The family of moisture meters from General Tools & Instruments features a variety of precision specialty instruments that are affordable, versatile and high-tech.

• Fed will 'most likely' need more cuts but should 'proceed cautiously'
• If Fed cuts too far past neutral, inflation could re-acclerate
• Difficult to know how many Fed rate cuts may be needed, and how soon they need to happen
• Fed has made a great deal of progress in bringing inflation down
• Fed not quite back to price stability yet
• US economic activity is resilient
• Labor market cooling gradually but not weakening materially
• Sees upside risk to inflation, downside risk to employment, says financial conditions may pose biggest challenges for monetary policy
• If bond yields continue to rise, the Fed may need less-restrictive policy

Logan last spoke in late October and wasn't quite this hawkish. I think the Fed cuts in December but takes a pause after that and waits to see how things play out.

Leading artificial intelligence companies including OpenAI, Google, and Anthropic are facing "diminishing returns" from their costly efforts to build newer AI models, according to a new Bloomberg report. The stumbling blocks appear to be growing in size as Apple continues a phased rollout of its own AI features through Apple Intelligence.


OpenAI's latest model, known internally as Orion, has reportedly fallen short of the company's performance expectations, particularly in handling coding tasks. The model is said to be lacking the significant improvements over existing systems when compared to the gains GPT-4 made versus its predecessor.

Google is also reportedly facing similar obstacles with its upcoming Gemini software, while Anthropic has delayed the release of its anticipated Claude 3.5 Opus model. Industry experts who spoke to Bloomberg attributed the challenges to the increasing difficulty in finding "new, untapped sources of high-quality, human-made training data" and the enormous costs associated with developing and operating new models concurrently with existing ones.

Silicon Valley's belief that more computing power, data, and larger models will inevitably lead to better performance, and ultimately the holy grail – artificial general intelligence (AGI) – could be based on false assumptions, suggests the report. Consequently, companies are now exploring alternative approaches, including further post-training (incorporating human feedback to improve responses and refining the tone) and developing AI tools called agents that can perform targeted tasks, such as booking flights or sending emails on a user's behalf.

"The AGI bubble is bursting a little bit," said Margaret Mitchell, chief ethics scientist at AI startup Hugging Face. She told Bloomberg that "different training approaches" may be needed to make AI models work really well on a variety of tasks. Other experts who spoke to the outlet echoed Mitchell's sentiment.

How much impact these challenges will have on Apple's approach is unclear, though Apple Intelligence is more focused in comparison, and the company uses internal large language models (LLMs) grounded in privacy. Apple's AI services mainly operate on-device, while the company's Private Cloud Compute encrypted servers are only pinged for tasks requiring more advanced processing power.

Apple is integrating AI capabilities into existing products and services, including writing tools, Siri improvements, and image generation features, so it can't be said to be competing directly in the LLM space. However, Apple has agreed a partnership with OpenAI that allows Siri to optionally hand off more open-ended queries to ChatGPT. Apple has also reportedly held discussions with other LLM companies about similar outsourcing partnerships.

It's possible that the challenges faced by major AI companies pursuing breakthrough general-purpose AI models could ultimately validate Apple's more conservative strategy of developing specific AI features that enhance the user experience. In that sense, its privacy-first policy may not be the straitjacket it first seemed. Apple plans to expand Apple Intelligence features next month with the release of iOS 18.2 and then via further updates through 2025.

AI chatbots such as ChatGPT and other applications powered by large language models (LLMs) have exploded in popularity, leading a number of companies to explore LLM-driven robots. However, a new study now reveals an automated way to hack into such machines with 100 percent success. By…

1. 'Red Monster' galaxies in early universe challenge current models of galaxy formation  Nanowerk
2. James Webb Space Telescope’s data unveils galaxies that could lead to a dark matter revolution  The Times of India
3. NASA James Webb Telescope Data Points to Dark Matter Alternative: 10 Key Insights  Moneycontrol
4. ‘Red Monster’: Scientists discover three ultra-massive galaxies, challenging previous formation theories  Business Today
5. James Webb Space Telescope finds galaxies pointing toward a dark matter alternative  Space.com

Menzies, J; Paulen, R C. Geoconvention 2020 abstracts; 2020 p. 1-3
<a href="https://geoscan.nrcan.gc.ca/images/geoscan/20190526.jpg"><img src="https://geoscan.nrcan.gc.ca/images/geoscan/20190526.jpg" title="Geoconvention 2020 abstracts; 2020 p. 1-3" height="150" border="1" /></a>

Thomas, M D. Geological Survey of Canada, Current Research (Online) 2022-1, 2022, 22 pages, https://doi.org/10.4095/328244
<a href="https://geoscan.nrcan.gc.ca/images/geoscan/gid_328244.jpg"><img src="https://geoscan.nrcan.gc.ca/images/geoscan/gid_328244.jpg" title="Geological Survey of Canada, Current Research (Online) 2022-1, 2022, 22 pages, https://doi.org/10.4095/328244" height="150" border="1" /></a>

Whitmeyer, S J; Kellett, D A; Tikoff, B; Williams, M L. Laurentia: turning points in the evolution of a continent; Geological Society of America, Memoir vol. 220, 2023 p. 7-16, https://doi.org/10.1130/2022.1220(001)
<a href="https://geoscan.nrcan.gc.ca/images/geoscan/20210545.jpg"><img src="https://geoscan.nrcan.gc.ca/images/geoscan/20210545.jpg" title="Laurentia: turning points in the evolution of a continent; Geological Society of America, Memoir vol. 220, 2023 p. 7-16, https://doi.org/10.1130/2022.1220(001)" height="150" border="1" /></a>

OpenAI's latest large language model, known internally as Orion, has fallen short of performance targets, marking a broader slowdown in AI advancement across the industry's leading companies, according to Bloomberg, corroborating similar media stories in recent days. The model, which completed initial training in September, showed particular weakness in novel coding tasks and failed to demonstrate the same magnitude of improvement over its predecessor as GPT-4 achieved over GPT-3.5, the publication reported Wednesday. Google's upcoming Gemini software and Anthropic's Claude 3.5 Opus are facing similar challenges. Google's project is not meeting internal benchmarks, while Anthropic has delayed its model's release, Bloomberg said. Industry insiders cited by the publication pointed to growing scarcity of high-quality training data and mounting operational costs as key obstacles. OpenAI's Orion specifically struggled due to insufficient coding data for training, the report said. OpenAI has moved Orion into post-training refinement but is unlikely to release the system before early 2024. The report adds: [...] AI companies continue to pursue a more-is-better playbook. In their quest to build products that approach the level of human intelligence, tech firms are increasing the amount of computing power, data and time they use to train new models -- and driving up costs in the process. Amodei has said companies will spend $100 million to train a bleeding-edge model this year and that amount will hit $100 billion in the coming years. As costs rise, so do the stakes and expectations for each new model under development. Noah Giansiracusa, an associate professor of mathematics at Bentley University in Waltham, Massachusetts, said AI models will keep improving, but the rate at which that will happen is questionable. "We got very excited for a brief period of very fast progress," he said. "That just wasn't sustainable." Further reading: OpenAI and Others Seek New Path To Smarter AI as Current Methods Hit Limitations.

Read more of this story at Slashdot.

If you’re wondering about sustainable business models, bear with me while I rant about ABC’s free episode streaming, or just skip the first bunch of paragraphs to the Sustainable Business Models heading.  Ohterwise bear with me, I’ve got a couple lung-fulls to spend talking about why I’m not having the best time I could be [...]

Russian girls of model quality seeking love and marriage to western men: Russian, Ukrainian, Belarus and Eastern European girls. Every week we add 50-100 new Russian girls to our database.

Russian girls of model quality seeking love and marriage to western men: Russian, Ukrainian, Belarus and Eastern European girls. Every week we add 50-100 new Russian girls to our database.

[The Conversation Africa] We're increasingly aware of how misinformation can influence elections. About 73% of Americans report seeing misleading election news, and about half struggle to discern what is true or false.

Both Cadillac and Mercedes plan to expand their electric car offerings to the SUV market segment in the near future.

The post Large Electric SUV Models Coming From Cadillac & Mercedes appeared first on CleanTechnica.

Fr. Apostolos encourages us to let the light of Christ shine through us.

Process Modeling has been a very active research topic for the last decades. One of its main issues is the externalization of knowledge and its acquisition for further use, as this remains deeply related to the quality of the resulting process models produced by this task. This paper presents a method and a graphical supporting tool for process elicitation and modeling, combining the Group Storytelling technique with the advances of Text Mining and Natural Language Processing. The implemented tool extends its previous versions with several functionalities to facilitate group story telling by the users, as well as to improve the results of the acquired process model from the stories.

This research explores vehicular ad hoc networks (VANETs) and their extensive applications, such as enhancing traffic efficiency, infotainment, and passenger safety. Despite significant study, widespread deployment of VANETs has been hindered by security and privacy concerns. Challenges in implementation, including scalability, flexibility, poor connection, and insufficient intelligence, have further complicated VANETs. This study proposes leveraging cloud computing to address these challenges, marking a paradigm shift. Cloud computing, recognised for its cost-efficiency and virtualisation, is integrated with VANETs. The paper details the nomenclature, architecture, models, services, applications, and challenges of VANET-based cloud computing. Three architectures for VANET clouds - vehicular clouds (VCs), vehicles utilising clouds (VuCs), and hybrid vehicular clouds (HVCs) - are discussed in detail. The research provides an overview, delves into related work, and explores VANET cloud computing's architectural frameworks, models, and cloud services. It concludes with insights into future work and a comprehensive conclusion.

In order to further improve the public health assurance system and the infectious diseases early warning system to give play to their positive roles and enhance their collaborative capacity, this paper, based on the big and thick data analytics technology, designs a 'rolling-type' data synergy model. This model covers districts and counties, municipalities, provinces, and the country. It forms a data blockchain for the public health assurance system and enables high sharing of data from existing system platforms such as the infectious diseases early warning system, the hospital medical record management system, the public health data management system, and the health big and thick data management system. Additionally, it realises prevention, control and early warning by utilising data mining and synergy technologies, and ideally solves problems of traditional public health assurance system platforms such as excessive pressure on the 'central node', poor data tamper-proofing capacity, low transmission efficiency of big and thick data, bad timeliness of emergency response, and so on. The realisation of this technology can greatly improve the application and analytics of big and thick data and further enhance the public health assurance capacity.

Digital Transformation seeks to improve the processes of an organisation by integrating digital technology in all its areas, this is inevitable due to technological evolution that generates new demands, new habits and greater demands on customers and users, therefore Digital Transformation is important. In organisations to maintain competitiveness. In this context, universities are no strangers to this reality, but they find serious problems in their execution, it is not clear how to deal with an implementation of this type. The work seeks to identify tools that can be used in the implementation of Digital Transformation in universities, for this a systematic review of literature is carried out with a method based on three stages, 23 models, 13 frameworks and 8 roadmaps are identified. The elements found are analysed, obtaining eight main components with their relationships and dependencies, which can be used to generate more optimal models for universities.

Many degree courses at technical universities include the subject of control systems engineering. As an addition to conventional approaches Fuzzy Control can be used to easily find control solutions for systems, even if they include nonlinearities. To support further educational training, models which represent a technical system to be controlled are required. These models have to represent the system in a transparent and easy cognizable manner. Furthermore, a programming tool is required that supports an easy Fuzzy Control development process, including the option to verify the results and tune the system behavior. In order to support the development process a graphical user interface is needed to display the fuzzy terms under real time conditions, especially with a debug system and trace functionality. The experiences with such a programming tool, the Fuzzy Control Design Tool (FHFCE Tool), and four fuzzy teaching models will be presented in this paper. The methodical and didactical objective in the utilization of these teaching models is to develop solution strategies using Computational Intelligence (CI) applications for Fuzzy Controllers in order to analyze different algorithms of inference or defuzzyfication and to verify and tune those systems efficiently.

Aim/Purpose: The understanding of disease spread dynamics in the context of air travel is crucial for effective disease detection and epidemic intelligence. The Susceptible-Exposed-Infectious-Recovered-Hospitalized-Critical-Deaths (SEIR-HCD) model proposed in this research work is identified as a valuable tool for capturing the complex dynamics of disease transmission, healthcare demands, and mortality rates during epidemics. Background: The spread of viral diseases is a major problem for public health services all over the world. Understanding how diseases spread is important in order to take the right steps to stop them. In epidemiology, the SIS, SIR, and SEIR models have been used to mimic and study how diseases spread in groups of people. Methodology: This research focuses on the integration of air traffic network data into the SEIR-HCD model to enhance the understanding of disease spread in air travel settings. By incorporating air traffic data, the model considers the role of travel patterns and connectivity in disease dissemination, enabling the identification of high-risk routes, airports, and regions. Contribution: This research contributes to the field of epidemiology by enhancing our understanding of disease spread dynamics through the application of the SIS, SIR, and SEIR-HCD models. The findings provide insights into the factors influencing disease transmission, allowing for the development of effective strategies for disease control and prevention. Findings: The interplay between local outbreaks and global disease dissemination through air travel is empirically explored. The model can be further used for the evaluation of the effectiveness of surveillance and early detection measures at airports and transportation hubs. The proposed research contributes to proactive and evidence-based strategies for disease prevention and control, offering insights into the impact of air travel on disease transmission and supporting public health interventions in air traffic networks. Recommendations for Practitioners: Government intervention can be studied during difficult times which plays as a moderating variable that can enhance or hinder the efficacy of epidemic intelligence efforts within air traffic networks. Expert collaboration from various fields, including epidemiology, aviation, data science, and public health with an interdisciplinary approach can provide a more comprehensive understanding of the disease spread dynamics in air traffic networks. Recommendation for Researchers: Researchers can collaborate with international health organizations and authorities to share their research findings and contribute to a global understanding of disease spread in air traffic networks. Impact on Society: This research has significant implications for society. By providing a deeper understanding of disease spread dynamics, it enables policymakers, public health officials, and practitioners to make informed decisions to mitigate disease outbreaks. The recommendations derived from this research can aid in the development of effective strategies to control and prevent the spread of infectious diseases, ultimately leading to improved public health outcomes and reduced societal disruptions. Future Research: Practitioners of the research can contribute more effectively to disease outbreaks within the context of air traffic networks, ultimately helping to protect public health and global travel. By considering air traffic patterns, the SEIR-HCD model contributes to more accurate modeling and prediction of disease outbreaks, aiding in the development of proactive and evidence-based strategies to manage and mitigate the impact of infectious diseases in the context of air travel.

Aim/Purpose: Acquisitions play a pivotal role in the growth strategy of a firm. Extensive resources and time are dedicated by a firm toward the identification of prospective acquisition candidates. The Indian manufacturing sector is currently experiencing significant growth, organically and inorganically, through acquisitions. The principal aim of this study is to explore models that can predict acquisitions and compare their performance in the Indian manufacturing sector. Background: Mergers and Acquisitions (M&A) have been integral to a firm’s growth strategy. Over the years, academic research has investigated multiple models for predicting acquisitions. In the context of the Indian manufacturing industry, the research is limited to prediction models. This research paper explores three models, namely Logistic Regression, Decision Tree, and Multilayer Perceptron, to predict acquisitions. Methodology: The methodology includes defining the accounting variables to be used in the model which have been selected based on strong theoretical foundations. The Indian manufacturing industry was selected as the focus, specifically, data for firms listed in the Bombay Stock Exchange (BSE) between 2010 and 2022 from the Prowess database. There were multiple techniques, such as data transformation and data scrubbing, that were used to mitigate bias and enhance the data reliability. The dataset was split into 70% training and 30% test data. The performance of the three models was compared using standard metrics. Contribution: The research contributes to the existing body of knowledge in multiple dimensions. First, a prediction model customized to the Indian manufacturing sector has been developed. Second, there are accounting variables identified specific to the Indian manufacturing sector. Third, the paper contributes to prediction modeling in the Indian manufacturing sector where there is limited research. Findings: The study found significant supporting evidence for four of the proposed hypotheses indicating that accounting variables can be used to predict acquisitions. It has been ascertained that statistically significant variables influence acquisition likelihood: Quick Ratio, Equity Turnover, Pretax Margin, and Total Sales. These variables are intrinsically linked with the theories of liquidity, growth-resource mismatch, profitability, and firm size. Furthermore, comparing performance metrics reveals that the Decision Tree model exhibits the highest accuracy rate of 62.3%, specificity rate of 66.4%, and the lowest false positive ratio of 33.6%. In contrast, the Multilayer Perceptron model exhibits the highest precision rate of 61.4% and recall rate of 64.3%. Recommendations for Practitioners: The study findings can help practitioners build custom prediction models for their firms. The model can be developed as a live reference model, which is continually updated based on a firm’s results. In addition, there is an opportunity for industry practitioners to establish a benchmark score that provides a reference for acquisitions. Recommendation for Researchers: Researchers can expand the scope of research by including additional classification modeling techniques. The data quality can be enhanced by cross-validation with other databases. Textual commentary about the target firms, including management and analyst quotes, provides additional insight that can enhance the predictive power of the models. Impact on Society: The research provides insights into leveraging emerging technologies to predict acquisitions. The theoretical basis and modeling attributes provide a foundation that can be further expanded to suit specific industries and firms. Future Research: There are opportunities to expand the scope of research in various dimensions by comparing acquisition prediction models across industries and cross-border and domestic acquisitions. Additionally, it is plausible to explore further research by incorporating non-financial data, such as management commentary, to augment the acquisition prediction model.

Aim/Purpose: Previous research has generally concentrated on identifying the variables that most significantly influence customer churn or has used customer segmentation to identify a subset of potential consumers, excluding its effects on forecast accuracy. Consequently, there are two primary research goals in this work. The initial goal was to examine the impact of customer segmentation on the accuracy of customer churn prediction in the banking sector using machine learning models. The second objective is to experiment, contrast, and assess which machine learning approaches are most effective in predicting customer churn. Background: This paper reviews the theoretical basis of customer churn, and customer segmentation, and suggests using supervised machine-learning techniques for customer attrition prediction. Methodology: In this study, we use different machine learning models such as k-means clustering to segment customers, k-nearest neighbors, logistic regression, decision tree, random forest, and support vector machine to apply to the dataset to predict customer churn. Contribution: The results demonstrate that the dataset performs well with the random forest model, with an accuracy of about 97%, and that, following customer segmentation, the mean accuracy of each model performed well, with logistic regression having the lowest accuracy (87.27%) and random forest having the best (97.25%). Findings: Customer segmentation does not have much impact on the precision of predictions. It is dependent on the dataset and the models we choose. Recommendations for Practitioners: The practitioners can apply the proposed solutions to build a predictive system or apply them in other fields such as education, tourism, marketing, and human resources. Recommendation for Researchers: The research paradigm is also applicable in other areas such as artificial intelligence, machine learning, and churn prediction. Impact on Society: Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. Future Research: Build a real-time or near real-time application to provide close information to make good decisions. Furthermore, handle the imbalanced data using new techniques.

Aim/Purpose: This study aims to evaluate the success of deep learning in building user profiles for personalized information access. Background: To better express document content and information during the matching phase of the information retrieval (IR) process, deep learning architectures could potentially offer a feasible and optimal alternative to user profile building for personalized information access. Methodology: This study uses deep learning-based models to deduce the domain of the document deemed implicitly relevant by a user that corresponds to their center of interest, and then used predicted domain by the best given architecture with user’s characteristics to predict other centers of interest. Contribution: This study contributes to the literature by considering the difference in vocabulary used to express document content and information needs. Users are integrated into all research phases in order to provide them with relevant information adapted to their context and their preferences meeting their precise needs. To better express document content and information during this phase, deep learning models are employed to learn complex representations of documents and queries. These models can capture hierarchical, sequential, or attention-based patterns in textual data. Findings: The results show that deep learning models were highly effective for building user profiles for personalized information access since they leveraged the power of neural networks in analyzing and understanding complex patterns in user behavior, preferences, and user interactions. Recommendations for Practitioners: Building effective user profiles for personalized information access is an ongoing process that requires a combination of technology, user engagement, and a commitment to privacy and security. Recommendation for Researchers: Researchers involved in building user profiles for personalized information access play a crucial role in advancing the field and developing more innovative deep-based networks solutions by exploring novel data sources, such as biometric data, sentiment analysis, or physiological signals, to enhance user profiles. They can investigate the integration of multimodal data for a more comprehensive understanding of user preferences. Impact on Society: The proposed models can provide companies with an alternative and sophisticated recommendation system to foster progress in building user profiles by analyzing complex user behavior, preferences, and interactions, leading to more effective and dynamic content suggestions. Future Research: The development of user profile evolution models and their integration into a personalized information search system may be confronted with other problems such as the interpretability and transparency of the learning-based models. Developing interpretable machine learning techniques and visualization tools to explain how user profiles are constructed and used for personalized information access seems necessary to us as a future extension of our work.

Aim/Purpose: This study examines the adoption and continued use of m-learning in Iraqi universities amidst an unstable environment by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Expectation-Confirmation Model (ECM) models. The primary goal is to address the specific challenges and opportunities in Iraq’s higher education institutions (HEIs) due to geopolitical instability and understand their impact on student acceptance, satisfaction, and continued m-learning usage. Background: The research builds on the growing importance of m-learning, especially in HEIs, and recognizes the unique challenges faced by institutions in Iraq, given the region’s instability. It identifies gaps in existing models and proposes extensions, introducing the variable “civil conflicts” to account for the volatile context. The study aims to contribute to a deeper understanding of m-learning acceptance in conflict-affected regions and provide insights for improving m-learning initiatives in Iraqi HEIs. Methodology: To achieve its objectives, this research employed a quantitative survey to collect data from 399 students in five Iraqi universities. PLS-SEM is used for the analysis of quantitative data, testing the extended UTAUT2 and ECM models. Contribution: The study’s findings are expected to contribute to the development of a nuanced understanding of m-learning adoption and continued usage in conflict-affected regions, particularly in the Iraqi HEI context. Findings: The study’s findings may inform strategies to enhance the effectiveness of m-learning initiatives in Iraqi HEIs and offer insights into how education can be supported in regions characterized by instability. Recommendations for Practitioners: Educators and policymakers can benefit from the research by making informed decisions to support education continuity and quality, particularly in conflict-affected areas. Recommendation for Researchers: Researchers can build upon this study by further exploring the adoption and usage of m-learning in unstable environments and evaluating the effectiveness of the proposed model extensions. Impact on Society: The research has the potential to positively impact society by improving access to quality education in regions affected by conflict and instability. Future Research: Future research can expand upon this study by examining the extended model’s applicability in different conflict-affected regions and assessing the long-term impact of m-learning initiatives on students’ educational outcomes.

Aim/Purpose: The objective of this paper is to review the vast literature of user-centric in-formation science and inform about the emerging themes in information behaviour science. Background: The paradigmatic shift from system-centric to user-centric approach facilitates research on the cognitive and individual information processing. Various information behaviour theories/models emerged. Methodology: Recent information behaviour theories and models are presented. Features, strengths and weaknesses of the models are discussed through the analysis of the information behaviour literature. Contribution: This paper sheds light onto the weaknesses in earlier information behaviour models and stresses (and advocates) the need for research on social information behaviour. Findings: Prominent information behaviour models deal with individual information behaviour. People live in a social world and sort out most of their daily or work problems in groups. However, only seven papers discuss social information behaviour (Scopus search). Recommendations for Practitioners : ICT tools used for inter-organisational sharing should be redesigned for effective information-sharing during disaster/emergency times. Recommendation for Researchers: There are scarce sources on social side of the information behaviour, however, most of the work tasks are carried out in groups/teams. Impact on Society: In dynamic work contexts like disaster management and health care settings, collaborative information-sharing may result in decreasing the losses. Future Research: A fieldwork will be conducted in disaster management context investigating the inter-organisational information-sharing.

British Vogue's editor says skinny models are back "in", partly fuelled by weight loss drug Ozempic.

As we harness AI’s potential, specialised models for sectors like climate change offer a promising path forward.

The "International Workshop Decision Models and Population Management" will take place from 2 to 4 February, 2014 in Paris, France. The three days international and interdisciplinary workshop is devoted to the decision making, in particular in presence of multiple actors with or without interaction. These problems occur in a natural way in management of populations, where the dynamics are strongly related to the decisions. The workshop aims to bring together Mathematicians, Computer Scientists and Ecologists around the problem of populations management. The population dynamics, viability theory and game theory form an umbrella of helpful mathematical tools in this context. On the other hand, the computer sciences bring the online and algorithmic mechanism design.

The workshop is motivated by concrete problems proposed by ecologists and aims to create a synergy between scientists from different backgrounds to address the challenging modelling of decision making in the context of ecological paradigms.

Invited Speakers
- Michel BENAIM (Université de Neuchâtel, Neuchâtel, Swtizerland)
- Renato CASAGRANDI (Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano, Italy)
- Denis COUVET (Muséum National d'Histoire Naturelle, Paris, France)
- Sylvain DUCTOR (LIP6, UPMC, Paris, France)
- Marino GATTO (Dipartimento di Elettronica, Informazione e Bioingegneria-Politecnico di Milano, Italy) 
- Ihab HAIDAR (Sorbonne Universités, Paris, France)
- Sophie MARTIN (UR LISC - IRSTEA)
- Nicolas MAUDET (LIP6, UPMC, Paris, France) 
- Paco MELIÀ (Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano, Italy)
- Jean-Baptiste MIHOUB (UPMC-Sorbonne Universités, Paris, France)
- Vianney PERCHET (Université Denis Diderot, Paris, France)
 - Karl SIGMUND (University of Vienna, Wien, Austria)
- Sylvain SORIN (IMJ-PRG, UPMC, Paris, France)
- Jean-Philippe TERREAUX (IRSTEA-ADBX, Bordeaux, France)
- Tristan TOMALA (École des hautes études commerciales de Paris, Paris, France)
- Vladimir VELIOV (Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, Vienna, Austria)
- Yannick VIOSSAT (Université Paris-Dauphine, Paris, France)

The conference covers scenarios and modelling applications in marine, freshwater and terrestrial systems, across all relevant disciplines of natural and social sciences. It is open to scientists and experts working in the field, policy makers and practioners. The conference focuses on: (i) Exploring recent advances in modelling human impacts on biodiversity and ecosystem services, (ii) Addressing the use of scenarios and models for decision support, (iii) Mobilising observations of biodiversity and ecosystem services for model development and testing, (iv) Capacity building for developing scenarios and models and for their use in decision making, (v) Horizon scanning and addressing gaps in knowledge.

More information available on the conference website.

The 10th  International Conference on Ecological Informatics 'Facing Global Change by Sharing Data and Models' wil take place on 24‐28 October 2016 in Dubrovnik, Croatia