Extreme Risk Protection Orders (ERPOs) are civil court orders that temporarily restrict firearm access for people at risk of harming themselves or others. In some cases, courts mandate respondents to complete a behavioral health evaluation (BHE), which may create an opportunity to connect people with unmet behavioral health needs to behavioral health services. However, little is known about how often these evaluations are ordered, what factors shape these decisions, or how judges explain their reasoning when a BHE is not ordered. This mixed-methods study examined patterns in court-ordered BHEs among granted ERPOs in Washington State from 2017-2023. Quantitative analyses examined how often BHEs were ordered, whether orders varied by respondent or case characteristics, and the frequency of BHE orders over time. We also conducted a qualitative content analysis of judges' written explanations in cases where an evaluation was considered appropriate but not ordered, using an inductive coding approach to identify common themes. Between 2017 and 2023, BHEs were ordered in less than half of granted ERPOs. Rurality was significantly (p < 0.05) associated with differences in BHE orders, but differences in BHE orders among other respondent or case characteristics were also observed. BHE orders varied per year, with more than half (55.8%) of ERPOs ordering a BHE in 2023. Judges most often cited recent or ongoing treatment, recent involuntary psychiatric holds, or parallel criminal proceedings as reasons for not ordering an evaluation. Other explanations included procedural considerations, reliance on family supports, and limited ability to monitor compliance. Findings indicated that BHEs are not consistently used in ERPO cases in Washington State, and their use varies by respondent and case characteristics, geography, and service access. Addressing gaps in statutory clarity, strengthening systems for monitoring compliance, supporting coordination across legal systems, and expanding access to affordable, voluntary behavioral health services may help ensure that ERPOs more consistently support both firearm safety and connection to appropriate care.
Phase transitions in transition metal oxides involving oxygen exchange underpin functionalities ranging from ionic electronics to infinite-layer superconductivity, yet pathway selection and transient intermediates remain difficult to capture. Here, we resolve the atomistic dynamics of oxygen-mediated topotactic transformations in strontium ferrite (SrFeO3-x) using in-situ high-resolution transmission electron microscopy combined with molecular dynamics (MD) simulations. Reduction of SrFeO3 at 450 °C proceeds through SrFeO2.5 and a previously unreported vacancy-ordered SrFeO2+σ phase that retains a less compact three-dimensional Fe-O framework and acts as a structural/chemical buffer before collapse to infinite-layer SrFeO2. During reverse oxidation, the pathway bifurcates: at 200 °C a direct interface-driven phase front converts the SrFeO2 film to perovskite SrFeO3, whereas deep undercooling to 20 °C induces a spatiotemporal competition between the SrTiO3 substrate and ambient oxygen reservoirs that stabilizes a cascade of vacancy-ordered intermediates, including SrFeO2+σ, SrFeO2.75, and SrFeO2.875. MD simulations further reveal a crossover from cooperative, stepwise oxygen diffusion to more continuous oxygen incorporation, rationalizing when staged metastable trapping versus near-direct transformation occurs. Density functional theory calculations show that these trapped intermediates are electronically and magnetically distinct phases rather than merely structural stepping stones, underscoring nonequilibrium pathway selection as a route to functional states beyond the equilibrium phase diagram.
Because the separation efficiency is classically analyzed as total plate height H vs. the average mobile-phase flow velocity at selected retention factors, the complementary analysis of H vs. the retention factor at distinctly different velocities is rare. As a consequence, the impact of retention on eddy dispersion in chromatographic beds remains underinvestigated. Here, we illuminate the plate height-retention factor relationship in a randomly packed bed of sub-2 µm, mesoporous particles through our established multiscale simulation approach by beginning with a low flow velocity, for which analyte transport in the bed is diffusion-limited, and increasing the velocity well into the advection-dominated transport regime. This reveals that in the range of phase-based retention factors (k) most relevant to chromatographic practice (k = 1-10), the actually set mobile-phase flow velocity determines the basic H-k relationship, i.e., if H increases or decreases with k or remains relatively unaffected by k. At low velocity, band broadening is dominated by longitudinal diffusion and H increases with k; at intermediate velocity, H becomes independent of k; and at high velocity, when eddy dispersion gets most important, H decreases with k. In particular, the coupling between eddy dispersion and intraparticle analyte transport leads to a maximum in the H-k relationship between k = 0.5 and 1, which emerges gradually with increasing velocity as a result of two effects: (i) The interplay of mobile-zone and stationary-zone transport via short-range interchannel eddy dispersion makes the lateral exchange of analyte molecules between adjacent interparticle pores of the bed by diffusion through the particles increasingly unfavorable as k increases (because the effective intraparticle diffusion coefficient decreases with increasing k), causing H to increase. (ii) The uptake of analyte molecules by porous particles and their subsequent, random release at a different location is a simple, efficient, and general mechanism interrupting the velocity-memory imprint of the analyte molecules by the interparticle flow paths. In turn, the decay of velocity memory is accelerated with increasing k, which ultimately causes H to decrease between k = 1 and 10. The downward slope of the H-k plot at high velocity reflects the degree of eddy dispersion, thus, the degree of heterogeneity of the packed bed, as we could illustrate by simulations in an ordered packing, for which microstructural heterogeneity (as in the random packing) and the resulting pore-to-pore variation in the velocity distribution and associated short-range interchannel eddy dispersion are absent.
Solvated ions of the same valency and charge exhibit minor differences in bulk transport but may display strong ion-specific effects in nanoscale environments. Investigating such effects is challenged by the heterogeneous nature of conventional nanostructured membranes, which can smear out underlying structure-property correlations. We use a combination of electrochemical impedance spectroscopy, two-dimensional infrared spectroscopy, NMR relaxometry, and molecular dynamics simulations to systematically investigate anion transport in nanoporous polymers with uniform charged 1 nm scale pores. The pores are water-containing channels formed by lyotropic self-assembly of positively charged amphiphilic monomers that are then cross-linked to produce a highly ordered nanoporous polymer. Across a series of monovalent anions, we observe strong correlations of activation energy and conductivity with hydration enthalpy; more strongly hydrated species have higher conductivity and lower activation energies. These effects originate from differences in pore-wall interactions and solvation shell behavior, with more weakly hydrated species showing larger departures from their bulk behavior in their water coordination and activation energy in the membrane. Our results indicate that pore confinement amplifies the impact of water contributions to ion motion. Specifically, the ability to maintain hydration shell waters and concomitantly to avoid interactions with hydrophobic pore wall patches leads to significant differences in transport and to ion-specific trends that are unexpected in nanoporous materials. These results provide insight into ion transport in highly confined and hydration-limited geometries and suggest a mechanism by which ion selectivity can be explicitly manipulated.
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Accurate identification of indoor occupant presence is crucial for intelligent building energy management. Traditional monitoring methods are often invasive and lack standardized quantitative indicators. Therefore, this study proposes a non-invasive occupant presence state prediction method based on machine learning. Six machine learning algorithms-Logistic Regression, Decision Tree, k-Nearest Neighbor (KNN), Random Forest, CatBoost, and XGBoost-were evaluated across five building scenarios (hospitals, classrooms, dormitories, offices, and dwelling houses) using core environmental features including air temperature, relative humidity, sound pressure level, illuminance, CO2 concentration, formaldehyde (HCHO), PM2.5, PM1.0, and PM10. A temporally ordered walk-forward evaluation framework was adopted to prevent data leakage, with VIF screening and RFECV for feature selection. The best-performing models achieved mean accuracies ranging from 0.61 (dwelling) to 0.88 (office), with the optimal algorithm varying by scenario. SHAP-based interpretability analysis identified CO2 concentration, illuminance as the most consistently influential predictors, with PM2.5, temperature, and sound contributing in scenario-specific patterns. A leave-one-feature-out sensitivity analysis showed that removing CO2 caused the largest performance drop in the hospital scenario. This study provides a comparative analysis of explainable machine-learning predictors across five single-room building scenarios under a temporally aligned protocol, providing comparative observations that may inform subsequent multi-room and closed-loop studies of smart building management.
Substance use disorders (SUDs) represent a critical public health challenge, frequently compounded by co-occurring mental health conditions. While integrated care is the evidence-based standard of care, implementation gaps persist. This study utilized data from 3764 outpatient facilities from the 2023 National Substance Use and Mental Health Services Survey to evaluate mental health service integration and identify key predictors. Despite treating highly comorbid populations, 33% of facilities offered no mental health assessments. Integration levels varied sharply by facility type; opioid treatment programs (OTPs) served more clients with co-occurring disorders and were over twice as likely to provide comprehensive integration (43%) compared to non-OTPs (20%). Generalized ordered logistic regression revealed that private non-profit and public ownership, Joint Commission (TJC) accreditation, private insurance acceptance, and a higher co-occurring client volume were strong positive predictors of integrated care in the full sample. Conversely, CARF accreditation, Medicaid acceptance, and being in the Midwest were associated with a lower likelihood of integrated care. Stratified analysis uncovered critical variations by program type. Medicaid acceptance specifically lowered odds of integration in OTP. Furthermore, CARF accreditation was a negative predictor for OTPs but a positive predictor for non-OTPs. At the state level, location in a Medicaid managed care organization state significantly reduced integration odds solely for OTPs, while an increasing proportion of adults with a drug use disorder significantly increased likelihood of integration for non-OTPs. These results underscore how organizational structures, including facility type, accreditation, and state-level funding mechanisms shape care delivery. Addressing these systemic barriers, particularly financial and regulatory, is essential to closing the implementation gap and ensuring comprehensive care.
Evidence-based clinical prediction rules (CPRs) improve care delivery to children in the emergency department (ED); however, clinician-level factors may impact rule implementation. We aimed to investigate the association between clinician characteristics and their risk tolerances with CT scan ordering in children with blunt head trauma at very low risk for clinically important traumatic brain injuries (ciTBI). As part of a prospective multicentre study of children with minor head trauma (Glasgow Coma Scale scores ≥14), we collected data from ED clinicians on clinician demographics, clinical experience, self-reported risk tolerance and perceptions/self-reported use of CPRs. Children enrolled were considered very low risk for ciTBI if they were negative for the Pediatric Emergency Care Applied Research Network (PECARN) TBI CPRs. Survey results were linked to the children they enrolled in the analytic database. We performed multivariable logistic regression to identify clinician-level factors associated with CT ordering in children at very low risk of ciTBI. Of 481 clinicians, 421 (88%, 95% CI 84% to 90%) completed the survey. Among the 8957 children at very low risk for ciTBI, 654 (7.3%, 95% CI 6.8% to 7.9%) underwent CT scanning. In multivariable modelling, clinician-reported characteristics associated with ordering CTs in very low risk children included years of experience (adjusted OR (aOR) 1.02, 95% CI 1.00 to 1.03), caring for <50% children in one's practice (aOR 1.55, 95% CI 1.13 to 2.12) and avoidance of uncertain outcomes (aOR 1.31, 95% CI 1.02 to 1.69). Despite awareness of the PECARN TBI CPRs, some clinicians ordered CTs for children at very low risk for ciTBI. More years of practice, lower clinician risk tolerance and lower proportion of children in one's clinical practice were associated with higher CT use. Increasing involvement of providers with greater paediatric expertise in imaging decisions and addressing clinician perceptions and decision-making biases may further safely lower CT use in children with minor head trauma.
The global demographic transition indicates a rapid shift toward an aging population structure. Good mental health in advanced ages is critical for a healthy aging. Problems such as depression reduce life expectancy and impose a societal burden. This study aims to examine, through a multidimensional approach, the sociodemographic, economic, health-related, functional, and psychosocial factors affecting depression levels among individuals aged 65 and over in Türkiye. In this study, microdata from the Türkiye Older Adult Profile Survey conducted by Turkish Statistical Institute (TurkStat) in 2023 was utilized. The analysis included a total of 10,348 individuals aged 65 and over who personally responded to perception-related questions. The dependent variable, depression level, was categorized as "not depressed," "mild," and "severe" according to Geriatric Depression Scale scores. In the analysis of the data, a generalized ordered logit model and marginal effects were employed due to the violation of the parallel lines assumption. Depression was common among older adults, with 46.71% exhibiting mild depressive symptoms. Specifically, 34.26% were mildly depressed and 12.45% were severely depressed, while 53.29% were classified as non-depressed. According to the analysis results, female gender, living alone, low-income level, and experiencing financial hardship significantly increase the risk of depression. In terms of health status, poor self-rated general health, sensory impairments (vision/hearing difficulties), history of falls, and the need for home care are the main factors increasing the likelihood of depression. An increase in functional independence (KATZ index) and maintaining a physically active lifestyle exhibit protective effects. Additionally, strong neighbor support, a higher number of support people, and a sense of autonomy in decision-making processes significantly reduce the level of depression. Geriatric depression in Türkiye is a multidimensional phenomenon with complex socioeconomic and environmental determinants, in addition to biological factors. In particular, older women, individuals living alone, and economically disadvantaged groups are at high risk. The findings indicate that, to support healthy aging, clinical interventions alone are insufficient; strengthening social support networks, enhancing urban accessibility, and urgently developing inclusive public health policies that promote older adults'autonomy are required.
The Amazon biome harbors an exceptional diversity of wildlife, yet remains understudied regarding tick-borne pathogens. This study investigated the species richness of ixodid ticks and the occurrence of associated microorganisms in three natural areas of the Brazilian Amazon. During 2023-2024, a total of 1,084 ticks were collected from vegetation and vertebrate hosts and identified into 16 species, predominantly within the genus Amblyomma. A subset of 289 ticks was subjected to molecular screening for bacteria of the genera Rickettsia, Anaplasma, and Borrelia, as well as protozoa of the order Piroplasmida. No DNA of known pathogenic rickettsiae was detected. Instead, Rickettsia amblyommatis was identified in multiple Amblyomma species, while Rickettsia bellii and Rickettsia rhipicephali were detected at lower frequencies. Two isolates of R. amblyommatis were successfully established in Vero cell culture. Additionally, an Anaplasma sp. closely related to strains previously detected in South America, including in humans, and 'Candidatus Allocryptoplasma sp.' were identified in individual ticks. Borrelial DNA was detected in 11 ticks, with sequences clustering within the reptile-associated Borrelia group, including specimens collected from a red-footed tortoise, suggesting a potential reptile reservoir. No piroplasmid DNA was detected. These findings reveal a high richness of tick species and associated microorganisms in the Amazon, dominated by agents of unknown or uncertain pathogenicity. The apparent absence of confirmed pathogenic rickettsiae in preserved forest environments contrasts with other Brazilian regions and reinforces the need for continued epidemiological surveillance. Overall, this study provides baseline data and highlights the Amazon as a potential reservoir of neglected and emerging tick-borne agents.
Networks of coupled oscillators underpin fundamental studies of collective dynamics and emerging paradigms in physical computing. Spin Hall nano-oscillators (SHNOs) are particularly attractive due to their scalability and fast spin-wave-mediated interactions, yet mutual synchronization has so far been limited to small arrays and predominantly steady-state characterization. Here we demonstrate nanosecond phase ordering in lattices of up to N = 105,000 constriction-type SHNOs with widths of 10-20 nm. Microwave spectra reveal full mutual synchronization, a quality factor exceeding 106, power scaling as N and linewidth scaling as N-1. Time-resolved Brillouin light scattering shows a weak, approximately logarithmic increase in the synchronization time with array size. The synchronization time varies from 10 ns in arrays of 100 SHNOs to 45 ns for the largest arrays and is consistent with Kuramoto-type collective phase-ordering dynamics in a large two-dimensional oscillator lattice. These results establish spin-wave-mediated SHNO lattices as an experimentally accessible platform for exploring collective oscillator physics and for developing embedded-Ising and reservoir-computing architectures operating at tens of gigahertz.
Accurate hazardous household waste detection is critical for intelligent municipal solid waste sorting, as missed hazardous items may cause equipment damage, secondary pollution, and safety risks. However, complex backgrounds, object occlusion, scale variation, and high visual similarity between hazardous and ordinary waste make reliable detection challenging. To address these issues, this study proposes CE-DFINE, an enhanced detector based on D-FINE for hazardous waste identification. The model introduces three key improvements: a lightweight feature extraction module to reduce redundant computation, a hypergraph-based high-order semantic association mechanism with multi-scale channel attention to enhance contextual modeling under occlusion and deformation, and a selective attention decoder to suppress cluttered background interference. Experiments on the self-constructed HW-Dataset show that CE-DFINE achieves 86.3% AP50 and 58.8% AP50-95, outperforming the baseline D-FINE by 3.4 and 1.9 percentage points, respectively. Generalization experiments on the external TrashCan dataset demonstrate that the improved model maintains competitive detection performance in more complex environments, such as underwater waste scenes, and shows enhanced capability for small-object detection. Edge deployment on Raspberry Pi 5 achieves 10.40, 5.59, and 3.84 FPS at 320 × 320, 512 × 512, and 640 × 640 input resolutions, respectively. These results indicate that CE-DFINE provides an accurate, robust, and deployable vision-based detection solution for low-cost hazardous waste sorting applications.
Understanding how primate frontal circuits encode socially informative vocalizations is central to elucidating the neural basis of communication. In common marmosets, functional MRI has identified robust selectivity for conspecific vocalizations in the pregenual anterior cingulate cortex (pgACC; area 32), but the underlying neural dynamics driving this response remain unknown. Here, we investigated this by recording single-neuron activity in area 32 of five awake marmosets (3 female and 2 male) using acute and chronic Neuropixels probes and Utah microelectrode arrays during presentation of a standardized cross-species auditory stimulus set comprising marmoset, macaque, and human vocalizations as well as a suite of non-vocal sounds. Of 1,713 neurons recorded across 17 sessions, 945 (55%) showed significant auditory responsiveness, and 362 (21%) were categorically selective. Selective neurons exhibited their strongest responses to marmoset calls, and population preference indices revealed a robust bias toward conspecific vocalizations. Temporal population analyses showed rapid encoding: decoding accuracy for detection of marmoset calls exceeded chance within ∼50-100 ms, and representational similarity analysis demonstrated that marmoset calls formed a distinct cluster separate from macaque, human, and non-vocal stimuli. These results provide the first direct evidence that ACC area 32 contains a specialized neural representation for conspecific vocal signals. By linking fMRI-defined selectivity to single-neuron population dynamics, our findings identify area 32 as a key node in primate vocal-communication networks and illuminate how frontal circuits transform auditory input into socially meaningful categorical information.Significance Statement Primate communication relies on detection and interpretation of species-specific vocal signals, yet the underlying frontal-cortical mechanisms remain poorly understood. Using high-density recordings in awake marmosets, we show that the pregenual ACC (primarily area 32) contains neurons that strongly and selectively encode conspecific vocalizations, discriminate call types, and form a coherent population-level representation of marmoset calls. These results link fMRI-defined voice selectivity to single-neuron mechanisms and demonstrate that social auditory processing engages higher-order prefrontal circuits. Area 32 thus serves as an audiovocal hub with implications for primate communication and social cognition.
This preregistered study examined how school socioeconomic status (SES) relates to the development of mathematical skills. At the study's start, Chilean children were enrolled in kindergarten (n = 101; Mage = 5.9 years; 51 low SES; 48 boys), Grade 1 (n = 95; Mage = 6.8 years; 46 low SES; 52 boys), Grade 2 (n = 87; Mage = 7.9 years; 52 low SES; 47 boys), or Grade 3 (n = 84; Mage = 8.8 years; 52 low SES; 38 boys). Children completed cognitive (receptive vocabulary, number comparison, number ordering), mathematics (calculation, math fluency, applied problems), and literacy (letter-word identification, passage comprehension) tasks. Across domains, children from high-SES schools outperformed children from low-SES schools. Patterns consistent with widening SES differences were observed for mathematics outcomes that rely more heavily on formal instruction, including calculation, math fluency, and applied problems, though the timing and magnitude of these effects varied by outcome and grade. For cognitive precursors, SES differences were generally stable across grades, with the exception of receptive vocabulary, for which SES differences widened by Grade 3. Longitudinally, school SES predicted one-year gains in calculation and math fluency, but not literacy outcomes, after accounting for maternal education and cognitive precursors. These findings highlight the compounding role of school SES in children's mathematical development within a segregated education system, pointing to a need for policies and practices that promote equitable learning opportunities.
To meet the demand for high-sensitivity ammonia (NH3) detection at room temperature, this study develops an ultra-sensitive gas sensor by integrating the exceptional sensing properties of 2D MXene with the evanescent field advantages of optical microfibers. A carboxylated MXene-functionalized microfiber sensor was designed and fabricated. Density functional theory (DFT) calculations confirmed that carboxylation significantly enhances the adsorption energy between the MXene surface and NH3 molecules compared to pristine MXene, theoretically validating its superior sensitivity. Experimentally, a uniform and robust carboxylated MXene thin film was coated onto the microfiber, demonstrating excellent mechanical stability for practical sensing. Gas sensing tests show a strong linear response within the 10-250 ppm range. Notably, the sensitivity is an order of magnitude higher than previously reported microfiber sensors, with a practical detection limit of 10 ppm and a theoretical limit as low as 6.90 ppm. Furthermore, the sensor exhibits excellent long-term stability over a 10-day measurement and gas selectivity. This research provides a new strategy for developing high-performance room-temperature gas sensors with significant potential for environmental atmospheric monitoring.
Emerging evidence links chronic heat exposure to impaired neurodevelopment, yet research on higher-order cognitive domains remains limited. Furthermore, the utility of existing evidence is often constrained by the use of traditional static exposure counterfactuals (e.g., "high" vs. "low" temperature throughout a long time-frame) that fail to reflect plausible, real-world temperature trajectories. In this study, we assessed the association between realistic scenarios of temperature increases during the prenatal and postnatal periods and infant perceptual reasoning scores. Data were drawn from the French ELFE birth cohort (N=8,965). Perceptual reasoning was measured at age 3.5 using the British Ability Scale Picture Similarities subtest. Ambient heat was defined as the percentage of hot days where daily minimum temperature was higher than the 95th percentile across four temporal windows from conception to age 3.5. We estimated effects of incremental temperature shifts (+1°C to +3°C) using a sequentially doubly-robust estimator integrated with a SuperLearner machine learning ensemble. In addition, we assessed how these effects vary across a subset of covariates and explored critical windows of developmental vulnerability. While a +1°C increase showed no significant effect, population scores decreased by 0.7-0.8 points in the +2°C scenario and by 1.4-1.5 points in the +3°C scenario (all p <0.001). For every unit increase in the European Deprivation Index, the negative effect of a +3°C shift deepened by 0.035 points (p<0.04), while urban residence significantly intensified the heat-outcome relationship by 0.42 to 0.46 points (p<0.015). Exploratory period-specific analyses identified the prenatal period and the third year of life as windows of highest sensitivity across all temperature shift scenarios. Elevations of 2-3°C in nocturnal temperatures throughout early development are significantly associated with reduced perceptual reasoning scores. Both urban residency and social deprivation may exacerbate these heat-related risks. High-sensitivity windows were predominantly identified during the prenatal period and third year of life.
The order Gymnotiformes-Neotropical electric eels and knifefishes-comprise a distinctive lineage of freshwater fishes characterized by electrogenesis and electroreception. Despite their ecological, evolutionary, and neurobiological significance, phylogenetic relationships within the order remain incompletely resolved. We present the most taxonomically inclusive molecular phylogeny of Gymnotiformes to date, based on two mitochondrial and seven nuclear genes from 202 species representing 31 of 36 extant genera. Maximum likelihood and Bayesian inference analyses provide strong support for a concordant species-level phylogenetic arrangement that we use to propose a revised classification of Gymnotiformes, including new suprageneric names and reassignments of some taxa and species. We found support for six monophyletic families, including Electrophoridae (electric eels). We propose two new suborders corresponding to electric signal type: Pulseoidei (pulse-type) and Sinusoidei (wave-type), an arrangement that does not allow determination of whether a pulse or wave-type signal was ancestral. In Apteronotidae, the subfamilies Sternarchorhamphinae and Apteronotinae are supported; within Apteronotinae, we establish the tribe Steatogenini to include Porotergus, Sternarchella, and close relatives. We resolve the non-monophyly of Apteronotus with generic reassignments. For Sternopygidae, we support the monophyletic subfamilies Sternopyginae and Eigenmanniinae and resolve polyphyly in Rhabdolichops and Eigenmannia via generic reassignments. Within Gymnotus, we support six species groups. We confirm the sister-group relationship between Hypopomidae and Rhamphichthyidae and clarify their internal structure, including support for six species groups within the diverse genus Brachyhypopomus. Our study provides a robust phylogenetic framework for Gymnotiformes and establishes a foundation for future work on systematics, biogeography, trait evolution, and comparative neurobiology.
This paper is concerned with the problem of safety control for nonlinear systems subject to multiple constraints with different unknown relative degrees. A novel control design approach is developed to guarantee the safety of nonlinear systems by using a control input filter and a high-order switching differentiator (HOSD). In particular, the time-derivatives of the safety function are estimated to facilitate the control design. Also, the control design approach offers significant advantages in terms of the absence of the assumption that the relative degree of the system is known in any part of the state space. Finally, we illustrate the effectiveness of the proposed approach through both a numerical example and a practical problem based on a 2-mass lumped-parameter structure.
Inflammation plays a pivotal role in acute ischemic stroke (AIS), with neutrophil granulocytes acting as major contributors to the post-stroke immune response. Upon degranulation, neutrophils release matrix metalloproteinase-9 (MMP-9) and myeloperoxidase (MPO), both of which may influence functional outcomes following AIS. The aim of this study was to investigate the association between systemic levels of MMP-9 and MPO, functional outcome, and neutrophil counts in patients with moderate to severe AIS, with a focus on their pathophysiological relevance rather than standalone prognostic performance. This prospective single-center cohort study included patients with moderate to severe AIS in the anterior circulation (NIHSS score ≥ 6 points and /or indication for mechanical recanalization). Venous blood was sampled in order to assess plasma concentrations of MMP-9 via zymography as well as MPO plasma levels via ELISA. Furthermore, differential blood count was determined simultaneously to venous biomarker sampling. Poor outcome was defined as mRS score ≥ 3. Uni- and multivariable regression analyses were performed. Between 07/2020 and 09/2022 a total of 279 patients with blood samples taken up to 48 h after symptom onset with a median NIHSS score of 13 and a median age of 79 were included. Of these patients, 81.0% underwent mechanical recanalization and 42.7% received systemic thrombolytic therapy. Systemic MMP-9 and MPO plasma levels were significantly higher in patients with poor functional outcome compared to patients with good functional outcome at 3-months (MMP-9: 359.1 vs. 303.5 ng/ml, p = 0.0006; MPO: 27.0 vs. 19.2 ng/ml, p = 0.0010). Both biomarkers were associated with a poor outcome in unadjusted analyses [MMP-9: unadjusted OR = 15.63 (95% CI: 3.54-80.69), p = 0.0005; MPO: unadjusted OR = 4.02 (95% CI: 1.84-9.21), p = 0.0007]. In addition, MMP-9 and MPO concentrations correlated with neutrophil counts (MMP-9: r = 0.39, p < 0.0001; MPO: r = 0.31, p < 0.0001). In conclusion, systemic MMP-9 and MPO plasma concentrations are associated with functional outcome in patients with moderate to severe AIS and reflect neutrophil-driven inflammatory processes. Their value appears to lie in complementing established clinical predictors rather than serving as independent prognostic markers.
Talkative Power Conversion (TPC) is an instance of simultaneous information and power transfer, in which data modulation is integrated in the power conversion process. The TPC framework is generalized in this work to incorporate multiple-input multiple-output (MIMO) processing. In this innovative concept, called MIMO-TPC, power electronic converters with multiple switching units are applied that use coordinated or uncoordinated, data-dependent switching patterns in order to increase the degrees of freedom for data modulation. A selection of power converter topologies is investigated that fit within the framework of MIMO-TPC, including H-bridge DC/DC converters, multi-phase DC/DC converters, multi-phase DC/AC inverters, multi-input converters, multi-modular converters, multi-level converters, and multi-port converters. These switched-mode converter classes are characterized by an elementary building block and elementary combinations. Encouraging advantages of MIMO-TPC are elaborated, potential applications of MIMO-TPC are discussed, and open challenges as well as future research directions are suggested.