Hasil untuk "Special industries and trades"

Shivi Sharma, Neetha S.S., Pranav Arya et al.

The indicators of electric vehicle performance such as state of charge (SOC), remaining useful life (RUL), and charge demand need to be accurately forecasted to ensure maximum energy control and battery life. The models used are usually not able to capture the spatial and temporal correlation of battery data and be robust to the presence of noisy measurements. In this study, we model a sequential attention-based deep learning structure with convolutional neural networks, gated recurrent units, and an attention mechanism that can ultimately understand the local features, temporal relationships, and dynamic significance of various features in sequential battery data. The hybrid architecture of this model allows it to extract local spatial features, long-term sequential dependencies and dynamically find the importance of the critical time steps. We also develop a hybrid loss that is an accumulation of Huber loss and Mean Squared Error, which is much more resilient to outliers and at the same time has high prediction accuracy. It is experimentally proven that the proposed model has R2 values of 0.9575, 0.9558, and 0.9199 on SOC, RUL, and charge demand, respectively, which are better than the current single-architecture methods.

Mrugank Gandhi, Archana Y. Chaudhari, Rahesha Mulla

Abstract The increasing shift to sustainable transportation has fueled growing interest in electric vehicles, including the key issue of estimating the remaining driving range with high precision. The study develops a data-driven approach to predict the cargo electric vehicle’s remaining driving range that incorporates machine learning based estimation. Real-world operational data: a Musoshi Pop-Up Mini electric cargo vehicle was tested under various load and speed characteristics on a 2 km campus route, providing a possibility of high-resolution modeling of energy consumption patterns. After systematic preprocessing with feature engineering and segment-wise aggregation, seven regression algorithms: ElasticNet, Support Vector Regression, Random Forest, LightGBM, XGBoost, CatBoost, and ExtraTrees were optimized with Optuna-based Bayesian hyperparameter tuning and exhaustively compared in terms of RMSE, MAE, and R². Amongst these, the SVR model RMSE equal to 2.37, MAE equal to 1.75, and R² equal to 0.892 demonstrated the best performance and outperformed other ensemble and gradient boosting models. The obtained results prove that data-driven models, can reliably assess energy consumption and range for cargo EVs, which would ensure the safer and more reliable deployment of electric mobility systems.

Prabhakar Maheswari, Purushothaman Raja, Manoj Karkee et al.

Deep learning plays an important role in automating various operations in fruit crop production including irrigation, nutrition management, yield estimation and harvesting. Yield estimation is essential in fruit crop production as it helps farmers optimize cultivation, harvesting, logistics and marketing operations. Furthermore, fruit detection and localization is a very important step in the development of an automated fruit harvesting system. Hence, an intelligent system was proposed in this study for apple fruit detection and localization using modified DeepLabv3+, semantic segmentation based architecture. The finetuned customizations (such as modifying the activation function, optimization technique and loss function) were performed in the original architecture of DeepLabv3+ and its performance was analyzed. The modified model was trained with the training dataset of 2600 apple tree images. Images were split into 80 % of training and 20 % of validation. The modified architecture was also compared with the other variants of DeepLabv3+ architectures. After training, the model was tested with the unobserved test dataset of 101 images. The test results demonstrated the Mean Accuracy (MAcc) of 98.58 % and the Mean Intersection over Union (MIoU) of 96.66 % without compromising the inference time (i.e., 15 ms). The proposed model revealed the improved results than the original model which attained a MAcc of 92.12 % and MIoU of 88.94 % for the same dataset with the inference time of 40 ms. To ascertain further, the modified model was compared with other single stage detectors, including Fully Convolutional Network (FCN) and U-Net. FCN attained a MAccandMIoU of 77.5 % and 77.27 %, respectively whereas U-Net resulted a MAcc and MIoU of 83.95 % and 81.09 %, respectively. Results demonstrated that the modified DeepLabv3+ with ResNet18 is capable of detecting the apple fruits by mitigating the effects of class imbalance which is the major drawback in single stage detectors. Further, better detection and localization of apple fruits can lead to the precise picking by the robotic system.

Lanxin Li, Xianze Ao, Qiangyan Hao et al.

Accurately estimating atmospheric downward longwave radiation is critical for applications ranging from radiative cooling to building energy efficiency. The main challenge lies in its spectral variability, which depends strongly on sky conditions such as humidity and cloud cover. In this study, we propose a Black–Gray body atmospheric radiation model that divides the infrared spectrum into three regions, treating the atmosphere as a graybody in the 8–13 μm and a blackbody outside this band. The model integrates locally measured radiative power to dynamically capture temporal and spatial variations. Validation experiments were conducted using radiative cooling processes in three Chinese cities (Hefei, Lhasa, and Haikou) under different climates and weather conditions. The BG model consistently predicted radiative cooling power with high accuracy, with mean absolute percentage errors generally below 10 %, outperforming both the effective sky emissivity method and MODTRAN-based predictions. Furthermore, we introduce the concept of band-resolved atmospheric energy databases, analogous to solar radiation databases, and demonstrate it with a full-year case study in Hefei. This work provides a new modeling framework that enhances precision and enables broader applications in energy systems, climate studies, and environmental design.

Tianle He, Peixuan Xue, Zongtao Yu et al.

Fe-based oxygen carriers (OCs) exhibit significant potential for biomass chemical looping gasification (BCLG) to produce hydrogen. However, variations in OC composition and operating conditions strongly affect BCLG performance. In this study, Fe-based OCs were optimized by integrating experimental results with machine learning (ML) techniques, considering both material composition and operational parameters. Experimental evaluation identified Fe8Al2 as the most effective OC, achieving a hydrogen yield of 22.83 mmol/g biomass. These experimental data were combined with literature datasets to train an XGBoost model, yielding a robust predictive performance (R2 > 0.824). Interpretable ML analyses using Shapley Additive Explanations (SHAP) and partial dependence plots (PDP) revealed that the steam-to-biomass ratio and Fe content were the most influential factors for hydrogen production. This integrated approach demonstrates a viable pathway for OC optimization by supplementing limited datasets with targeted experimental data, thereby advancing hydrogen production from BCLG.

Hyunho Lim, Jae-Woo Kim, Ho Seong Hwang et al.

A robotic focal plane system using robotic fiber positioners enables multi-object spectroscopy for hundreds to thousands of galaxies by utilizing a dense array of positioners that are closely packed at the focal plane of a telescope. While this dense arrangement increases the number of observations, it also introduces the potential for collisions between adjacent positioners. A fiber positioner is designed similarly to a SCARA robot. It is driven by two series of BLDC motors. Each positioner is manufactured with an outer diameter of 16 mm. It operates within an annular workspace with an outer diameter of 33.6 mm and an inner diameter of 12.8 mm. As these positioners are arranged with a spacing of 16.8 mm, target assignment and motion planning are critical to avoid collisions caused by overlapping workspaces. To address this, we proposed an optimized step choice algorithm using a motion planning method based on optimization with the sequential quadratic programming algorithm. Simulation results demonstrated that paths for all positioners within a tile were successfully generated with a success rate of up to 93.75% across 80 tiles.

Roberto Carlos Lyra da Silva, Esther Cytrynbaum Young, Solange Travassos de Figueiredo et al.

Introdução O controle estrito e precoce da glicemia com insulinoterapia intensiva é essencial para reduzir as complicações crônicas da doença. Um dos grandes avanços da tecnologia para o tratamento do diabetes foi o desenvolvimento da técnica de leitura da glicose intersticial e sua correlação com a glicemia capilar (GC). A tecnologia de monitoramento Flash de glicose foi introduzida no arsenal tecnológico para a orientação terapêutica do diabetes, tendo como uma das principais vantagens, não necessitar de calibração com a glicemia capilar (GC) e apresentar menor custo quando comparada a CGM tradicional, associada ao não à bomba de insulina. O objetivo do estudo foi avaliar a efetividade do dispositivo Flash em pacientes com DM1, acompanhados em um serviço de referência terciária em um hospital universitário. Métodos Coorte prospectiva do tipo aberta, com amostragem não probabilística, formada por 30 pacientes com diagnóstico prévio de DM1, atendidos no Ambulatório de Endocrinologia do Hospital Universitário Gaffrée e Guinle no Rio de Janeiro (HUGG), no período de maio a agosto de 2019. O protocolo nº 94672518.0.0000.5258 foi aprova[1]do pelo Comitê de Ética e Pesquisa local. Foram incluídos pacientes com DM1, com pelo menos um ano de doença; idade superior a 18 anos; que estavam orientados a realizar glicemias capilares antes das principais refeições e ao deitar-se, com glicosímetros com possibilidade de download dos dados (Accu chek Active®) e que concordaram com a utilização do sensor FSL pelo período de 28 dias. Os indivíduos com DM1 gestantes e condições médicas graves foram excluídos. A estatística inferencial foi aplicada para avaliar a efetividade do sistema FSL no controle glicêmico, considerando as variáveis de desfecho: tempo no alvo (TA), hipoglicemia e hiperglicemia nível 2. Para avaliar se houve variação significativa nas medidas do sistema Accu chek 360® em relação ao FSL foi utilizado teste de Wilcoxon para dados numéricos e pelo teste de McNemar corrigido para dados categóricos. Resultados Dados de 24 pacientes foram analisados. A análise comparativa entre as medidas de glicemias alcançados com LIBRE® total (A+B), em relação ao Accu chek 360®, observou-se aumento significativo no número avaliações de glicose (3:(3-5) com Accu chek 360® versus 13:(8-9) com LIBRE®), aumento significativo do percentual de glicemia no alvo (70-180mg/dl), (43% com Accu chek 360® versus 65% com LIBRE®), e no percentual de hipoglicemia nível 2 (< 54mg/dl), (2% com Accu chek 360® versus 5% com LIBRE® total A +B). Observou-se um aumento significativo no percentual de pacientes com classificação glicemia média no alvo (70-180 mg/dl) atingido e no alvo (>250mg/dl) atingido com LIBRE® (total, A e B) em relação ao Accu chek 360®; e queda significativa no percentual de pacientes com alvo (< 54mg/dl) atingido do LIBRE® (total, 1 e 2) em relação ao Accu chek 360®. Não se observou variação significativa entre LIBRE® B e Accu chek 360®. Discussão e conclusões Em estudos de mundo real, o aumento da varredura de glicose com o sensor LIBRE® foi associado ao aumento do tempo de glicemia no alvo, diminuição no tempo de hiperglicemia e hipoglicemia (5). Ao comparar grupos de maior frequência de varreduras com os de menor frequência, tanto os estudos de Dunn et al. (6), quanto os de Gomez- Peralta et al. (7) e Jangam et al. (8) verificaram que um maior número de leituras está significativamente associado a menores níveis de HbA1c, menor tempo passado em hipoglicemia/hiperglicemia e maior tempo passado no intervalo alvo. Uma maior frequência de monitorização está relacionada a melhor controle glicêmico, como demonstrado em estudo em que o aumento da frequência da monitorização foi associado à melhora da HbA1c (9). A meta preconizada pelo Consenso Internacional de Tempo no Alvo de 2019 para a hipoglicemia clinicamente significativa (glicemia < 54mg/dl em proporção menor que 1% das medidas realizadas em 24 horas) não foi atingida com o uso dos dois sistemas avaliados, sendo a proporção de hipoglicemias maior com o uso do FSL (Accu chek 360® 2% versus LIBRE® 5%). Entretanto, o LIBRE® mostrou algum benefícios para manter a glicemia no alvo.

Hongtian Song, Yong Xiao, Shanshan Hu et al.

Abstract Super low frequency electric field measurements are crucial in analysing electromagnetic compatibility, assessing equipment status, and other related fields. Rydberg atom‐based super low frequency electric field measurements are performed by observing the Stark shift in the spectrum of the Rydberg state. In a specific range of field strength (E < Eavoid, where Eavoid is the threshold to avoid crossing electric fields), the Rydberg atomic spectrum experiences a quadratic frequency shift in relation to the field strength, with the coefficient being determined by the atomic polarisability α. The authors establish a dynamic equation for the interaction between the external electric field and the atomic system, and present the Stark structure diagram of the Caesium Rydberg atom. The mathematical formulae for α and Eavoid in different Rydberg states are also obtained: α = A × (n*)6 + B × (n*)7 and Eavoid = C/(n*)5 + D/(n*)7, where A(B) = 2.2503 × 10−9(7.49,948 × 10−11) and C(D) = 1.68,868 × 108(2.45,991 × 109). The error of α and Eavoid compared with the experimental values does not exceed 8% and is even lower in the low Rydberg states. Accurately calculating the values of α and Eavoid is crucial in incorporating the Rydberg atom quantum coherence effect into super low frequency electric field measurements in new power systems.

Shabbir Ahmed Osmani, Jongjin Baik, Roya Narimani et al.

Agricultural productivity is highly correlated with climatic variations, including drought events. This study is aimed at performing a comprehensive assessment of agricultural drought conditions in the Köppen–Geiger climate zones in Bangladesh, based on the temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI). The zones are classified as follows: temperate dry winter with a hot summer zone, tropical savannah zone, and tropical monsoon zone. The range of VHI is optimized based on its correlation with TCI and VCI. The correlation among the stratified drought indices is also examined to quantify and measure the strength and direction of the linear relationships between them. Moreover, Mann–Kendall test is conducted to assess the statistical trends in the spatiotemporal propagation of droughts across different climate zones. The balanced correlation approach for VHI reveals that vegetation health is governed by the temperature conditions. Certain variations in the drought intensity, frequency, and duration are observed across climatic zones in earlier years while the recent years are noted with less droughts. Normal or no drought conditions are noticed mostly in the tropical monsoon zone through VCI and VHI. The correlations among the drought classes indicate that VHI is more strongly correlated with TCI than with VCI, while NDVI exhibits stronger correlations with VCI than with either VHI or TCI. The Mann-Kendall test revealed that VCI has significant downward trends in drought categories and an upward trend in normal conditions, whereas VHI and TCI displayed inconsistency in statistical trends. By extensively exploring the agricultural drought conditions within specific climate zones, this study offers valuable insights for agronomists and stakeholders involved in climate resilience planning and agricultural development in Bangladesh.

Zhenxiang Zhou, Paul C. Struik, Junfei Gu et al.

While optimising leaf chlorophyll content ([CHL]) has been proposed as a relevant means to manipulate canopy light penetration and canopy photosynthesis, effects of modifying [CHL] on leaf photosynthesis are yet to be investigated thoroughly. A greenhouse experiment and a field experiment were conducted involving rice genotypes of different genetic backgrounds and their leaf-colour variants. Leaf photosynthesis was more influenced by alteration to yellow-leaf than to stay-green cases. Higher specific leaf area and stomatal conductance were observed in two yellow-leaf variants, while only one yellow-leaf variant showed significantly increased Rubisco carboxylation capacity (Vcmax), maximum electron transport rate (Jmax), and photosynthetic nitrogen-use efficiency (PNUE). Model analysis indicated that reducing leaf [CHL] decreased the energy loss via non-photochemical quenching, but improving Vcmax, Jmax, and PNUE would require an improved nitrogen distribution pattern within the leaf. Label-free quantitative proteomics confirmed that an increased investment of nitrogen in Cyt b6/f and Rubisco was observed in the yellow-leaf variant of the genetic background with improved Vcmax, Jmax, and PNUE, but not in the other background. Our results suggest that reducing [CHL] can improve leaf photosynthesis only if the saved nitrogen is optimally distributed to proteins that are more rate-limiting to photosynthesis.

Moslem Uddin, Huadong Mo, Daoyi Dong et al.

A microgrid, regarded as one of the cornerstones of the future smart grid, uses distributed generations and information technology to create a widely distributed automated energy delivery network. This paper presents a review of the microgrid concept, classification and control strategies. Besides, various prospective issues and challenges of microgrid implementation are highlighted and explained. Finally, the important aspects of future microgrid research are outlined. This study would help researchers, scientists, and policymakers to get in-depth and systematic knowledge on microgrid. It will also contribute to identify the key factors for mobilizing this sector for a sustainable future.

Yaqin Zhong, Chunlin Liao, Lihua Chen

L. N. Nikiforov, S. V. Krivoshchekov, N. E. Kolomiets et al.

Introduction. Lemna minor L. (duckweed) refers to the duckweed subfamily (Lemnaceae S. F. Gray) and widely distributed in ponds of Russia. Literature data confirm the possibility of harvesting significant volumes of this raw material in natural habitat and grown in aquaculture. The process of biosynthetic accumulation in duckweed fronds provides a variety of compounds with a wide spectrum of biological activity. Therefore, the use of raw materials Lemna minor L. is promising for the development of drugs and parapharmaceutical products. Thus, it is an urgent task to quantify active components of duckweed and standardize (determination of criteria for identification, quality and safety) plant material.Aim. Establish macro- and microscopic characteristics of raw materials and develop methods for the quantitative determination of the main groups of biologically active substances (BAS) for standardization of raw duckweed.Materials and methods. Samples of duckweed was collected in natural habitats of Western Siberia. Macro- and microscopic assay, HPLC, UV-spectrometry were used in research process.Results and discussion. Were established the criteria for identification of duckweed fronds by studying external (macroscopic) and microscopic features of raw material Lemna minor L. Was developed and validated the procedure of the quantitative determination of phenolcarboxylic acids in raw material Lemna minor L.Conclusion. The study of external (macroscopic) and microscopic features provided the criteria for identification of the raw material Lemna minor L. The technique for the quantitative analysis of polysaccharides using gravimetry does not need validation, because is a direct method of substance measurement. Was validated quantification method of phenolcarboxylic acids (in terms of chlorogenic acid) by criteria of linearity, repeatability, in-laboratory precision and accuracy. Was established quality criteria for identification and quantitative assay, which can be used in the draft for normative documents for medicinal plant raw material of Lemna minor L. «Duckweed fronds».

Likwan Cheng

Community water supply is the local, consumer-level segment of water supply. For its ubiquity, an ability to predict the energy demand for community water supply would help better quantify the water-energy nexus at the community level. However, communities differ in size and distance from the water source, among other variations; no formalism is currently in use for making this energy prediction. Here, we describe a conceptual model and mechanistic formulation for predicting the energy demand of a community water supply in the context of a regional, multi-community supply system. We make the novel methodological integration of scaling analysis and life cycle assessment to account for both network effects and life cycle effects on energy intensity. We also make the novel mechanistic integration of hydraulic energy loss and allometric energy scaling to account for both linear transmission energy and nonlinear distribution energy. With these integrative steps, we create a novel “leaf on a stem” network model and mechanistically parameterized formula for predicting the energy demand of a community water supply, given in life cycle quantity and as the sum of community distribution energy in sublinear scaling with community size and regional transmission energy in linear scaling with the community’s distance from the regional water source. We validate the predictive model by empirically verifying the model parameters through a detailed case study of consumption communities within a regional supply system in the US Great Lakes region. This simple, mechanistic predictive model may help substantiate the concept of water-energy nexus and facilitate energy assessment for community water supply.

Željko Despotović, Marko Tajdić, Jovan Kon

U radu će biti prikazana realizacija hibridnog sistema za napajanje telekomunikacione i merne opreme daljinskih mernih stanica, koje su integralni deo sistema za zaštitu od poplava. Napajanje je bazirano na korišćenju solarne energije u kombinaciji sa mrežnim napajanjem. Solarno napajanje je primarni izvor napajanja tokom dana, dok se u noćnim uslovima kao napajanje koristi distributivna mreža koja napaja uličnu rasvetu. Sistem napajanja je realizovan sa dva kontrolisana MPPT punjača (mrežni i solarni), baterijskom bankom 12V/110Ah i pripadajućim sistemom za njen monitoring. U radu će biti prikazani ključni eksperimentalni rezultati dobijeni tokom puštanja konkretno realizovane merne stanice nivoa i pripadajuće komunikacione opreme, na reci Ub, opština Ub.

Yu Tian, Su Chen, Qiang-Shuai Gu et al.

Kapustin Nikita O., Grushevenko Dmitry A.

Russian petroleum industry plays a vital part in both the country’s economy and international hydrocarbon market, providing a third of state budget revenues and over 13% of global liquid hydrocarbon exports. Yet, nowadays the industry is facing a number of serious challenges, which threaten to undermine its sustainability. These challenges include depletion of the conventional oil resources, technological and economic sanctions and stagnating demand for liquid fuels, especially apparent in Russian traditional export destinations – Europe. The authors attempted to evaluate the impact of these issues and compile a forecast of Russian oil industry using state-of-the-art modelling tools. The calculations show, that even under fairly negative scenario assumptions, Russia is capable of maintaining crude oil and refined products exports above 250 mtoe up to 2040, remaining the world’s second liquid hydrocarbon supplier. This, however, is still a huge drop from 425 mtoe of exports in 2018. To ensure sustainability the government and oil companies need to work in conjunction in several fields: facilitate geologic survey of conventional and promising oil and gas basins; domestic development of new oil extraction technologies for accessing unconventional and low-margin oil resources; provide transport infrastructure for remote fields; reforming tax system to better suit the new environment. This way, crude production can be maintained above 500 mtoe in the forecast period and exports even surpass 2018 levels. In any case, however, the need for massive investments and tax incentives coupled with global movement away from fossil fuels means, that in the future oil will be becoming less and less profitable for the state budget, thus Russian government needs to redouble efforts on economic diversification and energy transition.

Jae Moon Im, Jong Yeon Kim, Kwang Bok Shin et al.

In this paper, the topology optimization method was used to describe the lightweight design of link structures for an amphibious boat. Topology optimization was used to determine the optimum density distribution of the structure. The analysis revealed that the link structures for amphibious boat can be reduced up to 31 percent by weight without altering the design of the connected and supported parts. The structural integrity of the proposed lightweight link structures was evaluated via topology optimization and verified by finite element analysis and static test. The structural integrity of lightweight link structures was found to meet the design requirements. The running stability of amphibious boat with lightweight link structures was verified via ground and water driving tests.

Floor Brouwer, Georgios Avgerinopoulos, Dora Fazekas et al.

The Nexus concept is the interconnection between the resources energy, water, food, land and climate. Such interconnections enable to address trade-offs and seek for synergies among them. Several policy areas (e.g. bio-based economy, circular economy) increasingly consider the Nexus concept. Ignoring synergies and trade-offs between energy and natural flows, can generate misleading modelling outcomes. Several modelling tools are available to address energy and the Nexus. Based on six such models, this paper aims to support the design and testing of coherent strategies for sustainable development. Model improvements would be achieved by comparing model outcomes and including a common baseline. Keywords: Energy modelling, Nexus concept, Water, Food, Climate change

Mark Lemon, Michael G. Pollitt, Steven Steer

National and local energy policies are implemented within a complex energy landscape that makes any evaluation of their impacts far from straightforward. Drawing upon a case study of Leicester this paper argues that the ability of local authorities to deliver significant energy savings within this landscape is questionable, albeit with other additional benefits being realised (e.g. job creation, community engagement). It examines existing domestic energy demand and multiple deprivation data for Leicester and neighbouring cities and combines this with a qualitative description of the transition process. The paper identifies the need for a more systematic analysis of how national energy policy translates to the local level and concludes that it is problematic even for a leading, pro-active and innovative local authority to have a statistically meaningful energy policy. Even where energy policies are favourable, carbon reduction is less easy to realise than other – more local - co-benefits and that in the light of significant financial and co-ordination constraints more attention needs to be given to how local communities can be more effectively supported in their desire to meet (or exceed) national targets. Keywords: Local energy policy, Local authorities, Energy transition