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Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The IITM-ESM climate model, released in 2015, includes the following components: aerosol: prescribed MAC-v2, atmos: IITM-GFSv1 (T62L64, Linearly Reduced Gaussian Grid; 192 x 94 longitude/latitude; 64 levels; top level 0.2 mb), land: NOAH LSMv2.7.1, ocean: MOM4p1 (tripolar, primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: TOPAZv2.0, seaIce: SISv1.0. The model was run by the Centre for Climate Change Research, Indian Institute of Tropical Meteorology Pune, Maharashtra 411 008, India (CCCR-IITM) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Proso millet (Panicum miliaceum L.) is a drought tolerant underutilised crop cultivated in rainfed subsistence agricultural systems. Proso millet yields were simulated using a calibrated Agricultural Production Systems Simulator (APSIM) model for 95 locations in Sri Lanka. The yield maps were generated according to the Inverse Distance Weighting (IDW) model using ArcMap 10.7.1. The database contains Proso millet yield maps for current climate and yield change under 5 hypothetical climate change scenarios; 1oC, 1.5 oC and 2 oC temperature increments, 25% rainfall increment, and 25% rainfall reduction compared to the baseline (1980-2009) climate.

With the extensive growth in technology, healthcare sector has benefitted a lot recently. Looking into the academic research and validation in the area of medical image processing and visualization, many platforms and the open-source resources are available. Insight toolkit (ITK) and visualization toolkit (VTK) are extensively used for medical image processing and 3D visualization respectively. Resources used to develop an application using ITK-VTK and same resources be used to deliver it to the users such as, clinicians, doctors etc. This can be achieved by using respective hardware and the infrastructure. In the proposed article, the infrastructure and resource used to build and deploy the application and remote access given to the users are elucidated.

Siderophores are low molecular weight secondary metabolites produced by microorganisms under low iron stress as a specific iron chelator. In the present study, a rhizospheric bacterium was isolated from the rhizosphere of sesame plants from Salem district, Tamil Nadu, India and later identified as Bacillus subtilis LSBS2. It exhibited multiple plant-growth-promoting (PGP) traits such as hydrogen cyanide (HCN), ammonia, and indole acetic acid (IAA), and solubilized phosphate. The chrome azurol sulphonate (CAS) agar plate assay was used to screen the siderophore production of LSBS2 and quantitatively the isolate produced 296 mg/L of siderophores in succinic acid medium. Further characterization of the siderophore revealed that the isolate produced catecholate siderophore bacillibactin. A pot culture experiment was used to explore the effect of LSBS2 and its siderophore in promoting iron absorption and plant growth of Sesamum indicum L. Data from the present study revealed that the multifarious Bacillus sp. LSBS2 could be exploited as a potential bioinoculant for growth and yield improvement in S. indicum.

Second-generation bioethanol can be produced from various lignocellulosic biomasses such as wood, agricultural or forest residues. Lignocellulosic biomass is inexpensive, renewable and abundant source for bioethanol production. The conversion of lignocellulosic biomass to bioethanol could be a promising technology though the process has several challenges and limitations such as biomass transport and handling, and efficient pretreatment methods for total delignification of lignocellulosics. Proper pretreatment methods can increase concentrations of fermentable sugars after enzymatic saccharification, thereby improving the efficiency of the whole process. Conversion of glucose as well as xylose to bioethanol needs some new fermentation technologies to make the whole process inexpensive. The main goal of pretreatment is to increase the digestibility of maximum available sugars. Each pretreatment process has a specific effect on the cellulose, hemicellulose and lignin fraction; thus, different pretreatment methods and conditions should be chosen according to the process configuration selected for the subsequent hydrolysis and fermentation steps. The cost of ethanol production from lignocellulosic biomass in current technologies is relatively high. Additionally, low yield still remains as one of the main challenges. This paper reviews the various technologies for maximum conversion of cellulose and hemicelluloses fraction to ethanol, and it point outs several key properties that should be targeted for low cost and maximum yield.

The study explores the enhancement of wind-solar hybrid microgrids via the use of Swarm Intelligence Algorithms (SIAs). It assesses the efficacy of these algorithms in efficiently managing renewable energy sources, load demands, and battery storage inside the microgrid system. An examination of actual data highlights the influence of environmental elements on the production of electricity, as seen by the diverse wind speeds resulting in power outputs that range from 15 kW at 4 m/s to 30 kW at 7 m/s. This underscores the clear and direct relationship between wind speed and the amount of power created. Likewise, solar irradiance levels demonstrate oscillations ranging from 500 W/m² to 800 W/m², therefore yielding power outputs that include a range of 15 kW to 24 kW, so illuminating the profound impact of solar irradiance on energy capture. The dynamic energy consumption patterns are exposed by the varying load demands, whereby the demand levels oscillate between 20 kW and 28 kW. This highlights the crucial significance of demand variability in determining energy needs. In addition, the data on battery storage reveals a range of charge levels, ranging from 25 kWh to 40 kWh, which underscores its pivotal function in the equilibrium of energy supply and consumption. When evaluating SIAs, it becomes evident that Particle Swarm Optimization (PSO) surpasses both Ant Colony Optimization (ACO) and Genetic Algorithms (GA) in obtaining an impressive 80% renewable energy penetration rate. PSO effectively reduces operating costs by 15%, demonstrating its exceptional proficiency in optimizing microgrid operations. This study provides valuable insights into the intricate interplay among environmental conditions, load demands, battery storage, and algorithmic optimization in wind-solar hybrid microgrids.

<abstract> <p>The rising proportion of inverter-based renewable energy sources in current power systems has reduced the rotational inertia of overall microgrid systems. This may cause high-frequency fluctuations in the system leading to system instability. Several initiatives have been suggested concerning inertia emulation based on other integrated external energy sources, such as energy storage systems, to combat the ever-declining issue of inertia. Hence, to deal with the aforementioned issue, we suggest the development of an optimal fractional sliding mode control (FSMC)-based frequency stabilization strategy for an industrial hybrid microgrid. An explicit state-space industrial microgrids model comprised of several coordinated energy sources along with loads, storage systems, photovoltaic and wind farms, is considered. In addition to this, the impact of electric vehicles and batteries with adequate control of the state of charge was investigated due to their short regulation times and this helps to balance the power supply and demand that in turn brings the minimization of the frequency deviations. The performance of the FSMC controller is enhanced by setting optimal parameters by employing the tuning strategy based on an iterative teaching-learning-based optimizer (ITLBO). To justify the efficacy of the proposed controller, the simulated results were obtained under several system conditions by using a vehicle simulator in a MATLAB/Simulink environment. The results reveal the enhanced performance of the ITLBO optimized fractional sliding mode control to effectively damp the frequency oscillations and retain the frequency stability with robustness, quick damping, and reliability under different system conditions.</p> </abstract>

Dans des scénarios réalistes, la performance dynamique d'un cluster de micro-réseaux est largement affectée par la puissance intermittente des sources d'énergie renouvelables et les changements de charge fréquents. Pour résoudre ce problème, un contrôleur secondaire à double couche basé sur le temps fixe distribué est conçu pour améliorer les performances dynamiques inter-microgrid et intra-microgrid dans un temps de stabilisation fixe. Le contrôleur proposé est indépendant des valeurs de fonctionnement initiales par opposition à la loi de contrôle à temps fini. Chaque agent global dans un micro-réseau fonctionne pour atténuer le décalage de charge entre les autres agents globaux, tandis que chaque agent local dans un micro-réseau fonctionne pour réaliser un partage de courant de charge proportionnel et une régulation de tension moyenne entre eux dans un temps fixe. Cependant, comme l'atténuation de la non-concordance de chargement dans des conditions de charge légère affecte l'efficacité du système en raison de pertes de ligne importantes, le fonctionnement du cluster passe à une approche de minimisation des pertes distribuées, qui fonctionne en utilisant des mesures en ligne des micro-réseaux voisins. Pour caractériser le mode de fonctionnement dans la cyber-couche globale, un seuil de point de chargement critique pour le cluster est ainsi déterminé. La performance du cluster utilisant la stratégie proposée est simulée dans l'environnement MATLAB/SIMULINK pour divers scénarios afin de démontrer sa fiabilité et son efficacité. En escenarios realistas, el rendimiento dinámico de un grupo de microrredes se ve afectado en gran medida por la potencia intermitente de las fuentes de energía renovables y los frecuentes cambios de carga. Para abordar este problema, un controlador secundario de doble capa basado en tiempo fijo distribuido está diseñado para mejorar el rendimiento dinámico entre microrredes y entre microrredes dentro de un tiempo de asentamiento fijo. El controlador propuesto es independiente de los valores operativos iniciales en oposición a la ley de control de tiempo finito. Cada agente global en una microrred opera para mitigar el desajuste de carga entre otros agentes globales, mientras que cada agente local en una microrred opera para lograr un reparto de corriente de carga proporcional y una regulación de voltaje promedio entre ellos en un tiempo fijo. Sin embargo, como la mitigación de la falta de coincidencia de carga durante condiciones de carga ligera afecta la eficiencia del sistema debido a pérdidas de línea significativas, la operación del clúster cambia a un enfoque de minimización de pérdidas distribuidas, que opera utilizando mediciones en línea de las microrredes vecinas. Para caracterizar el modo de operación en la capa cibernética global, se determina así un punto crítico de umbral de carga para el clúster. El rendimiento del clúster que emplea la estrategia propuesta se simula en el entorno MATLAB/SIMULINK para varios escenarios para demostrar su confiabilidad y eficiencia. In realistic scenarios, the dynamic performance of a microgrid cluster is largely affected by the intermittent power of renewable energy sources and frequent load changes. To address this issue, a distributed fixed-time based dual layer secondary controller is designed to improve inter-microgrid and intra-microgrid dynamic performance within a fixed settling time. The proposed controller is independent of initial operating values as opposed to the finite time control law. Each global agent in a microgrid operates to mitigate loading mismatch between other global agents, whereas each local agent in a microgrid operates to achieve proportionate load current sharing and average voltage regulation between them in fixed time. However, as loading mismatch mitigation during light load conditions affects the system efficiency due to significant line losses, the cluster operation switches to a distributed loss minimization approach, which operates using online measurements from the neighboring microgrids. To characterize the mode of operation in the global cyber layer, a critical point of loading threshold for the cluster is thus determined. The performance of the cluster employing the proposed strategy is simulated in MATLAB/SIMULINK environment for various scenarios to demonstrate its reliability and efficiency. في السيناريوهات الواقعية، يتأثر الأداء الديناميكي لمجموعة الشبكات الصغيرة إلى حد كبير بالطاقة المتقطعة لمصادر الطاقة المتجددة والتغيرات المتكررة في الحمل. لمعالجة هذه المشكلة، تم تصميم وحدة تحكم ثانوية ثنائية الطبقة موزعة على أساس الوقت الثابت لتحسين الأداء الديناميكي بين الشبكات الدقيقة وداخلها في غضون وقت استقرار ثابت. وحدة التحكم المقترحة مستقلة عن قيم التشغيل الأولية بدلاً من قانون التحكم في الوقت المحدود. يعمل كل عامل عالمي في شبكة صغرى على التخفيف من عدم تطابق التحميل بين العوامل العالمية الأخرى، في حين يعمل كل عامل محلي في شبكة صغرى على تحقيق مشاركة تيار الحمل المتناسب ومتوسط تنظيم الجهد بينهما في وقت محدد. ومع ذلك، نظرًا لأن تخفيف عدم تطابق التحميل أثناء ظروف الحمل الخفيف يؤثر على كفاءة النظام بسبب الخسائر الكبيرة في الخطوط، تتحول عملية المجموعة إلى نهج تقليل الخسارة الموزعة، والذي يعمل باستخدام القياسات عبر الإنترنت من الشبكات الصغيرة المجاورة. لتوصيف طريقة التشغيل في الطبقة السيبرانية العالمية، يتم تحديد نقطة حرجة لعتبة التحميل للمجموعة. تتم محاكاة أداء المجموعة التي تستخدم الاستراتيجية المقترحة في بيئة ماتلاب/سيمولينك لسيناريوهات مختلفة لإثبات موثوقيتها وكفاءتها.