Impact of coking plant to heavy metal characteristics in groundwater of surrounding areas: Spatial distribution, source apportionment and risk assessments.

Coking industry is a potential source of heavy metals (HMs) pollution. However, its impacts to the groundwater of surrounding residential areas have not been well understood. This study investigated the pollution characteristics and health risks of HMs in groundwater nearby a typical coking plant. Nine HMs including Fe, Zn, Mo, As, Cu, Ni, Cr, Pb and Cd were analyzed. The average concentration of total HMs was higher in the nearby area (244.27 µg/L) than that of remote area away the coking plant (89.15 µg/L). The spatial distribution of pollution indices including heavy metal pollution index (HPI), Nemerow index (NI) and contamination degree (CD), all demonstrated higher values at the nearby residential areas, suggesting coking activity could significantly impact the HMs distribution characteristics. Four sources of HMs were identified by Positive Matrix Factorization (PMF) model, which indicated coal washing and coking emission were the dominant sources, accounted for 40.4%, and 31.0%, respectively. Oral ingestion was found to be the dominant exposure pathway with higher exposure dose to children than adults. Hazard quotient (HQ) values were below 1.0, suggesting negligible non-carcinogenic health risks, while potential carcinogenic risks were from Pb and Ni with cancer risk (CR) values > 10-6. Monte Carlo simulation matched well with the calculated results with HMs concentrations to be the most sensitive parameters. This study provides insights into understanding how the industrial coking activities can impact the HMs pollution characteristics in groundwater, thus facilitating the implement of HMs regulation in coking industries.

Method to Study the Survival Abilities of Foodborne Bacterial Pathogens Under Food Processing Conditions.

Properly using controllable atmospheric containers can facilitate investigations of the survival abilities and physiological states of key and emerging-foodborne pathogens under recreated applicable food processing environmental conditions. Notably, saturated salt solutions can efficiently control relative humidity in airtight containers. This chapter describes a practical experimental setup, with necessary prerequisites for exposing foodborne pathogens to simulated and relevant food processing environmental conditions. Subsequent analyses for studying cell physiology will also be suggested.

Exploring the therapeutic targets of stevioside in management of type 2 diabetes by network pharmacology and in-silico approach.

AIMS: The main objective of the current study is to investigate the pathways and therapeutic targets linked to stevioside in the management of T2D using computational approaches. METHODS: We collected RNA-seq datasets from NCBI, then employed GREIN to retrieve differentially expressed genes (DEGs). Computer-assisted techniques DAVID, STRING and NetworkAnalyst were used to explore common significant pathways and therapeutic targets associated with T2D and stevioside. Molecular docking and dynamics simulations were conducted to validate the interaction between stevioside and therapeutic targets. RESULTS: Gene ontology and KEGG analysis revealed that prostaglandin synthesis, IL-17 signaling, inflammatory response, and interleukin signaling were potential pathways targeted by stevioside in T2D. Protein-protein interactions (PPI) analysis identified six common hub proteins (PPARG, PTGS2, CXCL8, CCL2, PTPRC, and EDN1). Molecular docking results showed best binding of stevioside to PPARG (-8 kcal/mol) and PTGS2 (-10.1 kcal/mol). Finally, 100 ns molecular dynamics demonstrated that the binding stability between stevioside and target protein (PPARG and PTGS2) falls within the acceptable range. CONCLUSIONS: This study reveals that stevioside exhibits significant potential in controlling T2D by targeting key pathways and stably binding to PPARG and PTGS2. Further research is necessary to confirm and expand upon these significant computational results.

Pollution area identification, receptor model-oriented sources and probabilistic health hazards to prioritize control measures for heavy metal management in soil.

Identifying the primary source of heavy metals (HMs) pollution and the key pollutants is crucial for safeguarding eco-health and managing risks in industrial vicinity. For this purpose, this investigation was carried out to investigate the pollution area identification with soil static environmental capacity (QI), receptor model-oriented critical sources, and Monte Carlo simulation (MCS) based probabilistic environmental and human health hazards associated with HMs in agricultural soils of Narayanganj, Bangladesh. The average concentration of Cr, Ni, Cu, Cd, Pb, Co, Zn, and Mn were 98.67, 63.41, 37.39, 1.28, 23.93, 14.48, 125.08, and 467.45 mg/kg, respectively. The geoaccumulation index identified Cd as the dominant metal, indicating heavy to extreme contamination in soils. The QI revealed that over 99% of the areas were polluted for Ni and Cd with less uncertain regions whereas Cr showed a significant portion of areas with uncertain pollution status. The positive matrix factorization (PMF) model identified three major sources: agricultural (29%), vehicular emissions (25%), and industrial (46%). The probabilistic assessment of health hazards indicated that both carcinogenic and non-carcinogenic risks for adult male, adult female, and children were deemed unacceptable. Moreover, children faced a higher health hazard compared to adults. For adult male, adult female, and children, industrial operations contributed 48.4%, 42.7%, and 71.2% of the carcinogenic risks, respectively and these risks were associated with Ni and Cr as the main pollutants of concern. The study emphasizes valuable scientific insights for environmental managers to tackle soil pollution from HMs by effectively managing anthropogenic sources. It could aid in devising strategies for environmental remediation engineering and refining industry standards.

Natural background level, source apportionment and health risk assessment of potentially toxic elements in multi-layer aquifers of arid area in Northwest China.

Groundwater contaminated by potentially toxic elements has become an increasing global concern for human health. Therefore, it is crucial to identify the sources and health risks of potentially toxic elements, especially in arid areas. Despite the necessity, there is a notable research gap concerning the sources and risks of these elements within multi-layer aquifers in such regions. To address this gap, 54 phreatic and 24 confined groundwater samples were collected from an arid area in Northwest China. This study aimed to trace the sources and evaluate the human health risks of potentially toxic elements by natural background level (NBL), positive matrix factorization (PMF) model, and health risk model. Findings revealed exceeding levels of potentially toxic elements existed in phreatic and confined aquifers. Source apportionment and NBL results indicated that mineral dissolution, evaporation, redox reactions, and human activities were the main factors for elevated concentrations of potentially toxic elements. High Fe and Mn concentrations were attributed to reduction environments, while F accumulation resulted from slow runoff, and irrigation from the Yellow River. Due to high F levels, more than one-third of groundwater samples (phreatic: 33.14 %, confined: 56.22 %) posed non-carcinogenic health risks to population groups. Adults displayed higher carcinogenic risks (phreatic: 19.47 %, confined: 34.16 %) than infants (phreatic: 0 %, confined: 0 %) and children (phreatic: 1.26 %, confined: 7.97 %) owing to the toxic elements of Cr. The confined aquifer presented greater health risks than the phreatic aquifer. Consequently, controlling the levels of F and Cr in multi-layered aquifers is key to reducing health risks. These findings provide valuable insights into protecting groundwater from contamination by potentially toxic elements in multi-layered aquifers worldwide.

Optimizing number of Raman spectra using an artificial neural network guided Monte Carlo simulation approach to analyze human cortical bone.

This study presents a novel methodology for optimizing the number of Raman spectra required per sample for human bone compositional analysis. The methodology integrates Artificial Neural Network (ANN) and Monte Carlo Simulation (MCS). We demonstrate the robustness of ANN in enabling prediction of Raman spectroscopy-based bone quality properties even with limited spectral inputs. The ANN algorithms tailored to individual sex and age groups, which enhance the specificity and accuracy of predictions in bone quality properties. In addition, ANN guided MCS systematically explores the variability and uncertainty inherent in different sample sizes and spectral datasets, leading to the identification of an optimal number of spectra per sample for characterizing human bone tissues. The findings suggest that as low as 2 spectra are sufficient for biochemical analysis of bone, with R2 values between real and predicted values of v1/PO4/Amide I and ∼I1670/I1640 ratios, ranging from 0.60 to 0.89. Our results also suggest that up to 8 spectra could be optimal when balancing other factors. This optimized approach streamlines experimental workflows, reduces data and acquisition costs. Additionally, our study highlights the potential for advancing Raman spectroscopy in bone research through the innovative integration of ANN-guided probabilistic modeling techniques. This research could significantly contribute to the broader landscape of bone quality analyses by establishing a precedent for optimizing the number of Raman spectra with sophisticated computational tools. It also sets a novel platform for future optimization studies in Raman spectroscopy applications in biomedical field.

Surface roughness associated with bacterial adhesion on dental resin-based materials.

This study investigates the surface quality and bacterial adhesion properties of various dental materials, including indirect composites, veneering composites, direct composites, polyether ether ketone (PEEK), and two millable polymethyl methacrylate (PMMA). Material specimens were processed following manufacturer instructions, initially evaluated for surface roughness and Streptococcus sanguinis (S. sanguinis) adhesion. Subsequently, toothbrushing simulation was employed to simulate aging, and changes in material surfaces were assessed via roughness measurements and bacterial adhesion testing. Prior to simulated aging, direct and indirect composites exhibited the lowest roughness values. However, after the simulated toothbrushing, veneering composites displayed the highest roughness levels. Both PMMA materials demonstrated the highest S. sanguinis adhesion levels, both before and after artificial aging. Interestingly, the indirect composite material showed a reduction in bacterial adhesion following toothbrushing simulation. Surprisingly, this study did not reveal a clear correlation between roughness and bacterial adhesion.

Optimal parameters for the efficient microwave ablation of liver tumor from the 3D-IRCADb-01 database.

Purpose: Microwave ablation is a minimally invasive thermal modality for cancer treatment with high survival and low recurrence rates. Despite the unquestionable benefits of microwave ablation, the interaction between the medical instruments and the tissue may cause damage to the healthy tissue around the tumor. Such damages can be removed by clarifying the conditions for their development. In addition to clinical methods, computer simulations have become very effective tools for optimizing microwave ablation performance. Methods: The study was focused on the determination of the optimal input power for complete microwave tumor ablation with an ade-quate safety margin avoiding injury to the surrounding healthy tissue. In three-dimensional simulations, the liver tumor model was based on a real tumor (1.74 cm × 2.40 cm × 1.43 cm) from the 3D-IRCADb-01 database. Calculations were performed for a 10-slot antenna proven to achieve a higher degree of ablation zone localization than a standard single-slot antenna. The temperature-dependent dielectric and thermal properties of healthy and tumoral liver tissue, blood perfusion, and water content were included in the model. Results: The obtained simulation results revealed that the proper choice of input power ensures that necrotic tissue is mainly located in the tumor with minimal damage to the surrounding healthy tissue. Conclusions: This study may represent a step forward in the planning of individual microwave ablation treatment for each patient.

Three-Dimensional Heart Modeling of Hypertrophic Obstructive Cardiomyopathy for In Situ Patient-Specific Simulation to Optimize Septal Myectomy.

OBJECTIVE: Hypertrophic obstructive cardiomyopathy (HOCM) develops in at least 1 out of 715 young adults. Patients who are refractory to medical therapy qualify for septal myectomy. Due to anatomy, serious complications such as ventricular septal defect and heart block may occur. Establishing cardiovascular magnetic resonance (CMR)-based 3-dimensional (3D) models as part of preoperative planning and training has the potential to decrease procedure-related complications and improve results. METHODS: CMR images were used to segment cardiac structures. Left ventricular wall thickness was calculated and projected on top of the in silico model. A 3D model was printed with a red layer indicating a wall thickness exceeding 15 mm and used for preoperative resection planning and patient counseling. To provide preoperative patient-specific in situ simulation, the planned resection volume was replaced with silicone in a second model. For perioperative quality control, resected silicone was compared with resected myocardial tissue. The impact of the models was evaluated descriptively through consultation of both the cardiothoracic surgeon and patients and through patient outcomes. RESULTS: Three-dimensional in silico and 3D-printed heart models of 5 patients were established preoperatively. Since the introduction of the models in October 2020, the surgeon feels better prepared, more confident, and less difficulty with making decisions. In addition, patients feel better informed preoperatively. CONCLUSIONS: Using 3D heart models optimized preoperative planning and training, intraoperative quality control, and patient consultation. Reduction of procedure-related complications and clinical outcome should be studied in larger cohorts.

Simulation extractable versions of Groth's zk-SNARK revisited.

Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) are the most efficient proof systems in terms of proof size and verification. Currently, Groth's scheme from EUROCRYPT 2016, Groth 16 , is the state-of-the-art and is widely deployed in practice. Groth 16 is originally proven to achieve knowledge soundness, which does not guarantee the non-malleability of proofs. There has been considerable progress in presenting new zk-SNARKs or modifying Groth 16 to efficiently achieve strong Simulation extractability, which is shown to be a necessary requirement in some applications. In this paper, we revise the Random oracle based variant of Groth 16 proposed by Bowe and Gabizon, BG18, the most efficient one in terms of prover efficiency and CRS size among the candidates, and present a more efficient variant that saves 2 pairings in the verification and 1 group element in the proof. This supersedes our preliminary construction, presented in CANS 2020 (Baghery et al. in CANS 20, volume 12579 of LNCS, Springer, Heidelberg. pp 453-461, 2020), which saved 1 pairing in the verification, and was proven in the generic group model. Our new construction also improves on BG18 in that our proofs are in the algebraic group model with Random Oracles and reduces security to standard computational assumptions in bilinear groups (as opposed to using the full power of the generic group model (GGM)). We implement our proposed simulation extractable zk-SNARK (SE zk-SNARK) along with BG18 in the Arkworks library, and compare the efficiency of our scheme with some related works. Our empirical experiences confirm that our SE zk-SNARK is more efficient than all previous simulation extractable (SE) schemes in most dimensions and it has very close efficiency to the original Groth 16 .

Polymyxin B1 in the E. coli inner membrane: a complex story of protein and lipopolysaccharide mediated insertion.

The rise in multi-drug resistant Gram-negative bacterial infections has led to an increased need for 'last-resort' antibiotics such as polymyxins. However, the emergence of polymyxin-resistant strains threatens to bring about a post-antibiotic era. Thus, there is a need to develop new polymyxin-based antibiotics, but a lack of knowledge of the mechanism of action of polymyxins hinders such efforts. It has recently been suggested that polymyxins induce cell lysis of the Gram-negative bacterial inner membrane (IM) by targeting trace amounts of lipopolysaccharide (LPS) localized there. We use multiscale molecular dynamics (MD) including long-timescale coarse-grained (CG) and all-atom (AA) simulations to investigate the interactions of polymyxin B1 (PMB1) with bacterial IM models containing phospholipids (PLs), small quantities of LPS, and IM proteins. LPS was observed to (transiently) phase separate from PLs at multiple LPS concentrations, and associate with proteins in the IM. PMB1 spontaneously inserted into the IM and localized at the LPS-PL interface, where it cross-linked lipid headgroups via hydrogen bonds, sampling a wide range of interfacial environments. In the presence of membrane proteins, a small number of PMB1 molecules formed interactions with them, in a manner that was modulated by local LPS molecules. Electroporation-driven translocation of PMB1 via water-filled pores was favored at the protein-PL interface, supporting the 'destabilizing' role proteins may have within the IM. Overall, this in-depth characterization of PMB1 modes of interaction reveals how small amounts of mislocalized LPS may play a role in pre-lytic targeting and provides insights that may facilitate rational improvement of polymyxin-based antibiotics.

Simulation with a standardised patient to reduce stigma towards people with schizophrenia spectrum disorder among nursing students: A quasi-experimental study.

OBJECTIVES: This study examined the effectiveness of simulation with a standardised patient on the perception of stigma associated with schizophrenia among undergraduate nursing students. It also assessed the reliability of the AQ-27 questionnaire in this context. METHOD: A quasi-experimental study without a control group was conducted on a non-probabilistic sample. The simulation programme used a standardised patient portrayed by a nurse with mental health experience. RESULTS: After simulation, statistically significant stigma improvements were found in six out of nine dimensions; anger and help obtained larger effect sizes (r = 0.392 and 0,307, respectively). Regarding gender, the intragroup analysis revealed that simulation improved stigma among women in six dimensions and among men in four dimensions, with anger and fear showing the highest effect size (r = 0.414 and 0.446, respectively). Regarding previous contact with mental illness among the study participants, the intergroup analysis did not show differences. In the intragroup analysis, simulation improved fear only in the contact group (p = 0,040, r = 0.353). In contrast, simulation changed the response in six dimensions in the no-contact group, similar to the entire group. CONCLUSION: Simulation with a standardised patient is an effective teaching tool for reducing the stigmatisation of people with schizophrenia, thus reducing people's perception of internal causal attribution. It allows for experiencing situations that may be anticipated in clinical practice and reflectively addressing emerging aspects during simulation.

The Effectiveness of Technology-Enhanced Learning in Prosthodontic Education: A Systematic Review.

OBJECTIVE: To compare the effectiveness of technology-enhanced learning (TEL) with traditional learning in prosthodontic education. METHODS: The protocol was registered before commencement [CRD42023390277]. The search was conducted to identify randomized controlled trials (RCTs) focused on comparative assessment of TEL and traditional learning in prosthodontic education up to 6 June 2024. A comprehensive search strategy was formulated, encompassing multiple electronic databases. Screening, data extraction and risk of bias assessment using the Cochrane tool (RoB 2) were performed. Educational outcomes were evaluated by Kirkpatrick's four-level evaluation model. RESULTS: Of 2830 records identified, 14 RCTs were included. All the trials were judged at high risk of bias or had some concerns. The included studies assessed the short-term benefits of various TEL approaches, including e-learning, blended learning, video demonstration specialized self-evaluation software and virtual reality simulation in prosthodontic education for undergraduates. Promising results were found in individual studies, particularly in terms of perceived learning conditions with problem-based learning (PBL) and self-reported understanding and confidence when using video demonstrations. Most studies reported comparable outcomes to traditional learning in practical skills assessments, while the results regarding the acquisition of theoretical knowledge through the incorporation of TEL approaches were contradictory. One study reported promising results in perceived learning conditions with the PBL approach, as well as improved self-reported understanding and confidence with the use of video demonstrations. Furthermore, there was a limited number of studies assessing the effect of TEL approaches on students' behaviour. CONCLUSION: There is a lack of high-quality evidence to support the adoption of TEL approaches in prosthodontics. However, the incorporation of virtual reality simulation has demonstrated promising results, particularly in improving practical clinical skills.

Electro-Optically Configurable Synaptic Transistors With Cluster-Induced Photoactive Dielectric Layer for Visual Simulation and Biomotor Stimuli.

The integration of visual simulation and biorehabilitation devices promises great applications for sustainable electronics, on-demand integration and neuroscience. However, achieving a multifunctional synergistic biomimetic system with tunable optoelectronic properties at the individual device level remains a challenge. Here, an electro-optically configurable transistor employing conjugated-polymer as semiconductor layer and an insulating polymer (poly(1,8-octanediol-co-citrate) (POC)) with clusterization-triggered photoactive properties as dielectric layer is shown. These devices realize adeptly transition from electrical to optical synapses, featuring multiwavelength and multilevel optical synaptic memory properties exceeding 3 bits. Utilizing enhanced optical memory, the images learning and memory function for visual simulation are achieved. Benefiting from rapid electrical response akin to biological muscle activation, increased actuation occurs under increased stimulus frequency of gate voltage. Additionally, the transistor on POC substrate can be effectively degraded in NaOH solution due to degradation of POC. Pioneeringly, the electro-optically configurability stems from light absorption and photoluminescence of the aggregation cluster in POC layer after 200 °C annealing. The enhancement of optical synaptic plasticity and integration of motion-activation functions within a single device opens new avenues at the intersection of optoelectronics, synaptic computing, and bioengineering.

Should all athletes use the same twisting strategy? The role of anthropometry in the personalisation of optimal acrobatic techniques.

Choosing the best acrobatic technique for each athlete remains a challenge for coaches. Predictive simulations may support coaches, but only a few athlete morphologies have been simulated yet. It is assumed that the optimal acrobatic techniques are somehow generalisable across athletes. However, anthropometry characteristics can influence the twist rotation outcome of an acrobatic technique. Our objective was to assess the differences in optimal techniques caused by the anthropometric differences between athletes. Anthropometry-specific techniques of double pike forward somersaults ending with 112 or 212 twists were generated using predictive simulations and the measurements of 18 acrobatic athletes presenting a wide range of anthropometry. We found that anthropometry had an impact on the optimal acrobatic techniques by modifying the amplitude of the strategies used or, more drastically, by modifying the strategies used. Some athletes had a morphological advantage for twist creation, which was measured using the combined twist potential, a metric introduced in the current study. This metric was very strongly correlated with the complexity of the techniques; models with an advantage for twist creation needed fewer/shorter limb movements to generate twists. This research shows that coaches should consider their athletes' anthropometry to offer them better guidance.

Deep learning insights into spatial patterns of stable isotopes in Iran's precipitation: a novel approach to climatological mapping.

Stable isotope techniques are precise methods for studying various aspects of hydrology, such as precipitation characteristics. However, understanding the variations in the stable isotope content in precipitation is challenging in Iran due to numerous climatic and geographic factors. To address this, forty-two precipitation sampling stations were selected across Iran to assess the fractional importance of these climatic and geographic parameters influencing stable isotopes. Additionally, deep learning models were employed to simulate the stable isotope content, with missing data initially addressed using the predictive mean matching (PMM) method. Subsequently, the recursive feature elimination (RFE) technique was applied to identify influential parameters impacting Iran's precipitation stable isotope content. Following this, long short-term memory (LSTM) and deep neural network (DNN) models were utilized to predict stable isotope values in precipitation. Interpolated maps of these values across Iran were developed using inverse distance weighting (IDW), while an interpolated reconstruction error (RE) map was generated to quantify deviations between observed and predicted values at study stations, offering insights into model precision. Validation using evaluation metrics demonstrated that the model based on DNN exhibited higher accuracy. Furthermore, RE maps confirmed acceptable accuracy in simulating the stable isotope content, albeit with minor weaknesses observed in simulation maps. The methodology outlined in this study holds promise for application in regions worldwide characterized by diverse climatic conditions.

[Structural design of "fitness" and "feasibility" of body-fitted stent and its mechanical analysis].

To address the conflict between the "fitness" and "feasibility" of body-fitted stents, this paper investigates the impact of various smoothing design strategies on the mechanical behaviour and apposition performance of stent. Based on the three-dimensional projection method, the projection region was fitted with the least squares method (fitting orders 1-6 corresponded to models 1-6, respectively) to achieve the effect of smoothing the body-fitted stent. The simulation included the crimping and expansion process of six groups of stents in stenotic vessels with different degrees of plaque calcification. Various metrics were analyzed, including bending stiffness, stent ruggedness, area residual stenosis rate, contact area fraction, and contact volume fraction. The study findings showed that the bending stiffness, stent ruggedness, area residual stenosis rate, contact area fraction and contact volume fraction increased with the fitting order's increase. Model 1 had the smallest contact area fraction and contact volume fraction, 77.63% and 83.49% respectively, in the incompletely calcified plaque environment. In the completely calcified plaque environment, these values were 72.86% and 82.21%, respectively. Additionally, it had the worst "fitness". Models 5 and 6 had similar values for stent ruggedness, with 32.15% and 32.38%, respectively, which indicated the worst "feasibility" for fabrication and implantation. Models 2, 3, and 4 had similar area residual stenosis rates in both plaque environments. In conclusion, it is more reasonable to obtain the body-fitted stent by using 2nd to 4th order fitting with the least squares method to the projected region. Among them, the body-fitted stent obtained by the 2nd order fitting performs better in the completely calcified environment.

Unleashing surgical skills: Ultra-high fidelity trauma thoracotomy training on knowledge donor platform.

BACKGROUND: Resuscitative thoracotomies are a time-sensitive emergency surgical procedure with an immediate risk of mortality. We hypothesize that a high-fidelity whole-body donor simulation model, referred to as a Knowledge Donor (KD), with mechanical lung ventilation and expired human blood perfusion could increase learner confidence in performing this critical procedure. METHODS: General surgery residents and faculty were invited to participate in KD training. Surveys were collected to track participation and confidence. RESULTS: Simulated resuscitative thoracotomies were performed involving PGY levels I-IV. Mean confidence was highest for residents with both KD and Live Patient experience (5.6 â€‹± â€‹1.7), followed by Live Patient only (4.3 â€‹± â€‹2.5), and KD only (2.6 â€‹± â€‹1.3). The mean confidence rating for residents with neither training opportunity was 1.4 â€‹± â€‹1.0. CONCLUSIONS: The KD platform is a hyper-realistic training modality that closely replicates live surgery. This platform allows residents to practice complex surgical procedures in a safe environment, without risking patient safety. This pilot program yielded early results in improving resident procedural confidence for high-risk surgical procedures, specifically resuscitative thoracotomies.

Review of hydraulic conditions optimization for constructed wetlands.

Hydraulic conditions exert a comprehensive and vital influence on constructed wetlands (CWs). However, research on this subject is relatively limited. Hydraulic parameters can be categorized into design and operational parameters based on their properties. The design parameters are represented by the hydraulic gradient, substrate porosity, and aspect ratio, while operational parameters are represented by the hydraulic retention time, hydraulic loading rate, and water depth. These parameters directly or indirectly affect the operational lifespan and pollutant removal performance of CWs. Currently, the primary measures for optimizing the hydraulic conditions of CWs involve hydraulic structure and numerical simulation optimization methods. In this review, we aimed to elucidate the impact of hydraulic conditions on CW performance and summarize current optimization strategies. By highlighting the significance of hydraulic parameters in enhancing pollutant removal and extending operational lifespan, this review provides valuable insights for improving CW design and management. The findings will be useful for researchers and practitioners seeking to optimize CW systems and advance the application of nature-based solutions for wastewater treatment.

Psychological first aid in operation for military healthcare providers : a study on pre-deployment training.

INTRODUCTION: During operational missions, while the management of physical injuries in the field remains the priority, the identification of operational incapacity of psychological origin is necessary as it is equally crucial for the safety of the individual, the group and the mission. The French Military Health Service has developed a Psychological First Aid Training in Operation (PFATO) course based on relational simulations, for military service members. The aim is to identify the early signs of psychological distress in a comrade and to adopt an adapted and protective attitude. PFATO training is also offered to healthcare providers. METHODS: We conducted a descriptive cross-sectional study using a self-administered online questionnaire which was sent after deployment to all physicians or nurses trained in PFATO between July 2019 and July 2021 (n=80). The main objective of our work was to evaluate the relevance of this awareness training among physicians and nurses and to identify specific complementary expectations in operational psychiatry for this population. RESULTS: We obtained a response rate of 55%. Significantly, 21.62% of participants used PFATO during their last deployment and another 20% observed a team member using PFATO. The circumstances of use as reported by participants included acute stress related to combat, conflict with hierarchy or comrades, and suicidal crisis. Among those who used PFATO, the training helped 87.5% of them to identify signs of psychological distress and 100% of them to assist combatants . All respondents stressed the added value of practical simulations during PFATO education. Moreover, this study also makes it possible to identify adaptations needed to optimize this module for healthcare providers. CONCLUSION: The results suggest the value for healthcare provider of training in first-response psychological care using relational simulation based on the model of raising awareness about PFATO.