Search Results (263)

The Gulf of Taranto (Ionian Sea) is a key transitional sector between the Southern Apennines collisional belt and the Calabrian Arc system, where the expression of Pleistocene–Holocene deformation in the shallow stratigraphic record remains debated. This study focuses on the Taranto Canyon area, the main morphologic feature of the northeastern Gulf of Taranto slope. We integrate high-resolution multibeam bathymetry (10 m grid) with Sparker seismic profiles to (i) define the shallow seismo-stratigraphic framework and (ii) document spatial relationships between shallow discontinuities, morphostructural lineaments, and submarine channel network organization. A simplified tie to the Livia 001 well constrains the subdivision of the shallow succession into four seismic units: the late Pleistocene–Holocene unit (PtH), the Santerno Formation (SNT), the Calcarenite di Gravina (GRA), and the Cupello Limestones (CPL). The PtH interval shows the strongest lateral variability and includes widespread acoustically disturbed bodies and recurrent sub-vertical fluid escape acoustic anomalies. Steep discontinuities producing reflector terminations, minor vertical separation, and localized bending affect PtH and, locally, SNT, with normal fault geometries prevailing where resolvable. Bathymetric mapping reveals multiple lineament families and preferred channel orientations that persist across higher Strahler orders, supporting a structurally conditioned template that guides seafloor morphology, sediment routing, and canyon–slope evolution in the northeastern Gulf of Taranto. Full article

Flexible interconnection systems (FISs) improve distribution flexibility, yet they remain vulnerable to pronounced nonlinear instability and potentially severe DC-link voltage collapse during large disturbances such as constant power load (CPL) surges. Conventional linear control methods are often unable to prevent deep transient voltage dips under these conditions. To address this issue, this paper proposes a novel large-signal stability criterion based on mixed potential function (MPF) theory. Unlike conventional Lyapunov-based approaches, the proposed formulation explicitly incorporates the dynamics of the DC capacitor, thereby enabling the derivation of a closed-form stability boundary. On this basis, the proportional gains of the outer voltage loop are first optimized to guarantee an adequate static stability margin. Subsequently, a power differentiation feedforward control strategy is developed. Rather than passively counteracting transients, the proposed method dynamically adjusts the DC voltage reference according to the rate of change in power, thereby actively reshaping the transient trajectory. In this way, the simple PI control framework is preserved while avoiding the heavy computational burden associated with advanced methods such as model predictive control. Simulation results show that the proposed strategy increases the permissible CPL step power by 8.7%, from 92 kW to 100 kW. Moreover, under severe load surges and weak grid conditions, the method prevents voltage collapse and maintains the transient trajectory above the practical 600 V safe-operation threshold. This computationally efficient strategy significantly improves the robustness and continuity of operation of practical FISs. Full article

With the high penetration of distributed energy resources (DERs), which are characterized by stochasticity and intermittency, traditional centralized optimization methods face challenges such as communication packet loss, low reliability, and poor scalability in large-scale DC microgrids. Therefore, distributed optimization methods have attracted attention due to their robustness and scalability. This paper extends our previous conference work by proposing a convex-relaxation-based distributed control strategy for DC microgrids with constant power loads (CPLs) and maximum power point tracking (MPPT)-controlled distributed generations (MPPT-DGs). Furthermore, a control strategy based on distributed observers is designed to achieve global optimal control under sparse communication networks. First, an exact convex relaxation method is applied to transform the original non-convex optimal power flow (OPF) problem into a convex problem, with theoretical guarantees of exactness. Then, the Karush–Kuhn–Tucker (KKT) conditions are equivalently transformed into a consensus-based optimality condition and integrated into the distributed control framework. Next, small-signal stability analysis is performed to verify the system’s robustness. To reduce communication costs, a distributed observer-based control strategy is proposed, which can achieve optimal control under sparse communication networks. The impact of communication delays on system stability is also investigated. Finally, the simulation results verify the accuracy of convex relaxation, the effectiveness of the proposed control strategy, and its performance under communication delay. Full article

The optical and thermal behaviors of a chiral europium(III) β-diketonate complex, Eu(D-facam)₃ (facam: 3-(trifluoromethylhydroxymethylene)-(+)-camphorate), were examined in the presence of imidazolium-based ionic liquid 1-ethyl-3-methylimidazolium acetate (EMImOAc). The addition of EMImOAc to Eu(D-facam)₃ butanol solutions enhanced their luminescence intensity by up to 74-fold and induced clear circularly polarized luminescence (g_CPL = −0.28 for the ⁵D₀ → ⁷F₁ transition). When Eu(D-facam)₃ was dissolved directly in EMImOAc, the Eu(III) complex also exhibited distinct circularly polarized luminescence (g_CPL = −0.22). In addition, compared with the thermal stability of luminescence in 1-butanol, the ionic liquid solution exhibited superior thermal robustness, retaining approximately 30% of its room-temperature emission intensity even at 100 °C. Arrhenius analysis of the solutions was performed using their emission intensity and lifetime to evaluate the emission stability at higher-temperature regions near 70–100 °C. In EMImOAc, the thermal acceleration of the nonradiative decay of the ligands was suppressed; thus, the energy transfer from the ligand to the Eu(III) ion was stabilized even at higher temperatures. These results highlight the role of ionic liquids as effective media toward achieving thermally robust and highly emissive chiral Eu(III) systems. Full article

The growing prevalence of antibiotic-resistant bacterial infections poses a serious burden on healthcare systems worldwide. Endolysins are promising candidates for a new type of antibiotic due to their strong bacteriolytic activity. However, important limitations, including reduced activity and short persistence in the bloodstream, must still be addressed. We evaluated the key physicochemical and biological factors limiting the activity and stability of the endolysins Cpl-1 and Pal in blood. The analysis included ionic composition and strength, pH, bystander proteins, physiological temperature, and proteolytic activity. Our results indicate that the aforementioned factors significantly affect Cpl-1 and Pal, suggesting that physiological conditions in human circulation markedly restrict the anti-bacterial potential of endolysins. To overcome these limitations, we designed a set of Cpl-1 and Pal variants with modified amino acid compositions aimed at increasing their resistance to such physiological constraints. One variant demonstrated improved performance in an ex vivo mouse model and lacked a cleavage site for blood proteases. Full article

Photovoltaic-storage direct current (DC) flexible (PEDF) systems are susceptible to DC bus voltage disturbances, with the constant power load (CPL) characteristics further exacerbating the risk of system instability. To address these challenges, a collaborative control scheme integrating distributed consensus and demand-side response (DSR) based on a consensus protocol is proposed in this study. A fully distributed control architecture is constructed, wherein the upper layer achieves power coordination through voltage deviation of parallel DC/DC converters and neighborhood interaction, whilst the lower layer dynamically optimizes inter-unit power allocation via the DSR mechanism. Distributed state estimation (DSE) is incorporated to enhance voltage control accuracy. Simulations conducted in the MATLAB (R2022a)/Simulink environment demonstrate that the proposed strategy enables rapid stabilization of bus voltage under load step changes and photovoltaic fluctuation scenarios, with system disturbance rejection capability being effectively enhanced. The effectiveness of the approach in maintaining stable system operation and optimizing power distribution is validated. The results indicate that the voltage deviation of the PEDF system remains below 2% under compound disturbances, with the steady-state error being controlled within 2%. The proposed control strategy, through the integration of the power DSR mechanism, effectively improves the system’s anti-disturbance capability. Compared with conventional droop control methods, which typically result in voltage deviations of 3–5%, the proposed strategy achieves a reduction in voltage deviation of over 50%, demonstrating superior voltage regulation performance. Full article

To address the issue of negative damping instability easily induced by DC/DC converters under constant power load (CPL) in DC microgrids and to enhance the control robustness of the system under uncertainties such as parameter perturbations, this paper designs a controller based on the linear active disturbance rejection control (LADRC) theory. Firstly, by establishing an equivalent model of the DC microgrid with CPL, the intrinsic relationship between the equivalent incremental admittance of the hybrid load and the system damping is revealed. Subsequently, treating the nonlinear characteristics of the CPL and model parameter variations as external disturbances, the linear extended state observer (LESO) is employed to estimate and compensate for the total system disturbance in real time. This effectively eliminates the risk of negative damping instability caused by the CPL and enhances the system’s robustness against parameter variations. Then, theoretical analysis is conducted from three perspectives, the convergence of disturbance estimation error, the stability of the closed-loop system, and robustness against parameter variations, thereby ensuring the reliability of the proposed control strategy. Finally, the proposed control strategy is validated through simulations and experiments. The results confirm that, even in the presence of negative damping effects and parameter variations, the strategy can effectively maintain fast tracking and stable control of the output voltage. Full article

This article introduces an integrated control scheme combining an Adaptive Extended Kalman Filter (AEKF) with a Passivity-Based Control (PBC) approach to stabilize a DC-DC boost converter feeding both constant voltage and constant power loads (CPLs) in DC microgrids. Unlike conventional observers, the AEKF adapts its covariance matrices in real time to accurately estimate both system states and the unknown load dynamics introduced by CPLs, thereby eliminating the need for additional sensors and enhancing estimation convergence. Coupled with the PBC, the estimated disturbances are compensated via a feedforward path, significantly improving the system’s resilience to input voltage fluctuations and load variations. Through a Lyapunov-based stability analysis, the combined strategy is proven to ensure large-signal stability while maintaining a rapid transient recovery profile, even under significant parametric uncertainties. The simulation of this algorithm was implemented using PLECS, thoroughly validating the effectiveness and robustness of the proposed method. Full article

The development of nanoscale chiral materials with enhanced optical properties holds significant promise for advancing technologies in light-emitting devices and enantioselective sensing. Here, we report the self-assembly of chiral metal–organic cages from an axially chiral, AIE-active binaphthyl dicarboxylate ligand. This supramolecular architecture functions as a multifunctional platform, demonstrating a high affinity for anionic guests through synergistic electrostatic and hydrogen-bonding interactions. The rigid cage framework not only enhances the ligand’s intrinsic aggregation-induced emission (AIE) but also serves as a highly effective chiral amplifier. Notably, MOCs significantly boost the circularly polarized luminescence (CPL), achieving a luminescence dissymmetry factor (|g_lum|) of 1.2 × 10⁻³. This value represents an approximately five-fold enhancement over that of the unassembled ligand. The photophysical properties of this chiral supramolecular system provide a strategic blueprint for designing next-generation optical nanomaterials. Full article

This paper formulates a control framework grounded in prescribed performance control (PPC) and combined with a dynamic error modulation function. The proposed framework addresses the control challenges of DC–DC boost converters under sudden power variations caused by constant power loads (CPLs). A sine kernel-based prescribed performance function with smoothly decaying characteristics is designed to form a dynamic performance boundary that gradually tightens as the system state evolves. Furthermore, to effectively eliminate the restriction of traditional PPC on the system’s initial state, a time-varying modulation function is introduced. This function dynamically scales the tracking error, thereby improving the system’s adaptability at the initial state. A neural network disturbance observer (NNDO) is employed to approximate and compensate for unknown nonlinearities and external disturbances, thereby enhancing system robustness and adaptability. Consequently, a prescribed performance controller that integrates dynamic error modulation and a dual-channel NNDO is proposed. The proposed controller not only guarantees that the tracking error satisfies the prescribed performance constraints but also avoids the computation of high-order derivatives. Simulation results demonstrate that the proposed method maintains bounded convergence of the tracking error and achieves smooth voltage regulation during CPL variations. The results further exhibit excellent dynamic response and steady-state performance. Full article

The rapid growth of online chess has intensified the challenge of distinguishing engine-assisted from authentic human play, exposing the limitations of existing approaches that rely solely on deterministic evaluation metrics. This study introduces a proof-of-concept hybrid framework for discriminating between engine-like and human-like chess play patterns, integrating Stockfish’s deterministic evaluations with stylometric behavioral features derived from the Maia engine. Key metrics include Centipawn Loss (CPL), Mismatch Move Match Probability (MMMP), and a novel Curvature-Based Stability (ΔS) indicator. These features were incorporated into a convolutional neural network (CNN) classifier and evaluated on a controlled benchmark dataset of 1000 games, where ‘suspicious’ gameplay was algorithmically generated to simulate engine-optimal patterns, while ‘clean’ play was modeled using Maia’s human-like predictions. Results demonstrate the framework’s ability to discriminate between these behavioral archetypes, with the hybrid model achieving a macro F1-score of 0.93, significantly outperforming the Stockfish-only baseline (F1 = 0.87), as validated by McNemar’s test (p = 0.0153). Feature ablation confirmed that Maia-derived features reduced false negatives and improved recall, while ΔS enhanced robustness. This work establishes a methodological foundation for behavioral pattern discrimination in chess, demonstrating the value of combining deterministic and human-centric modeling. Beyond chess, the approach offers a template for behavioral anomaly analysis in cybersecurity, education, and other decision-based domains, with real-world validation on adjudicated misconduct cases identified as the essential next step. Full article

Density functional theory (DFT) and its extension, time-dependent DFT (TD-DFT), have become fundamental tools for modeling chiral excited states and supporting experimental chiroptical spectroscopies. In this connection, the interest in understanding the asymmetric emission through the circularly polarized luminescence (CPL) technique peaked in the current decade. In the present work, we are computationally faced with an emerging class of luminophores which combines the luminogenic source of the BODIPY unit with the intrinsic chirality of the helicene pendant to obtain a chiral radiative deactivation. In particular, a meso-substituted BODIPY-[6]helicene was deeply examined through a DFT multistep approach to attain an appreciable level of theory for the CPL simulation. Among the multitude of alternatives, TPSSTPSS exchange-correlation functional with 6-311G(d,p) basis set revealed to be the best computational protocol to emulate the CPL spectral profile with regard to peak intensity, band position, and chiral sign for both M and P form. Full article

This paper presents the validation of a voltage balancing converter for a bipolar DC microgrid designed to ensure reliable operation in both grid-connected and islanded modes. This microgrid includes unipolar constant power loads (CPL), a unipolar Battery Energy Storage System (BESS), and local PV generation. The BESS converter employs a V–I droop strategy using only inductor current feedback, reducing sensing requirements while maintaining plug-and-play capability and ensuring smooth transitions between connected and islanded modes. In such a microgrid, the voltage balancing converter regulates the differential voltages under severe unbalanced load conditions and during transients caused by changes in unipolar loads and sources. The experimental results validate the voltage balancing strategy across various scenarios in a small-scale prototype. The results show tight voltage regulation under unbalanced conditions, and smooth transitions during load transients and unintentional islanding, even if there is no dc voltage source in one of the poles of the bipolar dc bus. For both conditions, the imbalance between the unipolar voltages is less than 0.5% of the total bipolar voltage. Full article

Due to their extensive common-sense knowledge and linguistic understanding, large language models (LLMs) have demonstrated remarkable capabilities in text comprehension and logical reasoning for natural language processing tasks. Traditional prompt-based learning methods, which rely on contextual pattern matching, have proven to be effective in extracting knowledge from LLMs. However, these approaches are constrained by training data pattern matching, overlook reasoning processes, and consequently suffer from suboptimal prompt performance and limited interpretability. Moreover, considering that the intermediate steps generated by single-chain reasoning may not effectively assist LLMs in identifying the sentiment polarity of aspect terms, and that multiple reasoning paths often exist for complex reasoning tasks to reach correct conclusions, this paper proposes a Multi-Chain Thought Prompt Learning framework (MT-CPL). Starting from fundamental concepts, this method simulates human multi-path reasoning patterns to progressively construct comprehensive thought processes and deeply explore sentiment cues. Based on syntactic structures and the semantic logic of text, the framework incorporates four distinct perspectives of text comprehension: hierarchical reading, experiential reading, keyword-based reading, and analogical reading. It establishes a multi-chain prompt template and employs voting mechanisms to select correct reasoning path outcomes. The MT-CPL approach aims to guide LLMs in mining multi-dimensional textual information from different perspectives, gradually uncovering hidden contextual sentiment clues, while mitigating issues caused by irrelevant sentiment cues in intermediate reasoning steps. By decomposing main tasks incrementally, the method achieves progressive reasoning, effectively reduces the difficulty of direct analysis, and further enhances model interpretability through the integration of inherent common-sense knowledge. Full article

We report the synthesis and characterization of the two enantiomeric forms of a thienyl-substituted diketopyrrolopyrrole (DPP) derivative bearing chiral alkyl chains. Thin films were prepared either by spin-coating and drop-casting and analyzed by UV–Visible absorption, electronic circular dichroism (ECD), and circularly polarized (CP) luminescence (CPL). ECD spectra confirmed the opposite chirality of the (R) and (S) isomers, while CPL measurements of the S enantiomer demonstrated solid-state chiroptical activity. Preliminary device tests showed promising optoelectronic behavior, highlighting these chiral DPP materials as potential candidates for CP organic light-emitting diodes (CP-OLEDs) applications, combining strong chiroptical response with good film quality. Full article