Search Results (10,514)

Industrial bioprocesses remain constrained by their limited ability to monitor intracellular events in real time. Most rely on external measurements—nutrient or metabolite levels in the culture medium—that provide only delayed and indirect information about the cell’s internal state. Riboswitches, RNA elements that respond to specific small molecules, offer a complementary route to direct intracellular sensing. Acting as genetically encoded biosensors, they bind metabolites with nanomolar-to-micromolar affinity, and ligand binding drives rapid conformational changes in the RNA. When coupled to gene regulatory outputs, riboswitches can, in principle, support dynamic feedback control that allows cells to sense metabolic imbalances and adjust their own metabolism. This Commentary argues that the central opportunity is conceptual: reframing intracellular biosensing as a foundational layer for adaptive, self-regulating cell factories. It distinguishes what riboswitch technology already demonstrates at laboratory scale from what remains a forward-looking vision, and outlines the engineering barriers, specificity, dynamic range, context-dependence, metabolic burden, evolutionary stability, and validation in production settings that must be addressed before autonomous bioprocess control becomes routine. Importantly, the functional response time of such systems is governed not by binding kinetics alone but by transcription, translation and mRNA turnover, a distinction that matters for feedback stability. Full article

Background/Objectives: This study investigated the effect of sprint intensity on spatiotemporal variables and joint kinematics during the maximal-velocity phase at each intensity level in experienced sprinters. Methods: Twenty experienced master sprinters (18 men and two women, age: 38.2 ± 12.1 years, height: 1.80 ± 0.06 m, body mass: 81.8 ± 8.8 kg, 100 m PB: 12.65 ± 1.06) performed nine 50 m sprints with increasing intensity each time (60–100%), during which step-by-step spatiotemporal parameters and joint kinematics during the maximal-velocity phase of each sprint were measured. Results: The main findings were that sprint times decreased significantly with each increase in intensity, together with a significant increase in maximal sprint velocity. The spatiotemporal parameters contact and flight times decreased, step frequency increased, while step length increased until 75% and decreased again after 90%. Joint angles of the ankle and knee changed, while those of the hip joint did not change at touchdown and toe-off with increasing intensity. All peak step-by-step angular joint velocities during the maximal-velocity phases increased with increasing sprint intensity. However, the changes in joint kinematics across joints did not occur at the same time between intensity levels. Conclusions: These findings highlight that spatiotemporal variables and joint kinematics do not follow a linear development as intensity increases; rather, they adopt distinct mechanical strategies at very high intensities to accommodate reduced time for force application and limb repositioning. Based on the findings it is suggested that training targeting rapid hip-extension mechanics, efficient limb repositioning, and the ability to maintain effective propulsion under shortened contact times may be particularly beneficial. Full article

Accurate blood glucose prediction is essential for glycemic management in people with diabetes, but the size of many high-performing models complicates execution on resource-constrained artificial pancreas controllers. We propose GluKDnet, a lightweight glucose-forecasting model for prospective Android-smartphone-based mobile edge controllers. GluKDnet transfers the representational capacity of a time-series foundation model to a compact causal CNN through heterogeneous knowledge distillation. The teacher model, MOMENT, is adapted to continuous glucose monitoring (CGM) data through risk-event-aware masking, which prioritizes abnormal glucose levels, rapid glucose fluctuations, and CGM-defined dawn phenomenon and Somogyi effect patterns during masked reconstruction. A transient-state and steady-state distillation module jointly aligns ordered patch-level dynamics and day-level summaries between teacher and student. Using DLCP3 for teacher pretraining and leave-one-patient-out evaluation on OhioT1DM, GluKDnet achieves RMSE values of 20.04, 32.04, and 45.33 mg/dL for 30, 60, and 120 min prediction, respectively, with about 53K parameters. Auxiliary evaluation on T1D-UoM shows a similar offline accuracy–parameter count pattern. On a vivo V2072A Android smartphone, the 30 min model achieved a mean API inference latency of 0.470 ms (P95: 0.855 ms), a maximum sampled process proportional-set-size memory of 47.06 MiB, and a median incremental device energy estimate of 0.277 mJ per inference. These device measurements characterize the exported student model under one hardware and software configuration; insulin dosing and prospective closed-loop clinical evaluation remain outside the scope of this study. Full article

The adoption of circular economy (CE) in agri-food systems has been accelerated by rising environmental pressures and resource inefficiencies. This systematic review examines the thematic and conceptual evolution of CE research in agri-food systems between 2018 and 2025, focusing on the transition from waste management toward the strategic integration of circular principles across food value chains. By combining bibliometric mapping with a structured literature review, the study analyzes publication trends, intellectual structures, dominant research themes, and emerging directions within the field. The findings reveal a rapid growth in scientific output and a shift from conceptual frameworks toward applied management strategies, including performance measurement, organizational innovation, and decision-support tools. Technological solutions for waste valorization increasingly support regenerative food system models. Despite these advances, important research gaps remain, particularly regarding geographical concentration, the limited integration of consumer perspectives, and the need for broader assessment frameworks capable of evaluating the systemic impacts of circular strategies. Although nutrition-related aspects appear only marginally in the current literature, they represent a promising avenue for future interdisciplinary research rather than a dominant research theme. Overall, the field is evolving toward a more systemic and implementation-oriented perspective capable of supporting resilient and sustainable food systems. Full article

Flow-path switching is a key operating condition that enables flow regulation, zonal conversion, and filling-path redirection in sand-control completions. The associated flow characteristics directly govern the operational stability and service reliability of intelligent filling tools. Most existing studies have addressed erosion only under simple geometries such as pipe contractions and expansions, leaving the dominant erosion-controlling factors and rapid erosion-rate prediction methods for sand-control filling tools under flow-path switching conditions insufficiently understood. This study developed a Fluent-based numerical model of solid–liquid two-phase erosion for intelligent filling tools and characterizes the wall-erosion distribution pattern during flow-path switching. Guided by field practice, multi-factor simulations were performed over the reduction angle, cutting particle size, inlet flow capacity, flow-switching direction, opening area, and structural form. On this basis, a maximum-erosion-rate prediction model was constructed using a logarithmic transformation combined with a stepwise quadratic response-surface method. This regression-based approach was deliberately chosen over machine-learning black-box models, whose limited interpretability and small-sample reliability make it difficult to reveal the underlying physical mechanisms; in contrast, the proposed model yields an explicit algebraic expression whose significant interaction and quadratic terms directly reflect the coupling between structural and operating parameters, while the logarithmic transformation accommodates erosion-rate fluctuations spanning several orders of magnitude. The results show that the factors rank in influence as follows: flow-switching direction > opening area > reduction angle > inlet flow capacity > cutting particle size > structural form. The established prediction model attained a coefficient of determination of about 0.951 and an adjusted coefficient of determination of about 0.901; combined with the significance test and a residual analysis, the model can effectively characterize the coupling influence of structural parameters and operating parameters on the erosion rate. These findings provide quantitative guidance for the erosion-resistant structural design, field operating-parameter selection, and preliminary service-life assessment of intelligent filling tools in offshore sand-control well-completion operations. The prediction model is further validated through jetting-erosion bench tests; the measured erosion rates agree closely with the model-predicted values, confirming the practical reliability of the model and its capability to serve as an engineering reference for the erosion-resistant design and service-life assessment of intelligent filling tools in sand-control well-completion operations. Full article

Tourism is an activity that contributes positively to economic growth. Although tourism should be supported because of its benefits for a region’s development, it can place a strain on the environment. Integrating the principles of sustainable development into tourism is essential to ensuring its long-term viability while protecting natural and cultural resources. The purpose of this study is to determine the extent of tourism pressure, its trends, and its impact on sustainable development in three major rural tourist regions of Romania, namely Humor, Câmpulung Moldovenesc, and Dorna, located in the historical province of Bucovina, Suceava County. An analysis of the trends in the three evaluation indicators (density, duration, and pressure level) at the rural region level (Humor Region, Câmpulung Moldovenesc Region, and Dorna Region) is warranted, as these regions are the main centers of attraction and development for rural tourism in Suceava County. Between 2001 and 2023, the density indicator (arrivals/km²) showed significant increases in all three rural regions analyzed—over 6000% in Humor, 2200% in Dorna, and 2000% in Câmpulung Moldovenesc, significantly amplifying the pressure on these three areas; the duration indicator (overnight stays/km²) recorded considerable increases of over 7400% in Humor, 2400% in Dorna, and 2250% in Câmpulung Moldovenesc, proving to be a much more severe pressure indicator than the density indicator; and the pressure level indicator (arrivals/resident population) increased substantially, by over 8000% in Humor, 2100% in Dorna, and 2000% in Câmpulung Moldovenesc, indicating profound sociocultural pressure on the rural communities analyzed. The importance of this research and the originality of this study are closely linked to an understanding of the evolution and intensity of tourism pressure in the three regions analyzed, with a view to informing future strategies for sustainable rural development. An analysis of the environmental pressure caused by tourism in the three regions of Bucovina for the period 2001–2023 reflects a shift from a localized and negligible impact to an increasingly significant pressure. Currently, the regions face the challenge of balancing rapid economic development with the critical need to preserve their unique heritage. Identifying increases in tourist pressure can facilitate the implementation of effective mitigation measures, which are essential for ensuring the sustainability of tourism-related activities in the most popular tourist regions of Bucovina. Full article

Manual visual diagnosis of apple diseases suffers from low efficiency, strong subjectivity and poor scalability for large commercial orchards. Existing research mainly targets diseases on single plant organs, whereas full-growth-cycle detection has to cope with extreme multi-scale differences among lesions. For instance, Valsa canker on tree trunks leads to extensive cortical necrosis, while early-stage anthracnose on fruits appears as tiny spots spanning only a few pixels. These significant scale gaps necessitate robust spatial feature aggregation and anti-noise ability to resist complex background interference. Aiming to achieve rapid and precise detection of diseases on multiple apple organs including leaves, fruits, trunks and branches, this work presents an enhanced YOLOv11 model equipped with the Information Integration Attention (IIA) module. The IIA module is embedded into the key fusion layers of the backbone and neck networks. It strengthens the extraction of fine-grained lesion features, recovers spatial location information via a bidirectional attention mechanism, and suppresses noise induced by uneven lighting and intricate backgrounds. To guarantee stable convergence on low-resource computing devices, a tailored training scheme is designed. Experimental results on a seven-category dataset with 7406 images demonstrate that YOLOv11-IIA reaches a precision of 0.763, a recall of 0.819, mAP@50 of 0.857 and mAP@50-95 of 0.699, which achieves clear performance improvements over the original YOLOv11 (mAP@50 improved from 0.485 to 0.857) and other attention-augmented detectors. The model operates stably on an NVIDIA GTX 1050 4GB GPU with an inference speed of 16 FPS for 640 × 640 input images; comprehensive quantitative computational metrics including parameter count, FLOPs and memory consumption will be fully measured in subsequent UAV deployment experiments. The proposed method provides a reliable technical reference for intelligent apple disease monitoring in smart orchard systems. Full article

Rapid urbanization has intensified pressure on public services, infrastructure systems, and spatial equity, highlighting the need for integrated approaches that jointly assess service deficits and emerging urban growth. This study proposes an explainable urban analytics framework for identifying service-gap risk and emerging urban patterns using harmonized spatial, service, population, and digital-readiness indicators. The framework integrates an MCP-enabled data harmonization pipeline, composite service-availability and population-adjusted service-stress features, and a Dual-Head MLP architecture that supports shared representation learning across two related prediction tasks. SHAP-based explainability is employed to interpret the relative contribution of service, stress, digital-readiness, and spatial-context features. The framework was evaluated using Saudi district-level data and proxy-based external city datasets to assess cross-city transferability. Compared with classical machine-learning and single-task neural baselines, the Dual-Head MLP demonstrated stronger task-wise predictive performance, while the external evaluation indicated stable but context-dependent transfer across heterogeneous urban settings. The findings suggest that the proposed framework can support urban planners and municipal decision-makers in prioritizing infrastructure investment, identifying underresourced areas, and interpreting early urban transformation patterns. However, the outputs should be regarded as proxy-based decision-support indicators rather than official administrative measures of service adequacy. Full article

With the rapid development of the Thai economy and the continuous improvement of national living standards, the Thai people’s demand for freshness, quality, and diversity of agricultural products is constantly increasing. Efficient agricultural product logistics can not only ensure the supply and quality of agricultural products, but also promote the optimization of resource allocation and logistics infrastructure construction, with direct implications for reducing food loss, lowering energy consumption, and enhancing the resilience of regional food systems—key dimensions of sustainable agricultural development. In order to analyze the development status of agricultural product logistics in Thailand, the paper constructed an evaluation system consisting of four dimensions and seven indicators. Factor analysis and natural break point method were used to measure the development level of agricultural product logistics in 77 provinces of Thailand from 2012 to 2023, and the spatial pattern evolution characteristics were analyzed. The research conclusion is as follows: (1) From 2012 to 2023, the comprehensive score of agricultural product logistics in Thailand increased from 24.01 to 30.89, indicating a significant enhancement in overall strength; (2) the logistics level of agricultural products in various provinces of Thailand shows heterogeneous characteristics; (3) by calculating the strength of agricultural product logistics attraction between cities, the Central Plains Agricultural Product Logistics Circle centered on Bangkok, the Northern Plains Agricultural Product Logistics Circle centered on Chiang Mai Province, and the Southern Logistics Circle centered on Pattani and Yala provinces were determined. Full article

Air-void distribution is an important internal characteristic of asphalt mixtures, as it affects compaction quality, moisture susceptibility, permeability, and long-term pavement durability. Conventional air-void testing methods generally provide only an average volumetric parameter and cannot effectively describe the spatial distribution of air voids within cylindrical specimens. To address this limitation, this study proposes a capacitance-based method for characterizing the vertical and radial air-void distribution of asphalt mixtures using a saturated reference field. An annular capacitive sensor was designed for cylindrical asphalt mixture specimens, and its structural dimensions were optimized using capacitance sensitivity and sensitivity-field distribution uniformity as evaluation indicators. Asphalt mixture specimens with different gradations and compaction conditions were prepared and tested under a saturated reference-field measurement scheme. Dielectric indicators derived from capacitance measurements were used to characterize the variation in air-void distribution along the specimen height and across radial regions. Layer-wise air-void measurements were further conducted to validate the vertical distribution results, while radial partition-based indicators were introduced to quantitatively describe the air-void distribution characteristics from the center to the edge of the specimen. In addition, rotation-angle and saturated-condition stability tests were performed to evaluate the robustness of the proposed method. The results indicate that the saturated reference-field capacitance method can effectively reflect the spatial variation in air voids in asphalt mixtures and provides a low-cost, rapid, and non-destructive approach for evaluating air-void distribution characteristics in laboratory-compacted specimens. Full article

Large-amplitude water sloshing in high-filling storage tanks can generate transient roof-impact pressures that threaten structural safety and operational reliability. This study develops a simplified momentum-conservation-based model for estimating the maximum dynamic impact pressure (MDIP) in rectangular tanks. The impact load is represented by the rate of momentum change within the roof-wetted liquid region, and the model parameters are calibrated using shaking-table tests. A total of 130 sinusoidal-excitation cases covering three filling depths and multiple excitation frequencies and amplitudes were conducted. The experimental results indicate that the roof-pressure response is strongly non-sinusoidal, with short-duration impulsive peaks and pronounced cycle-to-cycle variability. In the representative cases, the MDIP occurs after the first roof-contact event and varies non-monotonically with excitation amplitude. After calibration, the model provides reasonable estimates of the measured MDIP for most test conditions. The proposed formulation is physically interpretable and computationally efficient, making it suitable for rapid preliminary estimation of sloshing-induced roof-impact pressure in high-filling rectangular water tanks. Full article

In practical photovoltaic grid-tied four-leg inverter systems (PV-GTFLIs), changes in radiation, temperature, and grid voltage magnitude cause significant disturbances, including DC-link voltage disturbance and power unbalance, which can impact the system’s dynamic responses, control performance, grid power quality, efficiency, and reliability. To deal with these problems, this article proposes an improved active disturbance rejection control (ADRC) methodology for optimizing DC-link voltage regulation in PV-GTFLIs. The proposed ADRC approach incorporates a nonlinear high-gain observer (NHGO) within the external DC-link voltage control loop to estimate and mitigate disturbances. The suggested ADRC approach adopts the NHGO instead of the traditional extended state observer due to its superior characteristics, which include excellent dynamic responses, rapid and accurate disturbance estimation and rejection, and enhanced resilience against measurement noise. Thus, it enhances the stability of the DC bus voltage, enhances the system’s dynamic responses, improves its steady-state performance, increases its ability to reject voltage disturbances, and improves the reliability of the PV-GTFLI, as well as reducing the cost and size. The effectiveness of the proposed ADRC approach based on the NHGO is confirmed through software-in-the-loop real-time validation tests, including changes in irradiation and PV cell temperature, sag in grid voltage amplitude, and internal uncertainties, using the OPAL real-time digital simulator. Full article

This study assessed environmental changes observed during an integrated programme of microbiological bioremediation and macrophyte management in the Pasternik Reservoir in Starachowice, Poland. The monitoring programme included water and sediment analyses, repeated measurements of soft organic fraction thickness, observations of macrophyte management, Sentinel-2 Maximum Chlorophyll Index mapping, and historical catchment modelling. During the monitoring period, the mean thickness of soft organic fractions decreased by 78%, sediment dry matter increased, and several water quality variables showed favourable temporal changes. Rapid macrophyte regrowth required repeated cutting and increased the practical demands of vegetation management. Sentinel-2 imagery revealed marked spatial and seasonal variation in the red edge optical signal within the reservoir. The Maximum Chlorophyll Index was interpreted as a relative optical indicator rather than as a quantitative chlorophyll a product. Nutrient Delivery Ratio modelling was used only to provide historical catchment context for 1990–2018. Because the study involved one reservoir and did not include an untreated reference site, the observed changes cannot be attributed exclusively to the management programme. The study shows the value of combining field measurements, satellite observations, and catchment information in the adaptive monitoring of small eutrophic reservoirs. Full article

Large-scale physical model experiments play a critical role in understanding the coupled thermo–hydro-mechanical responses during hydrate dissociation. In this study, a specially designed large-scale physical simulation apparatus (effective volume: 1178 L) was employed to investigate the depressurization-induced dissociation behavior of CO₂ hydrate, which was used as a model system to simulate the macroscopic response of hydrate-bearing sediments under controlled laboratory conditions. Key reservoir parameters—including temperature, pressure, electrical resistivity, gas production rate, and stratum displacement—were continuously monitored using an integrated array of temperature sensors, pressure transducers, electrical resistivity probes, and displacement meters. During depressurization, the system pressure decreased from 3 MPa to 1 MPa (matching the backpressure), while the internal temperature dropped from 3.5 °C to approximately 1 °C due to the endothermic dissociation of the hydrate. Gas production exhibited a three-stage evolution: an initial slow release, a rapid increase as the dissociation front propagated through the sediment, and a plateau upon completion of hydrate dissociation. Based on the measured gas production and CO₂ consumption, the hydrate saturation was estimated to be approximately 0.248. The dissociation process led to measurable sediment settlement, with a maximum vertical displacement of 88.3 mm (approximately 5.88% of the model height). Analysis of the evolution of effective stress indicates that depressurization reduced pore pressure and increased vertical effective stress by approximately 0.55 MPa, while hydrate dissociation weakened the sediment skeleton, jointly causing settlement. This study demonstrates the feasibility of using a large-scale apparatus to capture the coupled processes during hydrate dissociation. It provides benchmark experimental data for validating numerical models of hydrate-bearing sediment behavior. Further validation is required before these results can be extrapolated to CH₄ hydrate systems. Full article

Quarry waste piles are heterogeneous engineered embankments that are susceptible to slope instability, yet early detection of pre-failure surface changes remains challenging due to complex surface conditions and measurement uncertainty. This study presents an integrated remote sensing-based framework for monitoring quarry waste pile instability by combining multi-temporal change detection with scale-dependent surface roughness analysis. The original contribution of the proposed framework lies in linking displacement-based change detection with multi-scale characterization of surface roughness, enabling both observed surface movement and topographical conditions associated with developing instability to be evaluated within a unified monitoring approach. Multi-epoch Unmanned Aerial Vehicle (UAV)-mounted Light Detection and Ranging (LiDAR) and photogrammetric point clouds were acquired before and after documented failure events at an active quarry site at active quarry sites located northeast of Montreal, Quebec, Canada. The regional climatic conditions, characterized by seasonal freeze–thaw cycles, rapid snowmelt, and periods of heavy rainfall, can promote water infiltration and elevated pore-water pressures, thereby increasing the susceptibility of these heterogeneous waste piles to slope instability. A standardized workflow was implemented, including precision alignment using a Recursive Iterative Closest Point (R-ICP) registration strategy, vegetation filtering with a multiscale CANUPO classifier, and uncertainty quantification through a Level of Detection (LoD) analysis. The resulting LoD thresholds were 10–15 cm for LiDAR-to-LiDAR comparisons and 34–36 cm for mixed-sensor datasets. Multi-scale roughness analysis revealed that zones which later experienced instability exhibited consistently higher and more heterogeneous roughness than adjacent stable areas within a well-defined linear scale range. A roughness-based A/D indicator enabled objective delineation of hazardous zones prior to failure. Post-failure monitoring showed surface smoothing following major displacement, followed by renewed roughness increases associated with secondary movements. These results demonstrate that scale-dependent roughness provides complementary information to displacement-based change detection, enabling potentially unstable areas to be identified and prioritized before substantial displacement becomes evident. The integrated framework can assist quarry managers in targeting field inspections and monitoring efforts toward higher-risk areas and support earlier preventive actions to reduce slope-failure risk. Full article