Generation Resources Research Articles

Enhancing paediatric and congenital cardiology e-learning postgraduate education: insights from the Association for European Paediatric and Congenital Cardiology directed webinar series.

A series of webinars covering widespread knowledge on paediatric cardiology and cardiac surgery topics was initiated by Association for European Paediatric and Congenital Cardiology, serving towards preparation for the Association for European Paediatric and Congenital Cardiology certification in paediatric and congenital cardiology. This study investigated the impact of webinars as educational tools for junior paediatric cardiologists in the post-COVID-19 pandemic era. A cross-sectional survey design study using an online survey as a tool for the assessment of trainees. An open and closed-ended SurveyMonkey questionnaire was used to document the learners' opinions on webinars. Results were reported using descriptive statistical analyses. Twenty-seven Association for European Paediatric and Congenital Cardiology junior members participated in the online survey from twelve different countries. Most of the participants were trainees in paediatric cardiology (56%), and the remainder were junior consultants in paediatric cardiology. Approximately 70% found no difficulties in participating in the webinars. The webinars were appreciated by participants, who found the webinars interactive and highly educational with contents highly applicable to everyday clinical practice. Significant heterogeneity emerged in training programmes across Europe and worldwide in terms of programme duration, number of fellows, teaching approach, and assessments. Training opportunities such as courses, grants, and more webinars were suggested as tools to support continuous learning by the Association for European Paediatric and Congenital Cardiology. The Association for European Paediatric and Congenital Cardiology webinar series has confirmed the crucial role of online-based learning resources in the new generation of junior paediatric cardiologists. Association for European Paediatric and Congenital Cardiology webinars and the examination in paediatric cardiology may help standardise training across Europe, promoting the highest standards in patient care.

An optimized method for short-term load forecasting based on feature fusion and ConvLSTM-3D neural network

As renewable energy continues to penetrate modern power systems, accurate short-term load forecasting is crucial for optimizing power generation resource allocation and reducing operational costs. Traditional forecasting methods often overlook key factors such as holiday load variations and differences in user electricity consumption behavior, resulting in reduced accuracy. To address this, we propose an optimized short-term load forecasting method based on time and weather-fused features using a ConvLSTM-3D neural network. The Prophet algorithm is first employed to decompose historical electricity load data, extracting feature components related to time variables. Simultaneously, the SHAP algorithm filters weather variables to identify highly correlated weather features. A time attention mechanism is then applied to fuse these features based on their correlation weights, enhancing their impact within the time series. Finally, the ConvLSTM-3D model is trained on the fused features to generate short-term load forecasts. A case study using real-world data validates the proposed method, demonstrating significant improvements in forecasting accuracy.

Green Practices: Way to Sustain with Small-Scale Enterprises

Environmental sustainability, once primarily associated with large corporations, is now a growing focus for small-scale enterprises (SSEs). These businesses are vital to global economic development, contributing around 40% to GDP in emerging economies and generating more than 50% of employment opportunities, according to the International Finance Corporation (IFC). However, despite their economic significance, the environmental impact of SSEs cannot be overlooked. Collectively, they contribute to issues such as high energy consumption, waste generation, and improper resource utilization. Adopting green practices offers SSEs an opportunity to address these challenges while unlocking significant benefits. Sustainable operations can lead to cost savings through reduced resource usage, improved brand reputation among eco-conscious consumers, and enhanced compliance with increasingly stringent environmental regulations. Furthermore, green initiatives align businesses with global sustainability goals and prepare them for future resource and market demands. This chapter delves into how small-scale businesses can effectively incorporate sustainable practices into their operations. It examines the specific challenges they face, such as financial constraints and lack of awareness, and offers practical strategies to overcome these hurdles. Ultimately, it underscores the importance of sustainability as a strategic imperative for long-term success in the evolving global marketplace. Keywords: Green Practices, Small-Scale Enterprises, Sustainability.

Forecasting geothermal temperature in western Yemen with Bayesian-optimized machine learning regression models

Geothermal energy is a sustainable resource for power generation, particularly in Yemen. Efficient utilization necessitates accurate forecasting of subsurface temperatures, which is challenging with conventional methods. This research leverages machine learning (ML) to optimize geothermal temperature forecasting in Yemen’s western region. The data set, collected from 108 geothermal wells, was divided into two sets: set 1 with 1402 data points and set 2 with 995 data points. Feature engineering prepared the data for model training. We evaluated a suite of machine learning regression models, from simple linear regression (SLR) to multi-layer perceptron (MLP). Hyperparameter tuning using Bayesian optimization (BO) was selected as the optimization process to boost model accuracy and performance. The MLP model outperformed others, achieving high R2 values and low error values across all metrics after BO. Specifically, MLP achieved R2 of 0.999, with MAE of 0.218, RMSE of 0.285, RAE of 4.071%, and RRSE of 4.011%. BO significantly upgraded the Gaussian process model, achieving an R2 of 0.996, a minimum MAE of 0.283, RMSE of 0.575, RAE of 5.453%, and RRSE of 8.717%. The models demonstrated robust generalization capabilities with high R2 values and low error metrics (MAE and RMSE) across all sets. This study highlights the potential of enhanced ML techniques and the novel BO in optimizing geothermal energy resource exploitation, contributing significantly to renewable energy research and development.

A Mechanism Framework for Clearing Prices in Electricity Market Based on Trusted Capacity of Power Generation Resources

A reasonable capacity market mechanism is conducive to exploring the capacity value of different power generation resources and ensuring the adequacy of power supply capacity in power systems. In response to the challenges faced by the existing capacity market mechanism under the background of energy transformation, such as the unreasonable quantification of the support effect of different power generation resources on the power capacity of power system and the imperfect pricing mechanism of power capacity, a capacity market mechanism for power systems with high proportion renewable energy has been designed. To quickly clarify the capacity support effect of different power generation resources, a capacity credibility factor is introduced to quantify the actual contribution of different power generation resources in capacity supply and to deeply explore the capacity value of power generation resources. Based on the uniform marginal clearing price in the capacity market, the marginal clearing price of different power generation resources is corrected by using the cost ratio factor, which includes the difference in the cost structure of power generation resources. By comparing and analyzing examples, the proposed cost ratio factor can effectively optimize the capacity price; the maximum price difference is 18.2 yuan/MW, the overall capacity cost of the system is reduced by 53.70%, and the effective connection between fixed cost and variable cost of power generation resources is realized.

Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption.

Energy is integral to the socio-economic development of every country. This development leads to a rapid increase in the demand for energy consumption. However, due to the constraints and costs associated with energy generation resources, it has become crucial for both energy generation companies and consumers to predict energy consumption well in advance. Forecasting energy needs through accurate predictions enables companies and customers to make informed decisions, enhancing the efficiency of both energy generation and consumption. In this context, energy generation companies and consumers seek a model capable of forecasting energy consumption both in the short term and the long term. Traditional models for energy prediction focus on either short-term or long-term accuracy, often failing to optimize both simultaneously. Therefore, this research proposes a novel hybrid model employing Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) to simultaneously predict both short-term and long-term residential energy consumption with enhanced accuracy measures. The proposed model is capable of capturing complex temporal and spatial features to predict short-term and long-term energy consumption. CNNs discover patterns in data, LSTM identifies long-term dependencies and sequential patterns and Bi-LSTM identifies complex temporal relations within the data. Experimental evaluations expressed that the proposed model outperformed with a minimum Mean Square Error (MSE) of 0.00035 and Mean Absolute Error (MAE) of 0.0057. Additionally, the proposed hybrid model is compared with existing state-of-the-art models, demonstrating its superior performance in both short-term and long-term energy consumption predictions.

Two-stage multi-objective framework for optimal operation of modern distribution network considering demand response program.

To improve the inadequate reliability of the grid that has led to a worsening energy crisis and environmental issues, comprehensive research on new clean renewable energy and efficient, cost-effective, and eco-friendly energy management technologies is essential. This requires the creation of advanced energy management systems to enhance system reliability and optimize efficiency. Demand-side energy management systems are a superior solution for multiple reasons. Firstly, they empower consumers to actively oversee and regulate their energy consumption, resulting in substantial cost savings by minimizing usage during peak hours and enhancing overall efficiency. The Demand Response Program (DRP) and optimal power sharing have gained significant attention to provide technical and economic benefits, while they require an efficient operation framework. Therefore, a two-stage framework is proposed for multi-objective operation of a distribution network with several generation resources. The first stage applies DRP to maximize the distribution network operator's (DNO) profit by optimizing common incentive rate for all consumers participate in DRP and an individual curtailed power for each consumer. In addition to an individual incentive rate for each consumer participates in DRP which is a new solution in the field of demand side management. The second stage achieves optimal power sharing among generation resources, while considering multiple objectives and incorporating the modified load of the first stage. The multi-objective problem is formulated to reduce energy losses, voltage deviation, total operational cost, gas emissions, and maximize the voltage stability index. The problem is optimized using a combination of the technique for order of preference by similarity to ideal solution (TOPSIS) and the elephant herding optimization (EHO) technique. The framework is validated using a modified IEEE 33-bus that incorporates photovoltaic system, diesel generators, and wind generation system. The proposed framework based on an individual incentive rate DRP provides superior response compared to common incentive rate DRP which reduces the total energy losses by 38.13%, reduces the total generation cost by 9.468%, and reduces the emission by 5.9%.

Optimal Siting, Sizing, and Energy Management of Distributed Renewable Generation and Storage Under Atmospheric Conditions

Integrating new generation and storage resources within power systems is challenging because of the stochastic nature of renewable generation, voltage regulation, and the use of microgrids. Classical optimization methods struggle with these nonlinear, multifaceted issues. This paper presents a novel optimization framework for integrating, sizing, and siting distributed renewable generation and energy storage systems in power distribution networks. To accurately reflect load variability, the framework considers four distinct load models—constant impedance, current, power, and ZIP (constant impedance, constant current, constant power). Our approach utilized three metaheuristic approaches to enhance the efficiency of power system management. The validation results on the IEEE 33 Bus System conclude that the Elephant Herding Optimization (EHO) emerged as the best performer regarding voltage stability and real power loss reduction with a voltage stability index of 0.0031346. Modified Ant Lion Optimization (ALO) achieved a best voltage stability index of 0.0024115 and power losses of 7.5092 MVA. The Red Colobus Monkey Optimization (RMO) algorithm realized a voltage stability index of 0.0052053 and real power losses of 20.7564 MVA. Overall, the results conclude that ALO is the most effective approach for optimizing distributed renewable energy systems under different climatic conditions. According to the analysis, the algorithm works best in ideal circumstances when the percentages of wind and irradiance are 60% or greater.

Do people prefer AI-generated patient educational materials over traditional ones?

This study aimed to assess people's preference between traditional and Artificial Intelligence (AI)-generated colon cancer staging Patient Education Materials (PEMs). We assessed preference among patients and companions being seen for a non-cancer diagnosis at the UT Health Austin Colon and Rectal Surgery Clinic. Participants were blinded to the study concept of AI and generation method of PEMs (Traditional: National Cancer Institute and the American Cancer Society; AI-generated: ChatGPT and Google Bard). Participants completed an anonymous demographic survey and ranked their PEM preference for randomized, de-identified materials. We hypothesized that there would be no difference in preference between the AI-generated and traditional PEMs. This study is IRB approved (STUDY00005203). 35 participants were recruited, ages 27-79 years (mean 46; SD 14 years) and 57 % female. 54 % were four-year college graduates. Among the participants, 51 % ranked the traditional PEMs as most preferred, while 49 % ranked the AI-generated PEMs as most preferred (p = NS). People do not have a dominant preference for AI-generated or traditional PEMs. Further investigation on the use of AI for PEM generation is warranted. AI may be capable of generating PEMs that incorporate the strengths of traditional PEMs while also reducing the required resources for PEM generation.

Adapting Southeast Asian Best Practices for Sustainable Development in Banyumas Regency

This study explores the adaptation of successful sustainable development practices from Southeast Asia to Banyumas Regency, Indonesia, as local implementation of the Sustainable Development Goals (SDGs) becomes crucial. Banyumas, with its unique socio-economic and environmental challenges, offers an opportunity to test the adaptability of these practices. The research examines Southeast Asian initiatives, focusing on renewable energy adoption, sustainable agriculture, eco-tourism, and community-based natural resource management. Key success factors and innovative approaches are identified through case studies from Singapore, Malaysia, and Thailand, considering geographical and cultural similarities. The study assesses the current state of sustainable development in Banyumas, highlighting local challenges and opportunities. By comparing regional best practices with the specific needs of Banyumas, the study proposes a tailored framework for implementing sustainable strategies. Findings suggest that while direct replication of Southeast Asian practices may be impractical, localisation efforts make adaptation feasible. The study recommends a phased approach, emphasising community engagement, capacity building, and policy alignment. This research contributes to understanding SDG localisation, providing insights for policymakers and development practitioners in Banyumas Regency to enhance the quality of life while preserving natural resources for future generations.

Ball Street Bridge Conservation Management Plan

This Conservation Management Plan focuses on Ball Street Bridge, a Grade II listed Victorian-era bridge in Sheffield, England, recognized for its architectural and historical significance. The plan identifies key management challenges, including structural aging, environmental impacts, community use, and the absence of heritage interpretation. A detailed action plan outlines strategies to address these issues, ensuring the bridge’s structural integrity, environmental sustainability, and role as a community and heritage resource. The plan emphasizes regular structural inspections, the implementation of environmental monitoring systems, and measures to engage the community while mitigating vandalism. Additionally, it recommends the installation of interpretive signage and the development of educational programs to enhance public understanding of the bridge’s historical and cultural importance. Through these initiatives, the plan aims to preserve Ball Street Bridge as a vital part of Sheffield's industrial heritage and a resource for future generations.

Fair charging management of PHEVs in radial distribution networks with DG resources-a case study.

Considering the widespread use of PHEVs in advanced societies and the issues ahead, researchers' thinking has focused more on this issue. The important issue is that the use of EVs is increasing due to the advantages, but the necessary infrastructure for their charging stations in the distribution networks does not exist. The high penetration level of EVs can create a potential risk for the existing distribution network; the fair charging of EVs has a special value. This paper presents a new model for the fair charging management of EVs at the medium voltage level of a distribution network equipped with dispatchable and non-dispatchable distributed generation (DG) resources. A fuzzy controller is used to adjust the charging rate of EVs within the permissible periods for charging stations, At the same time, the voltage control and reactive power management tool is also available for the distribution network operator through DG resources that can be dispatched, such as diesel generators. Numerical studies are used on a 25-bus IEEE test distribution system in the presence and absence of DG resources. The simulation results show that the presence of DG resources and voltage control and reactive power management at the different buses along the feeder causes a larger number of electric vehicles in different charging stations of the distribution network can be provided their consumption energy from network. In addition, the time difference for EV charging is minimized, and only the number of EVs that can be charged at the various stations will be different. Volt/Var control tools through DSO cause an increase in the number of charged EVs at various stations.

The Energy Potential of Residual Biomass Gasification Integrated with Internal Combustion Engine in Córdoba, Colombia using Artificial Neural Network Techniques

In this study, the energy potential of waste biomass gasification integrated to internal combustion engine in Cordoba, Colombia was investigated using artificial neural network techniques. A model was trained with proximate and elemental analysis data of different biomasses and this model was used to estimate the gasification potential of the four most abundant biomasses in Cordoba. The model developed achieved an adjusted determination coefficient (R2) of 0.9293 for validation and 0.9048 for training, demonstrating high predictive accuracy. The results indicate that temperature positively influences energy generation potential, while moisture content and air-to-fuel ratio have a negative impact. Among the biomass types analyzed, cassava stands out with the highest energy potential, exceeding 9 GWh/year, followed by plantain at approximately 3 GWh/year, maize cobs below 2 GWh/ year, and rice husk with <0.5 GWh/year. These findings provide critical insights for optimizing biomass gasification processes and harnessing regional biomass resources for energy generation.

Antimicrobial peptide production with Corynebacterium glutamicum on lignocellulosic side streams

BackgroundBiorefineries usually focus on the production of low-value commodities, such as bioethanol, platform chemicals or single cell protein. Shifting production to bioactive compounds, such as antimicrobial peptides, could provide an opportunity to increase the economic viability of biorefineries.ResultsRecombinant production of the antimicrobial peptide pediocin PA-1 in Corynebacterium glutamicum was transferred from yeast extract-based media to minimal media based on lignocellulosic spent sulfite liquor. Induced batch, fed batch, and extended batch process modes were compared for highest pediocin PA-1 production.ConclusionFor pediocin PA-1 production on lignocellulosic residues, extended batch cultivation was identified as the optimal process mode, producing up to ≃ 104 mg/L active pediocin PA-1. Moreover, the production of pediocin PA-1 on this sustainable second generation resource exceeded its state-of-the-art production on yeast extract-based media ≃ 1.5-fold.

Use of special knowledge in the investigation of crimes against the environment

The article is devoted to the actual problem of using special knowledge in the investigation of criminal offenses against the environment. The article examines the necessity and peculiarities of applying such knowledge in the process of uncovering and investigating crimes that harm the environment. The paper analyzes the current state of the problem, considers various types of environmental crimes and their consequences. Special attention is paid to the role of forensic examination and the use of modern technologies in the investigation process. In addition, the possibilities of forensic examinations to establish the truth in the investigation of crimes against the environment are analyzed. The work of scientists aimed at the development of forensic means and methods of investigation of environmental crimes, which contributed to the development of the methodology of investigation of crimes against the environment, improvement of their forensic characteristics, the role of special knowledge in the investigation of such crimes, was studied. Attention is focused on forensic examinations and their types, in particular forensic-ecological. The task of forensic environmental examination is to establish cause-and-effect relationships between the state of human health and its change and the harmful impact of human activity on the surrounding natural environment, as well as the study of violations of environmental safety rules. It is necessary to apply expertise from various fields of science, since the objects of research in the specified cases are extremely diverse. The results of the research can be used to develop new legislative norms and improve the practice of combating environmental crimes in Ukraine and beyond. Thus, the study proves that the effective application of special knowledge in the process of investigating environmental crimes is an important component of the struggle for environmental protection. However, in order to achieve maximum results, it is necessary not only to improve technical means and methods, but also to improve the personnel training system, regulatory and legal support, as well as interdepartmental interaction between various law enforcement subjects. Only a comprehensive approach will allow to significantly increase the effectiveness of the fight against environmental crimes and preserve natural resources for future generations.

Engaging Biodiversity Management Committee (BMC) Members in Conservation Training Programs: A Case Study from Kerala, India

The participation of BMC members in training programs on biodiversity conservation holds immense importance in fostering effective stewardship of natural resources. These training initiatives equip BMC members with essential knowledge, skills, and tools necessary for understanding and addressing biodiversity challenges within their communities. By enhancing their capacity, BMC members can play a pivotal role in implementing conservation strategies, promoting sustainable practices, and safeguarding local ecosystems. Moreover, their active involvement in training programs fosters collaboration, knowledge sharing, and collective action, thereby strengthening the overall effectiveness of biodiversity conservation efforts at the grassroots level. Ultimately, BMC member participation in training programs not only enhances their capabilities but also contributes significantly to the conservation and sustainable management of biodiversity resources for present and future generations. The BMC member's participation in training programmes on biodiversity conservation was investigated in this study. Based on the biodiversity richness and ecological sensitivity four blocks each were selected purposively from low lands of Kannur and Alappuzha districts, mid lands of Kollam and Malappuram districts and the high ranges of Wayanad and Idukki districts of Kerala. A sample of 180 BMC members were selected from the 6 districts for the study. The results of the study indicate that approximately 18.89 per cent of BMC members have not received any training on biodiversity conservation. In contrast, 40 per cent of the members have participated in two training sessions, while 32.22 per cent have attended at least one training program on the subject. Only 8.89 per cent of the members have participated in three or more training programs related to biodiversity conservation. These findings suggest that there is a need to enhance the participation of BMC members in training programs related to biodiversity conservation. Increasing the number of training sessions and ensuring the availability of relevant resources can help enhance the capacity of BMC members to implement conservation strategies, promote sustainable practices, and safeguard local ecosystems.

Age of Information Analysis for Multi-Priority Queue and Non-Orthoganal Multiple Access (NOMA)-Enabled Cellular Vehicle-to-Everything in Internet of Vehicles.

With the development of Internet of Vehicles (IoV) technology, the need for real-time data processing and communication in vehicles is increasing. Traditional request-based methods face challenges in terms of latency and bandwidth limitations. Mode 4 in cellular vehicle-to-everything (C-V2X), also known as autonomous resource selection, aims to address latency and overhead issues by dynamically selecting communication resources based on real-time conditions. However, semi-persistent scheduling (SPS), which relies on distributed sensing, may lead to a high number of collisions due to the lack of centralized coordination in resource allocation. On the other hand, non-orthogonal multiple access (NOMA) can alleviate the problem of reduced packet reception probability due to collisions. Age of Information (AoI) includes the time a message spends in both local waiting and transmission processes and thus is a comprehensive metric for reliability and latency performance. To address these issues, in C-V2X, the waiting process can be extended to the queuing process, influenced by packet generation rate and resource reservation interval (RRI), while the transmission process is mainly affected by transmission delay and success rate. In fact, a smaller selection window (SW) limits the number of available resources for vehicles, resulting in higher collisions when the number of vehicles is increasing rapidly. SW is generally equal to RRI, which not only affects the AoI part in the queuing process but also the AoI part in the transmission process. Therefore, this paper proposes an AoI estimation method based on multi-priority data type queues and considers the influence of NOMA on the AoI generated in both processes in C-V2X system under different RRI conditions. Our experiments show that using multiple priority queues can reduce the AoI of urgent messages in the queue, thereby providing better service about the urgent message in the whole vehicular network. Additionally, applying NOMA can further reduce the AoI of the messages received by the vehicle.

The evolution of cMOOC learners’ resource use behaviour

ABSTRACT As a representative pedagogical practice of Connectivism, cMOOC emphasises learners’ connected learning based on various types of resources. By systematically classifying the types of resource-use behaviours in connectivist learning, we measure the level of resource-use behaviours of each learner based on 61,496 real-time behavioural data and 544,073 words of textual content of the learners on the cMOOC platform, to portray the evolution of the resource-use behaviours of the learners as a whole. Then, the evolutionary pattern of individual learners’ resource use behaviours was revealed through lagged sequence analysis. It was found that the pre-determined resource use behaviours showed a decreasing trend as the number of course weeks increased; the generative resource use behaviours of recommended resources, outcome resources and interactive resources climbed up to the peak in the first and middle stages of the course, and then declined gradually; the evolution of the various types of resource use behaviours in the mid-and late stages of the course showed the phenomenon of “9-week recovery”; learners tend to inherit traditional resource use behaviours and tend to mutate resource use behaviours under the new curriculum concept. The results further develop the understanding of resource use behaviours in connectivist learning theory.

Trends in the use of biomass for energy generation for a province in the Central Region of Argentina

In this work, analyses are performed on international reports applicable to Argentina regarding the use of biomass for energy generation. More specific trends in the national context are also analyzed, and then a more detailed study is performed for Córdoba, one of the three provinces in the so-called Central Region. Córdoba was chosen not only due to its large availability of biomass and wide range of agricultural production, but also because of its favorable regulation promoting energy generation with regional biomass. It becomes clear that projects using dry biomass are very limited in number and scale in this province. Considering previous studies, including articles by the authors on energy applications of regional biomass such as sorghum, esparto grass or corn, it can be hinted that the use of dry biomass resources for energy generation is well below the capacity in the chosen province.

Uncertainties as Generative Resources in Research through Design: Three Dynamics for Moving in a Design Space

In HCI and design research, it has recently been suggested that, instead of risks and threats, uncertainties can be generative resources. This aligns particularly well with practice-based design research and research through design (RtD), where uncertainties are pervasive and unavoidable due to the approach’s interest in open-ended problems, contexts under transformation and unknown futures. Building on the idea that uncertainties drive design activity, we describe the design process as framing acts where a designer-researcher moves along perceived uncertainties. We identify three dynamics for a generative mode of working with uncertainties that can make designers aware of uncertainties, circle in towards those that appear essential while seeking to avoid others and stay with those that offer new perspectives. We exemplify these dynamics by drawing from our experiences from three RtD projects and highlight this viewpoint’s value in providing new reflective tools for designer-researchers.