Search Results (47,812)

The coalescence of Cu₃₀₉ and Ag₃₀₉ clusters and the subsequent thermal evolution of the resulting Cu-Ag alloy clusters are systematically investigated via molecular dynamics simulations. The effects of initial coalescence distance and contact orientation on the potential energy, free energy, shape factor, and atomic packing structures are examined in detail. The results demonstrate that the activation energy for coalescence is influenced by both initial coalescence distance and the contact orientation, with the facet-to-facet orientation generally exhibiting the highest energy barrier. During the initial coalescence stage, the free energy remains essentially constant until the clusters come into contact, after which distinct evolutionary pathways emerge depending on the orientation. Upon heating, the average potential energy reveals multiple stages of atomic rearrangement, structural transition, and melting, with the transition temperatures varying significantly with the initial conditions. Shape factor analysis indicates that most clusters evolve toward a nearly spherical morphology at high temperatures, while atomic packing visualizations confirm the formation of core@partial-shell, incomplete icosahedral, and fully molten configurations depending on the temperature and initial parameters. This work provides atomic-scale insights into the coalescence behavior and thermal stability of Cu-Ag alloy clusters, offering theoretical guidance for the design and synthesis of bimetallic nanoclusters with tailored structures. Full article

Vocational rehabilitation (VR) plays an important role in supporting successful school-to-work transitions for individuals with intellectual disabilities (IDs). However, the organizational conditions that enable effective implementation of VR practices remain insufficiently understood. Because participation outcomes among individuals with ID are influenced by interactions between intellectual functioning, adaptive abilities, and environmental supports, examining organizational implementation capacity provides an important systems-level perspective. This study examined professionals’ perceptions of organizational implementation capacity for VR during secondary transition in Saudi Arabia and explored differences according to institutional and professional characteristics. A cross-sectional survey was conducted among 89 professionals working in government secondary school programs and specialized education centers in Al-Kharj, Saudi Arabia. Participants completed the Vocational Rehabilitation Implementation Capacity Measure (VRICM), a context-specific, theoretically informed measure developed for this study. The VRICM examines four theoretically derived areas: Support and Related Services, Vocational Preparation Services, Collaborative Planning, and Workplace-Based Training. We conducted descriptive statistics and nonparametric group comparisons. Overall perceived organizational implementation capacity was moderate (M = 3.25, SD = 0.95). Support and Related Services and Vocational Preparation Services received higher ratings than Collaborative Planning and Workplace-Based Training. Differences were observed according to institutional sector and professional experience. Findings provide preliminary evidence regarding organizational factors supporting VR implementation within a Saudi Arabian secondary-city context. Further psychometric evaluation of the VRICM, including structural validity assessment, is required before broader application. Full article

For linear continuous-time systems subject to delays, this work constructs an $H_{\infty }$-based fault detection filter operating over a prescribed frequency range. By employing finite-frequency techniques, the design problem of the filter is recast as a delay-dependent $H_{\infty }$ filter design issue. The delay-dependent fault detection filter designed by this method satisfies the prescribed performance index $H_{\infty }$ over the specified frequency range. To characterize the finite-frequency features of faults and disturbances, the generalized KYP lemma is employed to establish a set of LMI-based sufficient conditions for filter existence. This avoids the complexity and potential inaccuracy introduced by weighting functions in existing approaches. Simulation results demonstrate that, under identical fault conditions, the proposed delay-dependent approach achieves better detection capability than the delay-independent approach. Full article

Photovoltaic power conversion systems require maximum power point tracking (MPPT) strategies capable of fast dynamic response with low computational burden, while remaining reliable under variable environmental conditions. While neural-network-based methods have been widely investigated, their practical deployment is often limited by the availability of representative training data and by the gap between offline algorithm development and real-time converter-level validation. This paper presents a reproducible hardware-in-the-loop workflow for the development and assessment of a lightweight neural MPPT controller applied to a photovoltaic system with a two-phase interleaved boost converter. The proposed approach generates a large synthetic training dataset using the single-diode photovoltaic model, leveraging only measurable quantities (PV voltage, PV current, and module temperature) as neural network inputs. The trained network estimates the voltage and current corresponding to the maximum power point, while a proportional-integral controller drives the converter toward the predicted operating point. The trained network is deployed on an STM32 microcontroller interfaced with the Typhoon HIL platform, allowing its real-time behavior to be tested against the emulated system. The measured neural MPPT execution time on the microcontroller is around 65 $\mathsf{\mu }$s, with an overall CPU occupancy of nearly 4%, considering the PI controller stage. The implemented setup reproduces the photovoltaic generator, converter dynamics, switching behavior, and realistic irradiance and temperature profiles under repeatable real-time conditions. The interleaved boost architecture also reduces input current ripple and distributes current stress, making the setup suitable for medium-power photovoltaic applications. The main contribution of this work lies in the integrated modeling, training, control, and hardware-in-the-loop validation procedure, supporting the implementation of neural MPPT strategies. Full article

Operational technology (OT) systems, such as power-grid controls and factory automation, are replacing cryptographic mechanisms vulnerable to future quantum computers with post-quantum (PQ) cryptography. However, older backups, standby systems, trust stores, and failover paths may retain weaker cryptography after the active system is upgraded. Legitimate recovery can reactivate these weaker states and allow them to regain privileged authority, reducing achieved protection. Existing work addresses PQ deployment, crypto-agility, secure recovery, rollback protection, attestation, and continuous authorization, but these mechanisms do not by themselves determine whether legitimate recovery can restore weaker cryptographic states that may regain privileged authority. We introduce PQReach-OT, which analyzes recovery paths before failure, keeps the required cryptographic protection level separate from the recoverable state, and requires fresh evidence before privileged authority is restored. We conducted a controlled mechanism-validation study using 570 deterministic recovery variants across 19 specified recovery scenario families. Seven configured mechanisms were exercised on the same variants, yielding 3990 primary records. The purpose of this matrix is to demonstrate and distinguish the registered recovery-security properties under controlled same-input cases, but it does not estimate the comparative effectiveness or weakness prevalence in operational OT deployments. Within these controlled cases, both PQReach-OT and the strong reactive experimental control satisfied post-transition grant safety. PQReach-OT additionally exercised the recovery-closure functions defined by the model: it identified all 510 current-compliant but recovery-unsafe variants before failure, selected a compliant alternative in all 390 applicable cases, rejected all 30 historical-floor replays, and detected all 30 exposures reachable only through multi-step recovery. Inventory completeness is an explicit assurance boundary: in the registered additive inventory-completion mutations, adding a compliant recovery state preserved Recovery-Closed Migration Coverage (RCMC) at 1.0, whereas adding a previously unrepresented below-floor state capable of regaining protected authority reduced RCMC from 1.0 to 0.0. Thus, RCMC is explicitly conditional on the represented recovery reachability: additive inventory completion can preserve the existing closure assessment or reveal an additional violation, but it cannot strengthen that assessment solely by enlarging the represented recovery space. These outcomes demonstrate the behavior and separability of the proposed recovery-closure mechanisms within the defined recovery semantics; evaluation in operational OT environments addresses the complementary question of external generalizability. Full article

To address excessive cab vibration observed after combine harvester manufacture, this study applies hybrid grey wolf–whale optimized variational mode decomposition (HGWO–VMD) to identify vibration characteristics during stationary no-load operation and evaluates targeted mechanical modifications for vibration reduction. Tests were conducted on a Linhai 4LZ-7A tracked combine harvester with only the engine running (Condition A) and with the engine and working components running (Condition B). HGWO–VMD was compared with conventional VMD using manually specified parameters. The target-frequency energy ratios reached 0.704 under Condition A and 0.821 under Condition B for the analyzed signals. Frequency components were consistent with engine rotation and second-order excitation, vibrating screen and cutter operation, and sixth-order threshing drum excitation. A component at 58.88 Hz was also observed and may be associated with the dynamic response of the cab system. The implemented measures comprised vibrating screen counterweight balancing, cutter speed adjustment and counterweight balancing, and inclined cab supports. Among the tested combinations, the selected scheme reduced the measured overall cab and header acceleration RMS values by 48.33% and 12.2%, respectively. These results describe the vibration reduction achieved on the tested machine under stationary no-load operation. Full article

This article examines women’s therapeutic knowledge through the traditional healing practices of the szeptunki (also szeptuchy, “whisperers”) of Podlachia, a multi-ethnic and multi-confessional borderland in north-eastern Poland. Their repertoire includes kołtun, urok, strach, wiatr and róża, treated through whispered Orthodox prayers, the sign of the cross, and materials such as wool, beeswax, ash, linen and flax. Drawing on published Polish-language ethnography, interview narratives, documentary films and fieldwork reported by other scholars, the article asks why a form of healing authority that remains locally meaningful is becoming difficult to transmit. It argues that the same conditions that authorise the szeptunka—older age, gendered domestic experience, religious discipline, secrecy, kin-based transmission and continual availability—also make succession increasingly demanding. The analysis links the female life course to embodied ritual practice, relations with Orthodox clergy and medical pluralism, while treating male successors and male znachorzy as important counterpoints to a simple women/men binary. It also shows how clerical delegitimisation shapes the way practitioners present their own work. Published evidence from 2010–2025 documents active practitioners while also recording growing uncertainty about succession. The szeptunki therefore illuminate not simply the persistence of a healing tradition, but the changing social conditions under which therapeutic authority can be reproduced. Full article

Nitrogen-doped carbon dots (N-CDs) have attracted considerable attention owing to their unique physicochemical and optoelectronic properties, as well as their promising potential for energy-related applications. However, only a limited number of studies have systematically investigated the influence of microwave-assisted synthesis conditions on the formation of these nanomaterials. In this work, five different N-CDs samples were synthesized from the same precursor system, consisting of citric acid and urea, by varying the microwave irradiation power and exposure time. The effects of these synthesis parameters on the formation and physicochemical characteristics of the carbon dots were systematically investigated. The synthesized materials were comprehensively characterized by ATR-FTIR, SEM, XPS, TG-DTA, UV-Vis absorption spectroscopy, photoluminescence spectroscopy, and electrochemical analyses. All samples exhibited similar chemical compositions and surface functional groups, while variations in the synthesis conditions led to noticeable differences in their thermal behavior, optical properties, and electrochemical responses. These findings highlight the important role of microwave synthesis parameters in fine-tuning the physicochemical properties of N-CDs and contribute to a deeper understanding of the relationship between synthesis conditions and the resulting material characteristics. The materials obtained were preliminarily tested as additives in the photoactive layer of perovskite solar cells. Overall, this study provides useful insights for the design and optimization of carbon dots with properties tailored for specific applications. Full article

Background: Environmental noise is one of the most pervasive ecological health issues in modern urban settings, contributing to the onset of hearing damage but also to the development of annoyance, sleep disturbance, cardiovascular diseases, and impaired communication. For people with hearing impairment of any age, the main challenge posed by noisy environments is not merely detecting sound but understanding speech against a competing background—a task heavily related to cognition. Methods: Narrative review; the literature has been identified through searches of PubMed, Scopus, and Google Scholar, combining terms related to cognition, speech perception, speech in noise, hearing-aid signal processing, environmental health, and aging. Results: The role of cognitive abilities in speech perception in noise has been evaluated, with particular attention to how artificial intelligence supports the interplay between cognition and hearing-aid signal processing in adverse environments. Within the Ease of Language Understanding (ELU) model, this paper describes how degraded acoustic input shifts processing from rapid, implicit lexical access toward explicit working-memory-dependent reconstruction. The actual evidence indicates (i) that working memory, in particular, could predict aided speech recognition under adverse conditions, (ii) that aging dissociates these processes, and (iii) that longitudinal studies link hearing-aid use to slower cognitive decline. Conclusions: Speech perception in noise is a coordinated neurocognitive process rather than a purely auditory task. Working memory is a central cognitive contributor, with effects that become evident specifically under adverse conditions. In particular, fast-acting compression and frequency lowering may be counterproductive for listeners in case working memory or speech processing are reduced. The success of different algorithms applied to audio signal processing of hearing aid, designed to compensate for difficult comprehension of words and phrases in noisy scenarios is also strictly dependent on working memory, and artificial intelligence significantly contributes to this goal. Full article

Organizations face a persistent tension between the need to hold employees accountable for results and the need to grant them the autonomy that fuels motivation, learning, and innovation. This conceptual paper theoretically develops and formalizes the tight–loose–tight (TLT) framework, a sequenced and cyclical approach to resolving this tension at the level of the individual employee. In the first tight phase, leaders and employees establish clear goals, expectations, and metrics that connect individual aspirations to organizational objectives. In the loose phase, leaders grant employees autonomy over how the work is accomplished, consistent with self-determination theory. In the final tight phase, leaders inspect what they expect through regular observation, feedback, and coaching, which then feeds forward into renewed clarity at the start of the next cycle. The framework integrates goal-setting theory, self-determination theory, and feedback and coaching research into a single dynamic process model, and it is distinguished from prior tight and loose constructs in the literature on organizational excellence, participative leadership, and national culture. Five research propositions are developed, together with guidance on measurement and research design, a rule that fixes each phase transition at the opening of the cycle, criteria for scoring cycle fidelity, and the condition under which the framework would be refuted. The framework’s application in an AI-enabled workplace is also considered. Implications for managers, limitations, and future research are discussed. Full article

The distributional specification in Markov-switching GARCH (MS-GARCH) models has historically been driven by empirical convention. This paper derives the regime-conditional Student-t innovation distribution from the Tsallis Maximum Entropy Principle, providing an information-theoretic foundation for the choice of heavy-tailed innovations. The GARCH variance dynamics and Markov-switching structure are standard modelling choices adopted independently of the MaxEnt derivation. The framework is applied to five major cryptocurrencies over January 2017 to March 2026, comprising 15,824 daily observations. Three principal findings emerge. First, Tsallis entropy maximisation under a variance constraint yields the q-Gaussian density, which coincides with the Student-$t_{{\nu }_k}$ distribution for $q_k=({\nu }_k+3)/({\nu }_k+1)$, with degrees of freedom determined endogenously from the empirical excess kurtosis. Second, calm-regime half-lives ${\tau }_C\in [1.21,2.37]$ days and stationary turbulent probabilities ${\pi }_T\in [0.254,0.437]$ confirm that both regimes are economically active across all assets; a Francq–Zakoïan stationarity verification confirms global ergodicity. Third, near-unity turbulent GARCH persistence suppresses the point-forecast advantage of regime-switching, consistent with a Fano-type Forecasting Irreversibility Bound; HAR-RV achieves the lowest QLIKE loss for three of five assets. Value-at-Risk backtests confirm adequate tail-risk calibration for four of five assets at the 1% and 5% levels, outperforming single-regime benchmarks. An empirical assessment of the VolShock extension identifies asset-class boundary conditions, motivating a proportional specification for future work. Full article

Electric vehicle (EV) wireless power transfer (WPT) systems face two fundamental challenges that hinder their widespread adoption: sensitivity to coil misalignment and the need for battery-friendly fast charging protocols. This paper presents a comprehensive and integrated framework that addresses both challenges through a three-pronged approach combining advanced coil geometry optimization, multi-stage current method (MSCM) charging, and reinforcement learning (RL)-based adaptive control. First, a memetic algorithm hybridizing global exploration and local refinement is employed to design coil geometries that are inherently resilient to misalignment. The optimized coils maintain strong magnetic coupling under lateral displacements up to ±75 mm by strategically sizing the secondary coil’s outer diameter to be smaller than the primary coil’s, ensuring it remains within the optimal magnetic flux region. Second, an MSCM charging protocol is developed and optimized with the objective of balancing charging speed against battery thermal stability and state of health (SoH). The proposed strategy determines optimal current levels for each charging stage, reducing temperature rise compared to conventional CC-CV charging. Third, a novel Deep Q-Network (DQN) RL agent is implemented for real-time adaptive control of the WPT system. The RL controller dynamically adjusts phase shift in response to varying coupling conditions, load disturbances, and battery state, outperforming traditional PI controllers with 23% faster settling time and improved efficiency under dynamic misalignment scenarios. Finite element analysis (FEA) simulations validate the electromagnetic performance of the optimized coils, while an experimental prototype demonstrates the integrated system’s performance. Results show that the combined approach achieves 91.2% DC-DC efficiency under nominal conditions and maintains over 83% efficiency under lateral misalignments up to ±75 mm, fully complying with SAE J2954 alignment tolerance requirements. The MSCM charging protocol, guided by the memetic algorithm, limits battery temperature rise during a full charge cycle, while the RL controller ensures stable power delivery under real-world dynamic conditions. This work establishes a new paradigm for holistic WPT system design, demonstrating that synergistic optimization of magnetic structures, charging protocols, and intelligent control can simultaneously achieve misalignment resilience, fast charging, and adaptive robustness. Full article

The production of volatile fatty acids (VFA) through acidogenic fermentation of organic waste is a promising strategy for resource recovery in circular bioeconomy systems, especially in those conditions where efficient resource utilisation is imperative, such as in future lunar missions. In this study, in collaboration with the Italian Space Agency, the organic waste conversion was investigated to produce VFA-rich effluent obtained under optimal conditions represents a promising potential feedstock for future downstream applications, such as photofermentative hydrogen production, although downstream biological validation was beyond the scope of the present study. In this experimental work, a synthetic mixture of organic waste and waste-activated sludge was fermented across varying organic loads (20–50 g_TVS/L) with and without zeolite, supplemented as an ammonium absorbent. Results showed that VFA production increased with organic loads, with the highest volatile fatty acid yields observed at 40 g_VS/L. Keeping the organic loads constant at 40 g_VS/L, zeolite addition further enhanced volatile fatty acid concentrations in a dose-dependent manner, reaching a maximum at 15 g/L and 20 g/L; the highest yield was obtained at 30 and 40 g_VS/L with 15 g/L, reaching a peak of 0.8 g_COD/g_VS. Zeolite addition reduced the ammonia conversion by about 6% at OL 40 and 50. Full article

Dielectrophoretic (DEP) enrichment is a powerful strategy to enhance bacterial capture efficiency and accelerate the response of electrochemical biosensors by actively concentrating target bioparticles at the sensing interface. In this study, a combined numerical–experimental framework is developed to rationally design a DEP-assisted electrochemical biosensor with improved bacterial capture and detection performance. A finite-element modeling (FEM) approach was used to model the coupled electric field, dielectrophoretic force, and particle-transport phenomena, providing a quantitative basis for comparing bacterial trapping efficiency across different interdigitated electrode geometries. The modeling reveals that a wave-shaped interdigitated electrode (W_IDE) generates extended high-field regions and an enlarged effective capture area, resulting in an improved bacterial capture efficiency of 19% compared to 12% for the conventional rectangular interdigitated electrodes (R_IDE) under positive DEP (pDEP) conditions. Based on these insights, the W_IDE was fabricated on a printed circuit board (PCB) substrate, modified with platinum-black (Pt-black) to increase electroactive surface area, and interfaced with a custom-built 16-channel portable potentiostat unit, enabling sequential impedance measurements. The developed biosensor was applied to pDEP-assisted impedance detection of Staphylococcus aureus and Micrococcus luteus using vancomycin as the capture probe. The pDEP-assisted operation enabled rapid and highly sensitive detection down to 10 CFU/mL within 30 min with a wide linear range (10–10⁵ CFU/mL) in 0.1× PBS, outperforming passive detection (10²–10⁵ CFU/mL) for both Staphylococcus aureus and Micrococcus luteus. In skim milk, however, the linear detection ranges shifted to 10²–10⁵ CFU/mL with pDEP-assisted detection and 10³–10⁵ CFU/mL under passive detection conditions. Overall, this work highlights the significance of combining FEM-optimized electrodes with DEP-driven enrichment to achieve improved bacterial capture, sensitivity, and robustness in electrochemical biosensors. Full article

Fire remains one of the most damaging exposures a concrete structure can face, yet how entrained air interacts with that damage is still not fully mapped out. This work looks at that gap directly, testing concretes with nominal total fresh-concrete air contents of approximately 2% (control), 4% (AE-4), and 6% (AE-6) after exposure to temperatures between 23 °C and 700 °C, followed by either air or water cooling. Six properties were measured: dry unit weight, thermal conductivity, compressive strength, flexural strength, ultrasonic pulse velocity (UPV), and dynamic modulus of elasticity (DEM), and each was then modeled with a dedicated feed-forward artificial neural network (ANN) using only AE content and temperature as inputs. The compressive strength of the control mix fell from 65.30 MPa at ambient temperature to 8.57 MPa at 700 °C, an 87% loss, with comparably steep reductions recorded for the other properties. At every temperature tested, water-cooled control specimens retained less strength and stiffness than their air-cooled counterparts. The ANN models, trained with the Levenberg–Marquardt algorithm, reproduced the experimental trends closely, returning coefficients of determination between 0.9364 and 0.9735. Sensitivity analysis placed temperature well ahead of AE content as the dominant driver of property change in every model (sensitivity ratio of 2.36–7.25 versus 1.14–1.87), although AE content was never negligible. The models provide accurate predictions within the investigated experimental ranges and may support preliminary assessment of comparable air-entrained concrete systems. Scanning electron microscopy tied these numbers to what was actually happening inside the material: the C–S–H structure held together reasonably well up to about 500 °C, then broke down visibly by 700 °C, with the AE’s air voids appearing to interrupt crack growth along the way. Together, the results offer both a practical dataset and a set of ready-to-use ANN tools for assessing the residual condition of air-entrained concrete after fire. Full article