On semble s'acheminer vers une vague En Marche de forte magnitude aux prochaines Législatives. Si Les Républicains espèrent limiter la casse, c'est à dire avec un perte d'une centaine de députés. L'enjeu de LR, c'est l'après avec en ligne de mire, un...

C’est entendu pour Emmanuel Macron, les violences auraient pour cause les Réseaux Sociaux. Je cite « il y a un changement anthropologique de nos sociétés qui vient des réseaux sociaux ». J’avoue que ce type de raisonnement aussi simplistes me laisse pantois....

1. Notre-Dame de Paris : discours d'Emmanuel Macron, première messe, ouverture au public… Découvrez le calendrier de la réouverture de la cathédrale  franceinfo
2. Réouverture de Notre-Dame : Emmanuel Macron s’exprimera «sur le parvis» de la cathédrale  Le Figaro
3. Réouverture de Notre-Dame de Paris : le calendrier dévoilé, une bonne nouvelle pour les visiteurs  actu.fr
4. Notre-Dame : la cathédrale ouvrira au public du 8 au 14 décembre "jusqu'à 22 heures"  RTL
5. Cérémonies, huit jours de fête, visites: ce qu'il faut savoir sur la réouverture de Notre-Dame de Paris  BFM Paris Ile-de-France

The confluence of the internet of things (IoT) with smart grids has ushered in a paradigm shift in energy management, promising unparalleled efficiency, economic robustness and unwavering reliability. However, this integrative evolution has concurrently amplified the grid's susceptibility to cyber intrusions, casting shadows on its foundational security and structural integrity. Machine learning (ML) and deep learning (DL) emerge as beacons in this landscape, offering robust methodologies to navigate the intricate cybersecurity labyrinth of IoT-infused smart grids. While ML excels at sifting through voluminous data to identify and classify looming threats, DL delves deeper, crafting sophisticated models equipped to counteract avant-garde cyber offensives. Both of these techniques are united in their objective of leveraging intricate data patterns to provide real-time, actionable security intelligence. Yet, despite the revolutionary potential of ML and DL, the battle against the ceaselessly morphing cyber threat landscape is relentless. The pursuit of an impervious smart grid continues to be a collective odyssey. In this review, we embark on a scholarly exploration of ML and DL's indispensable contributions to enhancing cybersecurity in IoT-centric smart grids. We meticulously dissect predominant cyber threats, critically assess extant security paradigms, and spotlight research frontiers yearning for deeper inquiry and innovation.

To achieve psychological and emotional analysis of massive internet chats, researchers have used statistical methods, machine learning, and neural networks to analyse the dynamic tendencies of texts dynamically. For long readers, the author first compares and explores the differences between the two psychoanalysis algorithms based on the emotion dictionary and machine learning for simple sentences, then studies the expansion algorithm of the emotion dictionary, and finally proposes an extended text psychoanalysis algorithm based on conditional random field. According to the experimental results, the mental dictionary's accuracy, recall, and F-score based on the cognitive understanding of each additional ten words were calculated. The optimisation decreased, and the memory and F-score improved. An <i>F</i>-value greater than 1, which is the most effective indicator for evaluating the effectiveness of a mental analysis problem, can better demonstrate that the algorithm is adaptive in the literature dictionary. It has been proven that this scheme can achieve good results in analysing emotional tendencies and has higher efficiency than ordinary weight-based psychological sentiment analysis algorithms.

Aim/Purpose: This paper presents a machine learning approach for analyzing Course Learning Outcomes (CLOs). The aim of this study is to find a model that can check whether a CLO is well written or not. Background: The use of machine learning algorithms has been, since many years, a prominent solution to predict learner performance in Outcome Based Education. However, the CLOs definition is still presenting a big handicap for faculties. There is a lack of supported tools and models that permit to predict whether a CLO is well written or not. Consequently, educators need an expert in quality and education to validate the outcomes of their courses. Methodology: A novel method named CLOCML (Course Learning Outcome Classification using Machine Learning) is proposed in this paper to develop predictive models for CLOs paraphrasing. A new dataset entitled CLOC (Course Learning Outcomes Classes) for that purpose has been collected and then undergone a pre-processing phase. We compared the performance of 4 models for predicting a CLO classification. Those models are Support Vector Machine (SVM), Random Forest, Naive Bayes and XGBoost. Contribution: The application of CLOCML may help faculties to make well-defined CLOs and then correct CLOs' measures in order to improve the quality of education addressed to their students. Findings: The best classification model was SVM. It was able to detect the CLO class with an accuracy of 83%. Recommendations for Practitioners: We would recommend both faculties’ members and quality reviewers to make an informed decision about the nature of a given course outcome. Recommendation for Researchers: We would highly endorse that the researchers apply more machine learning models for CLOs of various disciplines and compare between them. We would also recommend that future studies investigate on the importance of the definition of CLOs and its impact on the credibility of Key Performance Indicators (KPIs) values during accreditation process. Impact on Society: The findings of this study confirm the results of several other researchers who use machine learning in outcome-based education. The definition of right CLOs will help the student to get an idea about the performances that will be measured at the end of a course. Moreover, each faculty can take appropriate actions and suggest suitable recommendations after right performance measures in order to improve the quality of his course. Future Research: Future research can be improved by using a larger dataset. It could also be improved with deep learning models to reach more accurate results. Indeed, a strategy for checking CLOs overlaps could be integrated.

Aim/Purpose: The study focused on learner sentiments and experiences after using the Moodle assessment module and trained a machine learning classifier for future sentiment predictions. Background: Learner assessment is one of the standard methods instructors use to measure students’ performance and ascertain successful teaching objectives. In pedagogical design, assessment planning is vital in lesson content planning to the extent that curriculum designers and instructors primarily think like assessors. Assessment aids students in redefining their understanding of a subject and serves as the basis for more profound research in that particular subject. Positive results from an evaluation also motivate learners and provide employment directions to the students. Assessment results guide not just the students but also the instructor. Methodology: A modified methodology was used for carrying out the study. The revised methodology is divided into two major parts: the text-processing phase and the classification model phase. The text-processing phase consists of stages including cleaning, tokenization, and stop words removal, while the classification model phase consists of dataset training using a sentiment analyser, a polarity classification model and a prediction validation model. The text-processing phase of the referenced methodology did not utilise tokenization and stop words. In addition, the classification model did not include a sentiment analyser. Contribution: The reviewed literature reveals two major omissions: sentiment responses on using the Moodle for online assessment, particularly in developing countries with unstable internet connectivity, have not been investigated, and variations of the k-fold cross-validation technique in detecting overfitting and developing a reliable classifier have been largely neglected. In this study we built a Sentiment Analyser for Learner Emotion Management using the Moodle for assessment with data collected from a Ghanaian tertiary institution and developed a classification model for future sentiment predictions by evaluating the 10-fold and the 5-fold techniques on prediction accuracy. Findings: After training and testing, the RF algorithm emerged as the best classifier using the 5-fold cross-validation technique with an accuracy of 64.9%. Recommendations for Practitioners: Instead of a closed-ended questionnaire for learner feedback assessment, the open-ended mechanism should be utilised since learners can freely express their emotions devoid of restrictions. Recommendation for Researchers: Feature selection for sentiment analysis does not always improve the overall accuracy for the classification model. The traditional machine learning algorithms should always be compared to either the ensemble or the deep learning algorithms Impact on Society: Understanding learners’ emotions without restriction is important in the educational process. The pedagogical implementation of lessons and assessment should focus on machine learning integration Future Research: To compare ensemble and deep learning algorithms

Currently, Android is a booming technology that has occupied the major parts of the market share. However, as Android is an open-source operating system there are possibilities of attacks on the users, there are various types of attacks but one of the most common attacks found was malware. Malware with machine learning (ML) techniques has proven as an impressive result and a useful method for malware detection. Here in this paper, we have focused on the analysis of malware attacks by collecting the dataset for the various types of malware and we trained the model with multiple ML and deep learning (DL) algorithms. We have gathered all the previous knowledge related to malware with its limitations. The machine learning algorithms were having various accuracy levels and the maximum accuracy observed is 99.68%. It also shows which type of algorithm is preferred depending on the dataset. The knowledge from this paper may also guide and act as a reference for future research related to malware detection. We intend to make use of Static Android Activity to analyse malware to mitigate security risks.

Aim/Purpose: Although cassava is one of the crops that can be grown during the dry season in Northeastern Thailand, most farmers in the region do not know whether the crop can grow in their specific areas because the available agriculture planning guideline provides only a generic list of dry-season crops that can be grown in the whole region. The purpose of this research is to develop a predictive model that can be used to predict suitable areas for growing cassava in Northeastern Thailand during the dry season. Background: This paper develops a decision support system that can be used by farmers to assist them determine if cassava can be successfully grown in their specific areas. Methodology: This study uses satellite imagery and data on land characteristics to develop a machine learning model for predicting suitable areas for growing cassava in Thailand’s Nakhon-Phanom province. Contribution: This research contributes to the body of knowledge by developing a novel model for predicting suitable areas for growing cassava. Findings: This study identified elevation and Ferric Acrisols (Af) soil as the two most important features for predicting the best-suited areas for growing cassava in Nakhon-Phanom province, Thailand. The two-class boosted decision tree algorithm performs best when compared with other algorithms. The model achieved an accuracy of .886, and .746 F1-score. Recommendations for Practitioners: Farmers and agricultural extension agents will use the decision support system developed in this study to identify specific areas that are suitable for growing cassava in Nakhon-Phanom province, Thailand Recommendation for Researchers: To improve the predictive accuracy of the model developed in this study, more land and crop characteristics data should be incorporated during model development. The ground truth data for areas growing cassava should also be collected for a longer period to provide a more accurate sample of the areas that are suitable for cassava growing. Impact on Society: The use of machine learning for the development of new farming systems will enable farmers to produce more food throughout the year to feed the world’s growing population. Future Research: Further studies should be carried out to map other suitable areas for growing dry-season crops and to develop decision support systems for those crops.

Aim/Purpose: In the United States, the Centers for Disease Control and Prevention (CDC) tracks the disease activity using data collected from medical practice's on a weekly basis. Collection of data by CDC from medical practices on a weekly basis leads to a lag time of approximately 2 weeks before any viable action can be planned. The 2-week delay problem was addressed in the study by creating machine learning models to predict flu outbreak. Background: The 2-week delay problem was addressed in the study by correlation of the flu trends identified from Twitter data and official flu data from the Centers for Disease Control and Prevention (CDC) in combination with creating a machine learning model using both data sources to predict flu outbreak. Methodology: A quantitative correlational study was performed using a quasi-experimental design. Flu trends from the CDC portal and tweets with mention of flu and influenza from the state of Georgia were used over a period of 22 weeks from December 29, 2019 to May 30, 2020 for this study. Contribution: This research contributed to the body of knowledge by using a simple bag-of-word method for sentiment analysis followed by the combination of CDC and Twitter data to generate a flu prediction model with higher accuracy than using CDC data only. Findings: The study found that (a) there is no correlation between official flu data from CDC and tweets with mention of flu and (b) there is an improvement in the performance of a flu forecasting model based on a machine learning algorithm using both official flu data from CDC and tweets with mention of flu. Recommendations for Practitioners: In this study, it was found that there was no correlation between the official flu data from the CDC and the count of tweets with mention of flu, which is why tweets alone should be used with caution to predict a flu out-break. Based on the findings of this study, social media data can be used as an additional variable to improve the accuracy of flu prediction models. It is also found that fourth order polynomial and support vector regression models offered the best accuracy of flu prediction models. Recommendations for Researchers: Open-source data, such as Twitter feed, can be mined for useful intelligence benefiting society. Machine learning-based prediction models can be improved by adding open-source data to the primary data set. Impact on Society: Key implication of this study for practitioners in the field were to use social media postings to identify neighborhoods and geographic locations affected by seasonal outbreak, such as influenza, which would help reduce the spread of the disease and ultimately lead to containment. Based on the findings of this study, social media data will help health authorities in detecting seasonal outbreaks earlier than just using official CDC channels of disease and illness reporting from physicians and labs thus, empowering health officials to plan their responses swiftly and allocate their resources optimally for the most affected areas. Future Research: A future researcher could use more complex deep learning algorithms, such as Artificial Neural Networks and Recurrent Neural Networks, to evaluate the accuracy of flu outbreak prediction models as compared to the regression models used in this study. A future researcher could apply other sentiment analysis techniques, such as natural language processing and deep learning techniques, to identify context-sensitive emotion, concept extraction, and sarcasm detection for the identification of self-reporting flu tweets. A future researcher could expand the scope by continuously collecting tweets on a public cloud and applying big data applications, such as Hadoop and MapReduce, to perform predictions using several months of historical data or even years for a larger geographical area.

Aim/Purpose: The aim of this study was to analyze various performance metrics and approaches to their classification. The main goal of the study was to develop a new typology that will help to advance knowledge of metrics and facilitate their use in machine learning regression algorithms Background: Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. A performance metric can be defined as a logical and mathematical construct designed to measure how close are the actual results from what has been expected or predicted. A vast variety of performance metrics have been described in academic literature. The most commonly mentioned metrics in research studies are Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), etc. Knowledge about metrics properties needs to be systematized to simplify the design and use of the metrics. Methodology: A qualitative study was conducted to achieve the objectives of identifying related peer-reviewed research studies, literature reviews, critical thinking and inductive reasoning. Contribution: The main contribution of this paper is in ordering knowledge of performance metrics and enhancing understanding of their structure and properties by proposing a new typology, generic primary metrics mathematical formula and a visualization chart Findings: Based on the analysis of the structure of numerous performance metrics, we proposed a framework of metrics which includes four (4) categories: primary metrics, extended metrics, composite metrics, and hybrid sets of metrics. The paper identified three (3) key components (dimensions) that determine the structure and properties of primary metrics: method of determining point distance, method of normalization, method of aggregation of point distances over a data set. For each component, implementation options have been identified. The suggested new typology has been shown to cover a total of over 40 commonly used primary metrics Recommendations for Practitioners: Presented findings can be used to facilitate teaching performance metrics to university students and expedite metrics selection and implementation processes for practitioners Recommendation for Researchers: By using the proposed typology, researchers can streamline development of new metrics with predetermined properties Impact on Society: The outcomes of this study could be used for improving evaluation results in machine learning regression, forecasting and prognostics with direct or indirect positive impacts on innovation and productivity in a societal sense Future Research: Future research is needed to examine the properties of the extended metrics, composite metrics, and hybrid sets of metrics. Empirical study of the metrics is needed using R Studio or Azure Machine Learning Studio, to find associations between the properties of primary metrics and their “numerical” behavior in a wide spectrum of data characteristics and business or research requirements

Aim/Purpose: To predict the change-proneness of software from the continuous evolution using machine learning methods. To identify when software changes become statistically significant and how metrics change. Background: Software evolution is the most time-consuming activity after a software release. Understanding evolution patterns aids in understanding post-release software activities. Many methodologies have been proposed to comprehend software evolution and growth. As a result, change prediction is critical for future software maintenance. Methodology: I propose using machine learning methods to predict change-prone classes. Classes that are expected to change in future releases were defined as change-prone. The previous release was only considered by the researchers to define change-proneness. In this study, I use the evolution of software to redefine change-proneness. Many snapshots of software were studied to determine when changes became statistically significant, and snapshots were taken biweekly. The research was validated by looking at the evolution of five large open-source systems. Contribution: In this study, I use the evolution of software to redefine change-proneness. The research was validated by looking at the evolution of five large open-source systems. Findings: Software metrics can measure the significance of evolution in software. In addition, metric values change within different periods and the significance of change should be considered for each metric separately. For five classifiers, change-proneness prediction models were trained on one snapshot and tested on the next. In most snapshots, the prediction performance was excellent. For example, for Eclipse, the F-measure values were between 80 and 94. For other systems, the F-measure values were higher than 75 for most snapshots. Recommendations for Practitioners: Software change happens frequently in the evolution of software; however, the significance of change happens over a considerable length of time and this time should be considered when evaluating the quality of software. Recommendation for Researchers: Researchers should consider the significance of change when studying software evolution. Software changes should be taken from different perspectives besides the size or length of the code. Impact on Society: Software quality management is affected by the continuous evolution of projects. Knowing the appropriate time for software maintenance reduces the costs and impacts of software changes. Future Research: Studying the significance of software evolution for software refactoring helps improve the internal quality of software code.

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: While existing literature has extensively explored factors influencing the success of big data projects and proposed big data maturity models, no study has harnessed machine learning to predict project success and identify the critical features contributing significantly to that success. The purpose of this paper is to offer fresh insights into the realm of big data projects by leveraging machine-learning algorithms. Background: Previously, we introduced the Global Big Data Maturity Model (GBDMM), which encompassed various domains inspired by the success factors of big data projects. In this paper, we transformed these maturity domains into a survey and collected feedback from 90 big data experts across the Middle East, Gulf, Africa, and Turkey regions regarding their own projects. This approach aims to gather firsthand insights from practitioners and experts in the field. Methodology: To analyze the feedback obtained from the survey, we applied several algorithms suitable for small datasets and categorical features. Our approach included cross-validation and feature selection techniques to mitigate overfitting and enhance model performance. Notably, the best-performing algorithms in our study were the Decision Tree (achieving an F1 score of 67%) and the Cat Boost classifier (also achieving an F1 score of 67%). Contribution: This research makes a significant contribution to the field of big data projects. By utilizing machine-learning techniques, we predict the success or failure of such projects and identify the key features that significantly contribute to their success. This provides companies with a valuable model for predicting their own big data project outcomes. Findings: Our analysis revealed that the domains of strategy and data have the most influential impact on the success of big data projects. Therefore, companies should prioritize these domains when undertaking such projects. Furthermore, we now have an initial model capable of predicting project success or failure, which can be invaluable for companies. Recommendations for Practitioners: Based on our findings, we recommend that practitioners concentrate on developing robust strategies and prioritize data management to enhance the outcomes of their big data projects. Additionally, practitioners can leverage machine-learning techniques to predict the success rate of these projects. Recommendation for Researchers: For further research in this field, we suggest exploring additional algorithms and techniques and refining existing models to enhance the accuracy and reliability of predicting the success of big data projects. Researchers may also investigate further into the interplay between strategy, data, and the success of such projects. Impact on Society: By improving the success rate of big data projects, our findings enable organizations to create more efficient and impactful data-driven solutions across various sectors. This, in turn, facilitates informed decision-making, effective resource allocation, improved operational efficiency, and overall performance enhancement. Future Research: In the future, gathering additional feedback from a broader range of big data experts will be valuable and help refine the prediction algorithm. Conducting longitudinal studies to analyze the long-term success and outcomes of Big Data projects would be beneficial. Furthermore, exploring the applicability of our model across different regions and industries will provide further insights into the field.

The paper introduces a novel Personal Knowledge Management (PKM) concept and prototype system. The system’s objective is to aid life-long-learning, resourcefulness, creativity, and teamwork of individuals throughout their academic and professional life and as contributors and beneficiaries of organizational and societal performance. Such a scope offers appealing and viable opportunities for stakeholders in the educational, professional, and developmental context. To further validate the underlying PKM application design, the systems thinking techniques of the transdiscipline of Informing Science (IS) are employed. By applying Cohen’s IS-Framework, Leavitt’s Diamond Model, the IS-Meta Approach, and Gill’s and Murphy’s Three Dimensions of Design Task Complexity, the more specific KM models and methodologies central to the PKMS concept are aligned, introduced, and visualized. The extent of this introduction offers an essential overview, which can be deepened and broadened by using the cited URL and DOI links pointing to the available resources of the author’s prior publications. The paper emphasizes the differences of the proposed meme-based PKM System compared to its traditional organizational document-centric counterparts as well as its inherent complementing synergies. As a result, it shows how the system is closing in on Vannevar Bush’s still unfulfilled vison of the ‘Memex’, an as-close-as-it-gets imaginary ancestor celebrating its 70th anniversary as an inspiring idea never realized. It also addresses the scenario recently put forward by Levy which foresees a decentralizing revolution of knowledge management that gives more power and autonomy to individuals and self-organized groups. Accordingly, it also touches on the PKM potential in terms of Kuhn’s Scientific Revolutions and Disruptive Innovations.

Aim/Purpose: This paper introduces a Social Machine for collaborative sensemaking that the developers have configured to the requirements and challenges of transdisciplinary literature reviews. Background: Social Machines represent a promising model for unifying machines and social processes for a wide range of purposes. A development team led by the author is creating a Social Machine for activities that require users to combine pieces of information from multiple online sources and file types for various purposes. Methodology: The development team has applied emergent design processes, usability testing, and formative evaluation in the execution of the product road map. Contribution: A major challenge of the digital information age is how to tap into large volumes of online information and the collective intelligence of diverse groups to generate new knowledge, solve difficult problems, and drive innovation. A Transdisciplinary Social Machine (TDSM) enables new forms of interactions between humans, machines, and online content that have the potential to (a) improve outcomes of sensemaking activities that involve large collections of online documents and diverse groups and (b) make machines more capable of assisting humans in their sensemaking efforts. Findings: Preliminary findings suggest that TDSM promotes learning and the generation of new knowledge. Recommendations for Practitioners: TDSM has the potential to improve outcomes of literature reviews and similar activities that require distilling information from diverse online sources. Recommendation for Researchers: TDSM is an instrument for investigating sensemaking, an environment for studying various forms of human and machine interactions, and a subject for further evaluation. Impact on Society: In complex areas such as sustainability and healthcare research, TDSM has the potential to make decision-making more transparent and evidence-based, facilitate the production of new knowledge, and promote innovation. In education, TDSM has the potential to prepare students for the 21st century information economy. Future Research: Research is required to measure the effects of TDSM on cross-disciplinary communication, human and machine learning, and the outcomes of transdisciplinary research projects. The developers are planning a multiple case study using design-based research methodology to investigate these topics.

Aim/Purpose: The amount of information published has increased dramatically due to the information explosion. The issue of managing information as it expands at this rate lies in the development of information extraction technology that can turn unstructured data into organized data that is understandable and controllable by computers Background: The primary goal of named entity recognition (NER) is to extract named entities from amorphous materials and place them in pre-defined semantic classes. Methodology: In our work, we analyze various machine learning algorithms and implement K-NN which has been widely used in machine learning and remains one of the most popular methods to classify data. Contribution: To the researchers’ best knowledge, no published study has presented Named entity recognition for the Kikuyu language using a machine learning algorithm. This research will fill this gap by recognizing entities in the Kikuyu language. Findings: An evaluation was done by testing precision, recall, and F-measure. The experiment results demonstrate that using K-NN is effective in classification performance. Recommendation for Researchers: With enough training data, researchers could perform an experiment and check the learning curve with accuracy that compares to state of art NER. Future Research: Future studies may be done using unsupervised and semi-supervised learning algorithms for other resource-scarce languages.

Product innovation has become a crucial part of the sustainability of the coconut agro-industry in Indonesia, covering upstream and downstream sides. To overcome this challenge, it is necessary to create several model stages using a hybrid method that combines machine learning based on multiple criteria decision making and mass balance analysis. The research case study was conducted in Tembilahan district, Riau province, Indonesia, one of the primary coconut producers in Indonesia. The analysis results showed that potential products for domestic customers included coconut milk, coconut cooking oil, coconut chips, coconut jelly, coconut sugar, and virgin coconut oil. Furthermore, considering the experts, the most potential product to be developed was coconut sugar with a weight of 0.26. Prediction of coconut sugar demand reached 13,996,607 tons/year, requiring coconut sap as a raw material up to 97,976,249.

Do firms learn from their failed innovation attempts? Answering this question is important because failure is an integral part of exploratory learning. In this study, we explore whether and under what circumstances firms learn from their small failures in experimentation. Building on organizational learning literature, we examine the conditions under which prior failures influence firms' R&D output amount and quality. An empirical analysis of voluntary patent expirations (i.e., patents that firms give up by not paying renewal fees) in 97 pharmaceutical firms between 1980 and 2002 shows that the number, importance, and timing of small failures are associated with a decrease in R&D output (patent count) but an increase in the quality of the R&D output (forward citations to patents). Exploratory interviews suggest that the results are driven by a multi-level learning process from failures in pharmaceutical R&D. The findings contribute to the organizational learning literature by providing a nuanced view of learning from failures in experimentation.

In order to acquire resources, new ventures need to be perceived as legitimate. For this to occur, a venture must meet the expectations of various audiences with differing norms, standards, and values as the venture evolves and grows. We investigate how the organizational identity of a technology venture must adapt to meet the expectations of critical resource providers at each stage of its organizational life cycle. In so doing, we provide a temporal perspective on the interactions between identity, organizational legitimacy, institutional environments, and entrepreneurial resource acquisition for technology ventures. The core assertion from this conceptual analysis is that entrepreneurial ventures confront multiple legitimacy thresholds as they evolve and grow. We identify and discuss three key insights related to entrepreneurs' efforts to cross those thresholds at different organizational life cycle stages: institutional pluralism, venture-identity embeddedness and legitimacy buffering.

Carlo Lipizzi’s Societal impacts of artificial intelligence and machine learning offers a critical and comprehensive analysis of artificial intelligence (AI) and machine learning’s effects on society. This book provides a balanced perspective, cutting through the

PUTRAJAYA: The founding couple of FashionValet Sdn Bhd, linked to the investment loss of Khazanah Nasional Bhd (Khazanah) and Permodalan Nasional Bhd (PNB), continued giving their statement to the Malaysian Anti-Corruption Commission (MACC).

The vehicle carrying the couple arrived at MACC headquarters here at 2.50 pm.

Today marks the sixth day of their statements being recorded after the MACC detected several suspicious account transactions in its probe into investment losses totalling RM43.9 million.

MACC Chief Commissioner Tan Sri Azam Baki was reported to have said that the commission was reviewing and investigating the cash flow received by the e-commerce business platform founders.

MACC is also reported to have frozen several of the couple’s private and company bank accounts worth about RM1.1 million through Op Favish on Nov 6.

In the ever-changing global scenario, one country alone cannot decide the destiny of the world

The wethepeople "Chaos Machine 22" BMX Frame - 22 Inch is the signature BMX frame from the Australian BMX Trails legend Tyson Jones-Peni and comes in a 22" inch version for 22" inch wheels. The wethepeople "Chaos Machine 22" BMX Frame - 22 Inch features a long and stable geometry and mounts for a disc brake (and regular U-brake).

• Wheel Size: 20"
• Material: 100% 4130 Japanese CrMo, top tube and down tube gussets, butted tubes, integrated headset, integrated seatclamp, offset thickness dent resistant chainstays and mid bb for grind resistance
• Geometry:
  Top Tube Length (TT): 22.25"
  Chainstay Length (CS): 14" (35.56cm) - 14.75" (37.47cm)
  Head Tube Angle (HA): 74.25°
  Seat Tube Angle (SA): 71°
  Bottom Bracket Height: 12.25"
  Standover Height (SO): 9.75" (24.77cm)
• Seat Clamp: integrated
• Seat Post Diameter: 25.4mm
• Bottom Bracket: Mid BB
• Dropouts: Investment cast, 6mm thick, 14mm slots, CNC machined, 4130 CrMo
• Chain Tensioners: integrated
• Brake Type: U-Brake & Disc Brake
• Brakemounts: without
• Brakemounts included with delivery: No
• Gyro compatible: No
• Features: Tyson Jones-Peni Signature, long trail geometry, disc mount for 120mm - 160mm rotors, 127mm high head tube, strut tube with larger radius, stiffer & stronger back end, larger weld on the head tube, wider dropout for wide 2.4" tires
• Model Year: 2024

Bible study topics in Colossians, Chapter 1 cover the supremacy of Christ. The questions are designed for personal or group inductive style Bible study and discussion.

A eclectic group of stars - including reality TV's Ariana Madix, Broadway belter Idina Menzel, hip-hop's T-Pain, members of the WNBA champions New York Liberty and country duo Dan + Shay - will feature in this year's Macy's Thanksgiving Day Parade.

A judge in a rural Virginia city has ordered two officials there to certify the results of the election after they filed a lawsuit last month threatening not to certify unless they could hand-count the ballots.

A husband and wife from Long Island, New York, were sentenced this week for their role in a 2022 fire at their rented-out home in the Hamptons that killed two sisters from Potomac, Maryland.

The Biodiversity Data Journal (BDJ) and the associated Pensoft Writing Tool (PWT), launched on 16th of September 2013, offer several innovations - some of them unique - at every stage of the publishing process. The workflow allows for authoring, peer-review and dissemination to take place within the same online, collaborative platform.

Open access to content and data is quickly becoming the prevailing model in academic publishing, resulting in part from changes to policies of governments and funding agencies and in part from scientist's desire to get their work more widely read and used. Open access benefits scientists with greater dissemination and citation of their work, and provides society as a whole access to the latest research.

To publish effectively in open access, it is not sufficient simply to provide PDF files online. It is crucial to put them under a reuse-friendly license and to implement technologies that allow machine-readable content and data to be harvested by computers that can collate small scattered data into a big pool. Analyses and modelling of community-owned big data are the only way to confront environmental challenges to society, such as climate change, ecosystems destruction, biodiversity loss and others.

Manuscripts are not submitted to BDJ in the usual way, as word processor files, but are written in the online, collaborative Pensoft Writing Tool (PWT), that provides a set of pre-defined, but flexible article templates. Authors may work on a manuscript and invite external contributors, such as mentors, potential reviewers, linguistic and copy editors, and colleagues, who may read and comment on the text before submission. When a manuscript is completed, it is submitted to the journal with a simple click of a button. The tool also allows automated import of manuscripts from data management platforms, such as Scratchpads.

"This is the first workflow ever to support the full life cycle of a manuscript, from initial drafting through submission, community peer-review, publication and dissemination within a single, online, collaborative platform. By publishing papers in all branches of biodiversity science, including novel article types, such as data papers and software descriptions, BDJ becomes a gateway for either large or small data into the emerging world of "big data", said Prof. Lyubomir Penev, managing director and founder of Pensoft Publishers.

BDJ shortens the distance between "narrative (text)" and "data" publishing. Many data types, such as species occurrences, checklists, measurements and others, are converted into text from spreadsheets into a human-readable format. Conversely text from an article can be downloaded as structured data or harvested by computers for further use.

A novel community-based peer-review provides the opportunity for a large number of specialists in the field to review a manuscript. Authors may also opt for an entirely public peer-review process. Reviewers may opt to be anonymous or to disclose their names. Editors no longer need to check different reviewers' and author's versions of a manuscript because all versions can be consolidated into a single online document, again at the click of a button.

"The Biodiversity Data Journal is not just a journal, not even a data journal in the conventional sense. It is a completely novel workflow and infrastructure to mobilise, review, publish, store, disseminate, make interoperable, collate and re-use data through the act of scholarly publishing!" concluded Dr Vincent Smith from the Natural History Museum in London, the journal's Editor-in-Chief.

The platform has been designed by Pensoft Publishers and was funded in part by the European Union's Seventh Framework Program (FP7) project ViBRANT.

Smith V, Georgiev T, Stoev P, Biserkov J, Miller J, Livermore L, Baker E, Mietchen D, Couvreur T, Mueller G, Dikow T, Helgen K, Frank J, Agosti D, Roberts D, Penev L (2013) Beyond dead trees: integrating the scientific process in the Biodiversity Data Journal. Biodiversity Data Journal 1: e995. DOI: 10.3897/BDJ.1.e995

The discipline of taxonomy is highly reliant on previously published photographs, drawings and other images as biodiversity data. Inspired by the uncertainty among taxonomists, a team, representing both taxonomists and experts in rights and copyright law, has traced the role and relevance of copyright when it comes to images with scientific value. Their discussion and conclusions are published in the latest paper added in the EU BON Collection in the open science journal Research Ideas and Outcomes (RIO).

Taxonomic papers, by definition, cite a large number of previous publications, for instance, when comparing a new species to closely related ones that have already been described. Often it is necessary to use images to demonstrate characteristic traits and morphological differences or similarities. In this role, the images are best seen as biodiversity data rather than artwork. According to the authors, this puts them outside the scope, purposes and principles of Copyright. Moreover, such images are most useful when they are presented in a standardized fashion, and lack the artistic creativity that would otherwise make them 'copyrightable works'.

"It follows that most images found in taxonomic literature can be re-used for research or many other purposes without seeking permission, regardless of any copyright declaration," says Prof. David J. Patterson, affiliated with both Plazi and the University of Sydney.

Nonetheless, the authors point out that, "in observance of ethical and scholarly standards, re-users are expected to cite the author and original source of any image that they use." Such practice is "demanded by the conventions of scholarship, not by legal obligation," they add.

However, the authors underline that there are actual copyrightable visuals, which might also make their way to a scientific paper. These include wildlife photographs, drawings and artwork produced in a distinctive individual form and intended for other than comparative purposes, as well as collections of images, qualifiable as databases in the sense of the European Protection of Databases directive.

In their paper, the scientists also provide an updated version of the Blue List, originally compiled in 2014 and comprising the copyright exemptions applicable to taxonomic works. In their Extended Blue List, the authors expand the list to include five extra items relating specifically to images.

"Egloff, Agosti, et al. make the compelling argument that taxonomic images, as highly standardized 'references for identification of known biodiversity,' by necessity, lack sufficient creativity to qualify for copyright. Their contention that 'parameters of lighting, optical and specimen orientation' in biological imaging must be consistent for comparative purposes underscores the relevance of the merger doctrine for photographic works created specifically as scientific data," comments on the publication Ms. Gail Clement, Head of Research Services at the Caltech Library.

"In these cases, the idea and expression are the same and the creator exercises no discretion in complying with an established convention. This paper is an important contribution to the literature on property interests in scientific research data - an essential framing question for legal interoperability of research data," she adds.

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Original source:

Egloff W, Agosti D, Kishor P, Patterson D, Miller J (2017) Copyright and the Use of Images as Biodiversity Data. Research Ideas and Outcomes 3: e12502. https://doi.org/10.3897/rio.3.e12502

Integrating mechanisms into macroecology 

8^th Annual Meeting of the Specialist Group for Macroecology of the Ecological Society of Germany, Austria, and Switzerland (GfÖ)

Martin-Luther-University Halle-Wittenberg, Institute of Biology, Department Geobotany & Botanical Garden, Am Kirchtor 1, 06108 Halle (Saale), Germany

Linking processes and mechanistic approaches (e.g., from physiology, experimental ecology, demography, and evolution) with macroecological approaches, scales, and methods has long been discussed among macroecologists. Still, only few steps toward implementing such links have been taken, yet. With the 8th Annual Meeting of the Specialist Group for Macroecology of the GfÖ, we want to offer a forum for presenting and discussing both tested approaches and new ideas to make these links. Additionally, in order to move beyond discussion, we invite all participants to join the conference workshop on the 2nd day. Here, we plan to intensively discuss gaps in existing approaches and possible ways for bridging these gaps in several discussion groups and to take first steps towards publishing the summarized workshop results. 

http://www.macroecology.org/

Deep learning technology has enabled a machine to compose chorales in the style of Johann Sebastian Bach, according to researchers. Gaetan Hadjeres and Francois Pachet ... Read more

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