Search Results (79,535)

The accurate measurement of individual cognitive abilities in educational settings increasingly relies on personalized assessment systems that adapt to the learner’s current standing on the measured ability. These systems generate and use assessment items continuously and in real-time, bypassing the field-test cycle that traditional calibration requires. Item banks therefore contain a growing share of items whose parameters must be predicted first, and each test form must be assembled in real time to match the learner’s current ability level and content needs for effective personalized instruction. This study proposes an Ising framework that addresses both requirements by predicting item parameters from text with calibrated uncertainty estimates and assembling personalized test forms within the time budget of the assessment loop. This framework is validated through a factorial simulation and an empirical study on state-level English Language Arts items across Grades 3 through 12. In the simulation, the proposed uncertainty-aware method achieves a mean test information function (TIF) gap of 0.226, compared with 1.171 for the conventional baseline that ignores prediction uncertainty. The proposed method also produces feasible forms across the full operational region, whereas the chance-constrained and robust baselines become infeasible at high prediction error combined with a high share of predicted items. In the empirical study, predictive intervals achieve coverage within ten percentage points of the nominal level in the pooled condition, and the uncertainty-aware assembly reduces the TIF gap relative to the baseline, with the largest gains at the extremes of the ability distribution where accurate measurement matters most for instructional placement decisions. By separating prediction uncertainty from true individual differences in ability, this framework preserves the interpretability of cognitive ability estimates and provides a foundation for personalized assessment systems that support differentiated instruction under continuous item bank growth. Full article

Inverter-interfaced distributed generation (DG) can provide voltage support in unbalanced distribution feeders; however, sequential, phase-selective siting and sizing under local interconnection constraints have received limited attention. This study proposes a constructive Volt/VAR planning method that allocates DG according to the apparent demand and voltage-deficient phases at each candidate bus. A descending reactive power feasibility test rejects solutions that cause non-convergence, overvoltage, or deterioration of the minimum system voltage. The method was implemented through automated unbalanced three-phase power-flow simulations and tested on the IEEE 13-node feeder and the real 3849-bus CHIRIPA medium-voltage network in Honduras, using active power allocation factors of 0.7 and 0.9. The selected configurations were evaluated over 24 h profiles for representative sunny and rainy days, annualized, and assessed economically over a 25-year horizon. Annual energy losses decreased by 25.8% and 26.4% in the IEEE feeder, although local capacity constraints prevented all phase voltages from reaching the 0.95–1.05 p.u. range. In CHIRIPA, all medium-voltage bus-phase voltages satisfied this range at maximum demand, although this improvement persisted only during solar hours; annual losses fell by 15.1% and 18.7%. The economic feasibility threshold in CHIRIPA depended primarily on installed capacity (approximately 6.5–6.6 MW), rather than unit count. These results provide a reproducible framework for prioritizing phase-specific DG investments under technical and economic constraints. Full article

Carbon dioxide (CO₂) enrichment is an important means of increasing crop productivity in protected cultivation. However, local canopy CO₂ concentration in Chinese solar greenhouses is jointly affected by gas release, pipeline transport, and ventilation disturbances, making fixed-parameter proportional–integral–derivative (PID) control unable to simultaneously achieve rapid tracking, low overshoot, and fast disturbance recovery. This study developed a CO₂ enrichment system comprising controlled thermal decomposition of ammonium bicarbonate, condensation and water scrubbing, near-canopy delivery, and programmable logic controller (PLC)-based closed-loop control, and proposed a dynamic-target fuzzy PID (DT-FPID) strategy. Step-response tests were used to establish a first-order-plus-dead-time model linking heater duty cycle to local canopy CO₂ concentration, followed by fixed-target tracking, rule-based dynamic-target execution, and short-term ventilation-disturbance recovery tests in a local validation zone of a Chinese solar greenhouse. Relative to fixed-parameter PID, DT-FPID showed approximately 68–79% lower maximum overshoot and approximately 35–70% shorter ±20 ppm precision settling time (T₂₀) in simulation. At 600 ppm, the ±5% settling time was approximately 71% shorter, whereas at 800 and 1000 ppm it was broadly comparable to PID. In the greenhouse experiments, each controller–target combination included three independent runs. Based on descriptive comparisons of group means, DT-FPID showed approximately 47–49% lower mean maximum overshoot, approximately 36–40% shorter mean settling time, and approximately 77–80% shorter mean ventilation-disturbance recovery time; its mean maximum overshoot and settling time were also lower than those of conventional fuzzy PID. All three dynamic-target field runs completed the prescribed switches among the 600, 800, and 1000 ppm target levels. These results support control performance only under the short-term local validation conditions of this study; they are not used to determine physiologically or economically optimal CO₂ concentrations or to extrapolate whole-greenhouse uniformity or long-term production effects. Full article

Overtourism is often assessed through visitor intensity, although its effects also depend on infrastructure performance, resource demand, spatial development, and evidence availability. This study develops a comparative diagnostic framework and applies it to Mykonos, Patmos, and Spetses, representing three contrasting Greek island tourism models. Ten indicators were classified according to their analytical role. Seven indicators were scored within three dimensions—resource pressure, infrastructure system, and tourism demand—while coastal water quality was treated as environmental-state evidence and built-up and coastal development as descriptive spatial evidence. Evidence confidence was assessed separately. Sensitivity analysis included leave-one-indicator-out tests, omission of indicators based on shared tourism data, threshold variation, and alternative standardization. Mykonos recorded the highest resource-pressure (4.00) and tourism-demand (3.50) scores, together with extensive built-up and coastal development, but its centralized wastewater treatment system operated within reported compliance limits. Patmos recorded lower tourism pressure but documented wastewater treatment underperformance, while Spetses lacked centralized wastewater treatment and had important monitoring gaps. The Mykonos results are consistent with pressure-specific infrastructure buffering, although the available data do not support causal attribution. The framework provides a comparative diagnosis without producing a single overtourism score and helps identify areas where further monitoring and assessment are needed. Full article

Building facades and construction materials are continuously exposed to airborne particulate matter, organic pollutants, and microbial colonization, which cause progressive soiling, aesthetic degradation, and structural deterioration while imposing high maintenance and energy burdens. Photocatalytic titanium dioxide (TiO₂) has emerged as the most widely studied material for imparting self-cleaning functionality to building surfaces, owing to its ability to mineralize adsorbed contaminants under solar irradiation and to modulate surface wettability. This narrative review provides a structured account of TiO₂-based self-cleaning coatings for building materials, organized around three complementary themes: surface mechanisms, performance metrics, and outdoor durability. We first rationalize the two intertwined self-cleaning mechanisms—photocatalytic oxidative degradation and photoinduced superhydrophilicity—and their combination with physically repellent (superhydrophobic/superamphiphobic) wetting states. We then survey the principal coating-design strategies, including morphology and facet engineering, SiO₂-TiO₂ composites, metal/non-metal doping and heterojunction construction for visible-light activation, and dual-functional photocatalytic–superhydrophobic systems, and their integration into cementitious substrates, natural stone and cultural heritage, and transparent glass/photovoltaic surfaces. The quantitative metrics used to benchmark self-cleaning performance—water contact angle, dye photodegradation, NO_x and VOC abatement, and antimicrobial activity—are critically discussed together with the limitations of standardized laboratory tests. Finally, we analyze the weathering-induced deactivation pathways (photocatalyst leaching, surface contamination by soluble salts, and UV aging of organic matrices) and the emerging strategies for durable coatings, including inorganic binders, light-driven hydration, and defect- and heterojunction-engineered photocatalysts. The review concludes with an outlook on the open challenges that must be addressed to translate these coatings from laboratory demonstrations to long-lived, large-scale building applications. Full article

The shoe last is central to footwear design. Modern footwear CAD operates on parametric digital lasts, yet much last geometry—legacy collections and lasts that skilled last makers still sculpt by hand and copy by pantograph turning—exists only as physical models or as point-cloud scans lacking the structured parametric form that footwear CAD requires. This paper presents a complete template-based method for reconstructing a watertight parametric last from a segmented point cloud without intermediate triangulation. The only manual input is three landmark points—for which the system proposes standard positions—and the interactive confirmation of two boundary lines on the digitized last. From these, the method defines four feature points, a median plane, and a four-curve boundary network; all subsequent stages run without user interaction. A curvature-adaptive quadrilateral grid is constructed on the cloud by geodesic tracing and monitor-weighted area-orthogonality relaxation. A periodic Coons tube interpolates the grid and initializes the parameterization for a periodic tensor-product cubic B-spline surface fitted by penalized least squares with cyclic/open difference penalties, exact boundary interpolation, and toe-aware weighting. Cap surfaces close both collar and sole openings, and the model is exported as a watertight B-rep solid. Tests on sixteen industrial lasts using one fixed parameter set produced a mean one-sided deviation of 0.034 mm (RMS 0.058 mm) from the withheld industrial reference meshes in approximately 12 s per last. With synthetic noise at 50 dB SNR, the mean deviation increased by only 0.011 mm. A sampling-density study indicated near-second-order convergence before the control-net reaches an upper limit. The resulting solids import directly into CAD systems and support re-lasting, footwear design, and customization. Full article

Antimicrobial resistance (AMR) is a growing global health threat driven in part by inappropriate and unnecessary antibiotic use resulting from diagnostic uncertainty at the point of care. In outpatient and acute-care settings, clinicians are often unable to rapidly distinguish between viral and bacterial infections, leading to empiric antibiotic prescribing that contributes to the emergence and spread of resistant pathogens. This review examines current and emerging rapid diagnostic technologies for differentiating bacterial and viral infections, including molecular assays, rapid antigen tests, biomarker-based diagnostics, host-response platforms, hematologic methods, and artificial intelligence-based decision-support systems. These technologies are evaluated based on diagnostic accuracy, turnaround time, cost, accessibility, and clinical actionability within real-world healthcare settings. Although several emerging and point-of-care (POC) technologies can provide results within approximately 6–15 min, their ability to consistently align with the timing, workflow, and clinical decision-making requirements of frontline outpatient and emergency-care settings remains variable and incompletely established. Future progress in antimicrobial stewardship will depend on developing rapid, clinically actionable diagnostic systems that integrate seamlessly into patient care and reduce unnecessary antibiotic use. Full article

The cilium avoids energy loss due to bending deformation during vibration, thereby efficiently transmitting the acoustic driving force to the cross-beam. Meanwhile, a hollow cylindrical structure is adopted instead of the traditional solid one. While maintaining the same external geometry, this hollow design not only increases the effective acoustic area to enhance the acoustic driving force but also reduces the added mass of the cilium. This material–structure synergistic optimization improves the acoustic-to-mechanical transfer efficiency of the cilium in low-frequency acoustic fields, thus enhancing the device’s acoustic sensitivity in the low-frequency range. Parametric simulations are conducted using the COMSOL Multiphysics 6.3 platform to systematically optimize key geometric parameters such as the outer radius and height of the quartz cilium, and the optimal dimensions are determined. The sensitivity and directivity of the prototype are measured in the 20–1000 Hz frequency range using the standard hydrophone comparison method. Experimental results show that the quartz cilium vector hydrophone achieves a sensitivity of –179.1 dB (1 kHz, 0 dB = 1 V/μPa) at 1000 Hz. The frequency response curve exhibits good flatness in the low-frequency range and agrees well with the theoretical 6 dB per octave increase characteristic of pressure gradient sensors. Directivity tests reveal a typical figure-8 pattern at both 315 Hz and 630 Hz, with null depths exceeding 30 dB, demonstrating excellent vector detection capability. This study provides an effective approach for improving the low-frequency sensitivity of MEMS (Micro-Electromechanical System) vector hydrophones, and the designed device meets the requirements for underwater target detection with promising prospects for engineering applications. Full article

To address the susceptibility of polymer gels to syneresis and failure under high-temperature, high-salinity (HTHS) reservoir conditions, a zwitterionic terpolymer was synthesized from acrylamide (AM), 2-acrylamido-2-methylpropanesulfonic acid (AMPS), and dimethyldiallylammonium chloride (DMDAAC), and subsequently crosslinked with a phenolic system to produce an intramolecular salt-structured polymer gel with exceptional thermal and saline tolerance. Through systematic formulation optimization, the optimal composition was established as 0.3 wt% zwitterionic polymer, 0.03 wt% resorcinol, 0.3 wt% hexamethylenetetramine, and 0.1 wt% thiourea. X-ray photoelectron spectroscopy and zeta potential measurements confirmed that quaternary ammonium cations and sulfonate anions on the polymer chains associate via electrostatic attraction to form intramolecular salt bridges, which induce an anti-polyelectrolyte effect and thereby confer superior salt tolerance and thermal stability. Following aging for 140 days at 115 °C in formation water, the gel exhibited a syneresis rate below 30%, demonstrating favorable long-term stability under the tested high-temperature and high-salinity conditions. Thermogravimetric analysis and differential scanning calorimetry further characterized the thermal-transition behavior of the gel under programmed heating conditions. Scanning electron microscopy revealed a homogeneous honeycomb-like porous crosslinked network. Rheological testing demonstrated a storage modulus (G′) of 1.115 Pa and a loss modulus (G″) of 0.205 Pa, indicating an elasticity-dominated viscoelastic response and strong resistance to shear deformation. Single-core plugging experiments showed that at an injection volume of 0.2 PV, the gel achieved a plugging efficiency of 85.15% and a breakthrough pressure gradient of 18.6 MPa/m, with significant secondary plugging capability. In a heterogeneous dual-core model with a permeability contrast of approximately 10, the diversion rate into the low-permeability layer increased to 73.6%, effectively improving the water injection profile. This intramolecular salt-structured molecular design demonstrates potential for deep-profile control under high-temperature and high-salinity reservoir conditions. Full article

Generalization under deployment shifts remains a major challenge for visuomotor robot manipulation, especially when changes in camera placement and control behavior are unknown to the policy. This paper introduces the System-Context-Conditioned Diffusion Policy (SCE-DP), a history-conditioned manipulation policy designed to adapt its latent context to simulated changes in camera extrinsics, action scale, and bounded control delay without explicit recalibration or test-time model-weight updates. SCE-DP encodes a short history of issued commands and their subsequent visual and proprioceptive responses into a latent system context, which conditions the diffusion-based action denoising process. Auxiliary system-parameter regression and one-step response prediction further encourage the context to capture deployment-relevant system properties and their behavioral consequences. We evaluate SCE-DP on five independently trained ManiSkill manipulation tasks across held-out in-range configurations, a withheld camera-delay composition, and mild extrapolation settings. SCE-DP improves the five-task macro-average over domain-randomized Diffusion Policy by 14.5 percentage points on held-out configurations and 15.7 points on the withheld camera-delay composition. Across four shifted evaluation suites, it achieves an average success rate of 64.3%, compared with 48.9% for the domain-randomized baseline, while preserving nominal performance. Command-only, response-only, previous-action, recovery-source, and matched explicit-parameter controls indicate that the gain is not explained solely by command statistics, redundant command input, or recovery-data composition. These results show in simulation that command–response history is an effective source of online latent system context under coupled visual and control shifts; real-robot generalization remains to be established. Full article

Reliable determination of the surge limit of an installed centrifugal compressor cannot rely solely on OEM performance maps, theoretical predictions, or previously configured surge lines. The actual field boundary is influenced by installation-specific characteristics, including piping volumes, recycle-system dynamics, valve response, and instrumentation, all of which affect compressor behavior as surge is approached. At the same time, field surge testing is inherently demanding, because it is a safety-critical procedure that requires careful execution and often depends on the experience of specialist personnel. This paper presents a semi-automated supervisory framework for field surge limit verification intended for use by trained site engineers under operator supervision. The framework guides the testing process through readiness checks, instrumentation verification, recycle-valve assessment, gradual reduction of surge margin, real-time monitoring, surge-event identification, recovery actions, and structured data logging. Because early surge precursors are not consistently visible using standard plant instrumentation, the proposed approach relies on a practical signal-based event-recognition method derived from installed measurements and referenced to a pre-event baseline. The paper also introduces a structured method for tuning staged Recycle Trip^® recovery actions following surge limit verification using a limited number of field tests. The field evaluation included seven industrial compressor records: five independently confirmed surge cases, one shutdown/non-surge transient, and one confirmed no-surge case. Using a record-specific baseline threshold, all five confirmed surge events were recognized, with reported detection delays of 0–0.1 s. The supporting dynamic simulation environment was used for logic development and qualitative assessment of the supervisory and recovery-tuning concepts. Overall, the proposed approach provides a more structured and repeatable basis for supervised field surge limit verification while preserving operator authority. Full article

The increasing demand for electrified rail transportation has intensified power quality (PQ) challenges, including harmonics, voltage imbalance, and low power factor (PF). These issues have driven the development of advanced traction power supply systems, particularly co-phase configurations, to improve power quality, enhance grid-connected stability, and strengthen the operational resilience of railway power infrastructure. This paper proposes a co-phase power supply system for high-speed railways that facilitates high-speed train operation by integrating power quality compensation technologies while reducing the required number of neutral sections by half, thereby improving the continuity and robustness of traction power delivery. To address PQ issues, a capacitive-coupled hybrid power quality conditioner (HPQC) incorporating renewable energy sources (RESs) into its DC link is introduced. Given the highly dynamic and time-varying nature of railway loads, a sliding mode control (SMC)-based robust control method is developed based on the state space model of the co-phase power supply system and the HPQC to provide a stable and rapid response to load variations and operational disturbances. The effectiveness and real-time implementation capability of the proposed approach are validated through real-time control hardware-in-the-loop (CHIL) simulations. Results from MATLAB/Simulink simulations and real-time CHIL testing demonstrate substantial harmonic reduction, improved power factor, reduced negative-sequence currents, and enhanced overall system efficiency. These outcomes confirm the suitability of the proposed system for modern high-speed railway applications and highlight its contribution to resilient traction power supply systems capable of maintaining reliable operation under highly variable loading conditions. Full article

During the service life of karst tunnels, groundwater containing Ca²⁺ and CO₃²⁻ can migrate along the shotcrete–surrounding rock interface and induce calcite deposition, thereby changing the interfacial material composition and load transfer path. To clarify the effect of this process on interfacial bonding performance, this study used molecular dynamics simulations to construct CSH–SiO₂, SiO₂–calcite, CSH–calcite, and CSH–calcite–SiO₂ interface models. The interfacial density distribution, radial distribution function, number of hydrogen bonds, interaction energy, and normal tensile failure behavior were analyzed. The results show that all four models reached stable energy plateaus after relaxation, and clear atomic density overlap and short-range RDF peaks appeared in the interfacial regions. These descriptors indicate short-range contact and possible Ca–O electrostatic attraction, hydroxyl-related hydrogen bonding, and carbonate-associated interactions between calcite and both SiO₂ and CSH surfaces. Approximately 80 hydrogen bonds were formed at the SiO₂–calcite interface, approximately 32 at the CSH–calcite interface, and approximately 59 in total for the two hydrogen bond subtypes at the SiO₂–CSH interface, indicating that the hydroxyl state of different substrate surfaces controls the interfacial hydrogen bond network. Interaction energy analysis shows that the single CSH–calcite interface has the strongest interaction (−51,753.6 kcal/mol), approximately 1.90 times that of the SiO₂–CSH interface and 16.43 times that of the SiO₂–calcite interface. However, in the three-layer composite model, the interaction energy on the CSH–calcite side is only approximately 28.0% of that on the SiO₂–calcite side, suggesting that a continuous calcite interlayer introduces asymmetric interfacial constraints. Tensile simulations further show that the SiO₂–calcite model has the highest peak stress (approximately 3.23 GPa) and exhibits brittle failure, whereas failure in CSH-containing systems is more likely to transfer into the CSH layer or weakly connected regions. These results indicate that calcite deposition does not simply strengthen or weaken the interface. Instead, within the two idealized endpoint configurations tested here, its effect depends on deposition continuity, the surface chemistry of the two substrates, and the weak links within the serial interface. This study provides a nanoscale theoretical basis for evaluating relative trends in the long-term service performance of shotcrete–surrounding rock interfaces, and for guiding future multiscale validations of drainage and waterproofing measures in karst tunnels. Full article

Introduction: Rapid technological development requires continuous learning and adaptation, yet many older adults (OAs) face difficulties using digital technologies. The AGAPE project (Active aGeing And Personalised service Ecosystem) aimed to promote active and healthy ageing (AHA) among adults aged 65+ by supporting independent functioning reducing caregiver burden, and improving eHealth literacy through personalized, user-friendly digital services co-created and tested in Italy, Portugal, and Romania. Objective: AGAPE pilot phase evaluated the effectiveness and usability of innovation adoption strategies designed to improve technology acceptance, digital literacy, and behavioural change among older adults and caregivers. This study examined participants’ perceptions of digital technologies and self-reported digital efficacy during a multicomponent intervention combining repeated technology use, personalized coaching, digital literacy and health education, technical assistance, and support from informal and formal caregivers. Material and Methods: The multiple-sites uncontrolled pilot study combined home-based use of the AGAPE digital technologies with long-term combined personalized support. Data were collected at baseline (T0), intermediate (T1), and final (T2) assessments using the System Usability Scale (SUS), technostress questionnaire, digital adoption questionnaire (TAM-derived), EQ-5D-5L, CarerQoL-7D, and the UCLA Loneliness Scale, complemented by participant feedback, technology usage, and activity data. Results: The study included 217 participants, of whom 146 were OAs. Over the course of the intervention, participants showed reductions in technostress and improvements in perceived digital efficacy, although the magnitude and direction of change varied across pilot sites. The findings also indicated that contextual and social factors contributed to technology adoption and engagement. Conclusions: Participation in the multicomponent AGAPE intervention was associated with positive longitudinal changes in technology-related perceptions, technostress, and digital self-efficacy. The findings support the feasibility and potential of combining digital technologies with personalized coaching and broader interpersonal support to facilitate technology adoption among older adults. However, given the uncontrolled study design and the integrated nature of the intervention, the observed changes cannot be attributed independently to coaching or to any single intervention component. Full article

(1) Background: Information diffusion models developed for graph-based platforms such as Reddit and broadcast architectures such as Telegram identify temporal features—particularly the timing of peak spread—as dominant predictors of coverage. Whether these predictors generalise to platforms where content is distributed through algorithmic recommendation rather than social-graph contagion remains an open question. (2) Methods: We analyse the YouNiverse dataset, comprising 133,364 English-language YouTube channels observed weekly from January 2015 to September 2019 (18.9 million observations). We derive channel-level diffusion features—including time-to-peak, post-peak decay rate, diffusion volatility, and upload frequency—and train three machine learning models (Linear Regression, Random Forest, and LightGBM) on two tasks: predicting peak weekly view growth (regression) and identifying viral channels (classification). A single-feature naive baseline (subscriber count alone) establishes the marginal contribution of the broader feature set beyond subscriber count alone, and a temporal split experiment (training on channels peaking before 2018, testing on 2018–2019) assesses cross-temporal stability. Because subscriber count and subscriber rank are measured at the October 2019 crawl, this is a retrospective characterisation rather than a strict real-time forecasting design. (3) Results: LightGBM achieves $R^2=0.776$ (5-fold CV: $0.778\pm 0.003$) compared with $R^2=0.548$ for the naive baseline, a net gain of $+0.228R^2$. Because subscriber rank and subscriber count are near-perfectly collinear, we interpret them jointly as a channel-size dimension (42.2% of total mean absolute SHAP attribution), rather than as independent effects. Time-to-peak ranks fourteenth (1.1%), in contrast to its dominant role on Reddit ($r=0.995$, rank #1). For virality classification, LightGBM achieves ROC-AUC $=0.967$. Under the temporal split, Random Forest ($R^2=0.703$) outperforms LightGBM ($R^2=0.683$), showing greater cross-temporal stability within this retrospective split. (4) Conclusions: Within the 2015–2019 data, the results are consistent with algorithmic recommendation weakening the relationship between temporal diffusion dynamics and coverage magnitude at the channel level. Time-to-peak is weakly informative in this setting, while generalisation to the current recommendation system requires validation on newer data. Full article