Search Results (24,838)

Aesthetic perception of rural streetscapes reflects individuals’ cognitive responses to their surroundings and is central to understanding how landscape preferences are formed. Existing studies using Scenic Beauty Estimation (SBE) are constrained by incomplete indicator systems and overreliance on linear approaches. This study proposes an interpretable machine learning framework that integrates multi-source data to examine the nonlinear influences of streetscape features on SBE. Using Sanguan Village, a water-networked settlement in Jiangsu, we developed a 24-indicator system spanning color, spatial, natural, artificial, and cultural dimensions. Based on 523 panoramic images and aesthetic ratings from 1175 respondents, we compared OLS, DT, MLP, SVR, RF, and XGBoost models. The best-performing XGBoost, combined with SHAP analysis, revealed threshold effects and interaction patterns among variables. Green visibility, architectural aesthetics, building visibility, sky visibility, environmental coordination, and water are the top six feature variables most strongly associated with rural streetscape aesthetic perception, and each exhibits threshold effects. The saturation threshold for green visibility is 0.153, and architectural aesthetics can only make a positive contribution when its score exceeds 3.815. The appropriate range for building visibility is below 0.452, while the optimal value for sky visibility is approximately 0.194. We also explored the context-dependence of these threshold effects across urban and rural settings. This study proposes streetscape optimization strategies focusing on screening key factors, controlling their thresholds, and coordinating the allocation of streetscape features. The interpretable analytical framework for rural scenic beauty established in this research can facilitate evidence-based landscape optimization and provide scientific support for sustainable rural development and tourism in this case. Full article

Reverse osmosis (RO) desalination is a major technology for freshwater production in arid regions, but its energy demand becomes more challenging when the system is supplied by variable renewable energy. This study presents an offline digital-twin-assisted decision-support framework for dynamic RO under Kuwait solar-availability conditions. Within this framework, the predictive models are driven primarily by the dynamic RO process variables, while NASA Prediction Of Worldwide Energy Resources (POWER) data provide the Kuwait solar-availability context, and the PV power margin serves as a scenario-level energy indicator. The purpose is to predict instantaneous permeate flow rate, estimate specific energy consumption, and identify energy-efficient operating conditions using machine learning. Kuwait City was used as the solar case-study location. Hourly solar and meteorological data were obtained from NASA POWER, while dynamic RO membrane data were obtained from the open experimental wave desalination dataset published by the National Renewable Energy Laboratory (NREL) through Data.gov and the Marine and Hydrokinetic Data Repository. The RO dataset includes steady-state, ramp, sinusoidal, and Wave Energy Converter SIMulator (WEC-Sim) pressure/flow experiments. The process-flow image used in the system description was also taken from the same NREL dataset and is cited in the figure caption. The raw RO files were cleaned, harmonized, and transformed into a process-informed modeling dataset. Derived features included pressure rate, recovery ratio, salt rejection, estimated pump power, specific energy consumption (SEC), PV power margin, and rolling pressure/flow features. Three supervised regression models were tested: Gradient Boosting, Random Forest, and XGBoost. A representative subset of 60,000 records was used to preserve the main experimental conditions while reducing redundancy in the densely sampled sequential data. Results show that permeate flow rate can be predicted with high accuracy using Gradient Boosting (R² = 0.981; RMSE = 0.161 L/min). The moderate energy prediction performance yielded an R² of 0.654 and RMSE of 7.570 kWh/m³ for Random Forest. The accuracy of permeate conductivity predictions was lower (R² = 0.257; RMSE = 245.44 µS/cm) because membrane and feed characterizing parameters should be included for an adequate water quality control. The proposed approach is best suited as an offline decision-support framework for dynamic RO process analysis. Full article

This study examines the long-run relationship between terms of trade (TOT) and real fishing-sector GDP in a small open economy, focusing on the Peruvian case. Despite the strategic importance of fisheries for exports, employment and foreign exchange generation, the extent to which external price conditions are associated with fishing-sector performance remains insufficiently explored in sector-level research. Building on the notion that TOT summarise opportunities and constraints arising from the international environment, the paper evaluates whether persistent external conditions are linked to the long-run trajectory of the fishing sector. The analysis employs the Johansen cointegration approach and a bivariate Vector Error Correction Model (VECM) using quarterly data for the period 2001–2025. Seasonal effects are incorporated through quarterly dummy variables, while robustness is assessed by controlling for extreme El Niño–Southern Oscillation (ENSO) episodes and the COVID-19 pandemic. The results reveal the existence of a unique long-run equilibrium relationship between TOT and fishing-sector GDP. The error-correction mechanism indicates that deviations from equilibrium are actively corrected over time, whereas the adjustment coefficient for TOT is statistically insignificant. Robustness tests further show that El Niño episodes are negatively and significantly associated with short-run sectoral performance, while no statistically significant association is detected for La Niña. The COVID-19 control does not materially alter the long-run relationship identified by the model. The findings contribute sector-level evidence for a resource-dependent economy and suggest that long-run equilibrium and sectoral adjustment dynamics are important elements for understanding the long-run behaviour of the fishing sector. Full article

A robust fault-tolerant predictive asynchronous switching control method based on robust positively invariant sets is proposed for multi-phase batch processes subject to actuator faults, unknown disturbances, and asynchronous switching. To attenuate the effect of unknown disturbances, a min–max performance index is constructed, by which the robust control problem is formulated as a min–max optimization problem under worst-case disturbance conditions. To improve fault tolerance, robust positively invariant sets are introduced into the controller design so that the system states can remain within a constraint-satisfying feasible region under admissible actuator faults. Moreover, an online pre-switching mechanism is developed to address the phase mismatch between the system phase and controller. By updating the switching timing according to the real-time operating state, the controller can be adjusted to the corresponding control law before the system enters the next phase, thereby reducing the mismatched interval and suppressing state deviation. A case study on the injection and holding phases of the injection molding process shows that the proposed method improves tracking accuracy and operational smoothness under actuator faults, unknown disturbances, and asynchronous switching, demonstrating its effectiveness and applicability. Full article

Weather radar calibration is essential for ensuring data consistency and quantitative precipitation estimation in X-band radar networks. Existing absolute calibration methods (e.g., metal sphere, horn antenna) suffer from high cost, poor timeliness, and difficulty in automation due to meteorological conditions and airspace restrictions, while spatiotemporal matching methods based on volume scan data suffer from interpolation and matching inaccuracies. To address these issues, this study proposes a collaborative calibration method for X-band radar networks based on opposing Range–Height Indicator (RHI) scans. The method uses a rigorously calibrated reference radar as a benchmark and performs opposing RHI scans with the radar under calibration to obtain synchronized observations within the spatial overlap region. Precise spatial matching is achieved using the nearest-neighbor algorithm based on beam-broadening cross-coverage thresholds, and bias is extracted using both the midline 9-point averaging method (midline method) and spatially constrained regional Statistics method (regional method). Based on a total of 58 sets of opposing RHI scanning cases conducted under stratiform precipitation, scattered precipitation, and weak cloud conditions, the results show that under conditions where echo continuity is maintained near the midline of stratiform and scattered precipitation, both the midline method and the regional method can obtain stable matching data. The midline method achieves a median correlation coefficient (0.821–0.942) higher than that of the regional method (0.860–0.872), and its bias standard deviation remains relatively stable (midline method: 1.39–2.20 dB; regional method: 2.61–3.16 dB). Continuous RHI calibration tests confirm that within a 30-min window, the fluctuation of the data matching correlation coefficient is less than 0.05, and the fluctuation of the bias mean is controlled within ±0.3 dB. Under weak cloud conditions, although the midline method can still achieve a high correlation coefficient, the correctness of its results still requires auxiliary validation through other calibration means. This study provides a relatively efficient and effective technical approach for the automated collaborative calibration of dense X-band radar networks. Full article

Forest-inventory precision depends jointly on plot size and sample size. Using 42 stem-mapped 1000 m² plots in an uneven-aged hybrid fir (Abies × borisii-regis Mattf.) forest in Pertouli, Greece, we reconstructed 100–1000 m² concentric plots at the same centers. RSE was calculated using a simple-random-sampling variance working approximation. At $n=42$, 300 m² was the minimum acceptable plot size under an adopted ±10% practical agreement criterion relative to the 1000 m² reference; the minimum remained 300 m² at ±7.5% but increased to 400 m² for basal area and 700 m² for tree density at ±5%. Under a 5% RSE target, basal area met the target from 500 m², whereas tree density met it at no acceptable size; both attributes met a 10% target from 300 m². Controlled resampling and compartment-group analysis placed 95%–100% of group-by-size RSEs within same-sample-size resampling intervals. Pareto-efficient designs showed the sample-size–sampled-area trade-off. Holding total sampled area constant, more small plots reduced median RSE by 13%–23%. Designs requiring $n>42$ remain projections. Case-study plot-size values should not be transferred directly to other forest structures; the procedure provides a transferable way to define an acceptable plot-size range and evaluate sample-size and sampled-area trade-offs for a selected attribute and precision target. Full article

Botulinum neurotoxin type A (BoNT-A) is used across coexisting craniocervical conditions, and therapeutic response is often overinterpreted as evidence of a shared generator. The bruxism literature shows why. Across six controlled studies with event-level outcome measurement, reported effects on event frequency track not dose or injection field but whether the rule used to detect an event could follow the amplitude the toxin had just reduced; none yet combines a placebo arm with an amplitude-independent event definition. This targeted critical narrative review interprets representative evidence mechanistically rather than assessing efficacy. It examines chronic migraine, tension-type headache, myogenous temporomandibular disorders, and bruxism, with somatosensory tinnitus as a cross-modal boundary case. Efficacy is protocol-specific in chronic migraine and uncertain elsewhere. Tracing studies locate somatosensory routes to the cochlear nucleus in the spinal trigeminal and dorsal column nuclei; a direct mesencephalic-trigeminal-to-cochlear projection has not been demonstrated in the tracing literature reviewed, so somatic–auditory plausibility does not establish the masticatory proprioceptive route invoked by muscle-targeted rationales. Because BoNT-A affects motor output, peripheral nociceptive signaling, and muscle spindle input—the last of these probably differing in availability across injection fields—we frame it as a site-dependent, multi-output perturbation. That pharmacology is established; what is offered here is the inferential framing and the anatomical constraint following from it. Full article

To address the limited cooling range of ventilation in deep, high-temperature development headings and the lack of coordinated design between coil-based cooling and the ventilation system, this study proposes a coupled “twin-forcing–coil” cooling scheme. Building on conventional overlap (forcing–exhausting) ventilation, a rear-mounted second forcing duct is added to the conventional overlap (force–exhaust combined) auxiliary ventilation system, forming a dual-duct forcing, single-exhausting configuration—hereafter termed the “twin-forcing–single-exhausting” (TFSE) system—that provides a booster (relay) air supply to mitigate the along-path attenuation of cooling capacity and the short-circuiting of cold air; an in situ heat-exchange coil wall further provides supplementary cooling where ventilation-based temperature control weakens. Using a development heading at the 790 m level of a metal mine in Yunnan as the engineering background, a three-dimensional numerical model coupling the roadway, ventilation system, and coil wall was established and validated against nine field monitoring points, showing average relative errors of approximately 1% for temperature and 2–3% for humidity, comparable to the measurement uncertainty of the field instrumentation. Because the numerical model does not account for evaporative and condensation phase-change processes, two supplementary development headings with standing water at the face were used for validation; results showed that model error increases with water accumulation and heading length, indicating the model’s applicability is limited to conditions with intact surrounding rock and minimal seepage. Six operating cases were designed with duct placement and coil spacing as variables. Results show that single-duct ventilation cooling decays markedly beyond 30 m from the face, whereas twin-forcing booster (relay) air supply effectively extends the cooling range, reducing the 30–70 m section temperature by 2.7–2.9 K; the second duct should be positioned where the first duct’s cooling capacity begins to attenuate but is not yet depleted. Based on only two spacing configurations tested (10 m and 15 m), coil-staggered spacing showed limited effect on cooling performance under the field conditions examined; this preliminary finding requires validation across a broader range of spacings. Among the chilled-water conditions tested, an inlet temperature of 280.65 K and a flow velocity of 0.5 m/s offered a reasonable trade-off between cooling uniformity and economic efficiency. Under the boundary conditions and equipment parameters of this case, energy consumption estimates further indicate that the cooling effect per unit electricity consumption of twin-forcing ventilation is roughly 6–8 times that of coil-based cooling, primarily due to pumping losses over the ~240 m chilled-water delivery distance. This energy penalty indicates that coil-based cooling is better suited as a localized, short-distance supplementary measure rather than as a means of extending the cooling range over long distances. Full article

Distinguishing relapsing–remitting multiple sclerosis (RRMS) from secondary-progressive disease (SPMS) remains a clinical challenge, as no single laboratory test reliably identifies the transition. We explored whether plasma extracellular-vesicle (EV) microRNAs could separate the two phenotypes. Four candidate EV-miRNAs: miR-30a-5p, miR-223-5p, miR-155-5p, and miR-146a-5p, were quantified by qRT-PCR in plasma-derived EVs from 13 healthy controls (HC), 7 RRMS, and 16 SPMS patients, normalized to miR-16-5p. EVs were characterized by electron microscopy, dynamic light scattering, and zeta potential, confirming vesicles of expected morphology, size, and negative surface charge. Two miRNAs were informative. miR-155-5p was significantly downregulated in both RRMS and SPMS versus controls but did not differentiate the phenotypes (AUC 0.52), behaving as a disease-general marker. In contrast, miR-223-5p was selectively reduced in SPMS, to approximately one-quarter of control abundance, and provided the only signal distinguishing progressive from relapsing disease (AUC 0.73; rank-biserial −0.46). miR-30a-5p and miR-146a-5p were uninformative. These findings, consistent with the circulating/EV literature, suggest EV-miR-223-5p as a candidate marker of the progressive phenotype and EV-miR-155-5p as a disease indicator. Given the small cohort and near-detection-limit measurements, the study is hypothesis-generating and requires validation in larger, longitudinal cohorts. Full article

Mobile robots and autonomous vehicles require tightly regulated direct current (DC) power under rapidly varying load conditions, motivating control strategies that combine fast nonlinear regulation with predictive optimization. This paper proposes a Hybrid Sliding Mode Control and Model Predictive Control (Hybrid SMC + MPC) strategy for a DC-DC buck converter supplying a representative mobile-robot mission load. The controller employs a cascade SMC structure for fast inner-loop regulation and an MPC component that provides finite-horizon duty-cycle correction using planned load information. The MPC problem is formulated in condensed form and solved analytically without an external optimization solver. A Lyapunov-based analysis establishes a sufficient reaching condition for the sliding variable under the ideal averaged-model assumptions, and the condition is verified for the simulated mission. The proposed approach is evaluated in MATLAB using a 10-phase, 10 s load profile with resistance varying from 7 Ω to 100 Ω and is compared with SMC-only, MPC-only, PID, constant-duty, and reconstructed fuzzy-logic benchmarks. In the averaged-model study, the Hybrid SMC + MPC achieves a maximum absolute voltage deviation of 0.388 V, an RMSE of 0.0115 V, and a final-phase mean absolute error of 0.0076 V. It provides the lowest maximum voltage deviation among the principal closed-loop controllers, while PID achieves the lowest RMSE and final-phase error and SMC-only exhibits the shortest mean settling time. Relative to MPC-only, the Hybrid controller reduces the maximum voltage deviation by approximately 43.6% and the mean settling time by approximately 66.1%. An ablation study shows that the MPC contribution substantially improves overall and steady-state regulation accuracy, while load preview primarily reduces the worst-case voltage deviation. Switching-level MATLAB/Simulink validation with explicit 20 kHz PWM and converter parasitics confirms that the output remains within ±2% of the 25 V reference throughout the complete mission, with a maximum absolute deviation of 0.443 V and a maximum steady-state switching ripple of 21.6 mV peak-to-peak. These results demonstrate that the proposed Hybrid SMC + MPC architecture provides a favorable balance between worst-case transient regulation, steady-state accuracy, and predictive control capability for dynamically varying robotic power loads. Full article

Objectives: Women with a history of breast cancer are at increased risk of developing subsequent breast cancer, including ipsilateral recurrence and contralateral new primary breast cancer. This study evaluated the discriminatory performance of a mammogram-based artificial intelligence (AI) risk model for predicting subsequent breast cancer within one year after a negative screening mammogram. Methods: This enriched retrospective case–control study included women with a prior history of breast cancer who underwent screening digital breast tomosynthesis between January 2018 and December 2023 at three affiliated academic breast imaging centers. Digital breast tomosynthesis examinations classified as BI-RADS 1 or 2 were retrospectively analyzed using the ProFound AI^® Risk model version 1.0 to estimate 1-year breast cancer risk. Patients were classified according to whether they developed subsequent breast cancer within one year of the index screening examination. Model discrimination was evaluated using receiver operating characteristic analysis. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated at an exploratory cutoff selected by maximizing the Youden index. Results: The study included 96 women (mean age, 65.3 ± 8.7 years), of whom 32 developed subsequent breast cancer within one year, and 64 did not. The mean AI risk score was significantly higher in the subsequent breast cancer group than in the control group (1.18 ± 0.59 vs. 0.49 ± 0.41; p < 0.001). The AI model demonstrated an AUC of 0.824 (95% CI: 0.728–0.921). At an exploratory cutoff of 0.39, sensitivity was 81.3%, specificity was 76.6%, PPV was 63.4%, and NPV was 89.1%. In separate exploratory analyses, the AUC was 0.790 (95% CI: 0.641–0.939) for ipsilateral recurrence and 0.860 (95% CI: 0.752–0.974) for contralateral new primary breast cancer. AI risk scores were not significantly correlated with tumor size or age at subsequent breast cancer diagnosis. Conclusions: In this enriched retrospective case–control study, higher mammogram-based AI risk scores were associated with subsequent breast cancer within one year after a negative screening examination. The model demonstrated discriminatory performance for both ipsilateral recurrence and contralateral new primary breast cancer; however, these analyses were exploratory. Because the cohort was enriched for subsequent breast cancer events, the reported predictive values are specific to the study sample and should not be extrapolated to routine surveillance populations. Larger prospective cohorts are needed to validate discrimination, calibration, and clinical utility. Full article

The Myval balloon-expandable transcatheter heart valve was developed for transcatheter aortic valve implantation, but its broad 20–32 mm size matrix and controlled deployment have prompted use in non-aortic landing zones. This narrative review critically synthesizes Myval-specific case reports, case series, and observational cohorts, together with relevant platform-level evidence, addressing device design, anatomical selection, imaging, procedural strategy, clinical outcomes, and evidence gaps. Reported applications include mitral valve-in-valve, valve-in-ring, and valve-in-mitral annular calcification; tricuspid valve-in-valve and valve-in-ring; and pulmonary implantation in conduits, surgical bioprostheses, and selected native or patched right ventricular outflow tracts. Outcomes appear most predictable within circular stented surgical bioprostheses, whereas non-circular rings, severe mitral annular calcification, and compliant or aneurysmal outflow tracts present greater risks of inadequate anchoring, paravalvular regurgitation, embolization, frame deformation, left ventricular outflow tract obstruction, and coronary compression. Intermediate and extra-large diameters increase the available nominal sizing options; however, no clinical evidence demonstrates that this reduces embolization, paravalvular regurgitation, residual gradients, or reintervention. The available data document procedural feasibility in anatomically selected patients but are predominantly observational, with limited independent adjudication and follow-up. These procedures are generally off-label and should remain individualized Heart Team decisions; prospective multicenter studies are required to define comparative safety, antithrombotic management, durability, and lifetime reintervention strategies. Full article

Data centers are large and rapidly growing electricity consumers, and cooling systems account for a substantial share of their energy demand. A key contribution of this study is a climate-sensitive, hourly techno-economic comparison of three data-center cooling configurations under consistent operating assumptions: a conventional water-cooled centrifugal chiller baseline, a magnetic bearing chiller (MBC) system, and an MBC system integrated with aquifer thermal energy storage (ATES). The comparison is performed for Phoenix, Arizona, and Fairbanks, Alaska, which represent substantially different cooling climates in the U.S. Hourly simulations use identical information technology (IT) load profiles, identical aggregate installed chiller capacity represented by two 4058 kW chiller units, common water-side economizer controls, and site-specific weather and electricity tariffs. Results show that the MBC system reduces annual cooling-system electricity consumption from 1169.4 to 957.4 MWh in Phoenix (18.1%) and from 361.6 to 319.4 MWh in Fairbanks (11.7%). Peak cooling-system electrical demand decreases by 119.4 kW in Phoenix and 71.6 kW in Fairbanks. Relative to the centrifugal baseline, the MBC case gives a 5.8-year simple payback in Phoenix but is not economically attractive in Fairbanks under the assumed tariff. The MBC-only case gives the lowest annual cooling electricity use in both climates. The MBC+ATES case is treated only as a screening-level, discharge-assisted cold-storage scenario rather than a full techno-economic assessment of seasonal ATES, and no site-specific hydrogeological feasibility assessment is performed. Under the assumed O&M cost structure, MBC+ATES gives a higher discounted value of savings than MBC-only, but this economic result is not caused by additional cooling-electricity savings relative to MBC-only. The MBC+ATES case also has a longer payback period because of its higher capital cost. These results show that the value of advanced cooling configurations depends on climate, free-cooling availability, electricity pricing, storage assumptions, and economic assumptions within the modeling framework considered in this study. Full article

Background/Objectives: Neurocognitive disorders represent a growing burden among older adults. Although respiratory vaccines have demonstrated efficacy and safety in preventing acute respiratory events, their potential impact on the development of chronic neurocognitive disorders, such as cognitive impairment, dementia, and Alzheimer’s disease (AD), has not been extensively explored. The objective of this scoping review was to synthesize and analyze the available evidence on the association between respiratory vaccination and the risk of developing cognitive impairment, dementia, and Alzheimer’s disease. Methods: A scoping review was conducted in accordance with the PRISMA-ScR guidelines. A literature search was performed in the PubMed, Scopus, and Web of Science databases through 15 June 2026. Studies involving older adults (≥60 years), irrespective of their baseline neurocognitive status, were included. Observational studies (cohort and case–control studies) were considered. Editorials, narrative reviews, and other non-original articles were excluded. Results: Eighteen studies were included, with cohort studies being the predominant design. Four main categories of respiratory vaccines were evaluated: influenza, COVID-19, respiratory syncytial virus (RSV), and pneumococcal vaccines, in relation to the risk of cognitive impairment, dementia, and AD. Influenza vaccination was associated with a lower risk of dementia and AD, with a potential dose–response relationship observed, whereby a greater number of vaccinations was associated with a greater reduction in risk. Pneumococcal vaccination was also associated with a lower risk of dementia and AD, particularly among individuals who received a greater number of vaccine doses. In contrast, the evidence regarding COVID-19 and RSV vaccines was limited and yielded heterogeneous findings. Conclusions: Influenza and pneumococcal vaccination maybe associated with a lower risk of Alzheimer’s disease and dementia. However, the evidence regarding COVID-19 and RSV vaccines remains limited, and additional studies are needed to clarify their impact on the risk of developing neurocognitive disorders. Full article

Assessing the effectiveness of protected areas is crucial for refining conservation policies, yet it remains challenging in remote, biodiversity-rich tropics due to logistical constraints. To address this challenge, we developed a remote sensing-based Pressure–State–Benefit (PSB) framework comprising four dimensions: human activity, ecosystem pattern, ecosystem quality, and ecosystem services. Using the Hainan Tropical Rainforest National Park (HTRNP) as a case study, we evaluated conservation effectiveness at the park scale by comparing ecological changes before (2015–2020) and after (2020–2025) its establishment. We further compared the Core Protection Zone (CPZ) and the General Control Zone (GCZ) to reveal differences in conservation outcomes under distinct management regimes. The establishment of HTRNP effectively curbed cropland and built-up land expansion. During 2020–2025, landscape patterns improved, characterized by enhanced connectivity and reduced fragmentation. Concurrently, fractional vegetation cover increased at an accelerated rate, indicating improved ecosystem quality, while key ecosystem services showed upward trends. Notably, the CPZ maintained superior ecological conditions with steady improvements under strict protection, whereas the GCZ, being more sensitive to human disturbances, exhibited fluctuating outcomes. This study demonstrates the effectiveness of HTRNP in enhancing regional ecosystem conditions and highlights the importance of zoned management. Furthermore, the proposed PSB framework offers a transferable approach for comprehensively assessing conservation effectiveness in other protected areas. Full article