Abstract: FAM3A, FAM3B, FAM3C and FAM3D are members of “family with sequence similarity 3” (FAM3) gene family, an emerging class of cytokine-like proteins with a unique structural globular beta-beta-alpha fold and distinct biological functions. With widespread expression in tissue, organs and in many cell types, their specific roles in human diseases have been the focus of much research. FAM3A acts as a positive regulator of metabolic health, typically activating canonical pro-survival and metabolic pathways. FAM3B, also called PANDER (PANcreatic DERived Factor) exerts critical physiological functions in the regulation of glycemic levels via promotion of hepatic glucose production and pancreatic beta-cell insulin secretion. FAM3C, also named ILEI (Interleukin-like EMT inducer), is involved as inducer of epithelial-mesenchymal transition (EMT) and cancer metastasis, as well as osteoblast differentiation and bone mineralization. FAM3D is a gut secreted protein and potential regulator of gastrointestinal homeostasis and microbiota-induced inflammation. Here we provide an overview of previous studies supporting that FAM3 proteins can binding to putative membrane receptors and co-partners, including Fibroblast Growth Factor Receptor (FGFR), Leukemia Inhibitory Factor Receptor (LIFR), Formyl Peptide Receptor (FPR1/2), to activate diverse downstream signaling pathways on different cellular contexts. Basic and clinical studies suggest that FAM3 family influence both obesity, diabetes, and other metabolic disorders, thus its expression may have diagnostic potential. The differential and often cancer-specific expression patterns make members of the FAM3 family promising candidates for biomarkers and therapeutic targets of some types of neoplasia.

Abstract: Background/Objectives: Artificial intelligence (AI) tools for chest X-ray interpretation have gained relevance as support systems in diagnostic workflows, particularly in settings with high demand or limited specialist availability. This study evaluated the diagnostic performance of the qXR software (Qure.ai) for detecting high-risk pulmonary nodules, cardiomegaly, and pleural effusion in adult patients at Hospital Clínica Bíblica in San José, Costa Rica. Methods: Three radiologists independently interpreted 225 chest radiographs, serving as the reference standard. qXR results were compared against this standard for each finding. Sensitivity, specificity, Cohen’s kappa, and area under the curve (AUC) were calculated. Predictive values were not used for interpretation due to the artificial prevalence of the sample. Results: qXR showed higher agreement with radiologist assessments for pulmonary nodules and pleural effusion, achieving moderate to substantial concordance. Performance for cardiomegaly was more variable, with lower agreement across evaluators. Overall diagnostic accuracy was acceptable, although the magnitude differed by condition. Conclusions: These findings underscore the importance of validating AI diagnostic tools within local clinical environments and heterogeneous imaging conditions. qXR demonstrated potential as a complementary aid for detecting pulmonary nodules and pleural effusion, while its performance for cardiomegaly should be interpreted with caution. The study does not provide evidence of real-world clinical impact.

Abstract: This paper features a 1000 simulations of a set of 100 levered companies equity returns in a financial market. The goal was to generate a realistic distribution of company values that follow a Zipf-Mandelbrot power law. The returns should exhibit leverage effects, negative skewness, and feature Black Swan events of correlated down-turns. Realistic positive covariance structures of returns, systematic risk, plus evidence of long-memory properties. The Merton Model and two versions of the Platen Benchmark Asset Pricing Model (BAPM), the original model and the Stochastic Benchmark Process (SBP). The required market attributes were successfuly captured but the models proved to be highly sensitive to the chosen parameters. The BAPM model proved to be more flexible than the Merton Model and the SBP version more readily generated the stipulated financial market characteristics.

Abstract: Wheat production in Zimbabwe is strongly influenced by environmental variability, making it difficult for breeders to identify genotypes that are both high yielding and stable across locations. This study evaluated the yield performance and stability of pre-release bread wheat genotypes across contrasting environments in Zimbabwe. A total of 25 genotypes in 2020 and 24 genotypes in 2021 were tested using a randomized complete block design (RCBD) with three replications at three sites Gwebi Variety Testing Centre (GVTC), Harare Research Station (DR&SS), and Panmure, forming six test environments across two winter seasons. Grain yield and key agronomic traits were recorded and analyzed using combined analysis of variance, correlation analysis, and genotype plus genotype-by-environment (GGE) biplot methods. The combined analysis of variance revealed highly significant (p < 0.001) effects of location on all traits in both years, confirming strong environmental influence on wheat performance. Genotypic differences were also significant for most traits in each season. In 2020, genotype × location interaction for grain yield and grain weight was not significant, indicating relatively stable genotype ranking across environments. In contrast, significant genotype × location interaction in 2021 demonstrated strong crossover effects, with genotypes responding differently across sites. When the 15 genotypes common to both years were analyzed together, genotype × location interaction for grain yield was again not significant, indicating that this subset of genotypes expressed greater yield stability across environments. GGE biplot analysis revealed clear differences in genotype adaptation and stability. The mean versus stability view identified G10 and G4 as high yielding with moderate stability, while G5 and G8 were closest to the ideal genotype, combining high yield and wide adaptation. The which-won-where pattern separated the test environments into two main mega-environments, with G3 and G10 performing best in GVTC- and Harare-based environments, while G4, G5, and G8 were superior at Panmure-related environments. Environment E3 (Harare 2020) was identified as the most representative and closest to an ideal test environment, while E1, E2, E5, and E6 were more discriminating and useful for detecting genotype differences. The findings of this study demonstrated that both yield level and stability must be considered when selecting wheat genotypes for Zimbabwe. Genotypes G5 and G8 showed the best combination of high grain yield and stability and are therefore recommended for broad adaptation. Genotypes such as G3 showed high yield but strong environmental sensitivity and are better suited for specific environments. These findings provide valuable guidance for wheat breeding and variety recommendation in Zimbabwe’s diverse production environments.

Abstract: International agricultural trade plays a crucial role in balancing the global food supply and demand while facilitating the cross-regional allocation of cropland resources. This study examines the virtual cropland flows embedded in international wheat trade. Utilizing the telecoupling framework and wheat trade data from eight time points between 1995 and 2023, we developed a global virtual-cropland-flow network. Social network analysis (SNA) was used to characterize the structural features and identify telecoupling systems, whereas the quadratic assignment procedure (QAP) regression was applied to analyze the driving factors. The findings reveal that (1) the virtual cropland network shows structural vulnerability and evolutionary complexity amid increasing connectivity, with an overall rise in density and significant fluctuations in the average clustering coefficient and path length. (2) The network exhibits a distinct telecoupling structure. The sending system has shifted from U.S.-Canada dominance to a multipolar pattern involving Australia, Canada, Kazakhstan, and the United States. The receiving systems mainly comprise Asia, Africa, and Latin America, with China as the core inflow country. The United States and France, supported by major transnational agribusinesses, act as key spillover systems, consistently holding a high betweenness centrality. (3) Economic development and foreign demand significantly promote the establishment and intensification of trade relationships between countries. Geographical distance has a dual effect: it strongly negatively influences trade initiation but can be overcome by high complementarity between countries during trade deepening. (4) Although international wheat trade effectively conserves global cropland resources, it also introduces systemic risks and environmental spillovers in some countries. Developing nations that are highly dependent on wheat imports, such as Egypt, are more vulnerable to network fluctuations. By integrating multidisciplinary perspectives, this study provides a scientific basis for constructing sustainable food trade systems and agricultural resource governance. It offers valuable insights for advancing SDG 2 (Zero Hunger), SDG 12 (Responsible Consumption and Production), sustainable land systems, and the optimization of global land governance.

Abstract: The Surviving Sepsis Campaign (SSC) 1-hour bundle has transformed sepsis care in high-income countries. This bundle comprises rapid lactate measurements, blood cultures, broad-spectrum antibiotics, intravenous fluids, and vasopressors. However, in fragile systems such as Turkana County, Kenya, this protocol is largely impractical. This review synthesises current global and regional literature to contextualise the bundle’s limitations and propose evidence-based adaptations. Long travel distances, shortage of essential diagnostics and medicine, limited human resources, and inadequate critical care capacity remain significant systemic barriers. This review advocates for reframing the bundle from a fixed 1-hour metric to an “as soon as possible” (ASAP) framework, emphasising early recognition, timely empirical antibiotics, and pragmatic hemodynamic stabilisation using available resources. Key recommendations include replacing lactate measurements with clinical surrogates (such as capillary refill time), creating locally informed empirical antibiotic protocols, strengthening supply chains, investing in task-sharing and simulation-based training, and embedding community awareness initiatives. These adaptations can achieve meaningful mortality reduction and mitigate antimicrobial resistance.

Abstract: Diabetic retinopathy (DR) remains one of the most frequent and severe complications in patients with type 2 diabetes (T2DM), with significant implications for vision and quality of life. While classical screening methods are effective, they are not always accessible or systematically used. Sudoscan, a device that evaluates sweat gland function and reflects peripheral autonomic status, has recently attracted attention as a potential tool for early detection of microvascular complications. In this cross-sectional study, we investigated its utility in identifying DR among 271 adults with T2DM. DR was diagnosed in 35.8% of patients, and those affected showed lower Sudoscan scores in the lower limbs and higher scores indicating cardiovascular autonomic neuropathy. Statistical analyses, including ROC curve evaluation and multiple linear regression, revealed moderate diagnostic accuracy and significant correlations between Sudoscan parameters and DR severity. Our results suggest that Sudoscan could serve as a fast, painless, and informative screening tool, particularly valuable in settings with limited access to ophthalmologic services. Although it does not replace fundus examination, it may offer complementary insights and help stratify patients by risk level, guiding more targeted monitoring and intervention strategies.

Abstract:

Triboelectric nanogenerator (TENG) have gradually been applied in various practical scenarios, mainly focusing on core areas such as wearable motion monitoring devices, medical security systems, and natural resource exploration technology. However, it has the problem of low output energy and has not yet formed effective integration with mature commercially available products, which has hindered the industrialization process. This situation still significantly limits its global promotion and application. In this study, TENG was used as the sensing module for intelligent automotive airbags. We conducted tests on the voltage and current output characteristics of the system under different impact forces and frequency conditions. During the testing process, the electrical energy generated under different operating conditions is transmitted to the control system through Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) circuits. The system will quickly determine whether to trigger the airbag deployment based on the received electrical signals, and activate the ignition device when necessary to achieve rapid inflation and deployment of the airbag. Compared with traditional triggering mechanisms, the airbag system based on this designed sensor has higher sensitivity and reliability. The sensor can stably capture collision signals, and experiments have shown that as the collision speed increases, the slope of its open circuit voltage gradually approaches infinity. Applying TENG to automotive airbags not only effectively improves the triggering efficiency and accuracy of airbags, but also provides more reliable safety protection for drivers and passengers. The finite element simulation of vehicle airbags provides specific data support for safety performance evaluation. With the continuous advancement of TENG technology and further expansion of its application scenarios, we believe that such innovative safety technologies will play a more critical role in the future automotive industry.

Abstract:

Background: Older adults with Alzheimer’s disease (AD) face heightened risk of adverse hospital outcomes, including mortality. However, early identification of high-risk patients remains a challenge. While regression models provide interpretable associations, they may miss nonlinear interactions that machine learning can uncover. Objective: To identify key predictors of in-hospital mortality among AD patients using both survey-weighted logistic regression and explainable machine learning. Methods: We analyzed hospitalizations among AD patients aged ≥60 in the 2017 Nationwide Inpatient Sample (NIS). The outcome was in-hospital death. Predictors included demographics, hospital variables, and 15 comorbidities. Logistic regression used survey weighting to generate nationally representative inference; XGBoost incorporated NIS discharge weights as sample weights during 5-fold hospital-grouped cross-validation and used the same weights in performance evaluation. Missing-value imputation and feature scaling were performed within the cross-validation pipelines to prevent data leakage. Model performance was assessed using AUROC, AUPRC, Brier score, and log loss. Feature importance was assessed using adjusted odds ratios and SHapley Additive exPlanations (SHAP). A sensitivity analysis excluded palliative care and DNR status and was re-evaluated under the same grouped cross-validation. Results: In the full model, logistic regression achieved AUROC 0.879 and AUPRC 0.310, while XGBoost achieved AUROC 0.887 and AUPRC 0.324. Palliative care (aOR 6.19), acute respiratory failure (aOR 5.15), DNR status (aOR 2.20), and sepsis (aOR 2.26) were the strongest logistic predictors. SHAP analysis corroborated these findings and additionally emphasized dysphagia, malnutrition, and pressure ulcers. In sensitivity analysis excluding palliative care and DNR status, logistic regression performance declined (AUROC 0.806; AUPRC 0.206), while XGBoost performed similarly (AUROC 0.811; AUPRC 0.206). SHAP corroborated the dominant signals from end-of-life documentation and acute organ failure in the full model; in the restricted model (excluding DNR and palliative care), SHAP highlighted physiologic and frailty-related features (e.g., dysphagia, malnutrition, aspiration risk) that may be more actionable when end-of-life documentation is absent. Conclusion: Combining regression with explainable machine learning enables robust mortality risk stratification in hospitalized AD patients. Restricted models excluding end-of-life indicators provide actionable risk signals when such documentation is absent, while the full model may better support resource allocation and goals-of-care workflows.

Abstract: Cyber Security is an essential element for responding to serious threats posed by digital 2 technology. The Common Vulnerability Scoring System (CVSS) is a key indicator for eval- 3 uating software security risks. However, CVSS results—expressed as numerical scores 4 or vector strings—are difficult for general users and managers to intuitively understand 5 and judge. This complexity hinders effective risk management. This study aimed to im- 6 prove the usability and satisfaction of a cybersecurity assessment simulator by designing a 7 user-friendly UI/UX. The design proposal focused on three core principles for intuitive 8 understanding of detailed CVSS V4.0 indicator values: Firstly, data Visualization: Using a 9 clear color scheme (red/yellow/green) to distinguish risk levels at a glance. Tooltips were 10 implemented to provide detailed information on hover. secondly, clear Information Hier- 11 archy: The CVSS V4.0 groups (Base, Threat, Environment, Supplemental) were arranged 12 logically, with the Basic Group at the top center for visibility. Supplemental information 13 was provided using a drill-down approach. lastly, Interactivity and Accessibility: Features 14 like data filtering/sorting and a responsive UI were included. Accessibility was addressed 15 by providing patterns and text labels alongside colors for color vision deficiency. The 16 proposed dashboard-type UI/UX was implemented as a web service and tested against the 17 existing CVSS V4.0 calculator. Experiments showed a significant improvement in usability, 18 design satisfaction (e.g., visual satisfaction 8.9 points, readability 9.0 points), and overall 19 UI/UX satisfaction (83%) compared to the existing system. No significant difference was 20 found in items evaluating interaction or certain usability metrics. This was attributed to 21 the system being primarily information-providing rather than a two-way interactive tool. 22 The study successfully designed a visualized UI/UX for the CVSS V4.0 simulator, making 23 risk assessment results more accessible. Future work will focus on improving the system 24 structure to enable two-way interaction and enhance overall usability metrics.

Abstract:

Fermented foods are increasingly recognized for their potential to support gut and brain health via microbiome modulation. However, most research focuses on isolated probiotics or lab-prepared products, leaving limited evidence for real-world fermented foods with live bacteria. This study evaluated the effects of three commercially available fermented foods—dairy kefir, coconut kefir, and fermented red cabbage and beetroot—on gastrointestinal, cognitive, and emotional outcomes in healthy adults. Over a 4-week randomized controlled intervention, cognitive function was assessed using the CANTAB, emotional health via validated self-report measures, and stool samples analysed using the Genova Diagnostics GI Effects test. Dairy kefir improved decision-making, sustained attention, working memory, reduced depression, anxiety and stress. The coconut kefir reduced waiting impulsivity, enhanced short-term memory, improved total mood, and increased butyrate-associated commensals, Faecalibacterium prausnitzii, Bifidobacterium spp., Lactobacillus spp., and Anaerotruncus colihominis, alongside elevated butyrate levels. The fermented red cabbage and beetroot improved sustained attention, working memory, reduced stress, improved total mood, and increased both butyrate and propionate. In contrast, the control group showed a rise in Fusobacterium spp. These findings support fermented foods as functional dietary interventions for gut–brain health.

Abstract: Artificial intelligence (AI) has emerged as a transformative tool in biodiversity conservation, offering the potential to revolutionize ecological data collection, analysis, prediction, and decision-making processes. This literature review synthesizes insights from recent scholarship on AI applications, with a particular focus on the design, implementation, and governance of AI-driven frameworks. It concludes by proposing principles and research directions for the responsible and effective integration of AI in the service of global biodiversity.

Abstract: This review examines the combustion characteristics of hydrogenenriched natural gas with a specific focus on residential appliances, where safety, efficiency, and emission performance are critical. Drawing on experimental studies, numerical simulations, and regulatory considerations, the paper synthesizes current knowledge on how hydrogen addition influences flame stability, flashback phenomenon, thermal efficiency, pollutant formation, and flame geometry. Results across cooktop burners, boilers, and other domestic systems show that moderate hydrogen blending can reduce CO and CO₂ emissions and enhance combustion efficiency, but also increases burning velocity, diffusivity, and flame temperature, thereby elevating flashback and NOx risks. The review highlights the blending limits, design adaptations, and operational strategies required to ensure safe and effective integration of hydrogen into residential gas infrastructures, supporting its role as a transitional lowcarbon fuel.

Abstract:

This study investigates the application of Plasma Transferred Arc (PTA) surface treatment as an advanced method for the regeneration of railway wheels. Traditional wheel reprofiling, performed using semi-automatic lathes, involves the removal of at least 6 mm of metal from the running surface, leading to progressive rim thinning and eventual wheel replacement. Furthermore, the reprofiled surfaces lack any subsequent treatment to extend their operational lifespan. To address these limitations, PTA cladding was selected for its capability to produce enhanced surface layers with improved mechanical properties. Unlike commonly used diode laser treatments, PTA enables the deposition of alloying materials in wire form, providing a robust and controlled cladding process. The resulting surface structure comprises a heat-affected zone, a transition zone, and a remelted zone, all exhibiting significantly increased hardness compared to the untreated base metal. The cladding process allows for the incorporation of metal particles into the surface layer, facilitating the formation of a high-quality, wear-resistant top layer. These findings demonstrate the potential of PTA surface treatment to extend the service life of railway wheels by providing a durable and hard-wearing surface, thereby reducing maintenance frequency and costs [1–3].

Abstract: Automated face mask detection remains an important component of hygiene compli-ance, occupational safety, and public health monitoring, even in post-pandemic envi-ronments where real-time, non-intrusive surveillance is required. Traditional deep learning models offer strong recognition performance but are often impractical for de-ployment on embedded and edge devices due to their computational complexity. Re-cent research has therefore emphasized lightweight and hybrid architectures that maintain high detection accuracy while reducing model size, inference latency, and energy consumption. This review provides an architecture-centered examination of face mask detection systems, analyzing conventional convolutional models, light-weight convolutional networks such as the MobileNet family, and hybrid frameworks that integrate efficient backbones with optimized detection heads. A comparative per-formance analysis highlights key trade-offs between accuracy and computational effi-ciency, emphasizing the constraints of real-world and edge-oriented deployments. Open challenges, including improper mask detection, domain adaptation, model com-pression, and extending detection systems toward broader compliance-monitoring ap-plications, are discussed to outline a forward-looking research agenda. This work con-solidates current understanding of architectural strategies for mask detection and of-fers guidance for developing scalable, robust, and real-time deep learning solutions suitable for embedded and mobile platforms.

Abstract: The rapid expansion of artificial intelligence (AI) and digitalization in contemporary ed-ucation has reshaped global debates on sustainable education, often emphasizing effi-ciency, personalization, and technological innovation. However, this transformation has coincided with increasing technologization and commodification of education, raising critical questions about whether AI-driven education can genuinely support sustainability as a value-based and human-centered project. This study examines sustainable education in the age of artificial intelligence and digitalization through a value-critical analytical ap-proach grounded in a conceptual distinction between sustainable education, sustainabil-ity in education, and education for sustainable development. Methodologically, the article adopts a qualitative critical analysis of contemporary literature and policy-oriented de-bates to assess the ethical, social, and educational implications of AI integration. The analysis reveals a dual and context-dependent impact of AI on sustainable education: while AI can enhance educational quality, access, and personalization in well-resourced and well-governed contexts, it may also intensify educational inequalities, reinforce the commodification of knowledge, undermine academic integrity, and marginalize the hu-man dimension of education under market-driven and weakly regulated conditions. These challenges are particularly evident in culturally and religiously grounded educa-tional contexts, where AI reshapes epistemic authority and educational meaning. The study concludes that achieving sustainable education in the digital age depends not on AI adoption per se, but on reframing AI and digitalization within a coherent ethical and val-ue-based framework that subordinates technology to educational aims, social justice, and human dignity.

Abstract: Modern information systems suffer from a fundamental architectural flaw: data coherence depends on external validation layers, creating systemic entropy and computational waste. We present the G Model, a mathematical framework that redefines informationas points in a geometric space where incoherence is mathematically impossible. Through a triaxial formalization (Meaning, Location, Connection) and an intrinsic coherence operator (Φ), the system guarantees that only valid data can exist within the managed universe (Ω). We formalize this with four fundamental axioms ensuring coherence, uniqueness, acyclicity, and deterministic propagation. Our implementation, the SRGD system (Sistema Relacional Gestión de Datos), demonstrates practical viability through a stateless three-layer architecture and unified flow patterns. Preliminary results show significant advantages in critical infrastructure scenarios where error is inadmissible, providing a foundation for trustworthy AI training data and eliminating the validation overhead present in traditional RDBMS and NoSQL systems. This work represents a paradigm shift from “data storage systems” to “coherent information spaces".

Abstract: Drug discovery is a complex, multi-parameter optimization process. I argue that a greater emphasis on optimizing binding affinity will accelerate the development of new medicines. Note that “optimizing” is not always synonymous with “maximizing.” While affinity is certainly not the only thing that matters, the value of optimizing drug – receptor interactions is profound and often underappreciated. Optimizing affinity provides seven distinct benefits: achieving potent tool compounds more quickly; making compounds with increased potency; making more selective compounds; optimizing drug candidates more quickly; encouraging the pursuit of more synthetically challenging compounds; expanding chemical diversity during lead optimization; and minimizing interactions with "avoid-ome” targets that lead to poor ADME and tox properties. Affinity should be viewed as a key strategic component throughout the entire discovery process – balancing the level of on-target engagement appropriate to the specific mechanism being pursued alongside the need for chemical diversity and the proactive de-risking of off-targets including the avoid-ome. A “checklist” of practical suggestions is offered to enable project teams to more fully embrace the challenges of affinity optimization.

Abstract: Climate change is a major environmental determinant of health, capable of altering exposure pathways to toxic contaminants such as (Pb) [1,2]. Lead is a persistent global pollutant with no safe exposure threshold and disproportionately affects children and socioeconomically vulnerable populations [3–5,17,24]. This review examines how climate-related processes amplify lead mobilization and associated public health risks within a One Health framework. We conducted an integrated bibliometric and narrative review of peer-reviewed literature published between 1990 and 2025 using Web of Science, Scopus, and PubMed. Bibliometric mapping was combined with thematic synthesis. A total of 89 studies were analyzed. Results reveal a fragmented research landscape across disciplines and identify five convergent climate-sensitive lead exposure pathways: flood-driven remobilization [8,58], drought-related dust resuspension [7,22], temperature-mediated increases in bioavailability [6,28], urban amplification [9,20,21], and climate-influenced transport through water and food systems [13,40]. Climate change acts as a risk multiplier for lead exposure, reinforcing environmental health inequities. Integrating climate-sensitive exposure pathways into environmental surveillance and One Health–oriented public health policies is essential to reduce future lead-related disease burdens [35–38]. This review provides an integrated bibliometric and conceptual framework to support climate-sensitive lead surveillance and policy development.

Abstract: After approximately 60 years of service the 2856 MHz LINAC injector, of the Canadian Light Source (CLS), has been retired to make space for a new 3000.24 MHz LINAC injector, the frequency of which is a multiple of the 500.04 MHz CESR-B type superconductive radio frequency cavity used in the CLS storage ring. The new CLS LINAC injector has been designed and built by RI Research Instruments GmbH. The design is based on their robust S-band RF traveling wave accelerating structures technology, already serving other laboratories in the USA, Australia, Taiwan, Switzerland, and Sweden. In order to reduce cost and optimize space, the CLS has replaced its six accelerating RF structures, each 3.05 meters long, delivering 250 MeV electron beam with three 5.26 m long accelerating structures that will deliver the same beam energy. In order to do so, one RF structure is powered by one modulator-klystron and the last two RF structures receive their RF power from a second modulator-klystron that passes through a SLED system. The SLED system multiplies the peak power by a factor 5 to 6 and is then equally split to power each structure. We are reporting on the issues encountered during the commissioning of this new injector, on how we have tackled them and where the injector, compared to its technical specification, is standing today.