Search Results (1,213)

Recent advances in psychedelic research have renewed interest in their therapeutic potential for psychiatric disorders characterized by cognitive and behavioral rigidity. This review examines the rationale for using serotonergic psychedelics—particularly 5-HT₂A receptor agonists such as psilocybin—in the treatment of eating disorders (EDs), including anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED). The paper contextualizes these interventions within the broader serotonin hypothesis of EDs, emphasizing serotonergic dysregulation and impaired cognitive flexibility as central features of these conditions. Drawing from animal models, human neuroimaging studies, and emerging clinical trials, the authors outline how psychedelics may promote neuroplasticity and psychological insight through modulation of 5-HT₂A signaling. Preliminary evidence from open-label studies suggests psilocybin may improve ED symptoms and quality of life, though findings are early and methodologically limited. The paper also reviews data on ayahuasca, MDMA, and non-psychedelic serotonergic agents, highlighting both the promise and complexity of psychedelic-assisted therapy in EDs. The authors conclude that while further controlled trials are needed to clarify efficacy, safety, and optimal treatment parameters, psychedelics offer a novel, mechanistically distinct avenue for addressing entrenched ED psychopathology. Full article

Osteoporosis compromises bone quality and impairs implant osseointegration. Since an adequate bone bed is essential for implant stability and success, this study evaluated the effects of implant surface functionalization with zoledronic acid (ZOL), alone or combined with ruterpy (TERPY), on peri-implant bone healing in healthy (SHAM) and osteoporotic (OVX) rats. ZOL has antiresorptive properties, while TERPY exhibits osteoinductive potential. The hypothesis was that ZOL + TERPY would act synergistically by inhibiting bone resorption and promoting new bone formation. Sixty-six female Wistar rats (3 months old) were divided into six groups (n = 11) according to systemic condition (SHAM or OVX) and implant type: conventional (CONV), ZOL, or ZOL + TERPY. Surgeries (sham or bilateral ovariectomy) were performed on day 0, and implants were placed in the tibial metaphysis on day 90. Fluorochromes were administered on days 104 (calcein) and 114 (alizarin), and euthanasia was performed on day 118. Samples were analyzed histologically via confocal microscopy and micro-computed tomography (Micro-CT). The ZOL + TERPY groups demonstrated significantly accelerated peri-implant bone repair, showing greater bone formation and organization; improved BV/TV, Tb.N, and I.S.; and reduced Tb.Sp and Po.Tot compared to CONV and ZOL-alone groups. In conclusion, ZOL + TERPY enhances and speeds bone healing, even under osteoporotic conditions. Full article

We have developed a generalisable machine learning framework for reservoir quality prediction in deeply buried clastic systems. Applied to the Lower Jurassic deltaic sandstones of the Tilje Formation (Halten Terrace, North Sea), the approach integrates sedimentological facies modelling with mineralogical and petrophysical prediction in a single workflow. Using supervised Extreme Gradient Boosting (XGBoost) models, we classify reservoir facies, predict permeability directly from standard wireline log parameters and estimate the abundance of porosity-preserving grain coating chlorite (gamma ray, neutron porosity, caliper, photoelectric effect, bulk density, compressional and shear sonic, and deep resistivity). Model development and evaluation employed stratified K-fold cross-validation to preserve facies proportions and mineralogical variability across folds, supporting robust performance assessment and testing generalisability across a geologically heterogeneous dataset. Core description, point count petrography, and core plug analyses were used for ground truthing. The models distinguish chlorite-associated facies with up to 80% accuracy and estimate permeability with a mean absolute error of 0.782 log(mD), improving substantially on conventional regression-based approaches. The models also enable prediction, for the first time using wireline logs, grain-coating chlorite abundance with a mean absolute error of 1.79% (range 0–16%). The framework takes advantage of diagnostic petrophysical responses associated with chlorite and high porosity, yielding geologically consistent and interpretable results. It addresses persistent challenges in characterising thinly bedded, heterogeneous intervals beyond the resolution of traditional methods and is transferable to other clastic reservoirs, including those considered for carbon storage and geothermal applications. The workflow supports cost-effective, high-confidence subsurface characterisation and contributes a flexible methodology for future work at the interface of geoscience and machine learning. Full article

Laser powder bed fusion (L-PBF) is a unique technology that enables manufacturing geometrically complex metal alloys, including Ti-6Al-4V parts. The microstructure of Ti-6Al-4V is determined by its localized thermal history, which is affected by not only the L-PBF process but also the geometry of the part. Understanding the microstructure at specific locations in complex geometries is of great importance in predicting the mechanical performance of Ti-6Al-4V parts. This work investigates the effects of geometric features on the local microstructure. Three geometries, namely, holes, overhangs, and penholders, were designed and used for this study. Three different laser powers, namely 150 W, 250 W, and 350 W, were set to print those geometries. The use of a lower laser power results in improved print quality. While the martensite phase dominates the bulk of the L-PBF Ti-6Al-4V parts, a fine α+β lamellar structure can form at down-skin regions of printed horizontal holes and overhangs. Moreover, the direction of the columnar prime β grain can shift due to directional heat dissipation. The local microstructural evolution after heat treatment is investigated as well. Full article

In binder jetting additive manufacturing (BJAM), parts are fabricated layer by layer by depositing a liquid binder on selected regions of the powder bed. Powder particles in the hopper of the printer are dispensed onto the powder bed to form a layer of powder. Powder dispensing rate affects material usage and print quality. Too high dispensing rates can cause excessive powder dispensing, increasing powder waste, while too low dispensing rates may result in incomplete layer formation, leading to reduced density of printed parts. The present study investigates how ultrasonic vibration intensity and initial powder amount in the hopper affect powder dispensing rate in BJAM when using a bimodal powder. A set of experiments with full factorial design were conducted using three levels of ultrasonic vibration intensity (50%, 75%, and 100%) and three levels of initial powder amount (600 g, 1000 g, and 1400 g) in the hopper. The results show that both ultrasonic vibration intensity and initial powder amount, as well as their interaction, significantly influence powder dispensing rate. Powder dispensing rate was higher when ultrasonic vibration intensity was higher or initial powder amount was smaller. Increasing initial powder amount from 600 to 1400 g, resulted in a much bigger decrease in powder dispensing rate when ultrasonic vibration intensity was 50% than when ultrasonic vibration intensity was 100%. Full article

This research contributes to advance the sustainable production of biofuels and provides insights into the energy and exergy assessment of bio-oil, which is essential for developing environmentally friendly energy production solutions. Energy and exergy analyses were performed to evaluate the pyrolysis of beech wood biomass at 500 °C in an Auger reactor. To improve the quality of the obtained bio-oil, its catalytic deoxygenation was performed within an in-line fluidized catalytic bed reactor using a catalyst based on HZSM5 zeolite modified with 5 wt.% Iron (5%FeHZSM-5). A thermodynamic analysis of the catalytic and non-catalytic pyrolysis system was carried out, as well as a comparative study of the calculation methods for the energy and exergy evaluation for bio-oil. The required heat for pyrolysis was found to be 1.2 MJ/kg_biomass in the case of non-catalytic treatment and 3.46 MJ/kg_biomass in the presence of the zeolite-based catalyst. The exergy efficiency in the Auger reactor was 90.3%. Using the catalytic system coupled to the Auger reactor, this efficiency increased to 91.6%, leading to less energy degradation. Calculating the total energy and total exergy of the bio-oil using two different methods showed a difference of 6%. In the first method, only the energy contributions of the model compounds, corresponding to the major compounds of each chemical family of bio-oil, were considered. In contrast, in the second method, all molecules identified in the bio-oil were considered for the calculation. The second method proved to be more suitable for thermodynamic analysis. The novelties of this work concern the thermodynamic analysis of a coupled system of an Auger biomass pyrolysis reactor and a fluidized bed catalytic deoxygenation reactor on the one hand, and the use of all the molecules identified in the oily phase for the evaluation of energy and exergy on the other hand. Full article

Accurate melt pool geometry prediction is essential for ensuring quality and reliability in Laser Powder Bed Fusion (L-PBF). However, small experimental datasets and limited physical interpretability often restrict the effectiveness of traditional machine learning (ML) models. This study proposes a hybrid framework that integrates an explicit thermal model with ML algorithms to improve prediction under sparse data conditions. The explicit model—calibrated for variable penetration depth and absorptivity—generates synthetic melt pool data, augmenting 36 experimental samples across conduction, transition, and keyhole regimes for 316 L stainless steel. Three ML methods—Multilayer Perceptron (MLP), Random Forest, and XGBoost—are trained using fivefold cross-validation. The hybrid approach significantly improves prediction accuracy, especially in unstable transition regions (D/W ≈ 0.5–1.2), where morphological fluctuations hinder experimental sampling. The best-performing model (MLP) achieves R² > 0.98, with notable reductions in MAE and RMSE. The results highlight the benefit of incorporating physically consistent, nonlinearly distributed synthetic data to enhance generalization and robustness. This physics-augmented learning strategy not only demonstrates scientific novelty by integrating mechanistic modeling into data-driven learning, but also provides a scalable solution for intelligent process optimization, in situ monitoring, and digital twin development in metal additive manufacturing. Full article

Due to the high potential of shot peening to improve the surface quality of additively manufactured components, in this work, the influence on surface morphology and, thus, the surface topography and selected properties of the surface and subsurface regions of additively manufactured parts is analysed. For this, cubic specimens made of stainless steel 316L and AlSi10Mg were manufactured via powder bed fusion laser beam metal (PBF-LB/M), and subsequently, their “as-built” surfaces were shot peened. Shot peening was conducted with stainless steel or ceramic beads using pressures of 3 and 5 bar. The resulting morphologies were analysed regarding topography, microstructure and mechanical properties (hardness and cyclic deformation behaviour) in the subsurface region and the residual stresses. The results demonstrate a strong plastic deformation due to shot peening, resulting in a decreased surface roughness as well as an increased hardness and compressive residual stresses near the surface. These effects were generally more pronounced after using higher peening pressure and/or ceramic beads. Note that two sets of PBF-LB/M parameters were used to produce the AlSi10Mg specimens. The investigation of these specimens reveals an interrelation between the parameters used in shot peening and PBF-LB/M on the resulting surface morphology. Full article

To capture the complex metallic spatter and melt pool behavior during the rapid interaction between the laser and metal material, high-speed cameras are applied to record the laser powder bed fusion process and generate a large volume of image data. In this study, four deep learning algorithms are applied: YOLOv5, Fast R-CNN, RetinaNet, and EfficientDet. They are trained by the recorded videos to learn and extract information on spatter and melt pool behavior during the laser powder bed fusion process. The well-trained models achieved high accuracy and low loss, demonstrating strong capability in accurately detecting and tracking spatter and melt pool dynamics. A stability index is proposed and calculated based on the melt pool length change rate. Greater index value reflects a more stable melt pool. We found that more spatters were detected for the unstable melt pool, while fewer spatters were found for the stable melt pool. The spatter’s size can affect its initial ejection speed, and large spatters are ejected slowly while small spatters are ejected rapidly. In addition, more than 58% of detected spatters have their initial ejection angle in the range of 60–120$°$. These findings provide a better understanding of spatter and melt pool dynamics and behavior, uncover the influence of melt pool stability on spatter formation, and demonstrate the correlation between the spatter size and its initial ejection speed. This work will contribute to the extraction of important information from high-speed recorded videos for additive manufacturing to reduce waste, lower cost, enhance part quality, and increase process reliability. Full article

This study explores the efficacy of magnetic abrasive finishing (MAF) with planetary kinematics for post-processing Inconel 939 components fabricated by laser powder bed fusion (LPBF). Given the critical limitations in surface quality of LPBF-produced parts—especially in hard-to-machine superalloys like Inconel 939—there is a pressing need for advanced, adaptable finishing techniques that can operate effectively on complex geometries. This research focuses on optimizing the process parameters—eccentricity, rotational speed, and machining time—to enhance surface integrity following preliminary vibratory machining. Custom-designed samples underwent sequential machining, including heat treatment and 4 h vibratory machining, before MAF was applied under controlled conditions using ferromagnetic Fe-Si abrasives. Surface roughness measurements demonstrated a significant reduction, achieving Ra values from 1.21 µm to below 0.8 µm in optimal conditions, representing more than a fivefold improvement compared to the as-printed state (5.6 µm). Scanning Electron Microscopy (SEM) revealed progressive surface refinement, with MAF effectively removing adhered particles left by prior processing. Statistical analysis confirmed the dominant influence of eccentricity on the surface profile parameters, particularly Rz. The findings validate the viability of MAF as a precise, controllable, and complementary finishing method for LPBF-manufactured Inconel 939 components, especially for geometrically complex or hard-to-reach surfaces. Full article

Background and Objectives: Hospital mortality rates have repeatedly been used as important indicators of the quality of care provided and as a good monitoring and evaluation tool. Studies on hospital mortality in Saudi Arabia are scant, with most of the available literature focusing on the COVID-19 era. In this study, the patterns and trends in inpatient mortality at King Fahad Central Hospital in southwest Saudi Arabia from 2018 to 2022 were analyzed. Mortality characteristics, including age-specific mortality rates and associated factors, were also investigated. Materials and Methods: This was a retrospective study analyzing hospital mortality data in King Fahad Central Hospital (KFCH) from 2018 to 2022 using the largest hospital discharge database in the Jazan region. The mortality rates were calculated, and 95% confidence intervals (CIs) were reported. The analysis also documented some associations using logistic regression models. Results: Of the 62,534 patients admitted, 36,971 (59.1%) were females, and 25,543 (40.9%) were males. The mean age (standard deviation) was 24.6 (22.8) years. The overall hospital mortality was 4.8% [95% CI: 4.6–5.0] and was significantly higher among males [7.0%, 95% CI: 6.7–7.3] than females [3.2% 95% CI: 3.1–3.4] (p < 0.05). Mortality was significantly higher in the population aged 60 years and above [17.25%, 95% CI: 16.3–18.2] (p < 0.001). During the five-year period analyzed, mortality was low in 2018 (3.3%), with remarkably high rates during the COVID-19 period of 2020 and 2021 (5.6% and 6.0%, respectively). The disease groups with the highest prevalence of mortality include certain conditions originating in the perinatal period. In the logistic regression model, the male sex [odds ratio OR = 2.3, 95% CI = 2.01–2.43) was associated with an increased mortality risk. Compared to intensive care beds, general bed departments are associated with a 98% lower risk of mortality [OR = 0.015, 95% CI = 0.014–0.017]. Conclusions: This analysis of hospital data statistics revealed a relatively low hospital mortality rate in Jazan. However, the high mortality rates among male patients require further analysis and investigation. Customized interventions targeting high-mortality diseases are recommended. Full article

Circulating fluidized bed fly ash (CFBFA) stockpiles release alkaline dust, high-pH leachate, and secondary CO₂/SO₂—an environmental burden that exceeds 240 Mt yr⁻¹ in China alone. Yet, barely 25% is recycled, because the high f-CaO/SO₃ contents destabilize conventional cementitious products. Here, we presents a pressurized flue gas heat curing (FHC) route to bridge this scientific deficit, converting up to 85 wt% CFBFA into structural lightweight gravel. The gypsum dosage was optimized, and a 1:16 (gypsum/CFBFA) ratio delivered the best compromise between early ettringite nucleation and CO₂-uptake capacity, yielding the highest overall quality. The optimal mix reaches 9.13 MPa 28-day crushing strength, 4.27% in situ CO₂ uptake, 1.75 g cm⁻³ bulk density, and 3.59% water absorption. Multi-technique analyses (SEM, XRD, FTIR, TG-DTG, and MIP) show that FHC rapidly consumes expansive phases, suppresses undesirable granular-ettringite formation, and produces a dense calcite/needle-AFt skeleton. The FHC-treated CFBFA composite gravel demonstrates 30.43% higher crushing strength than JTG/TF20-2015 standards, accompanied by a water absorption rate 28.2% lower than recent studies. Its superior strength and durability highlight its potential as a low-carbon lightweight aggregate for structural engineering. A life-cycle inventory gives a cradle-to-gate energy demand of 1128 MJ t⁻¹ and a process GWP of 226 kg CO₂-eq t⁻¹. Consequently, higher point-source emissions paired with immediate mineral sequestration translate into a low overall climate footprint and eliminate the need for CFBFA landfilling. Full article

In this work, a model-based approach to inferentially obtaining information about the 3D fractal dimension of agglomerates produced in spray fluidized beds is presented. The method utilizes high-detail but scarce offline information from X-ray microcomputed tomography for establishing and training an inferential relationship with online information that is easy and fast to obtain. The online measurement information is the geometric roundness of the single agglomerate. To investigate the interpolation capability of the inferential approach, three different strategies are evaluated: correlation with individual process conditions; correlation with parameters adjusted to process parameters; and correlation with respect to a range of process conditions. It is shown that the approach incorporating process conditions provides sufficient accuracy over a wide range of conditions. The inferential evaluation of single agglomerate 3D fractal dimension is achieved in 5 ms on average. This enables the measurement of the distribution of 3D fractal dimension in an online setting for product quality monitoring and control. Several examples illustrate the capabilities of the approach, as well as current limitations. Full article

Background/Objectives: In Japan, task shifting reduces the working hours of pediatricians, who face excessive workloads. The status of task shifting under the Ministry of Health, Labor, and Welfare’s reforms remains unclear. This study aimed to evaluate the current status and barriers of task shifting in pediatric care in Japan. Methods: A questionnaire survey was conducted among pediatricians working in hospitals in Japan. The results were compared with those from 2020. Results: Questionnaires were sent to 835 hospitals, and valid responses were received from 815 pediatricians in 316 hospitals (response rate: 37.8%). The largest group (31.0%) was 40–49 years, and 34.4% of the participants were women. Among the items surveyed, most pediatricians indicated “shifted” in “Patient transfer (transporting between hospitals using an ambulance)” and “Intravenous injection of antibiotics.” Most physicians believed task shifting improved care quality; 10.3% felt it worsened. The most common estimate for daily working hour reduction due to task shifting was “1 to <2 h” (44.9%). Precisely 15.8% of pediatricians believed that task shifting had “not progressed at all,” with rural areas and non-university hospitals showing lower task-shifting implementation. National university hospitals had a higher likelihood of task shifting than public hospitals. No significant associations were observed for the total hospital bed count or the number of full-time pediatricians. Conclusions: Task shifting in pediatric care remains underdeveloped. While many pediatricians support the concept and report modest reductions in working hours, actual implementation remains limited. Future efforts must address systemic, institutional, and regulatory challenges to facilitate meaningful task redistribution and improve healthcare delivery. Full article

Multi-laser processing is increasingly adopted in laser powder bed fusion (L-PBF) to improve productivity and enable the fabrication of larger components, but its impact on part quality and performance remains a critical concern. This study investigates the microstructure, tensile properties, and fatigue performance of components fabricated by L-PBF using single- and multiple-laser configurations. Both strategies were evaluated under varying layer thicknesses and gas flow conditions with optimized process parameters. Microstructural analysis revealed defects such as lack-of-fusion, porosity and microcracks in multiple-laser builds with reduced gas flow. However, the density and microhardness results showed negligible differences between single and multiple-laser builds. Tensile testing indicated that single-laser builds exhibited superior strength and ductility, whereas multiple-laser builds demonstrated reduced performance due to localized defects such as lack-of-fusion and microcracks. Low-cycle fatigue testing results showed that optimized multiple-laser strategies could achieve performance comparable to that of single-laser builds while improving productivity. The results also revealed that the gas flow becomes more pronounced with multiple-laser processing, where more spatter is generated due to the interactions of the lasers in a small scan area, and that reduced gas flow leads to fatigue degradation due to increased defect density. The results from this study clearly highlight the importance of gas flow, laser overlap, border optimization, and defect mitigation strategies in producing multiple-laser produced components with mechanical properties and fatigue performance comparable to those of single-laser produced L-PBF components. Full article