Search Results (6,123)

Anaerobic digestion (AD) plays a key role in the circular bioeconomy by converting organic waste into renewable energy and facilitating the sustainable utilisation of waste materials. Agricultural and agro-industrial by-products are increasingly recognised as valuable AD feedstocks due to their widespread availability and significant bioenergy potential. However, many of these substrates contain plant-derived bioactive compounds, such as polyphenols, tannins, flavonoids and terpenes, which can influence microbial communities and process performance. Depending on their concentration and chemical characteristics, these compounds may inhibit microbial activity, impair process stability, and ultimately decrease methane production. This review critically synthesises current knowledge on the occurrence, bioavailability and biological activity of plant-derived bioactive compounds in agricultural feedstocks used for anaerobic digestion, with particular emphasis on their implications for process performance and reactor stability. The principal mechanisms through which phytochemicals influence anaerobic digestion include enzyme inhibition, membrane disruption, interference with syntrophic interactions and trace metal chelation. The available evidence demonstrates a pronounced dose-dependent response, whereby low concentrations may exert neutral or selective modulatory effects. In contrast, elevated concentrations disrupt microbial activity, leading to volatile fatty acid accumulation, prolonged lag phases and reduced methane production. Current mitigation strategies include substrate pretreatment, co-digestion, microbial adaptation, adsorbent-assisted detoxification and the use of DIET-promoting materials. An integrated evidence matrix is proposed to link phytochemical composition with reactor configuration, operational parameters and mitigation strategies, thereby providing a practical framework for feedstock-specific process optimisation. Overall, the available evidence demonstrates that reliable evaluation of agricultural feedstocks should extend beyond conventional biochemical methane potential assessment to incorporate phytochemical composition, microbial functional responses and key operational parameters. Such an integrated approach can improve the prediction of methane recovery and support evidence-based optimisation of anaerobic digestion within circular bioeconomy systems. Full article

Wastewater treatment is undergoing a transition from pollutant removal toward resource recovery, creating opportunities to integrate environmental remediation with circular bioeconomy principles. Filamentous algae have attracted increasing attention as multifunctional biological platforms because their attached growth habit facilitates biomass harvesting while supporting nutrient recovery and biomass valorization. This review synthesizes current knowledge on the roles of filamentous algae in wastewater treatment, with emphasis on nutrient and contaminant removal, biomass production, and the generation of bioenergy, biofertilizers, aquafeeds, and cellulose-based biomaterials. It highlights how filamentous algae differ from conventional suspended microalgae through improved biomass retention, simpler harvesting, and compatibility with attached-growth systems such as algal turf scrubbers and biofilm reactors. The review also examines the ecological interactions between filamentous algae and associated microbial communities that underpin nutrient cycling and treatment performance. Beyond wastewater treatment, it critically evaluates the opportunities and challenges associated with downstream biomass valorization, including biofuel production and the recovery of high-value products within integrated biorefinery frameworks. In addition, the review discusses the principal barriers to large-scale implementation, including limited field-scale validation, variability in biomass quality, contaminant safety, downstream processing requirements, regulatory uncertainty, and the need for comprehensive techno-economic and environmental assessments. Finally, it highlights emerging research directions involving systems biology, advanced process monitoring, artificial intelligence-assisted process control, and integrated biorefinery concepts that may support future development. By integrating biological, engineering, and sustainability perspectives, this review provides a comprehensive framework for understanding the potential of filamentous algae to support resilient wastewater treatment systems and accelerate the transition toward circular resource management. Full article

This paper presents a retrospective proof-of-concept development and preliminary evaluation of an interpretable decision-support system for daily fatigue-risk management in one elite women’s football club. The system integrates morning wellness, GPS-derived external load, and a daily wellness-duration internal-load proxy at the player-day level. It combines a player-day integration layer, an interpretable predictive layer, and a recommendation layer that returns one of three staff-facing actions: Reduce, Maintain, or Progress. The predictive model outputs a calibrated probability of elevated next-morning self-reported soreness. The target is a subjective questionnaire outcome, not an injury, medical diagnosis, or objective marker of recovery. The decision-support layer maps this probability to a three-state recommendation, informed by a review threshold, operational guardrails, and staff oversight. Using retrospective monitoring data from two competitive seasons (2024/25 and 2025/26) in a single professional team, we evaluated the proposed approach using rolling-origin temporal validation, leave-one-player-out cross-validation, and between-season validation. To separate genuine predictive signal from the day-to-day persistence of soreness, we report a baseline ladder ranging from a trivial persistence rule to the full model, with bootstrap confidence intervals for performance differences. Under rolling-origin validation across 16 monthly folds, the final logistic regression model achieved a mean ROC-AUC of $0.759$ ($SD=0.089$). Critically, a model excluding current soreness still outperformed the persistence baseline (ROC-AUC $0.738$ vs. $0.721$), and the isolated contribution of current soreness was modest but reliable ($\Delta$ROC-AUC $=+0.044$, 95% CI $[+0.027,+0.059]$). Between-season validation (train: 2024/25; test: 2025/26) yielded an ROC-AUC of 0.801. The three-state recommendation layer separated outcomes monotonically, with observed next-morning soreness rates of $0.054$ for Progress, $0.217$ for Maintain, and $0.326$ for Reduce ($p<0.001$ for the Progress-versus-Maintain contrast). These preliminary findings support the feasibility of the proposed approach within the club studied. However, because the model requires complete wellness, GPS, and proxy data, it operates only on the fully monitored on-pitch stratum (3386 of 17,703 player-days); the reported performance therefore applies to this stratum rather than to a typical player-day, and prospective evaluation and external validation by independent teams are required before broader implementation can be considered. Full article

In this study, an electrochemical immunosensor based on polyaniline/nano-Fe₃O₄ (PANI/Nano-Fe₃O₄) nanocomposite (PANI/Nano-Fe₃O₄/Anti-FLA/BSA/GCE) was developed for the highly sensitive and selective detection of trace levels of fluoranthene (FLA) in marine environments. Fluoranthene antibodies (Anti-FLA) were covalently immobilized on a glassy carbon electrode (GCE) modified with PANI/Nano-Fe₃O₄ via an EDC/NHS activation strategy, enabling specific recognition of FLA based on the antigen–antibody binding mechanism. The performance of the sensor was systematically optimized using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), linear sweep voltammetry (LSV), and differential pulse voltammetry (DPV). The results demonstrated a linear inverse relationship between peak current (I_p) and FLA concentration in the range of 0.5~80 ng/mL, with a regression equation of I = −1.55C + 174.602 (R² = 0.996). The limit of detection (LOD) was as low as 0.354 ng/mL (S/N = 3). In real seawater sample analysis, spiked recovery tests at three representative sites in the Maowei Sea, Guangxi, yielded recoveries of 95.44%~97.51%, with RSDs below 3%, confirming the sensor’s resistance to matrix interference. The synergistic effect of the porous conductive network of PANI and the high specific surface area of Nano-Fe₃O₄ significantly amplified the electrochemical signal, while the molecular specificity of the antibody ensured targeted recognition. This sensor provides a novel and effective approach for the on-site rapid detection of polycyclic aromatic hydrocarbon (PAH) pollutants in complex marine environments, offering both high sensitivity and selectivity. Full article

Background and objectives: Cerebrovascular strokes and transient ischemic attacks (TIAs) remain leading causes of global mortality and long-term disability. Emerging evidence suggests that pre-existing structural brain frailty—driven by chronic cerebral small vessel disease (CSVD)—critically limits neuroplastic compensation and determines overall recovery potential. This systematic review aims to synthesize current evidence evaluating brain frailty as an independent determinant of functional and cognitive outcomes. Methods: Following PRISMA and Cochrane SWiM guidelines, a comprehensive literature search was conducted across PubMed, Web of Science, Embase, and Scopus. Methodological quality was evaluated using the Newcastle–Ottawa Scale (NOS). Due to anticipated clinical and methodological heterogeneity, data were synthesized via a structured narrative approach. A total of 15 studies comprising more than 18,000 participants met the inclusion criteria, including prospective cohorts, retrospective cohorts, registry analyses, pooled cohort studies, and post hoc analyses of randomized controlled trials. Results: Structural disconnection burden emerged as one of the strongest predictors of post-stroke cognitive impairment, demonstrating remarkably large effect sizes at both 6 months (OR 9.96, 95% CI 2.21–52.40) and 36 months (OR 12.27, 95% CI 2.80–63.40). Additionally, blood–brain barrier (BBB) leakage was significantly associated with longitudinal cognitive decline (β −3.62 to −0.16, p < 0.001). Pre-existing brain frailty was also consistently associated with poorer functional recovery following acute ischemic stroke, even among patients undergoing advanced reperfusion strategies such as endovascular thrombectomy or intravenous thrombolysis. Conclusions: Brain frailty should be integrated into clinical prognostic frameworks as a key marker for predicting long-term cognitive function and functional independence in stroke patients. However, the substantial methodological and clinical heterogeneity observed across the included literature must be acknowledged as a key limitation of the current evidence base. Large-scale, prospective, multicenter longitudinal studies are required to better standardize brain frailty metrics and validate their utility in clinical practice. Full article

This paper presents an experimental study of vibration energy recovery from a horizontal seat suspension system in which a brushless direct current (BLDC) motor is used as both an active force actuator and a controllable regenerative braking element. The novelty of the study lies in the experimental validation of an active/regenerative switching strategy for a horizontal seat suspension and in the quantitative comparison of passive, fully active and regenerative operating modes under random vibration excitation and different inertial loads. The proposed system was evaluated using transmissibility functions, seat effective amplitude transmissibility (SEAT) factors, suspension travel, and electrical quantities generated in the braking branch. The results show that the fully active mode provides the highest vibration attenuation, whereas the regenerative mode reduces the SEAT factor compared with the passive suspension while simultaneously producing measurable electrical power in the braking resistor network. The maximum measured electrical power in the braking branch reached 7.692 W for the WN3 excitation signal and an 80 kg load. The obtained results confirm the practical potential of regenerative braking for potentially reducing the net energy demand of active seat suspension systems, while also highlighting the trade-off between vibration attenuation, suspension travel, and recoverable electrical power. Full article

Anti-amyloid therapy (AAT) with monoclonal antibodies (mAbs) modestly slow cognitive and functional decline in early Alzheimer’s disease (AD). However, both the magnitude of clinical benefit and the risk of treatment-related complications vary substantially even among patients with similar biomarker profiles. Frailty, both brain and systemic, is highly prevalent in older adults with AD and affects a large proportion of those considered for AAT. Despite this, it has been largely absent from current decision frameworks. Brain frailty, defined by structural and microvascular damage (e.g., small-vessel disease, microbleeds, and atrophy), limits the clinical benefit of amyloid clearance and increases susceptibility to amyloid-related imaging abnormalities. In contrast, systemic frailty, reflecting reduced physiological reserve, mainly affects treatment tolerance and recovery from adverse events. In this narrative, conceptual review, we synthesize evidence that both forms of frailty act as biologically grounded modifiers of AAT efficacy and safety and may limit the clinical benefit while increasing susceptibility to complications and decompensation. Importantly, the precise empirical thresholds at which frailty begins to exert harmful effects remain unknown. We further outline how MRI-based markers of brain frailty, combined with brief systemic frailty measures, could support risk stratification, patient selection, monitoring intensity, and shared decision-making, including deferring treatment when the benefit–risk balance is unfavorable, while avoiding exclusion of patients who may still benefit. Taken together, we propose that future studies should incorporate frailty measures and perform precise assessments of both brain and systemic frailty, as this may improve patient stratification and better characterize the effects of AAT. Full article

Despite the fact that smoking is still a significant global public health concern, current mathematical models of smoking dynamics primarily depend on conventional numerical solvers. A particular five-compartment smoking dynamics model has not yet been solved using deep neural network (DNN) techniques. In order to fill this research gap, this work creates a unique DNN framework that can simulate nonlinear smoking dynamics in a computationally efficient manner. The Levenberg–Marquardt backpropagation technique is used to improve a dual-hidden-layer network consisting of 20 radial basis activation function (RBAF) neurons and 40 log-sigmoid activation function (LSAF) neurons. With a minimum mean squared error (MSE) of $1.865\times {10}^{-6}$ and a coefficient of determination $R^2$ equal to or near unity across all model variables, the trained DNN offers instantaneous predictions while maintaining superior accuracy, in contrast to traditional numerical methods that necessitate the explicit re-solving of differential equations for each parameter change. Crucially, our DNN-based framework is appropriate for automated public health decision-support systems since it functions independently and does not require human intervention during the prediction phase. Key smoking behaviors, such as initiation, quitting efforts, relapse dynamics, and long-term recovery patterns, are successfully replicated by the framework, while relapse dynamics are captured through the recovered-to-potential smoker pathway, consistent with the original model formulation. These findings show that the proposed DNN approach not only closes the methodological gap in the application of deep learning to smoking dynamics but also offers a dependable and computationally effective tool for quick evaluation of intervention scenarios, supporting evidence-based public health decision making without compromising accuracy. Full article

Extracorporeal cardiopulmonary resuscitation (ECPR) has emerged as a promising strategy for selected patients with refractory cardiac arrest, improving survival and the likelihood of favorable neurological outcomes. However, neurological prognostication in this setting remains highly challenging and insufficiently standardized. The pathophysiological complexity of ECPR, including global ischemia–reperfusion injury, altered cerebral perfusion, systemic inflammation, anticoagulation and prolonged sedation, limits the reliability of conventional post-cardiac arrest prognostic tools. This narrative review provides a focused and clinically oriented synthesis of current evidence on brain injury and neuroprognostication in patients undergoing veno-arterial extracorporeal membrane oxygenation (V-A ECMO) for cardiac arrest. Key determinants of neurological outcome across pre-ECMO and peri-resuscitation phases are examined, alongside the role and limitations of multimodal monitoring strategies, including neurological examination, electroencephalography, neuroimaging, cerebral oximetry and circulating biomarkers. Particular attention is given to the timing of prognostication and the risk of premature or inaccurate predictions leading to self-fulfilling prophecies. Emerging data suggest that neurological recovery in ECPR patients may be delayed, supporting a more cautious and time-adapted approach. A pragmatic, multimodal framework for neurological assessment in this population is outlined. By addressing current gaps and proposing a structured approach, this review aims to inform clinical decision making and contribute to improved neurologically meaningful survival in ECPR-treated cardiac arrest. Full article

Caloric restriction (CR), defined as a 30–60% decrease in ad libitum food intake without malnutrition, has emerged as one of the most robust non-pharmacological interventions for promoting metabolic health and longevity in various species, including yeast, worms, flies, rodents, and perhaps non-human primates. In addition, CR has been shown to reduce the incidence of age-related disorders (for example, diabetes, cancer, and cardiovascular disorders) in mammals. Among the key organs influenced by CR, the pancreas—particularly the insulin-producing β-cells—plays a central role in maintaining glucose homeostasis and metabolic balance. A growing body of evidence suggests that CR exerts its beneficial effects, at least in part, through the modulation of nutrient-sensing pathways and epigenetic regulators. Sirtuins, a family of NAD⁺-dependent deacetylases and ADP-ribosyltransferases, have gained attention as pivotal molecular mediators of CR. By responding to changes in cellular energy status, sirtuins regulate diverse processes including gene expression, oxidative stress response, mitochondrial function, and autophagy. In the pancreas, sirtuins such as SIRT1, SIRT3, and SIRT6 have been implicated in preserving β-cell function, enhancing insulin secretion, and protecting against metabolic stress and inflammation. This review critically examines current evidence regarding the role of individual sirtuins in mediating the pancreatic response to caloric restriction, with particular emphasis on β-cell physiology, insulin secretion, mitochondrial function, autophagy, oxidative stress, and inflammatory signaling. We further discuss how these molecular mechanisms contribute to systemic metabolic homeostasis and may influence healthy longevity. Importantly, we integrate experimental findings with emerging clinical evidence demonstrating the recovery of β-cell function following dietary energy restriction and identify current controversies, limitations, and key knowledge gaps that should guide future translational research. Collectively, available evidence suggests that sirtuins represent central molecular links between caloric restriction and β-cell adaptation, highlighting their potential as therapeutic targets for preserving pancreatic function and preventing metabolic disease. Full article

This paper proposes a control method for electric vehicles’ powertrains that concentrates on improving the efficiency of the drivetrain as a whole by targeting regenerative energy recovery. The design of the system is based on a traditional six transistor voltage source inverter using an asymmetric control scheme together with a hybrid CNN-TD3 controller for speed control purposes. The asymmetric control method distinguishes the dynamics of the two operating modes of the inverter, that is, the traction and regenerative braking modes, by controlling the gain values based on the operating mode of the system. The TD3 algorithm adjusts the values of three continuous control parameters, Kpv, Kiv, and Ks, in real time. A multi-objective reward function aims to maximize the system’s energy efficiency, regenerative energy recovery efficiency, speed control accuracy, driving comfort, current harmonics reduction, and battery safety. Simulations performed using the WLTP Class 3 driving cycle show energy efficiency of 15.3% (25.2 to 21.44 kWh/100 km), 59.5% speed regulation error reduction, 19.6% increase in regenerative recovery efficiency from 65.2% to 77.34%, and 34.2% reduction in current THD from 8.58% to 5.65%. Full article

Direct current (DC) bus voltage stability in grid-connected microgrids with multiple energy-storage converter branches is governed by the coupled dynamics of photovoltaic generation, heterogeneous battery storage, and the grid-connected inverter. Conventional independent and droop control methods allocate branch power mainly from local voltage or state-of-charge information, and therefore cannot fully account for converter limits, battery condition, or inverter current admissibility. This paper proposes a constraint-aware predictive energy coordination (CAPEC) method for DC bus stabilization. A DC bus energy model with variable losses is combined with one-step disturbance prediction and a safety governor that allocates storage power according to state of charge (SOC), state of health (SOH), power reserve, thermal margin, current capability, and ramp limits. The grid-connected inverter is treated as a constrained energy regulation path with active power saturation and antiwindup correction. Small-signal and energy function analyses show that CAPEC increases equivalent DC bus damping and yields bounded voltage recovery for feasible references and bounded prediction errors. MATLAB R2023b studies for combined photovoltaic and load variation, grid interface disturbance, and heterogeneous storage conditions show that CAPEC limits the maximum voltage deviation to 2.86 to 6.35 V. A reduced power laboratory experiment further verifies the Case B breaker disturbance and confirms stable DC bus behavior during unplanned grid interface switching. Full article

Target speech extraction (TSE) aims to recover a target speaker’s speech from mixed speech using auxiliary speaker information. Recent diffusion-based TSE models have shown promising performance, but conventional training mainly optimizes prediction accuracy at individual timesteps without explicitly using adjacent diffusion predictions as supervision. This may limit stable recovery under severe speaker overlap or strong interference. In this paper, we propose a trajectory-aware teacher–student fine-tuning framework based on a pretrained SoloSpeech extractor. The proposed framework constructs a current-centered local diffusion prediction trajectory from the current and adjacent timesteps. A teacher mixer uses this trajectory to generate a refined latent-space teacher target for additional student supervision. Experiments on Libri2Mix show that the proposed method improves PESQ from 1.92 to 1.97, ESTOI from 0.77 to 0.79, and SI-SNR from 10.45 dB to 11.42 dB compared with the reproduced SoloSpeech baseline under the same evaluation pipeline. It also reduces input-relative degradation cases by 45.8%, 36.7%, and 43.2% for SI-SNRi, PESQi, and ESTOIi, respectively. Additional comparisons with normal fine-tuning and VCTK + DEMAND support the proposed supervision under different fine-tuning conditions. Since the teacher mixer is used only during fine-tuning and removed during inference, the proposed method improves the extractor while preserving the SoloSpeech inference structure. Full article

Quantum computing poses a significant threat to blockchain systems that rely on elliptic curve cryptography and other classical security mechanisms. Algorithms such as Shor’s and Grover’s can weaken or completely break the cryptographic foundations of current blockchain networks, exposing them to risks including private key recovery, transaction forgery, consensus manipulation, and harvest-now-decrypt-later attacks. This paper presents a systems framework for designing quantum-resilient blockchains by integrating post-quantum cryptographic standards, threat modeling, architectural redesign, governance mechanisms, and migration planning. The study evaluates major post-quantum cryptographic primitives, assesses their suitability for blockchain environments, and proposes a layered architecture grounded in crypto-agility, defense-in-depth, and forward secrecy. A structured migration strategy is also introduced to support the transition of existing blockchain networks toward post-quantum security while maintaining operational continuity and stakeholder trust. The framework provides practical guidance for researchers, developers, and policymakers preparing blockchain ecosystems for the post-quantum era. Full article

The goal of the study presented in this article is to investigate all current issues related to the proper deployment of load forecasting (LF) techniques in smart grids (SGs). The latter concept has recently emerged as a potential solution to the global energy problem as well as to the ever-increasing and diverse consumer demands. To this end, more flexible dispersed production units are involved, mainly based on renewable energy sources (RESs). Another key novelty of SGs is their ability to gather information directly from consumers and production units in real time, thus facilitating optimum network planning and recovery as well as minimization of outage probability. Hence, it is important to use appropriate advanced infrastructure, which, in combination with modern telecommunication networks, will enable the full exploitation of SGs. In this context, to make the electricity system more efficient, avoid voltage and frequency imbalance issues and implement optimal production and consumption planning, LF is a vital process and plays a key role in the management of future electricity systems. Therefore, recent state-of-the art approaches in LF methods are also presented and discussed. In the same context, current limitations and proposals for future work are identified as well. Full article