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We investigated the structural (internal) validity and classification performance of the Italian Short Negative Acts Questionnaire (SNAQ), a 9-item self-report instrument assessing bullying at work. Consistent with recent attention of researchers to control measurement error in predictive models (Jacobucci & Grimm, Perspectives on Psychological Science, 15(3), 809-816 2020 (See CR36)), classification performance was investigated through a proposed novel procedure that uses Structural Equation Modeling for building ROC curves. Participants included 357 workers (females = 50.4%) from various sectors. Our results showed that (a) the Italian SNAQ demonstrates adequate levels of structural validity; (b) its classification performance (in terms of self-labeled bullying) is outstanding; and (c) the ROC curves estimated by means of Structural Equation Modeling outperform those estimated with classical observed-variable approaches. In conclusion, we provided further evidence regarding the good psychometric properties of the Italian SNAQ and we also offered a novel approach for estimating ROC curves that does not neglect the issue of measurement quality., Author(s): Enrico Perinelli [sup.1] , Cristian Balducci [sup.2] , Franco Fraccaroli [sup.1] Author Affiliations: (1) https://ror.org/05trd4x28, grid.11696.39, 0000 0004 1937 0351, Department of Psychology and Cognitive Science, University of Trento, [...]

Integration between constrained optimization and deep networks has garnered significant interest from both research and industrial laboratories. Optimization techniques can be employed to optimize the choice of network structure based not only on loss and accuracy but also on physical constraints. Additionally, constraints can be imposed during training to enhance the performance of networks in specific contexts. This study surveys the literature on the integration of constrained optimization with deep networks. Specifically, we examine the integration of hyper-parameter tuning with physical constraints, such as the number of FLOPS (FLoating point Operations Per Second), a measure of computational capacity, latency, and other factors. This study also considers the use of context-specific knowledge constraints to improve network performance. We discuss the integration of constraints in neural architecture search (NAS), considering the problem as both a multi-objective optimization (MOO) challenge and through the imposition of penalties in the loss function. Furthermore, we explore various approaches that integrate logic with deep neural networks (DNNs). In particular, we examine logic-neural integration through constrained optimization applied during the training of NNs and the use of semantic loss, which employs the probabilistic output of the networks to enforce constraints on the output.

Rocky coastlines are characterised by steep cliffs, which frequently experience a variety of natural processes that often exhibit intricate interdependencies, such as rainfall, ice and water run-off, and marine actions. The advent of high temporal and spatial resolution data, that can be acquired through remote sensing and geomatics techniques, has facilitated the safe exploration of otherwise inaccessible areas. The datasets that can be gathered from these techniques, typically combined with data from fieldwork, can subsequently undergo analyses employing/applying machine learning algorithms and/or numerical modeling, in order to identify/discern the predominant influencing factors affecting cliff top erosion. This study focuses on a specific case situated at the Conero promontory of the Adriatic Sea in the Marche region. The research methodology entails several steps. Initially, the morphological, geological and geomechanical characteristics of the areas were determined through unmanned aerial vehicle (UAV) and conventional geological/geomechanical surveys. Subsequently, cliff top retreat was determined within a GIS environment by comparing orthophotos taken in 1978 and 2022 using the DSAS tool (Digital Shoreline Analysis System), highlighting cliff top retreat up to 50 m in some sectors. Further analysis was conducted via the use of two Machine Learning (ML) algorithms, namely Random Forest (RF) and eXtreme Gradient Boosting (XGB). The Mean Decrease in Impurity (MDI) methodology was employed to assess the significance of each factor. Both algorithms yielded congruent results, emphasising that cliff top erosion rates are primarily influenced by slope height. Finally, a validation of the ML algorithm results was conducted using 2D Limit Equilibrium Method (LEM) codes. Ten sections extracted from the sector experiencing the most substantial cliff top retreat, as identified by DSAS, were utilised for 2D LEM analysis. Factor of Safety (FS) values were identified and compared with the cliff height of each section. The results from the 2D LEM analyses corroborated the outputs of the ML algorithms, showing a strong correlation between the slope instability and slope height (R2 of 0.84), with FS decreasing with slope height.

Abstract Background Braak’s hypothesis states that sporadic Parkinson’s disease (PD) follows a specific progression of pathology from the peripheral to the central nervous system, and this progression can be monitored by detecting the accumulation of alpha-Synuclein (α-Syn) protein. Consequently, there is growing interest in understanding how the gut (commensal) microbiome can regulate α-Syn accumulation, as this could potentially lead to PD. Methods We used 16S rRNA and shotgun sequencing to characterise microbial diversity. 1H-NMR was employed to understand the metabolite production and intestinal inflammation estimated using ELISA and RNA-sequencing from feces and the intestinal epithelial layer respectively. The Na+ channel current and gut permeability were measured using an Ussing chamber. Immunohistochemistry and immunofluorescence imaging were applied to detect the α-Syn protein. LC-MS/MS was used for characterization of proteins from metabolite treated neuronal cells. Finally, Metascape and Ingenuity Pathway Analysis (IPA) bioinformatics tools were used for identification of dysregulated pathways. Results We studied a transgenic (TG) rat model overexpressing the human SNCA gene and found that a progressive gut microbial composition alteration characterized by the reduction of Firmicutes to Bacteroidetes ratio could be detected in the young TG rats. Interestingly, this ratio then increased with ageing. The dynamics of Lactobacillus and Alistipes were monitored and reduced Lactobacillus and increased Alistipes abundance was discerned in ageing TG rats. Additionally, the SNCA gene overexpression resulted in gut α-Syn protein expression and increased with advanced age. Further, older TG animals had increased intestinal inflammation, decreased Na+ current and a robust alteration in metabolite production characterized by the increase of succinate levels in feces and serum. Manipulation of the gut bacteria by short-term antibiotic cocktail treatment revealed a complete loss of short-chain fatty acids and a reduction in succinate levels. Although antibiotic cocktail treatment did not change α-Syn expression in the enteric nervous system of the colon, however, reduced α-Syn expression was detected in the olfactory bulbs (forebrain) of the TG rats. Conclusion Our data emphasize that the gut microbiome dysbiosis synchronous with ageing leads to a specific alteration of gut metabolites and can be modulated by antibiotics which may affect PD pathology.

The COVID-19 pandemic has presented significant challenges to the workforce, particularly concerning emotional and mental well-being. Given the prolonged periods of work-related stress, unexpected organizational changes, and uncertainties about work faced during the pandemic, it becomes imperative to study occupational health constructs under a dynamic methodological perspective, to understand their stable and unstable characteristics better. In this study, drawing on the Dynamic Structural Equation Modeling (DSEM) framework, we used a combination of multilevel AR(1) models, Residual-DSEM (RDSEM), multilevel bivariate VAR(1) models, and multilevel location-scale models to investigate the autoregression, trend, and (residual) cross-lagged relationships between emotional exhaustion (EmEx) and mental well-being (MWB) over the COVID-19 pandemic. Data were collected weekly on 533 workers from Germany (91.18%) and Italy (8.82%) who completed a self-reported battery (total number of observations = 3,946). Consistent with our hypotheses, results were as follows: (a) regarding autoregression, the autoregressive component for both EmEx and MWB was positive and significant, as well as it was their associated between-level variability; (b) regarding trend, over time EmEx significantly increased, while MWB significantly declined, furthermore both changes had a significant between-level variability; (c) regarding the longitudinal bivariate (cross-lagged) relationships, EmEx and MWB negatively and significantly affected each other from week to week, furthermore both cross-lagged relationships showed to have significant between-level variance. Overall, our study pointed attention to the vicious cycle between EmEx and MWB, even after controlling for their autoregressive component and trend, and supported the utility of DSEM in occupational health psychology studies.

Key clinical message The recurrence of respiratory symptoms without a clear clinical reason in children can be secondary to the presence of an unknown foreign body of the airways. In such cases, endoscopy of the airways is always necessary, regardless of the patient's age. Abstract The management of foreign bodies of the pediatric airway can be challenging. The clinical presentation may vary and, in case of recurrent respiratory symptoms without a clear clinical cause, the presence of a foreign body of the airway should be suspected. We describe the case of a 13‐month‐old patient, weight 11 kg, with a misdiagnosed subglottic foreign body causing dysphonia and worsening respiratory distress, removed by means of a direct laryngotracheoscopy in tubeless general anesthesia in spontaneous breathing.

Purpose: To protect the corneal endothelium, various ophthalmical viscoelastic devices (OVDs) are used during cataract surgery. In this study, we compared two sodium hyaluronate-based OVD, the bacteria-derived FIDIAL PLUS OVD (test) with the animal-derived IAL®-F OVD (reference) during the surgical procedure. Methods: Fifty patients with bilateral cataract participated in the study: 50 eyes randomly received FIDIAL PLUS and 50 eyes received IAL®-F (ratio 1:1). Noninferior efficacy of FIDIAL PLUS compared with reference OVD (first objective) was evaluated using a paired t-test comparing the mean percent loss of corneal endothelial cells (CECs) with FIDIAL PLUS against the mean percent loss with IAL®-F. Corneal endothelial protection during cataract surgery with FIDIAL PLUS without significant (≥30 mmHg) postoperative intraocular pressure (IOP) increase (main secondary objective) was assessed using a repeated measures logistic model comparing the incidence of significant postoperative IOP elevation between the two groups. Safety, tolerability, and efficacy were also evaluated by assessing secondary endpoints before and after surgery. Results: FIDIAL PLUS showed a statistically noninferior efficacy compared with IAL®-F; there was no significant increase in IOP in either group or different trends of the secondary endpoints were observed between the OVD groups. The safety profile was similar in both OVD groups and no adverse device effects were reported. Conclusions: This study demonstrates the equivalent efficacy, tolerability, and safety of the bacteria-derived FIDIAL PLUS compared with the animal-derived IAL®-F, confirming the clinical value of this product. [ABSTRACT FROM AUTHOR]

Organizational identification reflects the link between employees and their organization and it has been consistently found positively related to employee health and well-being (Steffens et al. 2017 (See CR47)). However, recent reviews and initial empirical evidence questioned the assumption of a uniform linear relation. We propose a mediation model, in which identification will be non-linearly related to changes in overcommitment over time, which in turn, will be related to employee psychological distress and job burnout. We tested the potential adverse effect of over-identification in a two-wave study (N = 85) across 18 months of both blue-collar and white-collar employees in an Italian manufacturing firm. The results confirmed our hypotheses and revealed a curvilinear effect of identification on overcommitment as well as indirect effects for health and burnout. We outline implications for theory in the form of the too-much-of-a-good-thing notion, and we discuss practical implications for managers who should be conscious of these effects when designing interventions to increase identification., Author(s): Lorenzo Avanzi [sup.1] , Lucia Savadori [sup.2] , Franco Fraccaroli [sup.1] , Valeria Ciampa [sup.3] , Rolf van Dick [sup.4] Author Affiliations: (1) grid.11696.39, 0000 0004 1937 0351, Department [...]

Chagas disease is an endemic American parasitosis, caused by Trypanosoma cruzi. The current therapies, benznidazole (BZN) and nifurtimox (NFX), show limited efficacy and multiple side effects. Thus, there is a need to develop new trypanocidal strategies. Ivermectin (IVM) is a broad-spectrum antiparasitic drug with low human and veterinary toxicity with effects against T. brucei and Leishmania spp. Considering this and its relatively low cost, we evaluate IVM as a potential repurposed trypanocidal drug on T. cruzi and other trypanosomatids. We found that IVM affected, in a dose-dependent manner, the proliferation of T. cruzi epimastigotes as well as the amastigotes and trypomastigotes survival. The Selectivity Index for the amastigote stage with respect to Vero cells was 12. The IVM effect was also observed in Phytomonas jma 066 and Leishmania mexicana proliferation but not in Crithidia fasciculata. On the epimastigote stage, the IVM effect was trypanostatic at 50 μM but trypanocidal at 100 μM. The assays of the drug combinations of IVM with BNZ or NFX showed mainly additive effects among combinations. In silico studies showed that classical structures belonging to glutamate-gated Cl channels, the most common IVM target, are absent in kinetoplastids. However, we found in the studied trypanosomatid genomes one copy for putative IMPα and IMPβ, potential targets for IVM. The putative IMPα genes (with 76% similarity) showed conserved Armadillo domains but lacked the canonical IMPβ binding sequence. These results allowed us to propose a novel molecular target in T. cruzi and suggest IVM as a good candidate for drug repurposing in the Chagas disease context.

Abstract Background Point-of-care lung ultrasound (LU) is an established tool in the first assessment of patients with coronavirus disease (COVID-19). Purpose of this study was to evaluate the value of lung ultrasound in COVID-19 intensive care unit (ICU) patients in predicting clinical course and outcome. Methods We analyzed lung ultrasound score (LUS) of all COVID-19 patients admitted from March 2020 to December 2020 to the Internal Intensive Care Unit, Ludwig-Maximilians-University (LMU) of Munich. LU was performed according to a standardized protocol at ICU admission and in case of clinical deterioration with the need for intubation. A normal lung scores 0 points, the worst LUS has 24 points. Patients were stratified in a low (0–12 points) and a high (13–24 points) lung ultrasound score group. Results The study included 42 patients, 69% of them male. The most common comorbidities were hypertension (81%) and obesity (57%). The values of pH (7.42 ± 0.09 vs 7.35 ± 0.1; p = 0.047) and paO2 (107 [80–130] vs 80 [66–93] mmHg; p = 0.034) were significantly reduced in patients of the high LUS group. Furthermore, the duration of ventilation (12.5 [8.3–25] vs 36.5 [9.8–70] days; p = 0.029) was significantly prolonged in this group. Patchy subpleural thickening (n = 38; 90.5%) and subpleural consolidations (n = 23; 54.8%) were present in most patients. Pleural effusion was rare (n = 4; 9.5%). The median total LUS was 11.9 ± 3.9 points. In case of clinical deterioration with the need for intubation, LUS worsened significantly compared to baseline LU. Twelve patients died during the ICU stay (29%). There was no difference in survival in both LUS groups (75% vs 66.7%, p = 0.559). Conclusions LU can be a useful monitoring tool to predict clinical course but not outcome of COVID-19 ICU patients and can early recognize possible deteriorations.

Simple Summary: Relapse and treatment-related side effects pose significant challenges for older patients with high-risk blood cancers (myelodysplastic syndrome (MDS)/acute myeloid leukemia (AML)) undergoing hematopoietic stem cell transplants from HLA-haploidentical donors. We compared two preparative treatment regimens to identify the most tolerable and effective approach: one using fludarabine, cyclophosphamide, and melphalan, and the other using fludarabine, cyclophosphamide, and treosulfan. Our goal was to determine which treatment offers better survival rates and fewer side effects. We found that both regimens resulted in similar survival outcomes. However, the melphalan regimen was associated with fewer relapses but more treatment-related deaths, while the treosulfan regimen had fewer side effects but exhibited poorer disease control. These findings suggest that treosulfan may be safer, though higher doses might improve disease control, providing valuable insights for future treatments. Relapse and regimen-related toxicities remain major challenges in achieving long-term survival, particularly among older patients with high-risk myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML) undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT). Previous studies have demonstrated the feasibility of treosulfan-based conditioning, noting stable engraftment and low non-relapse mortality (NRM) in patients undergoing HLA-matched allo-HSCT. However, data on treosulfan-based conditioning in the HLA-haploidentical transplantation (HaploT) setting are limited. We retrospectively compared conditioning with fludarabine–cyclophosphamide (FC)–melphalan (110 mg/m2) and FC-treosulfan (30 g/m2) prior to HaploT using post-transplantation cyclophosphamide (PTCy) in patients with high-risk MDS/AML patients ≥ 50 years, transplanted from 2009–2021 at our institution (n = 80). After balancing patient characteristics by a matched-pair analysis, we identified twenty-one matched pairs. Two-year OS and LFS were similar among the groups (OS 66% and LFS 66%, p = 0.8 and p = 0.57). However, FC-melphalan was associated with a significantly lower probability of relapse compared to FC-treosulfan (0% vs. 24%, p = 0.006), counterbalanced by a higher NRM (33% vs. 10%, p = 0.05). Time to engraftment and incidences of acute and chronic graft-versus-host disease (GvHD) did not differ significantly. In conclusion, HaploT using FC-treosulfan in combination with PTCy in patients aged ≥50 years with MDS/AML appears safe and effective, particularly in advanced disease stages. We confirm the favorable extramedullary toxicity profile, allowing for potential dose intensification to enhance antileukemic activity. [ABSTRACT FROM AUTHOR]

Rocky coastlines are characterised by steep cliffs, which frequently experience a variety of natural processes that often exhibit intricate interdependencies, such as rainfall, ice and water run-off, and marine actions. The advent of high temporal and spatial resolution data, that can be acquired through remote sensing and geomatics techniques, has facilitated the safe exploration of otherwise inaccessible areas. The datasets that can be gathered from these techniques, typically combined with data from fieldwork, can subsequently undergo analyses employing/applying machine learning algorithms and/or numerical modeling, in order to identify/discern the predominant influencing factors affecting cliff top erosion. This study focuses on a specific case situated at the Conero promontory of the Adriatic Sea in the Marche region. The research methodology entails several steps. Initially, the morphological, geological and geomechanical characteristics of the areas were determined through unmanned aerial vehicle (UAV) and conventional geological/geomechanical surveys. Subsequently, cliff top retreat was determined within a GIS environment by comparing orthophotos taken in 1978 and 2022 using the DSAS tool (Digital Shoreline Analysis System), highlighting cliff top retreat up to 50 m in some sectors. Further analysis was conducted via the use of two Machine Learning (ML) algorithms, namely Random Forest (RF) and eXtreme Gradient Boosting (XGB). The Mean Decrease in Impurity (MDI) methodology was employed to assess the significance of each factor. Both algorithms yielded congruent results, emphasising that cliff top erosion rates are primarily influenced by slope height. Finally, a validation of the ML algorithm results was conducted using 2D Limit Equilibrium Method (LEM) codes. Ten sections extracted from the sector experiencing the most substantial cliff top retreat, as identified by DSAS, were utilised for 2D LEM analysis. Factor of Safety (FS) values were identified and compared with the cliff height of each section. The results from the 2D LEM analyses corroborated the outputs of the ML algorithms, showing a strong correlation between the slope instability and slope height (R2 of 0.84), with FS decreasing with slope height. [ABSTRACT FROM AUTHOR]

Background. Percutaneous dilatational tracheostomy (PDT) has become the preferred method in several intensive care units (ICUs), but data on PDT performed in immunosuppressed and thrombocytopenic patients are scarce. This study aimed to analyze the feasibility of PDT in immunosuppressed and thrombocytopenic patients compared to conventional open surgical tracheostomy (OST). Methods. We retrospectively analyzed the charts of patients who underwent PDT or OST between May 2017 and November 2020. Our outcomes were stoma site infections and bleeding complications. Results. 63 patients underwent PDT, and 21 patients underwent OST. Distribution of gender ratio, age, SAPS II, time of ventilation before tracheostomy, and preexisting hematooncological diseases was comparable between the two groups. After allogeneic stem cell transplantation (alloSCT), patients were more likely to undergo PDT than OST (p=0.033). The PDT cohort suffered from mucositis more frequently (p=0.043). There were no significant differences in leucocyte or platelet count on the tracheostomy day. Patients with coagulation disorders and patients under immunosuppression were distributed equally among both groups. Stoma site infection was documented in five cases in PDT and eight cases in the OST group. Moderate infections were remarkably increased in the OST group. Smears were positive in six cases in the PDT group; none of these patients had local infection signs. In the OST group, smears were positive in four cases; all had signs of a stroma site infection. Postprocedural bleedings occurred in eight cases (9.5%) and were observed significantly more often in the OST group (p=0.001), leading to emergency surgery in one case of the OST group. Conclusion. PDT is a feasible and safe procedure in a predominantly immunosuppressed and thrombocytopenic patient cohort without an increased risk for stoma site infections or bleeding complications.

ObjectivesPoint-of-care lung ultrasound (LU) is an established tool in the first assessment of patients with coronavirus disease (COVID-19). To assess the progression or regression of respiratory failure in critically ill patients with COVID-19 on Intensive Care Unit (ICU) by using LU.Materials and methodsWe analyzed all patients admitted to Internal Intensive Care Unit, Ludwig-Maximilians-University (LMU) of Munich, from March 2020 to December 2020 suffering lung failure caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2). LU was performed according to a standardized protocol at baseline and at follow up every other day for the first 15 days using a lung ultrasound score (LUSS). Ventilation data were collected simultaneously.ResultsOur study included 42 patients. At admission to ICU, 19 of them (45%) were mechanically ventilated. Of the non-invasive ventilated ones (n = 23, 55%), eleven patients required invasive ventilation over the course. While LUS did not differ at admission to ICU between the invasive ventilated ones (at baseline or during ICU stay) compared to the non-invasive ventilated ones (12±4 vs 11±2 points, p = 0.2497), LUS was significantly lower at d7 for those, who had no need for invasive ventilation over the course (13±5 vs 7±4 points, p = 0.0046). Median time of invasive ventilation counted 18 days; the 90-day mortality was 24% (n = 10) in our cohort. In case of increasing LUS between day 1 (d1) and day 7 (d7), 92% (n = 12/13) required invasive ventilation, while it was 57% (n = 10/17) in case of decreasing LUS. At d7 we found significant correlation between LU and FiO2 (Pearson 0.591; p = 0.033), p/F ratio (Pearson -0.723; p = 0.005), PEEP (Pearson 0.495; p = 0.043), pplat (Pearson 0.617; p = 0.008) and compliance (Pearson -0.572; p = 0.016).ConclusionLUS can be a useful tool in monitoring of progression and regression of respiratory failure and in indicating intubation in patients with COVID-19 in the ICU.

The application of deep learning models to increasingly complex contexts has led to a rise in the complexity of the models themselves. Due to this, there is an increase in the number of hyper-parameters (HPs) to be set and Hyper-Parameter Optimization (HPO) algorithms occupy a fundamental role in deep learning. Bayesian Optimization (BO) is the state-of-the-art of HPO for deep learning models. BO keeps track of past results and uses them to build a probabilistic model, building a probability density of HPs. This work aims to improve BO applied to Deep Neural Networks (DNNs) by an analysis of the results of the network on training and validation sets. This analysis is obtained by applying symbolic tuning rules, implemented in Probabilistic Logic Programming (PLP). The resulting system, called Symbolic DNN-Tuner, logically evaluates the results obtained from the training and the validation phase and, by applying symbolic tuning rules, fixes the network architecture, and its HPs, leading to improved performance. In this paper, we present the general system and its implementation. We also show its graphical interface and a simple example of execution.

The aim of this study is to assess the potential of Earth Observation and climate data for the forestry sector focusing on the Copernicus Climate Change Service (C3S). Although forestry researchers recognize the importance of Earth Observation and climate data, forestry practitioners currently work mainly with land cover information, largely neglecting climate data. Understanding its potential for the forestry sector becomes thus important, as to align the vast offer of climate services in Europe to different forestry users and stakeholders’ necessities. Interviews, surveys, and dedicated workshops were used to collect a series of forestry end-users’ needs and requirements regarding climate data. End-user’s requirements were categorized through a SWOT analysis, which allowed to identify perceived internal strengths and weaknesses, external opportunities and threats to the increased use of the C3S. Results indicate that improved climate services for the forestry sector based on C3S data would benefit from enhanced training on the use of climate data, improved provision of services integrating climate with non-climate data, the provision of new variables and indicators, and the integration of machine learning techniques for developing data and information in support of the deployment of climate services. These findings are relevant to close the gap between demand and supply of climate services for the forestry sector and provide a basis for further exploring the value of climate data in serving a wide array of forestry stakeholders. Going forward, increased knowledge on user requirements from both forest practitioners and policy-makers can be beneficial to develop accessible tailored services.

Integrin-linked kinase (ILK) is an important mediator of integrin signaling. Here Park et al. show that mice with endothelial-specific deletion of Ilk develop vascular defects that resemble familial exudative vitreoretinopathy, and identify mutations in ILK in patients with exudative vitreoretinopathy suggesting a potential role in human pathogenesis.

The characterization of new materials for enabling gear design is definitely a fundamental objective in the gear industry and research. Single Tooth Bending Fatigue (STBF) tests can be performed to speed up this process. However, it is well known that STBF tests tend to overestimate material strength compared to tests performed directly on meshing gears (MG) which, in turn, require an excessively long test time. Therefore, it is common practice to use a constant correction factor fkorr of 0.9 to translate STBF results for designing actual MG (e.g., via ISO 6336). Recent works involving a combination of Finite Element Models (FEM) and multiaxial (non-proportional) fatigue criteria based on the critical plane concept have highlighted that the assumption of considering fkorr as a constant independent of the gear design parameters leads to inaccurate results. However, in previous studies, no correlation between fkorr and gear design parameters has emerged. In the present paper, the influence of the normal pressure angle (αn), the profile shift coefficient (x*), and the normal module (mn) on fkorr was investigated by analyzing FEM simulations with the Findley fatigue criterion. 27 gear geometries were studied by varying the above 3 parameters in 3 levels (full factorial DOE). These geometries were simulated in both MG and STBF configurations. The results of the 54 FEM simulations were analyzed by applying the Findley fatigue criterion and the corresponding fkorr were calculated. The correlation between fkorr and αn, x* and mn was investigated using the Analysis of Variance (ANOVA) technique. The results show that the only gear design parameter influencing fkorr is x* hence, a regression model for fkorr including x* has been developed. This latter has been then adopted for calculating and comparing fkorr values from other combination of the parameters found in literature, giving good correspondence.