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Rice husk has the potential to be used as a source of alternative energy e.g. as biofuel. Its high lignin content, however, poses difficulty to this use. This study investigates the use of microwave assisted acidic and alkaline pretreatment methods to improve the characteristics of rice husk for energy production. The use of microwave assisted 3.5% NaOH provided optimum pretreatment of rice husk; the lignin content was reduced from 17.8 to 10.2%, the cellulose content increased from 38.6 to 59.3% and the C/N ratio was within the optimum range of 25-35. The results showed enhanced potential of biofuel production from rice husk by this pretreatment.

This is the summary from the Condatis project “Decision support for restoring ecological networks in rapidly developing, biodiverse countries” for the case study in Malaysia. We would like to thank our project partners and everyone who helped us with this. For more information please see our website www.condatis.org.uk and the web app created by this project www.webapp.condatis.org.uk. The authors also acknowledge funding from the UK Natural Environment Research Council grant number NE/R009597/1.

This presentation was part of the EERA JP Wind SP8 WORKSHOP on Future market arrangements for large-scale offshore wind energy development in the European Seas (June 10).

This study addresses the optimized design of electro-hydrostatic regenerative shock absorbers to enhance vibrational energy recovery in ground vehicles, aiming to reduce carbon footprint. The design strategy focuses on maximizing regeneration efficiency while minimizing actuator volume. Important trade-offs are considered as constraints, such as ride comfort and road holding. The approach employs a multi-objective evolutionary genetic algorithm, validated through numerical analysis, and applied to design a prototype. Experimental results show a peak regeneration efficiency of 45%, and simulations on a class-B vehicle indicate an average regenerated power of 101 W per shock absorber, corresponding to a CO2 emission reduction of 5.25 g/km.

The development and diversification of machines and mechanisms with applications in all fields require new scientific researches for the systematization and improvement of existing mechanical systems by creating new mechanisms adapted to modern requirements, which involve increasingly complex topological structures. The modern industry, the practice of designing and building machinery is increasingly based on the results of scientific and applied research. Each industrial achievement has backed theoretical and experimental computer-assisted research, which solves increasingly complex problems with advanced computing programs using an increasingly specialized software. The robotization of technological processes determines and influences the emergence of new industries, applications under special environmental conditions, the approach of new types of technological operations, manipulation of objects in the alien space, teleoperators in the top disciplines like medicine, robots covering a whole field Greater service provision in our modern, computerized society. In this context, the present paper attempts to make a scientific and technical contribution to the kinematic analysis and geometric - kinematic synthesis of gears and gears, both as plane and spatial structures. By definition, these complex mechanisms are composed of bars and lever mechanisms (gears and racks). A remarkable problem with the use of conical gears is the spherical coupling design through three-axis spherical gear mechanisms. This modeling is extremely useful in the construction and kinematics of robots, especially the orientation mechanisms, which explains the particular interest in the use of gears and gears. The cinematic study methods of complex gears with bars and gears are particularly diverse, but a unitary method allows the adaptation and use of more efficient analytical and numerical resolution algorithms. The beginning of the use of gears and gears should be sought in ancient Egypt at least a thousand years before Christ. Here, for the first time, spur gears were used for crop irrigation and worm gears for cotton processing.

Biodiversity loss and widespread ecosystem degradation are among the most pressing challenges of our time, requiring urgent action. Yet our understanding of their causes remains limited because prevailing ecological concepts and approaches often overlook the underlying complex interactions of individuals of the same or different species, interacting with each other and with their environment. We propose a paradigm shift in ecological science, moving from simplifying frameworks that use species, population or community averages to an integrative approach that recognizes individual organisms as fundamental agents of ecological change. The urgency of the biodiversity crisis requires such a paradigm shift to advance ecology towards a predictive science by elucidating the causal mechanisms linking individual variation and adaptive behaviour to emergent properties of populations, communities, ecosystems, and ecological interactions with human interventions. Recent advances in computational technologies, sensors, and analytical tools now offer unprecedented opportunities to overcome past challenges and lay the foundation for a truly integrated Individual-Based Global Change Ecology (IBGCE). Unravelling the potential role of individual variability in global change impact analyses will require a systematic combination of empirical, experimental and modelling studies across systems, while taking into account multiple drivers of global change and their interactions. Key priorities include refining theoretical frameworks, developing benchmark models and standardized toolsets, and systematically incorporating individual variation and adaptive behaviour into empirical field work, experiments and predictive models. The emerging synergies between individual-based modelling, big data approaches, and machine learning hold great promise for addressing the inherent complexity of ecosystems. Each step in the development of IBGCE must systematically balance the complexity of the individual perspective with parsimony, computational efficiency, and experimental feasibility. IBGCE aims to unravel and predict the dynamics of biodiversity in the Anthropocene through a comprehensive study of individual organisms, their variability and their interactions. It will provide a critical foundation for considering individual variation and behaviour for future conservation and sustainability management, taking into account individual-to-ecosystem pathways and feedbacks.

The Atlantic Forest is a biodiversity hotspot that includes different types of phytophysiognomies, mainly forests. Hornschuchia, Annonaceae, is one of its endemic genera. Eight Hornschuchia species are found in the tropical moist forest, three of them both occurring in tropical moist forest and tabuleiro forest, a semideciduous seasonal forest, one occurs in the coastal semideciduous seasonal forest and two inhabit the semideciduous seasonal forest and deciduous seasonal forest far from the coast in Bahia. Here we describe the twelfth species of Hornschuchia, which occurs in the Bahian semideciduous seasonal forest, and present a morphological description, illustration, distribution map and assessment of its conservation status.

L'irradiation horizontale globale prévue (GHI) peut aider à la conception, au dimensionnement et à l'analyse des performances des systèmes photovoltaïques (PV), y compris les systèmes de pompage PV à eau utilisés pour les applications d'irrigation. Dans cet article, divers modèles de réseaux neuronaux profonds (DNN) pour la prédiction à un jour du GHI à Hail city (Arabie saoudite) sont développés et étudiés. Les modèles DNN considérés comprennent la mémoire à long terme (LSTM), le LSTM bidirectionnel (BiLSTM), l'unité récurrente fermée (GRU), le GRU bidirectionnel (Bi-GRU), le réseau neuronal convolutionnel unidimensionnel (CNN 1D ) et d'autres configurations hybrides telles que CNN-LSTM et CNN-BiLSTM.A. L'ensemble de données des enregistrements quotidiens GHI collectés entre le 1er janvier 2000 et le 30 juin 2020 auprès de la National Aeronautics and Space Administration (NASA) à un endroit aride (Hail, Arabie Saoudite) est utilisé pour développer et comparer les modèles basés sur DNN ci-dessus. Les paramètres affectant la précision des modèles ont également été analysés en profondeur. Seules les valeurs historiques du GHI quotidien ont été utilisées pour construire les modèles basés sur DNN, tandis que des paramètres météorologiques supplémentaires tels que la température de l'air, la vitesse du vent, la direction du vent, la pression atmosphérique et l'humidité relative ne sont pas pris en compte dans ce travail. La bibliothèque Keras et le langage Python ont été utilisés utilisé pour développer et comparer les modèles de prévision GHI. Les métriques d'évaluation telles que le coefficient de corrélation (r), l'erreur absolue moyenne en pourcentage (MAPE), l'erreur absolue moyenne (MAE), la fonction de distribution cumulative (CDF) et l'écart type (σ ) sont choisies pour évaluer la performance des modèles de prévision. Les résultats obtenus ont montré que les modèles DNN ont fourni de bonnes performances à l'échelle mondiale avec une valeur maximale atteinte de r = 96 %, pour la prévision quotidienne GHI. La irradiación horizontal global pronosticada (GHI) puede ayudar a diseñar, dimensionar y analizar el rendimiento de los sistemas fotovoltaicos (PV), incluidos los sistemas de bombeo de agua PV utilizados para aplicaciones de riego. En este documento, se desarrollan e investigan varios modelos de redes neuronales profundas (DNN) para la predicción de un día de anticipación de GHI en la ciudad de Hail (Arabia Saudita). Los modelos DNN considerados incluyen memoria a largo plazo (LSTM), LSTM bidireccional (BiLSTM), unidad recurrente cerrada (GRU), GRU bidireccional (Bi-GRU), red neuronal convolucional unidimensional (CNN 1D ) y otras configuraciones híbridas como CNN-LSTM y CNN-BiLSTM. Un conjunto de datos de grabaciones diarias de GHI recopiladas durante el 1 de enero de 2000 al 30 de junio de 2020 de la Administración Nacional de Aeronáutica y del Espacio (NASA) en una ubicación árida (Hail, Arabia Saudita) se utiliza para desarrollar y comparar los modelos basados en DNN anteriores. Los parámetros que afectan la precisión de los modelos también se han analizado profundamente. Solo se han utilizado valores históricos de GHI diarios para construir los modelos basados en DNN, mientras que los parámetros climáticos adicionales como la temperatura del aire, la velocidad del viento, la dirección del viento, la presión atmosférica y la humedad relativa no se consideran en este trabajo. Biblioteca Keras y lenguaje Python han sido utilizado para desarrollar y comparar los modelos de pronóstico de GHI. Las métricas de evaluación como el coeficiente de correlación (r), el error porcentual absoluto medio (MAPE), el error absoluto medio (MAE), la función de distribución acumulativa (CDF) y la desviación estándar (σ ) se optan para evaluar el rendimiento de los modelos de predicción. Los resultados obtenidos mostraron que los modelos DNN han proporcionado un buen rendimiento a nivel mundial con un valor máximo alcanzado de r = 96%, para el pronóstico diario de GHI. Forecasted global horizontal irradiation (GHI) can help for designing, sizing and performances analysis of photovoltaic (PV) systems including water PV pumping systems used for irrigation applications.In this paper, various deep neural networks (DNN) models for one day-ahead prediction of GHI at Hail city (Saudi Arabia) are developed and investigated.The considered DNN models include long-shortterm memory (LSTM), bidirectional-LSTM (BiLSTM), gated recurrent unit (GRU), bidirectional-GRU (Bi-GRU), one-dimensional convolutional neural network (CNN 1D ) and other hybrid configurations such as CNN-LSTM and CNN-BiLSTM.A dataset of daily GHI recordings collected during January 1, 2000 to June 30, 2020 from National Aeronautics and Space Administration (NASA) at an arid location (Hail, Saudi Arabia) is used to develop and compare the above DNN-based models.The parameters affecting the accuracy of the models have been also deeply analyzed.Only historical values of daily GHI have been used to build the DNN-based models whereas additional weather parameters such as air temperature, wind speed, wind direction, atmospheric pressure and relative humidity are not considered in this work.Keras library and Python language have been used to develop and compare the GHI forecasting models.The evaluation metrics such as correlation coefficient (r), Mean Absolute Percent Error (MAPE), Mean Absolute Error (MAE), cumulative distribution function (CDF) and standard deviation (σ ) are opted to evaluate the performance of the prediction models.The obtained results showed that the DNN models have provided globally good performances with a maximum reached value of r = 96%, for daily GHI forecasting. يمكن أن يساعد الإشعاع الأفقي العالمي المتوقع (GHI) في تصميم وتحجيم وتحليل أداء الأنظمة الكهروضوئية (PV) بما في ذلك أنظمة ضخ المياه الكهروضوئية المستخدمة في تطبيقات الري. في هذه الورقة، يتم تطوير نماذج مختلفة للشبكات العصبية العميقة (DNN) للتنبؤ قبل يوم واحد من GHI في مدينة حائل (المملكة العربية السعودية) والتحقيق فيها. تشمل نماذج DNN التي يتم النظر فيها الذاكرة قصيرة المدى (LSTM)، ثنائية الاتجاه - LSTM (BiLSTM)، وحدة متكررة مسورة (GRU)، ثنائية الاتجاه - GRU (Bi - GRU)، شبكة عصبية التفافية أحادية البعد (CNN 1D ) والتكوينات الهجينة الأخرى مثل CNN - LSTM و CNN - BiLSTM. يتم استخدام مجموعة بيانات من تسجيلات GHI اليومية التي تم جمعها خلال الفترة من 1 يناير 2000 إلى 30 يونيو 2020 من الإدارة الوطنية للملاحة الجوية والفضاء (ناسا) في موقع قاحل (حائل، المملكة العربية السعودية) لتطوير ومقارنة النماذج القائمة على DNN المذكورة أعلاه. كما تم تحليل المعلمات التي تؤثر على دقة النماذج بعمق. تم استخدام القيم التاريخية فقط لـ GHI اليومية لبناء النماذج القائمة على DNN بينما لا يتم النظر في معلمات الطقس الإضافية مثل درجة حرارة الهواء وسرعة الرياح واتجاه الرياح والضغط الجوي والرطوبة النسبية في هذا العمل. تستخدم لتطوير ومقارنة نماذج التنبؤ بمؤشر GHI. يتم اختيار مقاييس التقييم مثل معامل الارتباط (r)، متوسط النسبة المئوية المطلقة (MAPE)، متوسط الخطأ المطلق (MAE)، وظيفة التوزيع التراكمي (CDF) والانحراف المعياري (σ ) لتقييم أداء نماذج التنبؤ. أظهرت النتائج التي تم الحصول عليها أن نماذج DNN قد قدمت أداءً جيدًا عالميًا بحد أقصى للقيمة التي تم الوصول إليها r = 96 ٪، للتنبؤ اليومي بمؤشر GHI.