics, the suggested approach is a sensible option for actual EV applications. Full article

We study the optical susceptibility of a CdSe-based semiconductor quantum dot with a cascade exciton–biexciton configuration, which is coupled via the Coulomb interaction to a gold spheroidal nanoparticle, in the presence of a nearly resonant strong pump field and a weak probe field. We take both fields’ polarization vectors to be parallel to the interparticle axis, derive the equations of motion for the density matrix, and proceed with a perturbative expansion approach to calculate the components of the density matrix associated with the effective optical susceptibility, which describes processes to first order in the probe field and to all orders in the pump field. We present spectra of the effective susceptibility and examine their dependence on the metal nanoparticle’s geometric characteristics for various interparticle distances and pump field detunings, under both one- and two-photon resonance conditions. The role of the biexciton energy shift is also studied. Lastly, we introduce a dressed-state picture to elucidate the origin of the observed spectral features. Our calculations reveal that reducing the interparticle distance and increasing the metal nanoparticle aspect ratio enhance the exciton–plasmon coupling, leading to pronounced resonance splitting, spectral shifts, and broadened gain regions. Prolate nanoparticles aligned with the field polarization exhibit the strongest coupling and the widest gain bandwidth, whereas oblate geometries produce nearly overlapping resonances. Under exact resonance, the probe displays zero absorption with a negative dispersion slope, indicating slow-light behavior. These results demonstrate the tunability of hybrid CdSe-Au nanostructures for designing nanoscale optimal amplifiers, modulators, and sensors. Full article

To enhance the operational efficiency of energy storage converters in grid-connected systems with high renewable penetration, this study systematically investigates key technologies of active neutral-point clamped (ANPC) topology under “electrical–thermal–mechanical” multi-physical field coupling. The study reviews recent progress in structural design, modulation strategies, and fault-tolerant control, highlighting their impact on efficiency, reliability, and power density. At the structural stage, a hybrid SiC/IGBT device configuration combined with a three-dimensional stacked bus reduces conduction loss and achieves parasitic inductance. In the modulation stage, improved finite-set model predictive control and adaptive space vector modulation shorten computation time to 20 µs and keep total harmonic distortion (THD) within 2.8%. System-level evaluations demonstrate that a 250 kW ANPC converter attains a peak efficiency of 99.1%, a power density of 4.5 kW/kg, and a mean time between failure exceeding 150,000 h. These findings reveal a clear transition from single-objective performance improvement toward integrated multi-physics co-design. By unifying advanced modulation, intelligent fault-tolerant control, and multi-field coupling optimization, ANPC-based converters advance converters to a new stage of higher efficiency, reliability, and stability. The results provide essential technical support for next-generation power conversion systems in renewable-rich grids. Full article

A compact 8–15 GHz Doherty power amplifier (DPA) is proposed and fabricated in 22 nm FD-SOI CMOS. The proposed DPA relies on a quadrature-hybrid splitter and combiner to replace the bulky $\lambda$/4 impedance inverters at the input and the output of the conventional DPA enabling load modulation over a large fractional bandwidth (FBW = 61%) with efficient and compact integration. The proposed DPA achieves a peak gain of 19.6 dB; ≥17 dB across 8–15 GHz, 18 dBm P1dB, 19.5 dBm $P_{sat}$, and a peak PAE of 21% at 10 GHz, while sustaining 17% PAE at 6 dB back-off. The proposed DPA enables a modulation BW up to 200 MHz for a 256-QAM single carrier (SC) signal with a peak-to-average power ratio (PAPR) of 6 dB. Under this setting, the average output power (Pavg) is measured at 12.5 dBm with an RMS error vector magnitude (EVM) of $-24.1$ dB and an average PAE of 15%. Within the scope of CMOS power amplifiers in 22 nm FD-SOI, we found no published example that jointly demonstrates 8–15 GHz coverage and sustained PAE at 6 dB back-off using a quadrature hybrid. Full article