Search Results (398)

Background: Lower phylogenetic species are known to rebuild cut-off caudal parts with regeneration of the central nervous system (CNS). In contrast, CNS regeneration in higher vertebrates is often attributed to immaturity, although this has never been conclusively demonstrated. The emergence of stem cells and their effective medical applications has intensified research into spinal cord regeneration. However, despite these advances, the impact of clinical trials involving spinal cord-injured (SCI) patients remains disappointingly low. Long-distance regeneration has yet to be proven. Methods: Our study involved a microsurgical dorsal myelotomy in fetal rats. The development of pioneering long primary afferent axons during early gestation was examined long after birth. Results: A single cut triggered the intrinsic ability of the dorsal root ganglion (DRG) neurons to reprogram. Susceptibility to hypoxia caused the axons to stop developing. However, the residual axonal outgrowth sheds light on the intriguing temporal and spatial events that reveal long-distance CNS regeneration. The altered phenotypes displayed axons of varying lengths and different features, which remained visible throughout life. The previously designed developmental blueprint was crucial for interpreting these enigmatic features. Conclusions: This research into immaturity enabled the exploration of the previously impenetrable domain of early life and the identification of a potential missing link in CNS regeneration research. Central axon regeneration appeared to occur much faster than is generally believed. The paradigm provides a challenging approach for exhaustive intrauterine reprogramming. When the results demonstrate pre-clinical effectiveness in CNS regeneration research, the transformational impact may ultimately lead to improved outcomes for patients with spinal cord injuries. Full article

Reliable estimation of extreme waves is essential for offshore wind turbine system design; however, site-specific conditions limit the application of one-size-fits-all statistical methods. We analyzed extreme wave conditions at potential offshore wind farm sites in South Korea using high-resolution hindcast data (1979–2022) based on the Weather Research and Forecasting (WRF) model. While previous studies have typically relied on a limited combination of distribution types and parameter estimation methods, this study systematically applied various Weibull distribution models and parameter estimation techniques to the environmental contour (EC) method. The results show that the optimal statistical approach varied by site according to the tail characteristics of the wave height distribution. The inverse second-order reliability method (I-SORM) provided the highest accuracy in regions with rapidly decaying tails, achieving root mean square error (RMSE) values of 0.21 in Shinan (using the three-parameter Weibull distribution with maximum likelihood estimation, MLE) and 0.34 in Chujado (with the method of moments, MOM). In contrast, the inverse first-order reliability method (I-FORM) yielded superior performance in areas where the tail decays more gradually, such as Yokjido (RMSE = 0.47 with MLE using the exponentiated Weibull distribution) and Ulsan (RMSE = 0.29, with MLE using the exponentiated Weibull distribution). These findings underscore the importance of selecting site-specific combinations of statistical models and estimation techniques based on wave distribution characteristics, thereby improving the accuracy and reliability of extreme design wave predictions. The proposed framework can significantly contribute to the establishment of reliable design criteria for offshore wind turbine systems by reflecting region-specific marine environmental conditions. Full article

In this paper, the first demonstration of a highly strained In_0.8Ga_0.2As/In_0.2Ga_0.8Sb type-II superlattice structure grown on InAs substrates by molecular beam epitaxy (MBE) for long-wavelength infrared detection was reported. Novel methodologies were developed to optimize the As and Sb flux growth conditions. The quality of the epitaxial layer was characterized using multiple analytical techniques, including differential interference contrast microscopy, atomic force microscopy, high-resolution X-ray diffraction, and high-resolution transmission electron microscopy. The high-quality superlattice structure, with a total thickness of 1.5 μm, exhibited exceptional surface morphology with a root-mean-square roughness of 0.141 nm over a 5 × 5 μm² area. Single-element devices with PIN architecture were fabricated and characterized. At 77 K, these devices demonstrated a 50% cutoff wavelength of approximately 12.1 μm. The long-wavelength infrared PIN devices exhibited promising performance metrics, including a dark current density of 7.96 × 10⁻² A/cm² at −50 mV bias and a high peak responsivity of 4.90 A/W under zero bias conditions, both measured at 77 K. Furthermore, the devices achieved a high peak quantum efficiency of 65% and a specific detectivity (D*) of 2.74 × 10¹⁰ cm·Hz^1/2/W at the peak responsivity wavelength of 10.7 µm. These results demonstrate the viability of this material system for long-wavelength infrared detection applications. Full article

The accurate measurement of the tree diameter is vital for forest inventories, urban tree quality assessments, the management of roadside and railway vegetation, and various other applications. It also plays a crucial role in evaluating tree growth dynamics, which are closely linked to tree health, structural stability, and vulnerability. Although a range of devices and methodologies are currently under investigation, the widespread adoption of laser scanners remains constrained by their high cost. This study therefore aimed to compare high-end laser scanners (Trimble TX8 and GeoSLAM ZEB Horizon) with cost-effective alternatives, represented by the Apple iPhone 14 Pro and the LA03 scanner developed by mapry Co., Ltd. (Tamba, Japan). It further sought to evaluate the feasibility of employing these more affordable devices, even for small-scale forest owners or managers. Given the growing availability of 3D-based forest inventory algorithms, a selection of such processing pipelines was used to assess the practical potential of the scanning devices. The tested low-cost device produced moderate results, achieving a tree detection rate of up to 78% and a relative root mean square error (rRMSE) of 19.7% in diameter at breast height (DBH) estimation. However, performance varied depending on the algorithms applied. In contrast, the high-end mobile laser scanning (MLS) and terrestrial laser scanning (TLS) systems outperformed the low-cost alternative across all metrics, with tree detection rates reaching up to 99% and DBH estimation rRMSEs as low as 5%. Nevertheless, the low-cost device may still be suitable for scanning small sample plots at a reduced cost and could potentially be deployed in larger quantities to support broader forest inventory initiatives. Full article

The acquisition of plant phenotypic traits is essential for selecting superior varieties, improving crop yield, and supporting precision agriculture and agricultural decision-making. Therefore, it plays a significant role in modern agriculture and plant science research. Traditional manual measurements of phenotypic traits are labor-intensive and inefficient. In contrast, combining 3D reconstruction technologies with autonomous vehicles enables more intuitive and efficient trait acquisition. This study proposes a 3D semantic reconstruction system based on an improved ORB-SLAM3 framework, which is mounted on an unmanned vehicle to acquire phenotypic traits in tomato cultivation scenarios. The vehicle is also equipped with the A * algorithm for autonomous navigation. To enhance the semantic representation of the point cloud map, we integrate the BiSeNetV2 network into the ORB-SLAM3 system as a semantic segmentation module. Furthermore, a two-stage filtering strategy is employed to remove outliers and improve the map accuracy, and OctoMap is adopted to store the point cloud data, significantly reducing the memory consumption. A spherical fitting method is applied to estimate the number of tomato fruits. The experimental results demonstrate that BiSeNetV2 achieves a mean intersection over union (mIoU) of 95.37% and a frame rate of 61.98 FPS on the tomato dataset, enabling real-time segmentation. The use of OctoMap reduces the memory consumption by an average of 96.70%. The relative errors when predicting the plant height, canopy width, and volume are 3.86%, 14.34%, and 27.14%, respectively, while the errors concerning the fruit count and fruit volume are 14.36% and 14.25%. Localization experiments on a field dataset show that the proposed system achieves a mean absolute trajectory error (mATE) of 0.16 m and a root mean square error (RMSE) of 0.21 m, indicating high localization accuracy. Therefore, the proposed system can accurately acquire the phenotypic traits of tomato plants, providing data support for precision agriculture and agricultural decision-making. Full article

Efficient adventitious root formation is crucial for Lavandula angustifolia Mill. propagation. This study evaluated the effects of continuous and short-duration pulse applications (1 min, 1 h, and 1 day) of the auxin dichlorprop (DCP) and its prodrug dichlorprop-2-ethylhexyl ester (DCPE) at varying concentrations on adventitious rooting and callus formation. DCPE generally proved more effective than DCP in promoting rooting, especially at lower concentrations, with continuous application of 0.1 µM DCPE yielding the highest number of adventitious roots. Notably, a brief 1 min pulse of 2.5 µM DCPE induced superior rooting, including high root number and weight, while minimizing callus formation compared to longer exposures. In contrast, 1 h pulse treatments showed a positive correlation between auxin concentration and root number but led to substantial callus development. These findings highlight DCPE’s potential as an efficient auxin source for lavender propagation, likely due to its rapid hydrolysis to active DCP within plant tissues, facilitating systemic distribution. The enhanced rooting achieved with short pulse treatments offers significant implications for optimizing commercial propagation for this economically important aromatic plant. Full article

The conflicting demands of urban trees and walkable surfaces result in significant financial burdens for municipal administrators who understand that urban residents want tree-lined walkable surfaces. This study investigates three methodologies for mitigating this tension: suspended grating systems, modular box systems, and structural soils. A Multi-Criteria Analysis (MCA) was conducted to evaluate their suitability in dense urban areas, employing criteria categorized into Environmental, Economical, and Other considerations. The comparison focused on critical aspects such as the impact on tree health (root growth, water availability), installation complexity, initial costs, and overall suitability for diverse urban contexts. The MCA indicates that, under the given weighting of criteria, suspended grating systems (especially those suited for existing trees) rank the highest, primarily due to their superior root protection and minimal disturbance to established root systems. In contrast, modular box systems and structural soils emerge as particularly strong contenders for new tree plantings. Structural soils may have application at sites with existing trees, but the costs of removing native soil are a consideration. Sensitivity analysis suggests that modular box systems may become the preferred option when greater emphasis is placed on stormwater management and new plantings, rather than on challenges for existing trees or underground infrastructure. Structural soils score well in cost-effectiveness and installation speed but require careful implementation to address their lower root protection performance and long-term maintenance concerns. Ultimately, the optimal solution depends on unique site-specific conditions and budgetary constraints, emphasizing the necessity of tailored approaches to balance urban infrastructure with tree health. Full article

Accurate measurement uncertainty quantification and its propagation are critical for dimensional compliance in precision manufacturing. This study presents a novel framework that examines the evolution of measurement error along the axial length of CNC-turned components, focusing on spatial and material-specific factors. A systematic experimental comparison was conducted between a manual Digital Vernier Caliper (DVC) and an automated Coordinate Measuring Machine (CMM) using five engineering materials: Aluminum Alloy 6061, Brass C26000, Bronze C51000, Carbon Steel 1020 Annealed, and Stainless Steel 304 Annealed. Dimensional measurements were taken from five consecutive machining zones to capture localized metrological behaviors. The results indicated that the CMM consistently achieved lower expanded uncertainty (as low as 0.00166 mm) and minimal propagated uncertainties (≤0.0038 mm), regardless of material hardness or cutting position. In contrast, the DVC demonstrated significantly higher uncertainty (up to 0.03333 mm) and propagated errors exceeding 0.035 mm, particularly in harder materials and unsupported zones affected by surface degradation and fixture variability. Root-sum-square (RSS) modeling confirmed that manual measurements are more prone to operator-induced error amplification. While the DVC sometimes recorded lower absolute errors, its substantial uncertainty margins hampered measurement reliability. To statistically validate these findings, a two-way ANOVA was performed, confirming that both the measurement system and machining zone significantly impacted uncertainty, as well as their interaction. These results emphasize the importance of material-informed and zone-sensitive metrology, highlighting the advantages of automated systems in sustaining measurement repeatability and dimensional stability in high-precision applications. Full article

This study evaluated the effects of different types and rates of locally produced organic residues on soil organic matter (SOM) and soil health in highly degraded loamy soils of olive orchards in arid southern Tunisia. Three residues were tested: poultry manure, raw date palm waste, and composted date palm waste mixed with manure. A randomised field trial was conducted over three years. Two years after application, soil samples were analysed for physical and chemical properties, basal respiration, nitrogen mineralisation, microbial biomass, enzyme activities (dehydrogenase, phosphomonoesterase, β-glucosidase, urease, arylsulphatase), and community-level physiological profiles. All residues increased SOM and available phosphorus (Pi), with dose-dependent effects sustained over time, though significant increases were only observed at the highest application rates. The most notable improvements occurred in soils amended with composted date palm waste. In contrast, biological and biochemical parameters showed little response, even after remoistening to stimulate microbial activity. This limited response was attributed to the absence of vegetation and, consequently, of root exudates and plant residues. This will be further investigated by assessing changes in the same biological and biochemical properties following the implementation of an intercropping system, which is expected to enhance both SOM content and microbial activity in these soils. Full article

Traditional farming practices are becoming increasingly inadequate to meet global food demand due to water scarcity, prolonged production cycles, climate variability, and declining arable land. In contrast, aeroponic, smart, soil-free farming technologies offer a more sustainable alternative by reducing land use and providing efficient water use, given that aeroponics intermittently delivers water in mist form rather than maintaining continuous root zone moisture. However, aeroponics faces critical challenges in irrigation management due to non-standardized structures and limited real-time control. A key limitation is the inability to dynamically respond to temperature (T), relative humidity (RH), light intensity (L_i), electrical conductivity (EC), pH, and photosynthesis rate (P_n), resulting in suboptimal crop yields and resource wastage. Despite growing interest, there remains a research gap in integrating internet of things (IoT) and machine learning technologies into aeroponic systems for adaptive control. IoT-enabled sensors provide real-time data on ambient conditions and plant health, while ML models can adaptively optimize misting intervals based on the fluctuations in P_n and environmental inputs. These technologies are particularly well suited to address the dynamic, data-intensive nature of aeroponic environments. This review purposes a novel, standardized IoT–ML framework to control irrigation by emphasizing IoT sensing and ML-based decision making in aeroponics. This integrated approach is essential for minimizing water loss, enhancing resource efficiency, and advancing the sustainability of controlled-environment agriculture. Full article

Environmental monitoring systems often operate continuously, measuring various parameters, including carbon dioxide levels (CO₂), relative humidity (RH), temperature (T), and other factors that affect environmental conditions. Such systems are often referred to as smart systems because they can autonomously monitor and respond to environmental conditions and can be integrated both indoors and outdoors to detect, for example, structural anomalies. However, these systems typically have high energy consumption, data overload, and large equipment sizes, which makes them difficult to install in constrained spaces. Therefore, three challenges remain unresolved: efficient energy use, accurate data measurement, and compact installation. To address these limitations, this study proposes a two-to-one threshold sampling approach, where the CO₂ measurement is activated when the specified T and $RH$ change thresholds are exceeded. This event-driven method avoids redundant data collection, minimizes power consumption, and is suitable for resource-constrained embedded systems. The proposed approach was implemented on a low-power, small-form and self-made multivariate sensor based on the PIC16LF19156 microcontroller. In contrast, a commercial monitoring system and sensor modules based on the Arduino Uno were used for comparison. As a result, by activating only key points in the T and $RH$ signals, the number of CO₂ measurements was significantly reduced without loss of essential signal characteristics. Signal reconstruction from the reduced points demonstrated high accuracy, with a mean absolute error (MAE) of 0.0089 and root mean squared error (RMSE) of 0.0117. Despite reducing the number of CO₂ measurements by approximately 41.9%, the essential characteristics of the signal were saved, highlighting the efficiency of the proposed approach. Despite its effectiveness in controlled conditions (in buildings, indoors), environmental factors such as the presence of people, ventilation systems, and room layout can significantly alter the dynamics of CO₂ concentrations, which may limit the implementation of this approach. Future studies will focus on the study of adaptive threshold mechanisms and context-dependent models that can adjust to changing conditions. This approach will expand the scope of application of the proposed two-to-one sampling technique in various practical situations. Full article

Introduction: Successful root canal therapy relies on thorough cleaning and disinfection to eliminate microorganisms and residual pulp tissue. Advanced irrigation activation techniques, including Sonic, Ultrasonic, and Diode Laser activation, have improved cleaning efficacy, bacterial reduction, smear layer removal, and irrigant hydrodynamics. On the other hand, these irrigation activation techniques may lead to a temperature rise that may risk the surrounding periodontal tissue. Thus, this study aimed to investigate the temperature rise during different irrigation activation techniques at various time intervals and evaluate the efficacy of these techniques in removing biofilm-mimicking hydrogel BMH of a simulated root canal system in 3D-printed tooth models. Methods: Ten extracted human mandibular premolars, prepared to size 40/0.04 taper, and a hundred 3D-printed resin premolars with simulated main (0.25 mm) and lateral canals (0.15 mm at 3, 7, 11 mm from apex) were used; 50 of them were filled with biofilm-mimicking hydrogel (BMH). Five irrigation activation techniques were evaluated: Diode Laser, Ultrasonic, Sonic, XP-Finisher, and Control (n = 10). Temperature rises were measured on the extracted premolars after 30 and 60 s of activation using a thermographic camera in a controlled environment (23 ± 2 °C). Irrigant penetration, with and without BMH, was assessed in 3D-printed premolars using a 2.5% sodium hypochlorite-contrast medium mixture, visualized with a CMOS radiographic sensor. Penetration was scored (main canal: 3 points; lateral canals: 0–2 points) and analyzed with non-parametric tests. Results: Diode Laser activation technique resulted in the highest temperature rise on the external root surface, followed by the Ultrasonic, with no statistically significant difference observed among the remaining groups. In terms of efficacy, Ultrasonic and Sonic activation achieved significantly greater irrigant penetration in samples without BMH, and greater BMH removal in samples with BMH, compared to Diode Laser, XP-Finisher, and Control groups. Conclusions: In this in vitro study, Diode Laser caused the highest temperature rise, followed by Ultrasonic, with significant increases from 30 to 60 s. Temperature rise did not significantly affect penetration or BMH removal. Ultrasonic and Sonic irrigation techniques achieved the highest depth of penetration (without BMH) and biofilm-mimicking Hydrogel removal (with BMH) compared to Diode Laser, XP-Finisher, and Control. Full article

This study proposes an automatic gear shift classification algorithm in M1 category vehicles using data acquired through the onboard diagnostic system (OBD II) and GPS. The proposed approach is based on the analysis of identification parameters (PIDs), such as manifold absolute pressure (MAP), revolutions per minute (RPM), vehicle speed (VSS), torque, power, stall times, and longitudinal dynamics, to determine the efficiency and behavior of the vehicle in each of its gears. In addition, the unsupervised K-means algorithm was implemented to analyze vehicle gear changes, identify driving patterns, and segment the data into meaningful groups. Machine learning techniques, including K-Nearest Neighbors (KNN), decision trees, logistic regression, and Support Vector Machines (SVMs), were employed to classify gear shifts accurately. After a thorough evaluation, the KNN (Fine KNN) model proved to be the most effective, achieving an accuracy of 99.7%, an error rate of 0.3%, a precision of 99.8%, a recall of 99.7%, and an F1-score of 99.8%, outperforming other models in terms of accuracy, robustness, and balance between metrics. A multiple linear regression model was developed to estimate instantaneous fuel consumption (in L/100 km) using the gear predicted by the KNN algorithm and other relevant variables. The model, built on over 66,000 valid observations, achieved an R² of 0.897 and a root mean square error (RMSE) of 2.06, indicating a strong fit. Results showed that higher gears (3, 4, and 5) are associated with lower fuel consumption. In contrast, a neutral gear presented the highest levels of consumption and variability, especially during prolonged idling periods in heavy traffic conditions. In future work, we propose integrating this algorithm into driver assistance systems (ADAS) and exploring its applicability in autonomous vehicles to enhance real-time decision making. Such integration could optimize gear shift timing based on dynamic factors like road conditions, traffic density, and driver behavior, ultimately contributing to improved fuel efficiency and overall vehicle performance. Full article

Understanding rockfall behavior is essential for accurately predicting hazards in both natural and engineered environments, yet prior research has predominantly focused on spherical rocks or single-impact scenarios, leaving critical gaps in highlighting the dynamics of non-spherical rocks and multiple impacts. This study addresses these shortcomings by investigating the influence of rock shape and release angle on motion, energy dissipation, and impact behavior. To achieve this, an innovative approach rooted in the Video-Assisted Rockfall Kinematics Analysis (VARKA) procedure was introduced, integrating a custom-designed apparatus, controlled experimental setups, and sophisticated data analysis techniques. Experiments utilizing a pendulum-based release system analyzed various scenarios involving different rock shapes and release angles. These tests provided comprehensive motion data for multiple impacts, including trajectories, translational and angular velocities, and the coefficient of restitution (COR). Results revealed that non-spherical rocks exhibited significantly more erratic trajectories and greater variability in COR values compared to spherical rocks. The experiments demonstrated that ellipsoidal and octahedral shapes had substantially higher variability in runout distances than spherical rocks. COR values for ellipsoidal shapes spanned a wide range, in contrast to the tighter clustering observed for spherical rocks. These findings highlight the pivotal influence of rock shape on lateral dispersion and energy dissipation, reinforcing the need for data-driven approaches to enhance and complement traditional physics-based predictive models. Full article

Conventional micropropagation of cannabis struggles with excessive callus hyperhydration, slow growth, low rooting efficiency, and high contamination risk, all of which greatly restrict its feasibility for large-scale propagation. In contrast, photoautotrophic micropropagation (PAM) has emerged as an efficient and cost-effective propagation strategy that can significantly enhance plantlet growth and improve seedling quality by optimizing the LED lighting environment. This study investigated the effects of four light intensities (50, 100, 150, and 200 µmol m⁻² s⁻¹) and three photoperiods (16, 20, and 24 h d⁻¹) on the growth and rooting of two medicinal cannabis cultivars (the short-day cultivar ‘Charlotte’ and the day-neutral cultivar ‘Auto Charlotte’). Cluster analysis revealed that plantlets grown under the photoperiod of 20 h d⁻¹ and light intensity of 100–150 µmol m⁻² s⁻¹ exhibited optimal growth performance in terms of plant height, root length, leaf number, leaf area, biomass, and root activity. Moreover, increasing the light intensity from 50 to 100–150 µmol m⁻² s⁻¹ significantly enhanced net CO₂ exchange rates by 41.5% and 204.9% for Charlotte and Auto Charlotte, respectively, along with corresponding increases in dry matter accumulation of 44.3% and 27.9%. However, the plantlets exhibited photooxidative damage under continuous lighting and light intensity of 200 µmol m⁻² s⁻¹, as evidenced by reduced photosynthetic pigment content and suppressed antioxidant enzyme activity. Therefore, PAM of medicinal cannabis is recommended under the LED lighting environment with light intensity of 100–150 µmol m⁻² s⁻¹ and photoperiod of 20 h d⁻¹ to achieve optimal growth and rooting. These findings provide essential technical support for the large-scale propagation of vigorous, disease-free female plantlets with well-developed root systems and high genetic uniformity, thereby meeting the stringent quality standards for planting materials in the commercial cultivation of cannabis for medical and pharmaceutical use. Full article