Modeling Analysis Research Articles

Relationships Between Quality of Discharge Teaching, Readiness for Hospital Discharge, Self-Efficacy and Self-Management in Patients With First-Episode Stroke: A Cross-Sectional Study.

The study investigated the influence of quality of discharge teaching (QDT) on readiness for hospital discharge (RHD) and pathways involved in patients with first-episode stroke, aiming to provide a theoretical framework for enhancing RHD levels and reducing readmission rates. Cross-sectional study. A total of 372 inpatients completed the Quality of Discharge Teaching Scale, Readiness for Hospital Discharge Scale, Chronic Disease Self-efficacy Scale and Southampton Stroke Self-Management Questionnaire. Structural equation modelling and Pearson's correlation analysis were utilised to elucidate relationships and action pathways among these variables. The correlation analysis demonstrated significant positive pairwise correlations between QDT, RHD, self-efficacy and self-management (r = 0.376-0.678, p < 0.01). The final model exhibited a good fit with the following indices: χ2/df = 3.286, RMSEA = 0.078, SRMR = 0.0303, GFI = 0.984, AGFI = 0.926, CFI = 0.991 and TLI = 0.970. The impact of QDT on RHD in patients with first-episode stroke was observed through one direct and three indirect pathways: (1) QDT exerted a direct influence on RHD (p < 0.001); (2) QDT indirectly influenced RHD via self-efficacy (p < 0.001); (3) QDT indirectly affected RHD through self-management (p < 0.001); and (4) QDT had an indirect effect on RHD via both self-efficacy and self-management (p < 0.05). QDT was found to directly influence RHD in patients with first-episode stroke and also exerted indirect effects through self-efficacy and self-management, either independently or in combination. Early screening of RHD levels in patients before discharge is recommended, along with the enhancement of QDT through the development of tailored guidance plans according to different disease stages, ultimately improving RHD levels and facilitating a safer transition from hospital to home or community. Healthcare professionals should assess both QDT and RHD levels to provide targeted interventions. The establishment of transitional care teams and implementation of long-term poststroke management are essential for reducing stroke recurrence and mortality rates.

Revolutionizing Cybersecurity with AI: Predictive Threat Intelligence and Automated Response Systems

The sophistication and breadth of cyber threats are continuously expanding, making it more difficult for traditional security measures to keep up. Artificial intelligence is revolutionizing cybersecurity by equipping businesses to proactively counter threats with automated reaction systems and predictive threat intelligence. Data analytics, behavioral analysis, and machine learning enable AI-powered systems to anticipate cyber assaults, enabling more efficient and rapid threat detection. By automating reaction mechanisms and mitigating threats in real-time, AI systems can minimize human error and maximize damage mitigation. AI techniques, such as anomaly detection, predictive modeling, and real-time threat analysis; data privacy, ethics, and the risks of hostile attacks are among the subjects covered, as are the benefits and drawbacks of utilizing AI in cybersecurity. This article provides the framework for future intelligent, automated cyber defense methods and illustrates how AI may alter cybersecurity using real-life examples and case studies.

Associations between mother and father involvement with older adolescents: Mediating effects of parent‐adolescent and couple relationships

AbstractObjectiveThis study aims to illuminate the dynamic interplay among subsystems within the family context to understand how mother and father involvement in their adolescent children's lives may be related.BackgroundConsistent with different predictions offered by family spillover and by compensatory conceptual models, whether there is an association between mother and father involvement, as well as the direction of the association, is unclear thus far in the empirical literature. Whether more proximal dyadic relationships, such as parent–adolescent relationships, may mediate the association between mother and father involvement has also been underexamined.MethodThis study was conducted using the adolescent subsample drawn from Wave 3 of the Welfare, Children, and Families Project. A total of 463 ethnically diverse adolescents from low‐income households living with two biological parents were involved in this study.ResultsResults of a structural equation modeling analysis indicated that the total effect from mother involvement to father involvement was significant, but a direct association between them was not significant; closeness in parent–adolescent and coparental relationships mediated the relationship between the two. Study results also indicated that model fits and associations were roughly the same across adolescent sex and ethnicity groups.ConclusionThis study advanced the understanding of the father involvement process. The findings support that parental involvement interventions for parents from low‐income families should focus on developing culturally responsive strategies for promoting their parenting beliefs and healthy functioning.

Dynamic modeling and response analysis of misaligned rotor system with squeeze film dampers under maneuver loads

Dynamic modeling and response analysis of misaligned rotor system with squeeze film dampers under maneuver loads

Role of online health communities in patient compliance: a social support perspective

Purpose Online health communities (OHC) can transform the healthcare industry, particularly in developing economies. Technology advancement and increased health literacy pave the way for these communities to become powerful tools for empowering patients. The purpose of this study was to empirically validate the linkages between social support and how it overarchingly influences patient compliance. Following social support theory, this study delineates how support from the community affects the patient–physician relationship (PERP) and consequently patient compliance regarding the treatment plan. This study also invents the role of patient trust in an OHC in moderating the relationship between PERP and engagement. Design/methodology/approach This paper is based on social support and empowerment theories to investigate the importance of social support in improving patients’ health behaviours and health outcomes via patient empowerment, patient engagement and patient compliance. The authors surveyed users from three Facebook cancer communities in India to collect data. The authors used partial least squares structured equation modelling and necessary condition analysis (NCA) with 265 participants to support the proposed model. Findings The result demonstrates that PERP is a crucial factor for patient engagement in OHC, and patient engagement has a significant effect on patient compliance. The results also showed that trust was a significant moderator between PERP and engagement. The NCA analysis shows all the relationships are significant; however, emotional support is not a necessary condition for PERP. Research limitations/implications By empowering cancer patients and enabling them to meet their emotional and informational needs through OHCs, the study model can aid in the development of solutions that will improve compliance with their treatment in an emerging economic context. The findings indicate the potential chain reaction of social support and PERP in online cancer health communities. This study also contributes to quantifying the social impacts of online healthcare services and how to enhance the healthcare compliance framework. Originality/value This study combines social support and empowerment theory with patient, physician, and technology to provide a fine-grained picture of PERP in OHC. It explains how social support in OHC promotes self-care behaviour. This linkage validation enables readers and the community at large to gain a more nuanced understanding of how social support – through PERP, engagement and trust – enables patient compliance using primary data.

Fusion of spectral and topographic features for land use mapping using a machine learning framework for a regional scale application.

This study investigated the dynamics of land use and land cover (LULC) modelling, mapping, and assessment in the Kegalle District ofSri Lanka, where policy decision-making is crucial in agricultural development where LULC temporal datasets are not readily available.Employing remotely sensed datasets and machine learning algorithms, the work presented here aims to compare the accuracy of three classification approaches in mapping LULC categories across the time in the study area primarily using the Google Earth Engine (GEE). Three classifiers namely random forest (RF), support vector machines (SVM), and classification and regression trees (CART) were used in LULC modelling, mapping, and change analysis. Different combinations of input features were investigated to improve classification performance. Developed models were optimised using the grid search cross-validation (CV) hyperparameter optimisation approach. It was revealed that the RF classifier constantly outstrips SVM and CART in terms of accuracy measures, highlighting its reliability in classifying the LULC. Land cover changes were examined for two periods, from 2001 to 2013 and 2013 to 2022, implying major alterations such as the conversion of rubber and coconut areas to built-up areas and barren lands. For suitable classification with higher accuracy, the study suggests utilising high spatial resolution satellite data, advanced feature selection approaches, and a combination of several spatial and spatial-temporal data sources. The study demonstrated practical applications of derived temporal LULC datasets for land management practices in agricultural development activities in developing nations.

Optimizing Touchless Fingerprint Identification: A Machine Learning Approach to Modelling and Performance Evaluation

This paper explored the modelling and performance analysis of a smartphone-based fingerprint identification system using Convolutional Neural Networks (CNN). The research developed a theoretical framework to validate picture-based fingerprint identification as a feasible alternative to traditional touch-based methods. A modified Automated Fingerprint Identification System (AFIS) model served as the study's foundation. To enhance the model's capabilities, data from two databases, IIT India and SOCOFing, were utilized. The evaluation of the CNN architecture focused on mobile device fingerprint recognition. It emphasized key processes such as data pre-processing, model training, and the optimization of the CNN through a Siamese-CNN approach to boost accuracy and efficiency. Python scripts developed for this purpose were converted to Android code using TensorFlow for deployment on Android devices. Performance metrics, including identification accuracy, processing speed, and resource utilization, were analysed to determine the system's feasibility. The results demonstrated that CNN-based fingerprint identification systems hold significant promise for delivering robust and reliable biometric authentication on smartphones, highlighting both their practical applications and limitations. decrease medical as well as financial burden, hence improving the management of cirrhotic patients. These predictors, however, need further work to validate reliability.

Comparative Analysis of Armature Structure on Constant Force Characteristics in Long-Stroke Moving-Iron Proportional Solenoid Actuator

The influence of key design parameters on the constant force characteristics of long-stroke moving-iron proportional solenoid actuators (MPSAs) has been explored by a method combining finite element modelling and correlation analysis. First, the finite element model (FEM) of long-stroke MPSA was developed and validated. Subsequently, the two evaluation indexes, the average-output solenoid force and maximum-output solenoid force variability, were introduced to disclose the influence law of pole shoe parameters on the constant force characteristics of a long-stroke MPSA. After that, correlation analysis was employed to quantify the influence of several parameters and parameter interaction factors on the constant force characteristics. The results indicate a strong contradiction between the average-output solenoid force and maximum-output solenoid force variability; however, increasing the inner diameter of the cone helps enhance the average-output solenoid force without causing maximum-output solenoid force variability to increase. Among all the parameters examined, the cone angle is the most significant parameter affecting the constant force characteristics. Additionally, interactions between the cone angle and the cone length, the cone angle and the inner cone diameter, the cone angle and the outer cone diameter, the cone length and the outer cone diameter, as well as the inner cone diameter and the outer cone diameter also have an important influence on the constant force characteristics. This study deepens our understanding of how the key parameters affect the constant force characteristics and assists designers in optimizing these parameters for developing new structures.

Biochar affects organic carbon composition and stability in highly acidic tea plantation soil

Biochar amendments are effective in stabilizing soil aggregates and improving soil organic carbon (SOC) content. However, the effects of biochar on highly acidic soil and their relation with soil SOC stability remain understudied. The study aimed to investigate the impact of biochar on changes of aggregate distribution and SOC stability in a highly acidic tea plantation soils over an eight-year period. Soil samples were collected from plots with varying biochar application amounts (0, 2.5 t ha−1, 5 t ha−1, 10 t ha−1, 20 t ha−1 and 40 t ha−1). The content of SOC, iron bound organic carbon (OC-Fe), particulate organic carbon (POC), mineral-associated organic carbon (MAOC) and the functional group composition of SOC was analyzed. The results indicated that in the biochar application treatments, the value of soil pH, SOC, POC and MAOC contents were increased from 3.92 to 4.28, 6.68%–187.02%, 8.31%–66.78% and 13.07%–236.47% respectively, compared with CK, while the content of macro-aggregate (particle size>0.25 mm) and soil aggregates mean weight diameter (MWD) significantly increased with higher biochar application amounts. But dissolved organic carbon (DOC) and OC-Fe content exhibited downward trend, decreased from 2.43% to 6.97% and 4.18%–19.91%. Furthermore, aromatic-C levels increased, with increased biochar application amounts. The integration of biochar not only bolstered soil aggregate stability but also amplified the presence of aromatic-C, thereby enhancing the resilience of organic carbon in highly acidic tea garden soil (BC40 > BC20 > BC5>BC2.5 > BC10 > CK), with increases ranging from 6% to 47%. The principal component analysis and structural equation modeling identified soil pH, TN, SOC, POC, MAOC, R > 0.25 and MWD as key factors of soil organic carbon stability. These findings provide crucial insights into the mechanism underlying biochar's efficiency in fortifying organic carbon stability, particularly in the context of highly acidic soil.

Artificial Intelligence Integration and Social Innovation: Interdisciplinary Research Trends Aligned with the Sustainable Development Goals

This study investigates the integration of Artificial Intelligence (AI), Machine Learning, Natural Language Processing (NLP), and Prompt Engineering into the social sciences and their impact on collaborative networks, thematic developments, and research trends aligned with the Sustainable Development Goals (SDGs). Utilizing bibliometric analysis and topic modeling, the research analyzes a dataset of 389 publications from the Web of Science (WoS) database, spanning the last decade. The findings highlight significant growth in interdisciplinary research at the intersection of these technologies and social sciences, with notable contributions in management, business, and environmental studies. Key themes identified include AI-driven innovation in product development, energy sector advancements, and the role of AI in educational and healthcare settings. The study underscores the transformative potential of AI in driving sustainable development, while also emphasizing the importance of addressing ethical considerations and ensuring responsible implementation. This research contributes to a deeper understanding of how AI and related technologies are reshaping the social sciences and their role in achieving global sustainability goals.

COSMIC Database and Structural Modeling Analysis of CYP2D6 Mutations in Human Cancers.

Single nucleotide polymorphisms (SNPs) in cytochrome P450 (CYP450) enzymes alter the metabolism of a variety of drugs. Numerous medications, including chemotherapies, are metabolized by CYP450 enzymes, making the expression of this suite of enzymes in tumor cells relevant to prescription regimens for cancer patients. We analyzed the characteristics of mutations of the CYP2D6 enzymes in cancer patients obtained from the Catalogue of Somatic Mutations in Cancer (COSMIC), including mutation type, age of the patient, tissue type, and histology. Mutations were analyzed through the Cancer-Related Analysis of Variants Toolkit (CRAVAT) software along with CHASM and VEST4 algorithms to determine the likelihood of being a driver and/or pathogenic mutation. For mutations with significant CHASM and VEST4 scores, structural analysis of each corresponding mutant protein was performed. The effect of each mutation was evaluated for its impact on the overall protein stability and ligand binding using Foldit Standalone and SwissDock, respectively. Structural analysis revealed that several missense mutations in CYP2D6 resulted in altered stability after energy minimization. Three missense mutations of CYP2D6 significantly altered docking stability and those located on alpha-helices near the docking site had a more significant impact than those not found in secondary protein structures. In conclusion, we have identified a series of mutations to CYP2D6 enzymes with possible relevance to cancer pathologies. Significance Statement CYP2D6 is responsible for the metabolism of many anti-cancer drugs. This study identified and characterized a series of mutations in the CYP2D6 enzyme that occurred in tumors. We found it likely that many of these mutations would alter enzyme function, leading to changes in drug metabolism in the tumor. We provide a basis for predicting the likelihood of a patient carrying these mutations to identify patients who may benefit from a precision medicine approach to drug selection and dosing.

Experimenting with artificial shelters to assist habitat restoration for medium‐sized ground‐dwelling mammals

Many terrestrial vertebrates require microhabitat shelter structures for survival. Where anthropogenic or environmental disturbances have degraded or depleted shelter, artificial shelters are increasingly used to provide supplementary habitat for various taxa. However, their application to medium‐sized ground‐dwelling mammals (MGMs) remains largely unexplored. We installed rudimentary artificial shelters in a conservation reserve to emulate the vegetative cover used as refuge by three Australian MGMs: the long‐nosed (Perameles nasuta) and northern brown bandicoots (Isoodon macrourus), and the vulnerable long‐nosed potoroo (Potorous tridactylus). We used multi‐method occupancy modeling and behavioral analysis to compare the detections and behaviors of the target species with those of four non‐target species. Our study design included three plot types (treatments)—artificial shelter, baited lure, and disturbance control—enabling unambiguous evaluation of responses to the shelters. The bandicoots showed no difference in detection among treatments, whereas detection of the potoroo was highest at control plots. Detection of non‐target species was generally highest at baited plots. The target species demonstrated a much higher willingness to enter the shelters compared to the non‐target species. The use of the structures appeared to be exploratory, with no evidence that individuals remained in shelters during the day. There was limited evidence that the shelters reduced the target species' perceptions of predation risk and no evidence that predators were attracted to the shelters. The high availability of natural shelter and our small sample size likely influenced these findings. Nevertheless, they lay the foundation for research and refinement into more effective shelter designs.

Health-related quality of life and utility of maternity health states amongst post-partum Australians.

This study aimed to measure patient-reported health-related quality of life amongst post-partum women in Queensland, Australia. Patient-reported health-related quality of life data was prospectively collected from 134 post-partum women using the EQ-5D-5L at weekly intervals during the first six weeks following birth. Data across the five health domains of the EQ-5D-5L was converted to a single health utility value to represent overall health status. Linear mixed modelling and regression analysis were used to examine changes in utility over the first six weeks post-birth and determine associations between utility and clinical and demographic characteristics of post-partum women. Gestation at birth and weeks post-partum were significantly associated with utility values when considered in a multivariate linear mixed model. Mean utility values increased by 0.01 for every week increase in gestation at birth, and utility values were 0.70 at one week post-partum and increased to 0.85 at six weeks post-partum, with the largest increase occurring between one- and two-weeks post-birth. When controlling for variables that were found to predict utility values across the first six weeks post-partum, no single state of health predicted utility values at one-week post-partum. Maternity services can use our data and methods to establish norms for their own service, and researchers and maternity services can partner to conduct cost-effectiveness analysis using our more relevant utility values than what is currently available. Time since birth and gestational age of the woman's baby should be considered when selecting post-partum health state utility values for maternity services cost-effectiveness analyses.

Experiential value and environmentally responsible behavioral intention in rock-climbing tourism: The role of place attachment and biospheric value

With increasing emphasis on environmental issues in outdoor leisure tourism, understanding the environmentally responsible behavioral intention (ERBI) of tourists is crucial to sustainable development. We explored the novel chain-mediating effect of experiential value on ERBI through place attachment and biospheric value among 300 recreational climbers in Yangshuo County, Guilin City. Using confirmatory factor analysis (CFA) and structural equation modeling (SEM), we found that the experiential value of climbing resorts had a direct effect on place attachment and biospheric value. Experiential value and place attachment had a moderate effect on ERBI, while biospheric value had a positive effect on ERBI. However, experiential value and place attachment indirectly influenced ERBI through the mediating effect of biospheric value. Finally, experiential value indirectly affected ERBI via the chain-mediating effects of place attachment and biospheric value. These findings extend existing theoretical frameworks by elucidating previously underexplored chain-mediation relationships. Practical insights derived from this study include strategies for enhancing the management and environmental protection of rock-climbing resorts. These strategies should focus on deepening the emotional connection between recreational climbers and the environment through innovative marketing approaches that emphasize tailored environmental protection activities, community involvement, and sustainable tourism practices. Management implicationsThe findings suggest three innovative strategies, building on existing practices, to foster sustainable tourism in rock-climbing resorts by connecting tourists with the environment. First, immersive programs such as ecotourism, wildlife observation, and multimedia experiences would enhance experiential value. These programs can promote environmentally responsible behavior by actively engaging tourists in environmental education and encouraging deeper interactions with nature, fostering place attachment and biospheric values. Second, content marketing, including storytelling and environmental campaigns that emphasize the ecological significance of rock-climbing resorts could position them as advocates for environmental conservation, especially ecosystem and biodiversity preservation. Third, personalized marketing can strengthen place attachment. Tourists could share personal photos on social media and receive symbolic rewards, such as sustainable tourism certificates. These strategies will actively engage tourists in environmental conservation and sustainable tourism efforts, foster long-term participation, and enhance the unique appeal of resorts as leaders in eco-friendly tourism. Management implicationsThe findings suggest three innovative strategies, building on existing practices, to foster sustainable tourism in rock-climbing resorts by connecting tourists with the environment. First, immersive programs such as ecotourism, wildlife observation, and multimedia experiences would enhance experiential value. These programs can promote environmentally responsible behavior by actively engaging tourists in environmental education and encouraging deeper interactions with nature, fostering place attachment and biospheric values. Second, content marketing, including storytelling and environmental campaigns that emphasize the ecological significance of rock-climbing resorts could position them as advocates for environmental conservation, especially ecosystem and biodiversity preservation. Third, personalized marketing can strengthen place attachment. Tourists could share personal photos on social media and receive symbolic rewards, such as sustainable tourism certificates. These strategies will actively engage tourists in environmental conservation and sustainable tourism efforts, foster long-term participation, and enhance the unique appeal of resorts as leaders in eco-friendly tourism.

Effect of FinTech Adoption, Green Finance and Green Innovation on Sustainability Performance of Nepalese Financial Institutions

This study is aimed to investigate the impact of FinTech adoption on the sustainability performance of financial institutions of Nepal focusing the mediating role of green finance and green innovation. Employing a descriptive and causal comparative research design, the research systematically describes the characteristics of FinTech adoption among customers of financial institutions of the selected area and explores potential cause-and-effect relationships between FinTech adoption and sustainability performance. Data were collected from 180 respondents through a structured questionnaire distributed via an online survey platform, utilizing a 5-point Likert scale. Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) were used to test the measurement model and examine the relationships between the constructs. The study confirmed that FinTech adoption positively impacts sustainability performance and green finance, although it negatively influences green innovation. Mediation analysis revealed that neither green finance nor green innovation significantly mediate the relationship between FinTech adoption and sustainability performance. The findings underscore the necessity for financial institutions to integrate both technological advancements and sustainable practices comprehensively. This study contributes to the academic literature by providing empirical evidence from Nepal and offers practical insights for policymakers and financial institutions aiming to enhance sustainability through fintech. By addressing critical research gaps, this study advances our understanding of how FinTech can be effectively aligned with sustainable financial practices in Nepal, ensuring long-term environmental, social, and economic benefits.

MODELLING OF SOCIO-PSYCHOLOGICAL ADAPTATION OF AN INDIVIDUAL TO DIGITAL RISKS IN EDUCATION AND PROFESSIONAL SELF-DETERMINATION

Automated semantic analysis and topic modelling methods were first applied to a large corpus of public online communities and discussions to analyse strategies for responding to digital risks in modern society. A large array of public data on profiles of the social network “Vkontakte” and their subscriptions to online communities was used to train a predictive model of individual adaptation to risks in the field of education and professional self-determination. Based on the results of thematic modelling, three topics of subscriptions to online communities were identified as signs of psychological adaptation: preparation for exams, self-development and “popular” psychology, education and science. The developed version of the predictive model of psychological adaptation showed high accuracy and the importance of such factors as age, gender and place of residence for individual adaptation to digital risks in the field of education and professional self-determination.

Ethical Leadership as a Driver of Supervisor Technical and Social Effectiveness: A Triple Helix for Cultivating Employees' Sense of Purpose

ABSTRACTA sense of purpose is generated when individuals perceive an authentic connection between their work and a broader transcendent life purpose beyond the self. Academics have shown significant positive effects of this driving force in life for employees and organizations, and thus the literature demands studies that analyze its antecedents, i.e., the potential factors that shape an individual's sense of purpose in life. Following an Aristotelian approach to virtue ethics in business, we analyze (1) whether ethical leadership enhances the technical and social effectiveness of supervisors, and (2) whether this moral asset of leaders enhances employee sense of purpose, either directly or by interacting with their technical and social effectiveness‐related dimensions. Using data from 395 employees in the Iraqi insurance and health care industry, structural equation modeling analysis revealed that, as expected, the ethical dimension of supervisors can influence employees' sense of purpose, both directly and by improving their technical and social effectiveness as leaders. Our findings thus encourage managers to practice ethical leadership to become more effective in leadership and in encouraging employees to have a sense of purpose in what they do.

Mapping secondary data gaps for social simulation modelling: A case study of the journeys of Syrian asylum seekers to Europe

Simulation models of social processes may require data that are not readily available, have low accuracy, are incomplete or biased. The paper presents a formal process for collating, assessing, selecting, and using secondary data as part of creating, validating, and documenting an agent-based simulation model of a complex social process, in this case, asylum seekers’ journeys to Europe. The process starts by creating an inventory of data sources, and the associated metadata, followed by assessing different aspects of data quality according to pre-defined criteria. As a result, based on the typology of available data, we are able to produce a thematic map of the area under study, and assess the uncertainty of key data sources, at least qualitatively. We illustrate the process by looking at the data on Syrian migration to Europe in 2011–21. In parallel, successive stages of the development of a simulation model allow for identifying key types of information which are needed as input into empirically grounded modelling analysis. Juxtaposing the available evidence and model requirements allows for identifying knowledge gaps that need filling, preferably by collecting additional primary data, or, failing that, by carrying out a sensitivity analysis for the assumptions made. By doing so, we offer a way of formalising the data collection process in the context of model-building endeavours, while allowing the modelling to be predominantly question-driven rather than purely data-driven. The paper concludes with recommendations with respect to data and evidence, both for modellers, as well as model users in practice-oriented applications.

Digital transformation at what cost? A case study from Germany estimating the adoption potential of precision farming technologies under different scenarios

This study addresses the research gap in understanding the low adoption of Precision Agriculture Technologies (PATs) by extrapolating regional trends from in-depth farm-level analyses. Focusing on a smallholder region in Germany, it investigates the adoption limits of PATs, questioning whether the low rates are due to unknown economic thresholds and how these thresholds can be influenced. Six PATs are evaluated, along with the impact of six potential measures to increase adoption rates, using predictive economic modeling and scenario analysis. Findings confirm low adoption rates, peaking at 4.61 % for automatic guidance and 17.73 % for site-specific fertilization, highlighting that current strategies are only partially effective, especially for precision weeding, site-specific seeding, planting, and spraying. Fortunately, targeted interventions for less adopted PATs enhance average farm income, which may increase their broader acceptance. The study advocates for targeted, technology-specific strategies over broad measures like subsidies, emphasizing the effectiveness of private sector initiatives like machinery cooperatives and service providers in improving adoption. The study concludes that greater PAT uptake in agriculture is only possible if realistic expectations are set by all stakeholders and tailored strategies are developed to promote a comprehensive digital transformation of the sector.

Personalised High Tibial Osteotomy Surgery Is Accurate: An Assessment Using 3D Distance Mapping

Early-stage knee osteoarthritis is often suitable for treatment with high tibial osteotomy (HTO). This is an effective joint-preserving treatment, resulting in good postoperative outcomes. To overcome the limitations of traditional HTO, the surgical technique and correction accuracy can be enhanced by personalised procedures using three-dimensional digital planning and metal additive manufacturing, The purpose of this clinical trial study was to evaluate the three-dimensional accuracy of a new personalised HTO procedure, using modern imaging techniques, 3D modelling, and distance map analysis (DMA). Twenty-five patients were treated with the personalised HTO procedure. Before surgery and after 6 months, they underwent clinical evaluation scoring, radiographic imaging, and computed-tomography scanning to generate morphological models. Specifically, preoperative tibia models were used to plan the tibia correction and the design and position of the fixation plate. Preoperative, planned, and postoperative models were imported in computer-aided and designing software (Geomagic ControlTM 2014, 3D Systems, Rock Hill, SC, USA) for DMA implementation to assess geometrical differences between model surfaces. A very good reproduction of the planned tibia morphology was achieved postoperatively (average differences between −0.9 mm and 1.4 mm). DMA values associated with fixation-plate deformation were less than 1 mm, similar to those for plate-to-tibia surface-contour matching. Overall, personalised digitally planned HTO utilising three-dimensional printed surgical guides and plates enables accurate planned correction and plate placement.