Cause-effect Relationship Research Articles

Retinal Microvascular Characteristics and Outcomes in Hypertensive Disorders of Pregnancy.

Background Hypertensive disorders of pregnancy (HDP) is a continuum of chronic hypertension, gestational hypertension, preeclampsia, and eclampsia in increasing severity, associated with a higher risk of complicated pregnancies and poor neonatal outcomes. This multisystem involvement can be assessed by fundoscopy, which serves as an indicator for generalized microvascular abnormalities. Our study aims to evaluate the correlation of hypertensive retinopathy with the severity of HDP and maternal and fetal outcomes. Materials and methods The study was conducted at a tertiary care hospital in Vijayapura from October 2021 to March 2022 among admitted cases of HDP. Detailed history, blood pressure (BP) measurement, obstetric examination, and fundoscopy were performed for all cases. Patients were followed up until the 10th postnatal day. The mode of delivery, birth weight, gestational age at birth, and any other neonatal outcomes were noted. Results We included 94 preeclampsia/eclampsia patients with a median age of 23 years, 51 (54.3%) being primigravida. Patients with chronic hypertension, gestational hypertension, and chronic hypertension superimposed by preeclampsia/eclampsia were excluded. The most common symptom in mothers was headache (23.4%), followed by blurring of vision (20.2%) and epigastric pain (5.3%) with a significant association (p < 0.05). Thirty-two cases (34%) had preterm deliveries with a positive association with the severity of retinopathy (p < 0.05). The magnitude of hypertensive retinopathy was 56.3% (53 cases), the severity of which significantly correlated to the severity of HDP (p < 0.05). We report 8.5% neonatal mortality and 22.3% small for gestational age (SGA) with a positive association with HDP severity (p < 0.05). There was no correlation between serum creatinine levels and the severity of retinopathy and fetal outcome. Conclusion The occurrence and severity of hypertensive retinopathy increase with increasing severity of HDP. Complaints, such as headache, blurred vision, and epigastric pain, are reported higher in cases with retinopathy. The severity of retinopathy may be used as an indicator of fetal morbidity; however, studies with large sample sizes and advanced tools are required to quantify the cause-effect relationship. The retinopathy associated with HDP resolves naturally with BP control postnatally.

Experimental investigations on the formation mechanisms of shrink lines in powder bed fusion of metals using a laser beam

The powder bed fusion of metals using a laser beam enables the tool-free fabrication of complex part geometries with merging areas and rapid cross-sectional changes. Together, these geometry features represent a structural transition leading to the formation of shrink lines. These notches on the surface of the part reduce the dimensional accuracy and the fatigue resistance. Shrink lines arise in various materials, with the dimensions of the shrink line depending on the geometric design. The formation mechanisms and influencing parameters of shrink lines have not been investigated yet. This paper demonstrates the extent of influence of the part geometry on the shrink line formation, which was quantified by varying the design of a representative structural transition. In addition, the positions of the specimens on the build platform and the scanning strategy were varied for deriving a cause-effect relationship using process monitoring. The results demonstrated that the shrink line formation was mainly caused by a local overheating at the structural transition and the global cooling behavior. The radius at the structural transition indicated the most significant impact among the investigated geometric parameters. The shrink line dimensions depended significantly on the orientation of the specimens on the build platform and the local scanning strategy applied at the height of the structural transition. The results can be used to reduce shrink lines by re-designing the part and to adjust the manufacturing strategy for structural transitions.

Application of qualitative modelling to improve system understanding of the stressed elbe estuary

An estuary is a complex system that encompasses numerous, complex interactions between environmental factors and processes that are directly or indirectly influenced by human activities. A well-studied estuary is the Elbe estuary, which is under pressure from human activities. About 2300 publications focus on scientific aspects of its hydrology, morphodynamics, biology, chemistry or a combination of these, covering water, sediment and human interventions, among other topics. While it is important to understand the processes, selecting actions to improve the system should be based on a deep understanding of the estuary system as a whole and a confrontation with the complex interrelationship of the components that make up the estuary. This can be overwhelming, as most humans are able to understand only three or four indirectly related parameters simultaneously, whereas numerous variables are interlinked and affect each other in the environment. The resulting reluctance to address such an issue combined with a lack of common language of citizens, scientists and planning authorities can hamper public acceptance of management measures.In this paper, we use the software iModeler to describe the Elbe estuary in its complexity as a stressed system and present results from the application of the model by a group of scientists from different backgrounds. This model is not intended to be an alternative to – for example – mathematical-hydrological modelling. It also does not claim to be factually correct, and it is certainly not complete. It should be seen as an exercise to deal with complex interactions in a simple way and to develop a deeper understanding of the system. Participants in the exercise defined 46 factors and 112 direct linkages. The model identified contaminant availability, turbidity and nutrient concentrations as the stressors with the greatest impact on the quality of the Elbe estuary. Dredging of shipping channels was the activity with the greatest negative impact, and extending nature protection areas would have the highest positive effect.The results of the model, although subjective to some extent, were plausible when compared to the literature. The possibility of describing a more differentiated cause-effect relationship for some factors and their direct connection would have been beneficial. However, such collaborative qualitative modelling facilitates knowledge sharing, can reveal indirect effects and raises awareness of those factors that are strongly interwoven within system, and would have a large cumulative effect on the respective goal.

A developmental neurotoxicity adverse outcome pathway (DNT-AOP) with voltage gate sodium channel (VGSC) inhibition as a molecular initiating event (MiE).

The adverse outcome pathway (AOP) framework serves as a practical tool for organising scientific knowledge that can be used to infer cause-effect relationships between stressor events and toxicity outcomes in intact organisms. However, a major challenge in the broader application of the AOP concept within regulatory toxicology is the development of a robust AOPs that can withstand peer review and acceptance. This is mainly due to the considerable amount of work required to substantiate the modular units of a complete AOP, which can take years from inception to completion. The methodology used here consisted of an initial assessment of a single chemical hazard using the Integrated Approach to Testing and Assessment (IATA) framework. An evidence-based approach was then used to gather empirical evidence combining systematic literature review methods with expert knowledge to ensure the effectiveness of the AOP development methodology. The structured framework used assured transparency, objectivity and comprehensiveness, and included expert knowledge elicitation for the evaluation of key event relationships (KERs). This stepwise approach led to the development of an AOP that begins with binding of chemicals to Voltage Gate Sodium Channels (VGSC/Nav) during mammalian development leading to adverse consequences in neurodevelopment evidenced as deficits in cognitive functions. Disruption of the formation of precise neural circuits by alterations in VGSC kinetics during the perinatal stages of brain development may also underlie neurodevelopmental disorders. Gaps in our understanding include the specific critical developmental windows and the quantitative relationship of binding to VGSC and subsequent disruption and cognitive function. Despite the limited quantitative information at all KER levels, regulatory applications of this AOP for DNT assessment have been identified.

An Experimental Murine Model to Study Lipoatrophia Semicircularis.

Lipoatrophia semicircularis is a benign pathology characterized by subcutaneous tissue atrophy that affects the skin and related structures. Its etiology remains unclear; however, in the recent few years, it has been proposed that electrostatic charges could be a potential factor. Based on this hypothesis, the aim of this work is to study the cause-effect relation between electrostatic energy and LS, providing insights into the molecular mechanisms. For this purpose, an experimental murine model was created using obese mice. One group served as a control and the other groups involved charging clothes with varying connections to the ground: through the skin, through the clothes or not connected to the ground). Skin biopsies showed that the most significant lesions, including lipophagic granulomas with inflammatory infiltrate, were found in the first group (connected to the ground through the skin). Lipophagic reactions without an inflammatory infiltrate were observed in the other groups subjected to electrical discharges. In the control mice, no histological changes were observed. Oxidative processes were also measured in lower limbs tissue. Malondialdehyde levels significantly increased in the lower limbs after electrostatic discharges. However, the presence of ground through a wire attached to highly conductive clothes around the thigh significantly reduced the effect of electrostatic charges on lipid peroxidation. To our knowledge, this is the first study in which an experimental model has been used to reproduce LS induced by electrostatic energy, suggesting a cause-effect relationship between electrostatic charge and discharge with fat tissue lesion.

Disseminating land use land cover change scenarios for improving the role of blue and green infrastructure: Evaluation and perspectives

Land use land cover (LUCC) scenarios are often co-produced by researchers and a reduced group of local actors. While most scenarios’ evaluations analyse feedback from actors involved in the scenarios making, restricting the diffusion of scientific results to the co-construction group can limit their potential to support land management. In this study, we investigate if disseminating LUCC scenarios to a broad and heterogeneous audience is effective in overcoming socio-cultural and knowledge-related barriers to blue and green infrastructure (BGINs). We presented LUCC scenarios that study the impact of different blue and green infrastructure strategies on water resources and biodiversity. In 12 workshops, we collected feedback from 147 local actors, including representatives, water management technicians, farmers and students. Findings indicate that despite the original aim of the scenarios being to support the integration of BGINs in land management, the primary benefits were a deeper understanding of future land management challenges and cause-effect relationships between different landscape elements and socioeconomic drivers of change. In addition, we observed that respondents’ professional activity significantly influences the evaluation. We conclude that, while scenarios can be an effective tool in overcoming some of the barriers of BGINs, the outcomes of scenarios can be maximised by making the expected outcomes (including conceptual ones) explicit and by acknowledging local actors’ roles in land management during the dissemination.

A significant difference of synovial mast cells in synovium from rotator cuff arthropathy compared to rotator cuff tears: A histological pilot study

ObjectiveAim of the present study was to compare the presence of Mast Cells (MCs) in synovial samples from gleno-humeral osteoarthritis (OA) and from control group. MethodsSynovial tissue samples were obtained during arthroplasty from 23 patients with gleno-humeral OA due to rotator cuff arthropathy (RCA) and from 20 patients without OA, constituting OA group and control group respectively. Before surgery self-reported pain was assessed using VAS score and OSS was used to value functional ability. Shoulder radiograph (Antero-posterior, Y-view and Grashey views) was evaluated by musculoskeletal radiologist and graded according to modified Samilson-Prieto classification.Synovial tissue, obtained during arthroplasty and arthroscopic procedure, was prepared to immunohistochemical analysis with anti-CD31 and anti-CD117 antibodies, to detect respectively endothelial cells and MCs at 40x magnification. Synovitis scores have been assessed. Under the control of the image processing system the distribution and the total number of vessels and MCs were determined. ResultsThe numbers of MCs and the area fraction (20x magnification) occupied by them were significantly higher in OA samples than in control tissue. The synovitis score was higher in OA patients with a positive correlation. Vessels number and area fraction were higher in OA patients than in controls. Analysis of MC number in relation to clinical data indicated positive correlation with the VAS score. ConclusionsThe distribution of MCs on synovium significantly differ between OA and control groups. Despite the design of the study could not conclude the cause-effect relationship, the presence of MCs might have role in OA pathogenesis. Level of evidenceHistological study.

Social media influencer marketing and economic performance in the Spanish wine industry: unravelling the role of corporate social legitimacy

PurposeThis research focuses firstly on the impact of Social Media Influencer Marketing (SMIM) on the Economic Performance (EP) of Spanish wineries, and secondly on the mediating effect of Corporate Social Legitimacy (CSL) on this main relationship (SMIM-EP). Age, size and Protected Designation of Origin (PDO) membership are used to increase the precision of the cause-effect relationships examined.Design/methodology/approachThe study proposes a conceptual model based on previous studies, which is tested using structural equations (PLS-SEM) with data collected from 196 Spanish wineries between September 2022 and January 2023.FindingsThe results reveal a positive and significant relationship between the development of SMIM and the EP of Spanish wineries, as well as the partial mediation of CSL in the SMIM-EP relationship.Originality/valueThe uniqueness of this study can be attributed to several factors. First, it enhances the understanding and knowledge of the benefits associated with SMIM development in the winemaking context. Second, no previous research has conducted an empirical study on the development of SMIM as a catalyst for EP in the context of Spanish wineries. Third, to the authors' knowledge, no previous study has examined the mediating role of CSL in the SMIM-EP relationship, thus representing an opportunity to advance scientific knowledge related to SMIM.

Cross-prevalence of multiple sclerosis and stroke evaluated on cohorts of four independent specialty medical centers

BackgroundNumerous sources reported the increased risk of cerebrovascular accidents (CVA) in individuals with multiple sclerosis (MS), without a single study thus far challenging the conclusion. Before addressing hypothesis on potential cause-effect relationships, a question whether there are indeed frequent comorbidities between MS and CVA needed to be answered. MethodsAuthors designed a study to evaluate substantial populations of four independent neurology centers with the purpose to assess the prevalence of CVA diagnosis in patients with MS, and vice versa. Data of subjects meeting enrollment criteria were entered into a single database. Authors compared obtained data with the MS and CVA prevalences in the populations of specific countries. ResultsOf the 1881 subjects’ records, the main diagnosis was MS for 51.5% and CVA for 48.5%. The CVA prevalence as a secondary diagnosis among MS subjects was 1.47% in USA cohort and 1.65% in combined USA+ Russian Federation (RF) cohorts, which is lower than known prevalence of stroke in adult population of these regions. The prevalence of MS as a secondary diagnosis among subjects with stroke in USA cohort was 0.4% and in combined USA+RF cohorts was 0.33%, which is higher, but not statistically different from calculated MS prevalence in both USA and RF populations combined. ConclusionThus, authors found that prevalence of CVA in cohorts with MS of 2 independent centers was at least not higher, than that in general population, which differs from previously published data.

Associations between urinary level of bisphenol A, phthalates, 8-iso-prostaglandin-F2α, and emotional and behavioral problems among Chinese adolescents

BackgroundEmotional and behavioral problems (EBPs) of adolescents is a worldwide public health problem. Bisphenol A (BPA) and phthalate (PAEs) are prevalent and potentially toxic to human health. Therefore, this study aimed to investigate the associations between urinary level of BPA, PAEs, 8-iso-prostaglandin-F2α (8-iso-PGF2α), and EBPs. MethodsA total of 865 Chinese adolescents were included in this study and EBPs was assessed using the Strengths and Difficulties Questionnaire (SDQ). Urinary concentrations of BPA and seven PAEs metabolites in adolescents were determined by high performance liquid chromatography-tandem mass spectrometry. Urinary 8-iso-PGF2α concentration was detected by enzyme-linked immunosorbent assay (ELISA). Spearman rank correlation analysis, multivariate logistic regression analysis, restricted cubic spline functions were used to explore the relationship between the levels of BPA, PAEs, 8-iso-PGF2α and EBPs. ResultsBPA and PAEs metabolites were positively associated with EBPs in Chinese adolescents. And the 8-iso-PGF2α was significantly non-linearly correlated with emotional symptoms, conduct problems, peer problems and total difficulties. Furthermore, 8-iso-PGF2α may partially mediate the association between BPA and PAEs exposure and EBPs. LimitationsThis study was a cross-sectional study, the cause-effect relationship between BPA, PAEs exposure and EBPs could not be determined. A single spot urine sample for BPA and PAEs exposure characterization maybe could not represent their long-term exposure level. ConclusionsHigh exposure of BPA and PAEs are associated with EBPs, which may be partly mediated by oxidative stress among adolescents. The results of this study could provide certain ideas for subsequent related research.

Neuroinflammatory activation in sensory and motor regions of the cortex is related to sensorimotor function in individuals with low back pain maintained by nociplastic mechanisms: A preliminary proof-of-concept study.

Chronic pain involves communication between neural and immune systems. Recent data suggest localization of glial (brain immune cells) activation to the sensorimotor regions of the brain cortex (S1/M1) in chronic low back pain (LBP). As glia perform diverse functions that impact neural function, activation might contribute to sensorimotor changes, particularly in LBP maintained by increased nervous system sensitivity (i.e., nociplastic pain). This preliminary proof-of-concept study aimed to: (i) compare evidence of neuroinflammatory activation in S1/M1 between individuals with and without LBP (and between nociceptive and nociplastic LBP phenotypes), and (ii) evaluate relationships between neuroinflammatory activation and sensorimotor function. Simultaneous PET-fMRI measured neuroinflammatory activation in functionally defined S1/M1 in pain-free individuals (n = 8) and individuals with chronic LBP (n = 9; nociceptive: n = 4, nociplastic: n = 5). Regions of S1/M1 related to the back were identified using fMRI during motor tasks and thermal stimuli. Sensorimotor measures included single and paired-pulse transcranial magnetic stimulation (TMS) and quantitative sensory testing (QST). Sleep, depression, disability and pain questionnaires were administered. Neuroinflammatory activation was greater in the lower back cortical representation of S1/M1 of the nociplastic LBP group than both nociceptive LBP and pain-free groups. Neuroinflammatory activation in S1/M1 was positively correlated with sensitivity to hot (r = 0.52) and cold (r = 0.55) pain stimuli, poor sleep, depression, disability and BMI, and negatively correlated with intracortical facilitation (r = -0.41). This preliminary proof-of-concept study suggests that neuroinflammation in back regions of S1/M1 in individuals with nociplastic LBP could plausibly explain some characteristic features of this LBP phenotype. Neuroinflammatory activation localized to sensorimotor areas of the brain in individuals with nociplastic pain might contribute to changes in sensory and motor function and aspects of central sensitization. If cause-effect relationships are established in longitudinal studies, this may direct development of therapies that target neuroinflammatory activation.

The Chemistry-Process-Structure Relationships of a Functionally Graded Ti-6Al-4V/Ti-1B Alloy Processed with Laser-Engineered Net Shaping Creates Borlite.

We quantify the chemistry-process-structure-property relationships of a Ti-6Al-4V alloy in which titanium-boron alloy (Ti-B) was added in a functionally graded assembly through a laser-engineered net shaping (LENS) process. The material gradient was made by pre-alloyed powder additions to form an in situ melt of the prescribed alloy concentration. The complex heterogeneous structures arising from the LENS thermal history are completely discussed for the first time, and we introduce a new term called "Borlite", a eutectic structure containing orthorhombic titanium monoboride (TiB) and titanium. The β-titanium grain size decreased nonlinearly until reaching the minimum when the boron weight fraction reached 0.25%. Similarly, the transformed α-titanium grain size decreased nonlinearly until reaching the minimum level, but the grain size was approximately 2 μm when the boron weight fraction reached 0.6%. Alternatively, the α-titanium grain size increased nonlinearly from 1 to 5 μm as a function of the aluminum concentration increasing from 0% to 6% aluminum by weight and vanadium increasing from 0% to 4% by weight. Finally, the cause-effect relationships related to the creation of unwanted porosity were quantified, which helps in further developing additively manufactured metal alloys.

Predator in proximity: how does a large carnivore respond to anthropogenic pressures at fine-scales? Implications for interface area management.

Driven by habitat loss and fragmentation, large carnivores are increasingly navigating human-dominated landscapes, where their activity is restricted and their behaviour altered. This movement, however, raises significant concerns and costs for people living nearby. While intricately linked, studies often isolate human and carnivore impacts, hindering effective management efforts. Hence, in this study, we brought these two into a common framework, focusing on an interface area between the critical tiger habitat and the human-dominated multiple-use buffer area of a central Indian protected area. We employed a fine-scale camera trap survey complemented by GPS-collar movement data to understand spatio-temporal activity patterns and adjustments of tigers in response to anthropogenic pressures. We used an occupancy framework to evaluate space use, Bayesian circular GLMs to model temporal activity, and home range and step length analyses to assess the movement patterns of tigers. Further, we used predation-risk models to understand conflict patterns as a function of tiger presence and other habitat variables. Despite disturbance, a high proportion of the sampled area was occupied by 17 unique tigers (ψ = 0.76; CI [0.73-0.92]). The distance to villages (β ± SE = 0.63 ± 0.21) and the relative abundance of large-bodied wild prey (β ± SE = 0.72 ± 0.37) emerged as key predictors of tiger space use probability, indicating a preference for wild prey by tigers, while human influences constrained their habitat utilisation. Distance to villages was also identified as the most significant predictor of the tigers' temporal activity (μ ± σ = 3.03 ± 0.06 rad) that exhibited higher nocturnality near villages. A total of 11% of tiger home ranges were within village boundaries, accompanied by faster movement in these areas (displacement 40-82% higher). Livestock depredation probability by tigers increased with proximity to villages (P = 0.002) and highway (P = 0.003). Although tiger space use probability (P = 0.056) and wild prey abundance (P = 0.134) were non-significant at the 0.05 threshold, their presence in the best-fit predation-risk model suggests their contextual relevance for understanding conflict risk. The results highlight the importance of appropriately managing livestock near human infrastructures to effectively mitigate conflict. Shared space of carnivores and humans requires dynamic site-specific actions grounded in evidence-based decision-making. This study emphasises the importance of concurrently addressing the intricate interactions between humans and large carnivores, particularly the latter's behavioural adaptations and role in conflict dynamics. Such an integrated approach is essential to unravel cause-effect relationships and promote effective interface management in human-dominated landscapes.

Tomographic Reconstruction Of Combustion Flow Fields From Density Measurements Based On Physics-Informed Neural Networks

Achieving stable, low-emission combustion with green hydrogen is crucial for climate-neutral ground-based power generation in turbomachinery. Lean combustion modes with green hydrogen reduce fuel consumption but increase unsteadiness. Thus, multimodal detection techniques for parameters like density and flow velocity are essential to understand the interconnected behavior of combustion, advection velocity and noise production. We previously introduced a high-speed camera-based laser interferometric vibrometer system to detect thermoacoustic oscillations and record advection velocities, using interferometric detection of density fluctuations and correlation-based velocity estimation. However, this method only estimates integral velocity fields, necessitating the solution of the inverse problem. This is relying on iterative optimization, which is computationally expensive and struggles with high dimensional, noisy or limited data. Physics-Informed Neural Networks offer an innovative and efficient approach to these problems, combining neural network flexibility with physical law constraints to unravel intricate cause-effect relationships. Here an approach for reconstructing local velocity fields from a single projection velocity calculated from integral density data is presented. Using a U-net and model assumptions for coupling local and integral velocity fields, the training minimizes errors between measured integral input fields and the predicted local field integrals. The comparison of measured local velocity and the network prediction achieved a relative mean squared error of 3 %.

Heading for new shores? Longitudinal participation patterns in teacher professional development

ABSTRACT Recent research indicated the advantages of diversified teacher professional development participation patterns (PDPP) to foster teachers’ professionalisation. This study investigated teachers’ PDPP from both cross-sectional and longitudinal perspectives using data from the National Educational Panel Study (NEPS) utilising information on teachers’ professional development enrolment across three years (N = 3,539). Results from latent class and multiple group analyses indicated that teachers showing higher professional engagement and teachers who were granted paid temporary leave from teaching demonstrated more diversified PDPP. Multiple logistic regression models revealed that the same variables seemed to prevent changes to less diversified PDPP over time. If our study results are confirmed in replications and studies that allow to detect cause-effect relationships, implications for educational policy and practice could be to raise teachers’ professional engagement and grant engaged teachers a paid temporary leave from teaching to participate in professional development activities.

A Look at the Problems of Transhumance in Northeastern Anatolia with a Cause-Effect Relationship: Ardahan Example

A Look at the Problems of Transhumance in Northeastern Anatolia with a Cause-Effect Relationship: Ardahan Example

From single trees to country-wide maps: Modeling mortality rates in Germany based on the Crown Condition Survey

Most years in the period from 2018 to 2022 have been exceptionally dry in Central Europe. In Germany’s forests, this long-lasting drought has caused unprecedented tree mortality. Systematic ground-based surveys, such as the annual Crown Condition Survey, provide information on the vitality status of the different tree species and their mortality rates. However, models are needed to be able to map the spatial patterns of mortality for each tree species based on cause-effect relationships derived from field observations. In this study, logistic regression models were used to identify the most important drivers of mortality for the most important tree species in Germany. For this purpose, the dead and surviving trees from the Crown Condition Survey were combined with a large set of potential predictor variables from the domains of climate, topography, soil, land cover and deposition. After feature selection, the models were evaluated using the area under the curve (AUC) statistic. Norway spruce (Picea abies; AUC = 0.9) showed by far the greatest increase in mortality, with the country-wide average observed and predicted rates approaching almost 10% per year from 2020 to 2022, and much higher predicted rates at the regional level. Much of the spruce mortality was explained by the climatic water balance of the driest summer in previous years. The other main tree species also showed clear mortality responses to the drought conditions. However, in the case of European beech (Fagus sylvatica; AUC = 0.94) and Pedunculate and Sessile oak (Quercus robur and petraea; AUC = 0.88), the peaks in the time series of the country-wide mortality rates stayed below 1%. For these broadleaved species, mortality was more dependent on a range of site conditions, i.e., soil and topography. For Scots pine (Pinus sylvestris; AUC = 0.76), for which the observed mortality rate peaked at 1.2% in 2020, the given drivers could explain mortality only to a lesser degree than for the other species. The regression models were used for spatial prediction to produce country-wide maps of species-specific mortality rates at annual temporal and 100-m spatial resolution, covering all years from 1998 to 2022. The maps visualize the spatial patterns of mortality over time. The regions in western and central Germany, which were most seriously affected by spruce dieback can clearly be identified. The models and maps presented can be used for risk assessment, forest planning, and tree species selection, providing decision support for forest practitioners.

NeuroMotion: Open-source platform with neuromechanical and deep network modules to generate surface EMG signals during voluntary movement.

Neuromechanical studies investigate how the nervous system interacts with the musculoskeletal (MSK) system to generate volitional movements. Such studies have been supported by simulation models that provide insights into variables that cannot be measured experimentally and allow a large number of conditions to be tested before the experimental analysis. However, current simulation models of electromyography (EMG), a core physiological signal in neuromechanical analyses, remain either limited in accuracy and conditions or are computationally heavy to apply. Here, we provide a computational platform to enable future work to overcome these limitations by presenting NeuroMotion, an open-source simulator that can modularly test a variety of approaches to the full-spectrum synthesis of EMG signals during voluntary movements. We demonstrate NeuroMotion using three sample modules. The first module is an upper-limb MSK model with OpenSim API to estimate the muscle fibre lengths and muscle activations during movements. The second module is BioMime, a deep neural network-based EMG generator that receives nonstationary physiological parameter inputs, like the afore-estimated muscle fibre lengths, and efficiently outputs motor unit action potentials (MUAPs). The third module is a motor unit pool model that transforms the muscle activations into discharge timings of motor units. The discharge timings are convolved with the output of BioMime to simulate EMG signals during the movement. We first show how MUAP waveforms change during different levels of physiological parameter variations and different movements. We then show that the synthetic EMG signals during two-degree-of-freedom hand and wrist movements can be used to augment experimental data for regressing joint angles. Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. In this way, NeuroMotion was able to generate full-spectrum EMG for the first use-case of human forearm electrophysiology during voluntary hand, wrist, and forearm movements. All intermediate variables are available, which allows the user to study cause-effect relationships in the complex neuromechanical system, fast iterate algorithms before collecting experimental data, and validate algorithms that estimate non-measurable parameters in experiments. We expect this modular platform will enable validation of generative EMG models, complement experimental approaches and empower neuromechanical research.

Green supply chain management and sustainable performance: exploring the role of green ambidexterity innovation and top management environmental awareness

PurposeThis research focuses on analyzing the impact of Green Supply Chain Management (GSCM) on the Sustainable Performance (SP) of Spanish wineries, as well as the mediating effect of Green Ambidexterity Innovation (GAI) and the moderating role of Top Management Environmental Awareness (TMEA) on the main relationship posited (GSCM-SP). In addition, age, size and membership in a Protected Designation of Origin (PDO) were used to increase the precision of the cause-effect relationships examined.Design/methodology/approachThe study proposes a conceptual model based on previous studies, which is tested using structural equations (PLS-SEM) with data collected from 196 Spanish wineries between September 2022 and January 2023.FindingsThe results of the research reveal the existence of a positive and significant relationship between the development of GSCM and SP of Spanish wineries, as well as the partial mediation of GAI and the positive moderation of TMEA in this linkage.Originality/valueThe novelty of this study lies in its exploratory approach, as it ventures into new research territory by examining the mediating effect of GAI and the moderating influence of TMEA on the relationship between GSCM and SP, being, to the authors' knowledge, the first research to investigate these specific dynamics.

Multi-feature hybrid network for traffic flow prediction based on mobility patterns

Future flow prediction in spatiotemporal traffic data is a critical requirement for real-world applications, particularly for multi-feature and large-scale data with intricate forecasting mechanisms and varied predictability. Prior sequence-to-sequence studies have demonstrated the superiority of attention learning in prediction tasks by effectively capturing reliable dependencies/correlations. However, despite the promising results, the single-feature input pattern of traffic prediction causes an information utilization bottleneck. In this paper, we focus on discovering the cause-effect relationship of traffic dynamics by fusing the origin–destination (OD) flow. To achieve both high accuracy and universality, we design a scalable composition architecture—a Multi-Feature Hybrid Network (MFHN)—based on the existing framework of spatiotemporal feature modeling. In particular, we break with the preprocessing convention of feature composition and propose a Hybrid-Correlation mechanism by integrating similar subseries of the OD flow. Furthermore, inspired by graph learning, we introduce the mobility pattern based on the OD flow, which reveals the node-to-node dependencies. In the experiment, we consider various prediction tasks with state-of-the-art baseline models and find that the MFHN yields competitive accuracy in short- and long-term prediction.