Browse Items (2832 total)
Sort by:
-
Sacred Roots: Rethinking Urban Landscapes via Ethnobotanical Narratives
This chapter discusses the synthesis of interdisciplinary research on the integration of sacred ethnobotanical knowledge with artificial intelligence (AI) into the present-day urban planning. This chapter draws upon a wide range of literature in the field of ethnobotany, ecotheology, urban ecology and digital innovation to explore how relationships between religious worldviews (including TEK), AI and green infrastructure can be used toward the enhancement of sustainable development of the urban. Review of current academic discourse on sacred plant landscape is emphasized above all, also examining the academic discourse on the nature of faith based ecological ethics and AI assisted urban greening strategies. I begin by reviewing ethnographic approaches and field-based studies that discuss the cultural and spiritual significance of sacred plants in Hindu, Islamic and Christian traditions, then examine service and trust as both a source and outcome for social infrastructure. It is critically analyzed how theological frameworks are ecologically applicable on the plural urban context. The review of AI integrated urban gardening initiatives provides a glimpse of how sensor data, machine learning models as well as mobile platforms are used to monitor plant health and plant biodiversity and how these can also be problematic on ethical front, justice, appropriation of knowledge and autonomy of community. The case studies from projects in Tokyo, Singapore, Ethiopia and Barcelona are placed within a global context and globally applied with a thematic synthesis in order to explore how, in practice, the coalescence of sacred ecological values and technological interventions occurs. The chapter discusses challenges of implementing policy, of cultural commodification, and of current interfaith collaboration models. The end of the review discusses the best practices and policy recommendations that can assist cities to join spiritual stewardship with digital ecological management to coalesce inclusive, biodiverse, and culturally grounded urban ecosystems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Family Functioning and Differentiation in Indian Homeschooling Families: A Systems Perspective on Stress and Coping
Family system and functioning play an integral part in individuals emotional development and channel the various coping mechanisms adopted by individuals to deal with multiple situations in life. The study explored family functioning, family differentiation, family stress, and family coping strategies among homeschooling families. A sample of 115 homeschooling families was selected by snowball sampling from India. A quantitative approach using correlation and regression analysis was used for the study. There has been a surge in families opting for home-based education post-pandemic. There is a need for an in-depth exploration of how these families function, develop differentiation, experience stress, and cope in a collectivistic culture like India. The results indicate that balanced cohesion in families predicts better differentiation in the motherchild subsystem, while family satisfaction predicts better differentiation in the husband and wife subsystem. The research findings can form a basis for developing family therapy specific to homeschooling and enhance the knowledge and understanding regarding the benefits and costs of homeschooling. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Unpacking Insurance: The Challenges and Innovations in Tying and Bundling Practices in Insurance
The insurers market share mainly depends on the channels of distribution they have adopted to make the business cost-effective. IRDAIs working paper supported the idea that the lengthier the distribution channel, the more conflict of interest there was, and it had a mediating effect. There is a chance of misrepresenting policy information, which is an outcome of that effect. In India, as per IRDAI, the incurred insurance claim ratio, the ratio of claims settlement against the total premiums received in a year, varies yearly. Even though the ratio indicates the financial performance of each sector, it can also be a benchmark for explaining the scope of the product utilization by the insured. During 202223, the ratio ranges between 72 to 86% for the general insurance companies. It also differs for the Fire, Health, motors, and marine sectors. The highest ratio is for the health sector, around 86%. At the same time, the ratio for the domestic travel insurance business is around 19%. Travel insurance is an example of bundling an insurance policy with another product. However, there is no specific information about the scope of bundling of insurance policies and the claims arising out of it. To combat these challenges, innovative solutions must be explored. One such approach is the introduction of modular insurance plans, allowing consumers to customize their coverage based on specific needs without being forced into unnecessary add-ons. AI-driven policy comparison tools can further empower consumers by offering real-time insights into the value of bundled versus standalone options. Regulatory mandates for opt-out provisions in bundled offerings can ensure greater autonomy, allowing policyholders to reject unwanted components. Additionally, the implementation of blockchain-based smart contracts can bring transparency by clearly defining coverage terms and pricing, reducing hidden costs. By adopting these solutions, the insurance sector can enhance consumer trust, encourage informed decision-making, and create a more competitive marketplace, ultimately leading to fairer and more accessible insurance options for all. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Technology, Informality, and the Future of Work: Enhancing Socio-Economic Status of Domestic Workers in Karnataka
This study examines the impact of technology-based skill development on the socio-economic status of domestic workers in Karnataka, India. Using structural equation modelling with data from 433 female domestic workers across five districts, the research investigates how digital skill interventions influence employability, job security, and access to government welfare schemes. The findings reveal that technology-based skill development significantly enhances socio-economic outcomes through improved employability (? = 0.53, p < 0.001), with job security serving as a crucial mediating factor. However, government welfare schemes showed limited effectiveness in mediating the relationship between skill development and socio-economic advancement. The study highlights that 78% of respondents work part-time, with 88% receiving cash payments, indicating persistent informality. While digital skill programs create pathways for economic mobility, their success depends on facilitating stable employment rather than mere knowledge transfer. The research underscores the need for better integration between skill development initiatives and social protection systems to maximize benefits for marginalized domestic workers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Unlocking Access: Technology as a Tool for Transgender Economic Justice
The Transgender community across the world face discrimination from all walks of life. The transgender, even if educated and skilled, faces multiple hurdles during the employment process and within the workplace once employed. The challenges have even increased with COVID-19 and mass layoffs affecting employees across the globe. Transgender people have to be empowered to face the challenges of employment processes and workplace challenges as and when they arise. With the advancement of digital technologies, the Fourth Industrial Revolution can be leveraged for training, empowerment, awareness, network building, grievance redressal and sustainability for the transgender community. Digital technologies leverage the community to collaborate and be vocal about their needs and rights at a global level. Digital technology also helps the transgender community reach the proper forum to implement the required program for the benefit of the community. The decentralized and anonymous system for recording data and reporting incidents will also be a helpful, transparent process without being subject to unnecessary scrutiny. The technology-enabled system and practices will also help the companies and institutions to ascertain appropriate and efficient methods for creating gender gender-neutral environment devoid of any discrimination. Recently, there was a significant rise in concerns about employment practices for the LGBTQ community around the globe during the pandemic, such as lockdown protocols affecting employment conditions, vaccine access, support from employers, severance pay, mental health, etc. The International ecosystem has seen minimal regulatory discussion, and individual countries and companies are now implementing schemes to address the issue faced by transgender individuals in the employment and work ecosystem through the infusion of technologies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Export Rhythms in Indian Agriculture: Trend and Seasonal Decomposition of Indian Cereal Products Exports
This study investigates the long-term trends and seasonal dynamics of Indias cereal exports specifically Basmati rice, non-Basmati rice, other cereals, and wheat using trend modelling and decomposition techniques. Drawing on monthly export data from April 2006 to November 2024 (with wheat data beginning in 2013), linear, log-linear, and quadratic trend models were estimated alongside additive, multiplicative, and STL (Seasonal-Trend decomposition using Loess) seasonal models. Results indicate strong linear and exponential growth in Basmati and non-Basmati rice exports, wheat exports exhibited no statistically significant trend and displayed high volatility. Durbin-Watson statistics revealed serial autocorrelation in most models, highlighting the importance of incorporating seasonality and external shocks in trend analysis. Additive decomposition reveals pronounced seasonal effects in Basmati rice exports, STL analysis confirms these patterns. Wheat shows moderate seasonal strength, while non-Basmati rice and other cereals exhibit mild seasonality. These findings underscore the necessity of commodity-specific export strategies aligned with harvest cycles and global demand windows. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
The Mediating Role of Attitude Towards Behaviour Between Brand Image and Purchase Intention: Evidence from Indias Housing Market
This study investigates the association between Brand Image, Attitude Toward Behaviour, and Purchase Intention within the Indian real estate sector. The research employs the Theory of Planned Behaviour (TPB) to examine the impact of brand image on purchase intention, with attitude toward behaviour serving as a mediating role. Data were gathered from 422 respondents. The collected data is inspected using Structural Equation Modeling (SEM hereafter) utilising JMP Software. The findings demonstrate that brand image has a substantial effect on purchase intention, both directly as well as indirectly, via attitude towards behaviour. The results emphasize the need to establish a robust brand image to cultivate favourable consumer perceptions and influence purchasing decisions, providing essential insights for real estate developers in a competitive landscape. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Consumer Trust and Online Security in the UK Banking Sector
The expanded use of online banking has generated major worries about consumer trust and the security of their financial information. As online banking becomes more prevalent in the United Kingdom it becomes essential to ensure consumers trust the security measures of these platforms. This research examined what elements create trust and security among consumers in the UK online banking industry. The study examined critical components including perceived usefulness, navigation simplicity and security effectiveness to understand their impact on consumer trust. The study proves significant because it fills research gaps while delivering valuable insights that help financial institutions improve consumer trust through enhanced user experience and security measures. The research objectives were met through quantitative methods which involved gathering data using an online survey from 55 participants from 1st October to 30th October 2024. The survey targeted multiple aspects such as ease of navigation through the platform, the perceived usefulness of the system, user concerns about information security, and trust levels in online banking security measures. Through descriptive statistics, correlation analysis and linear regression techniques researchers analysed data to uncover how different factors affect consumer trust in online banking. The study utilized the Technology Acceptance Model (TAM) and Protection Motivation Theory (PMT) as guiding theoretical frameworks for analysis. Three main factors including ease of navigation, the perceived usefulness of the system, and security information concerns emerged as significant drivers of consumer trust in online banking security. Although customers trust existing security protocols they require ongoing improvements and clear communication to sustain their confidence levels. Banks can use these findings to improve online platform trust by enhancing security measures and user experience. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Evaluating the Effectiveness of GraphSAGE with Reinforcement Learning in Suicide Risk Prediction
Suicide is considered to be a major mental health issue that has affected most individuals worldwide. According to World Health Organization, it shows the rise of suicidal rates among students has increased drastically. This vulnerability shows the rising need to encounter this issue with immediate effect. Therefore, proper detection methods have to be incorporated so that we can reduce the number of suicidal rates. Many computational models were implemented to address this issue. This study was conducted to compare various algorithms such as traditional machine learning models random forest and also various deep learning models like GraphSAGE, Graph Convolutional Network, Convolutional Neural Network, and Convolutional Neural Network with Long Short Term Memory with the proposed GraphSAGE Reinforcement Learning. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Examining the Relationship Between Acceptance of Technology Integration and Dimensions of TPACK Among Higher Secondary School Teachers in Kerala
The study uses the Unified Theory of Adoption and Use of Technology (UTAUT) to investigate how higher secondary school teachers in Kerala, India, connect to Technological Pedagogical Content Knowledge (TPACK) and technology adoption. Using 496 teachers from diverse backgrounds, the study revealed some important positive correlations between TPACK characteristics and UTAUT results. Strong technologically minded teachers are interested in incorporating technology into the classroom. Crucially for the quality of education, PK, PCK, and CK have a modest influence on technology acceptance. The study emphasises the need for specific professional development and supportive policies to fit Kerala's unique educational scene. With consequences for the whole educational scene, TPACK can encourage improved technology acceptance, particularly in sectors connected to technology. Future research should look at long-term changes in technology use, geographical comparisons, and how new technologies impact teaching approaches. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Classic Models, Modern Threats: A Study on Adversarial Attack and Defense for Traditional ML Models
Adversarial attacks are a serious threat to machine learning models, both for conventional architectures, like neural networks, and for more sophisticated frameworks, like Vision Transformers (ViTs). Although a lot of work has been done to defend state-of-the-art deep learning models against attacks like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Gaussian noise perturbations, classical machine learning models like logistic regression, support vector machines (SVMs), and decision trees are relatively less explored despite their extensive use in situations where low computational complexity and high interpretability are needed. This work presents a rigorous evaluation of the adversarial vulnerability of binary and other classical models on the MNIST dataset and explores the effectiveness of various defense mechanisms, including adversarial training, input pre-processing (Gaussian smoothing), and defensive distillation. Experiments demonstrate that adversarial training is the most effective defense that improves model robustness with classification accuracies of up to 96% in all attack scenarios. In contrast, defensive distillation and input preprocessing make modest gains, with accuracy levels ranging from 61 to 81% based on the nature of the attack. Through adversarial threat analysis of typical machine learning models, this work points out their inherent susceptibility to adversarial perturbations and introduces robust defense techniques. These results identify the necessity for robust security and reaffirm the practical viability of typical models in the scenario of resource-constrained environments, contributing towards a more complete picture of adversarial defenses for the entire spectrum of machine learning architectures. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
A Cognitive Workload-Aware Machine Learning Model for Performance Enhancement in Cyber-Physical Systems
Cyber-Physical Systems increasingly demand seamless coordination between human operators and autonomous processes, which increases the complexity. High cognitive workload in those environments amounts to a degradation of performance, decision fatigue, and increased susceptibility to system failure and cyber threats. To address these challenges, we propose a Neuro-inspired Cognitive Workload Optimizer (NCO), a novel machine-learning-based model for the monitoring, prediction, and optimization of cognitive workload for CPS performance improvement. The NCO framework employs neuro-inspired deep learning techniques, with LSTM networks coupled with an attention mechanism for assessing workload patterns dynamically in time. The adaptive operation of the system depends on executing a contextual analysis of system data and operator interaction metrics, whereby NCO recognizes fluctuations in workload and adjusts the operations of the system in real-time to maintain an optimal state for cognitive functioning. Thus, the model implements an adaptive feedback loop that prioritizes task distribution, resource allocation, and security management based on cognitive load estimations. In this way, CPS environments are hereby enabled to proactively mitigate operator overloads, minimize latencies, and enhance accuracy in decision-making, all while ensuring this is happening under dynamic conditions ensuring robust system performance. Experimental results on simulated CPS datasets indicate that NCO can reduce workloads peaks by 35%, improve system throughput by 28%, and provide better anomaly detection performance in conditions of high stress. The NeuroCPS-Optimizer thus opens up a new paradigm for cognitive-aware CPS management, ensuring that human and machine components are kept within safe and efficient bounds. This research thereby advances the creation of resilient and intelligent CPS that can self-adjust and sustain performance levels in complex and demanding environments. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Transforming Teletherapy: Using Transfer Learning and NLP for Improved Mental Health Care
The increasing reliance on tele-therapy for mental health support highlights the need for advanced methodologies to improve diagnostic precision and patient outcomes. This study explores the transformative potential of transfer learning in natural language processing (NLP) to enhance the detection of mental health conditions during tele-therapy sessions. Leveraging a dataset sourced from mental health-related subreddits, which includes conversations mapped to five target categories (Stress, Depression, Bipolar Disorder, Personality Disorder, and Anxiety), we fine-tuned a pre-trained BERT model for multi-class classification. Our study's results highlight significant performance enhancements achieved through the implementation of transformer-based models. The proposed framework achieved an accuracy of 83%, with macro average precision, recall, and F1-score values of 0.84, 0.83, and 0.83, respectively. Class-specific analysis further underscores the model's robustness, with precision ranging from 0.75 to 0.92 and recall values exceeding 0.80 for most categories. These outcomes significantly outperform traditional machine learning models such as Random Forest (accuracy: 72.65%) and Support Vector Machines (accuracy: 69.71%), demonstrating the superior capacity of BERT to capture complex linguistic patterns and semantic nuances in patient interactions. This research underscores the transformative role of transfer learning in NLP applications for tele-therapy, offering a scalable and precise solution for mental health assessment and paving the way for personalized, AI-driven interventions. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Twins and Preterm Birth
About 3.2% of live births comprise twins, and 20% account for preterm labor, with delivery of twins before 37 and 32weeks of gestation approximately 60% and 10.7%. About five times elevated risk of initial neonatal and infant mortality is noted in the case of twin pregnancy in preterm parturition cases. Both spontaneous and indicated preterm labor is observed more in monochorionic twins than in dichorionic twins. Prediction and diagnosis of preterm labor is effectively done using transvaginal ultrasound to compute the length of the cervix before 24weeks without any risk. Vaginal administration of progesterone in women with less than 25 mm cervical length is beneficial to prevent preterm and neonatal obstacles in twin pregnancies. Physical evaluation showed women undergoing cerclage surgery, when cervical dilation is greater than 1cm, have lowered the risk of perinatal death and preterm labor at varied gestational stages. In a few studies, twin delivery is not directly associated with preterm and related complications. However, pregnancies weighing less than 1000g are associated with major disability around year 1 compared to singleton preterm. All these considerations are crucial in order to optimize the antenatal management of this group of pregnancies destinated to show an increasing trend. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Environment and Twins
Twins provide a unique context for studying the interaction between genetics and the environment on human traits. Monozygotic (MZ) twins share nearly identical genetic material, and dizygotic (DZ) twins share about 50% are influenced by environmental factors, especially during prenatal development. Chorionicity, or whether twins share the same placenta, plays a key role in shaping their in utero environment, influencing risks such as unequal nutrient distribution and conditions like twin-to-twin transfusion syndrome (TTTS). Postnatally, shared environments and lifestyles can further affect twin development, although their susceptibilities may lead to different outcomes. Factors like maternal nutrition, exposure to toxins, and prenatal care are critical, particularly in the context of neural tube defects (NTDs), where both genetic and environmental influences, such as parental occupation, maternal obesity, and folic acid deficiency, play a role. Understanding these risks is vital for preconception counseling and effective pregnancy management to optimize outcomes for twins. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Preterm Birth and Prematurity Prevention
About 3.2% of live births comprise twins, and 20% account for preterm labor, with delivery of twins before 37 and 32weeks of gestation approximately 60% and 10.7%. About five times elevated risk of initial neonatal and infant mortality is noted in the case of twin pregnancy in preterm parturition cases. Both spontaneous and indicated preterm labor are observed more in monochorionic twins than in dichorionic twins. Prediction and diagnosis of preterm labor is effectively done using transvaginal ultrasound to compute the length of the cervix before 24weeks without any risk. Vaginal administration of progesterone in women with less than 25 mm cervical length is beneficial to prevent preterm and neonatal obstacles in twin pregnancies. Physical evaluation showed that women undergoing cerclage surgery, when cervical dilation is greater than 1cm, have lowered risk of perinatal death and preterm labor at varied gestational stages. In a few studies, twin delivery is not directly associated with preterm and related complications. However, pregnancies weighing less than 1000g are associated with major disability around year 1 compared to singleton preterm. All these considerations are crucial in order to optimize the antenatal management of this group of pregnancies destined to show an increasing trend. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Recent Enzyme Discovery: Engineering Strategies for Biocatalysis and Its Applications
Biocatalysis has been growing over the past 30years because of the developments in many technical fields. Due to their biocompatibility, selectivity, and specificity in material, ability to continue operating under temperature and pH conditions, and relatively high interaction, biocatalysts have become significant replacements for conventional catalytic reactions. Several methods, such as enzyme structural improvements (such as protein engineering, direct evaluation), engineering approaches (such as electrode materials, supercritical fluid extraction), and sensory preservation (such as encapsulation, CLEAS), have been developed, which together are dominant instruments in the improvement of biotransformation and the synthesis of new products. This chapter summarizes the benefits of applying a performance-based approach to biocatalyst invention and engineering in cell culture for enhancing their development in critical modules and separation. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Advances in Food Packaging with Nanotechnology-Enhanced Biomaterials
The growing concern about food safety and the need for environmental sustainability has triggered the shift from traditional plastic packaging to eco-friendly packaging materials derived from biological resources, enhanced with nanotechnology. Different challenges, such as low mechanical strength, thermal instability, barrier properties and microbial susceptibility, are being observed in traditional packaging. In this chapter, we discuss nanomaterials that are integrated with biopolymers for food packaging to address these issues and increase their physicochemical properties, functionalities, as well as practical applications. Biopolymers such as polylactic acid (PLA), chitosan, gelatin, cellulose and starch-based films, are increasingly being used and further reinforced with organic nanoparticles (nanocellulose, nano starch, carbon dots, and protein nanoparticles) and inorganic nanoparticles (silver, zinc oxide, titanium dioxide etc.) are addressed in the chapter. This chapter classifies and analyses nanomaterials according to their origin and function, demonstrating the enhancement of barrier properties, antimicrobial activity, UV shielding, and thermal resistance in nano-biocomposites. Special attention is paid to their application in smart packaging systems, including active systems that release antimicrobials or antioxidants and intelligent systems containing nano sensors that check for freshness and contamination. Examples of enhanced shelf life and quality preservation are discussed in fruits, vegetables, dairy, meat and bakery goods. The chapter also examines important safety, nanoparticle migration, toxicity regulatory issues, and the environmental impact, highlighting the emerging need for globally unified rules. To sum up, the worlds targets for sustainable food systems, reducing waste, consumer health protection and safeguarding public health are enabled by nano-biomaterials, making it the most suitable innovative solution to the challenges of packaging. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Recent Advancements inPhotonic Quantum Computation
Photonic quantum computation has emerged as a promising paradigm for scalable and fault-tolerant quantum computing, leveraging the unique properties of photons, such as low decoherence and ease of manipulation. Recent advancements in integrated photonics, reduction of noise and error correction, and entanglement distribution have significantly enhanced the feasibility of large-scale photonic quantum processors. This article comprehensively reviews the latest developments in single-photon qubits, photonic quantum gates, photonic chips capable of performing quantum operations and communications, and hybrid quantum architectures. We also discuss breakthroughs in optimizing fidelity in quantum gates, reducing error rates, and chip-based quantum circuits that contribute to the rapid progress in this field. Furthermore, we analyze key challenges, including loss mitigation, ensuring the sustained preservation of quantum coherence across long distances, and the effect of temperature fluctuations and coupling between adjacent photonic waveguides while exploring potential solutions. By synthesizing recent research trends, this review aims to offer insights into the future trajectory of photonic quantum computation and its role in advancing quantum technologies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Effects of Climate Change on Natural Resources and Its Management Using Computer-Aided Techniques
The fast-paced climate change produces worse resource stress because it damages freshwater reservoirs and forests with arable land and biodiversity while creating major sustainability issues for urban spaces. Food shortages result from global warming along with uneven rainfall patterns and powerful weather systems that further intensify resource-related problems throughout entire ecosystems. Resolving these challenges requires computer technology solutions combining Quantum Computing (QC) with Machine Learning (ML), Geographic Information Systems (GIS), and Artificial Intelligence (AI) to optimise resources and develop predictive analyses as well as strengthen climate resilience strategies. AI technologies integrated with quantum algorithms give birth to improved climate modelling systems, which trigger instant emergency actions to control disasters while urban requirements shift. Climate risk reduction benefits from two successful techniques: NASA employs them through the Earth Observing System (EOS) while Google deploys their AI-based flood prediction model in India and Bangladesh. Environmental governance finds its legal and policy basis in two primary international agreements, namely the EU's Green Deal and the United Nations SDGs and the Paris Agreement (2015). Research evidence demonstrates that combined disciplinary methods effectively verify how computer-based processes solve sustainable urban expansion problems. The research indicates that climate resilience reaches its optimal potential through international establishments of standardised ethical frameworks and rules for innovative technology systems. Also, the strategic recommendations regarding AI implementation for natural resource defence during climate change need support from policymakers, urban planners, and researchers who must perform these tasks. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.