Long-term Power Research Articles

Thermoelectric textiles have garnered significant attention in energy harvesting and temperature sensing due to their comfort and reliable long-term power generation capabilities. However, existing thermoelectric textiles rarely realize antibacterial, high output performance, and sensing capabilities simultaneously. Here, we present a facile and scalable method for fabricating n-type silver selenide (Ag2Se) cotton threads with exceptional antibacterial, high power output, and advanced sensing capabilities. The Ag-Ag2Se segmented structures are prepared using the segmented selenization method. Subsequently, a thermoelectric textile consisting of 50 pairs of p-n legs is fabricated, which can generate a power density of 500 μW m-2 at a temperature difference of 30 K, and it can provide an output voltage of 24.7 mV when worn on the arm at room temperature. The textile-based sensor exhibits temperature detection (0.7 K) and a response time (2.49 s). Integrating Ag2Se cotton threads onto textiles enables the utilization of multipixel touchpads for writing and communication. Additionally, these sensors can be incorporated into gloves to accurately detect the surrounding objects' temperatures. This thermoelectric cotton thread not only facilitates energy harvesting but also establishes a solid foundation for widespread application in multifunctional textile electronics.

IoT technology has revolutionized hydroponic farming by enhancing the precision of real-time monitoring, automation, and nutrient and water management. This review explores 25 recent studies integrating IoT systems in hydroponic environments, focusing on methodologies, outcomes, and identified research gaps. Each study demonstrates the application of various IoT components such as microcontrollers, sensors, cloud platforms, and machine learning models to optimize growing conditions, minimize resource waste, and increase crop yields. Key findings indicate significant improvements in nutrient efficiency, water usage, and yield outcomes, with reductions in manual oversight ranging from 15% to 50%. IoT systems have also enabled advanced functionalities such as predictive analytics, remote monitoring, and automated nutrient dosing. However, the literature reveals consistent gaps in system scalability, long-term power efficiency, and data security, particularly concerning sensor reliability and integration in larger setups. Additionally, many systems struggle to maintain consistent performance in environments with intermittent connectivity or varying light conditions, such as solar energy. By identifying these limitations, this review underscores the potential for future research to address these challenges through more robust, scalable designs and low-power solutions for sustainable operation. This study provides an in-depth synthesis of IoT applications in hydroponics, establishing a foundation for developing enhanced, resilient IoT-based systems better suited for diverse agricultural environments and capable of advancing sustainable agriculture through intelligent automation and resource management

The research in the field of renewable energy has taken centre stage in the study of reliable and effective photovoltaic (PV) systems. These systems are essential to a future powered by renewable energy, where solar radiation is directly converted into electrical power. However, the photovoltaic arrays have limited conversion efficiency. Hence, highly accurate forecasting strategies are required to mitigate the impact of this challenge. This research focuses on proposing serial algorithms that combine machine learning and global optimization algorithms to solve stochastic optimization problems. Gated Recurrent Unit (GRU) architecture, Support Vector Machine (SVM) for Regression (SVR) models and Differential Evolution algorithm (DE) are used in developing the forecast of grid power generation across environmental variations. Initially, four serial GRU-SVR models will be trained to address the prediction for the seasonal evolution. Afterwards, a hybrid approach GRU-SVR-DE strategy will be defined to integrate four seasonal models, providing a robust forecasting strategy for PV power generation. In the end, the performances predictions will be analyzed to demonstrate the accuracy of the long-term forecasts.

Power systems hold huge potential for emission reduction, which has made the modeling and pathway simulations of their decarbonizing development a subject of widespread interest. However, current studies have not yet provided a useful modeling method that can deliver analytical probabilistic information about future system behaviors by considering various uncertainty factors. Therefore, this paper proposes a stochastic process-based approach that can provide analytical solutions for the uncertainty ranges, as well as their changing momentum, accumulation, and probabilistic distributions. Quantitative probabilities of certain incidents in power systems can be deduced accordingly, without massive Monte Carlo simulations. A case study on China’s long-term coal-fired power phaseout was conducted to demonstrate the practical use of the proposed approach. By modeling the coal-fired power system at the unit level based on stochastic processes, phaseout pathways are probabilistically simulated with consideration of national power security. Simulations span from 2025 to 2060, presenting results and accumulated uncertainties for annual power amounts, full-process emissions, and carbon efficiencies. Through this modeling and simulation, the probabilities of China’s coal-fired power system achieving carbon peaking by 2030 and carbon neutrality by 2060 are 91.15% and 42.13%, respectively. It is expected that there will remain 442 GW of capacity with 0.18 Gt of carbon emissions in 2060.

The aim of this study was to investigate whether regular use of infrared sauna (IRS) after training can promote neuromuscular performance and positive changes in body composition during a 6-week training period. Forty female team sport athletes were pair-matched into two groups: IRS (n = 20) and control (CON; n = 20). Physical performance tests, body composition and hypertrophy measurements (dual-energy x-ray absorptiometry and ultrasound of m. vastus lateralis) were performed PRE and POST a 6-week strength and power training period, involving 2-3 sessions per week. Performance tests included a 20 m sprint, squat jump (SJ), countermovement jumps with body weight (CMJ) as well as with 15, 25, and 50% additional load (CMJ15%, CMJ25%, and CMJ50%), and a maximal isometric leg press (MVC). Participants in the IRS-group used IRS (10 min, 50℃) after training three times per week. Training improved neuromuscular performance and muscle hypertrophy in both groups (p < 0.05). Following the discovery of an interaction effect for CMJ15% height (p = 0.002) and peak power (p = 0.010), post hoc tests revealed higher jump height POST-IRS (p = 0.006) and PRE-CON (p = 0.023) compared to PRE-IRS, and higher peak power POST-IRS (p = 0.002) compared to PRE-IRS. Furthermore, an interaction effect was observed for 5 m split time of the 20 m sprint (p = 0.020), but no differences were found between groups and timepoints. There were no interactions for the hypertrophy measures. Incorporating post-exercise IRS bathing does not significantly impact hypertrophy gains, but might boost long-term power production capabilities.

The half-Heusler TiNiSn alloy is a promising candidate for thermoelectric power generation, capable of directly converting waste heat into electric power, due to its high thermoelectric performance over a wide temperature range from 500 to 1000 K. However, the thermoelectric performance of p-type TiNiSn is much lower than that of its n-type counterpart. Here, we demonstrate that Hf substitution in the p-type half-Heusler TiNi0.8Co0.2Sn alloy significantly enhances thermoelectric performance within the miscibility gap region of the binary TiNiSn-HfNiSn phase diagram. The Seebeck coefficient, which is below 80 μV/K for TiNi0.8Co0.2Sn, is substantially improved to 170 μV/K in Ti0.5Hf0.5Ni0.8Co0.2Sn at 650 K, owing to modifications in the electronic band structure induced by Hf substitution. Moreover, the lattice distortion and point defects introduced by Hf substitution effectively reduce thermal conductivity, from 5.0 W/mK for TiNi0.8Co0.2Sn to 3.3 W/mK for Ti0.5Hf0.5Ni0.8Co0.2Sn at 900 K. Consequently, the thermoelectric figure of merit (ZT) significantly increases from 0.03 for TiNi0.8Co0.2Sn to 0.26 for Ti0.5Hf0.5Ni0.8Co0.2Sn at 800 K. This enhancement of thermoelectric performance in p-type TiNiSn enables the construction of thermoelectric modules composed solely of half-Heusler TiNiSn based alloys, which are expected to exhibit high thermal stability useful for long-term thermoelectric power generation.

The stochastic and variable nature of power generated by photovoltaic (PV) systems can impact grid stability. Accurately predicting the output power of a solar PV power generation system is crucial for addressing this challenge. While short-term PV power prediction is highly accurate, the accuracy of medium- to long-term photovoltaic power predictions will face great challenges. In order to improve the accuracy of medium and long-term photovoltaic power prediction, a unique hybrid deep learning model named interactive feature trend transformer (IFTformer) has been designed. Initially, deep isolated forest (DIF) and local anomaly factor (LOF) are used to construct a parallel framework that serves as the data preprocessing module, removing outliers from raw data. The time series are subsequently decomposed into seasonal and trend components, which are modelled separately for independent study. Ultimately, the predicted trend components with the seasonal components predicted by the ProSparse Self-attention mechanism based on information interaction are fitted by the multilayer perceptron (MLP) for medium- to long-term PV power prediction. The comprehensive experimental results show that the predictive performance of IFTformer is superior to that of baseline models, with a normalised root mean square error (NRMSE) of 3.64% and a normalised mean absolute error (NMAE) of 2.44%. The IFTformer model proposed in this paper is an effective approach for medium- to long-term PV power prediction, can mitigate the impact of outliers, enhance the feature extraction ability, and improve the prediction accuracy, generalizability and robustness of medium- to long-term predictions, providing a novel perspective on medium- to long-term PV power prediction methods based on deep learning methods.

Abstract This paper disputes the popular view that Australia has embraced a balancing posture against China after ditching its previous hedging policy. This is because a balancing perspective does not reconcile with the fact that Australia still actively cultivates economic ties with China. Instead, this paper proposes a new dual-track approach to explain Australia’s China policy. It posits that states can simultaneously pursue balancing and cooperation toward rising powers even after the establishment of threat certainty. Gains from trade with rising powers enhance their long-term aggregate power, which in turn enables more effective balancing against rising powers. Australia, a typical trading state, is particularly motivated to pursue this dual-track strategy toward China to maximize both its security and power. Although this dual-track approach also exhibits the co-presence of balancing and cooperation, it is very different from the hedging approach. While the hedging approach rests on the uncertainty of threats posed by a rising power, the dual-track approach is defined by such certainty. Whereas the hedging approach pursues limited and restrained balancing to manage the security dilemma with rising powers, the dual-track approach practices hard balancing. While the hedging approach uses economic engagement to shape a rising power’s intentions and behaviors, the dual-track approach only seeks profit from a rising power’s economic ascent. Finally, while the hedging approach allows largely unrestrictive economic cooperation, the dual-track approach incorporates geoeconomics to restrict cooperation in security sensitive domains. This dual-track perspective not only offers a new and novel perspective on how states respond to power shifts but also explains the “fear and greed” motto that underlies Australia’s China policy.

Introduction: The contraction–relaxation pattern of the left atrial (LA) walls is opposite to that detected in the left ventricle, which includes thinning in radial, lengthening in longitudinal, and widening in circumferential directions in the systolic reservoir phase of LA function as measured by three-dimensional speckle-tracking echocardiography (3DSTE). Global longitudinal strain (GLS) is a quantitative feature of longitudinal wall contraction referring to the whole LA. The present study aims to clarify the expected prognostic impact of peak LA-GLS as assessed by 3DSTE in healthy participants during a long-term follow-up period. Methods: The study consisted of 142 healthy adults (with an average age of 32.1 ± 12.7 years; 72 of the participants were men), in whom complete two-dimensional Doppler echocardiography and 3DSTE were performed on a voluntary basis. Results: Thirteen adults suffered from a cardiovascular event, including two cardiac deaths during a mean follow-up of 8.35 ± 4.20 years. Peak LA-GLS ≥ 20.9%, as assessed by 3DSTE, was found to be a significant predictor for cardiovascular event-free survival by using ROC analysis (specificity 74%, sensitivity 62%, area under the curve 0.69, p = 0.025). Healthy individuals with peak LA-GLS < 20.9% had a lower LV-EF and a significantly higher ratio of cardiovascular events compared to cases with peak LA-GLS ≥ 20.9%. Subjects who experienced cardiovascular events had lower peak LA-GLS and the ratio of subjects with peak LA-GLS < 20.9% proved to be significantly increased compared to that of cases without cardiovascular events. Conclusions: 3DSTE-derived peak LA-GLS representing LA lengthening in the end-systolic reservoir phase of LA function predicts future cardiovascular events in healthy adults.

A hybrid chirped pulse amplification (CPA) system is demonstrated based on a fiber seed source and a three-stage Yb:YAG crystal amplifier. We investigate the mechanism of gain narrowing in a CPA system through theoretical and experimental research. The gain narrowing can be suppressed by pre-shaping the pulse spectrum via a fiber Bragg grating (FBG) with a specifically designed spectral characteristic before amplification. The compact compressor is based on a transmission diffraction grating with a line density of 1739 lines/mm. The high-order dispersion within the CPA system is compensated by a tunable dispersion stretcher. At a repetition rate of 200 kHz, the compressed average power of 64.15 W and pulse duration of 431 fs, corresponding to a pulse energy of 321 μJ and a peak power of 744 MW, is obtained. The polarization extinction ratio of the pulses is about 23 dB, and the M2 factor is less than 1.16. In addition, the long-term power stability is tested, and the root mean square of the average power is below 0.17% over 39.5 hours. Such good performance is beneficial to ultrafast material processing in scientific research and industrial fields.

While tradable green certificates (TGCs) and carbon emission trading (CET) play key roles in achieving peak carbon and carbon neutrality, the coupling effects between these two policies on the medium- and long-term electricity market and the spot market are still uncertain. In this study, we firstly construct a multi-scale market trading framework to sort out the information transfer of four markets. Secondly, we establish a multi-scale market system dynamics-coupled trading model with five sub-modules, including the medium- and long-term power markets, the spot market, and the carbon market. Subsequently, we adjust the policy parameters (carbon quota benchmark price, carbon quota auction ratio, and renewable energy quota ratio) and set up five policy scenarios to compare and analyze the impacts of the CET and TGC mechanisms on the power market and carbon emission reduction when they act alone or in synergy, in order to provide a theoretical basis for the adjustment of strategies of market entities and the setting of parameters. The results show that CET can increase spot electricity prices and promote renewable energy to enter the spot market, while TGCs can promote a high proportion of renewable energy consumption but lower spot electricity prices for a long time. The coordinated implementation of the CET and TGC mechanisms can improve the power market’s adaptability to high renewable energy penetration, but it may also result in policy redundancy.

Microfluidic technologies are transforming wearable and implantable biomedical devices by enabling precise, real-time analysis and control of biofluids at the microscale. Integrating soft, biocompatible materials with advanced sensing and fabrication techniques, these systems offer promising solutions for continuous health monitoring, targeted drug delivery, and responsive therapeutics. This review outlines critical design considerations, material strategies, and fluid handling mechanisms essential for device performance and biocompatibility. We systematically examine key fabrication approaches-including soft lithography, 3D printing, laser micromachining, and textile-based methods-highlighting their advantages and limitations for wearable and implantable applications. Representative use cases such as sweat analysis, interstitial fluid sampling, ocular diagnostics, wound monitoring, and in vivo therapeutic systems are explored, alongside current challenges in long-term stability, power management, and clinical translation. Finally, we discuss future directions involving bioresorbable materials, AI-assisted diagnostics, and wireless integration that may drive the next generation of personalized microfluidic healthcare systems.

Recycling americium (Am) from spent nuclear fuels is an important option considered for the future nuclear fuel cycle as americium is the main contributor to the long-term radiotoxicity and heat power of the ultimate waste. The AmSEL flowsheet aims at recovering and purifying americium from a PUREX raffinate. This separation can be achieved by co-extracting lanthanide(III) (Ln) and actinide(III) cations (Am(III) and Cm(III)) into an organic phase containing the TODGA extractant (N,N,N’,N’-tetraoctyl diglycolamide), and then stripping Am(III) selectively towards curium and lanthanides. The water-soluble ligand SO3-Ph-BTBP (6,6′-bis(5,6-dialkyl-1,2,4-triazin-3-yl)-2,2′-bipyridine) is used to selectively strip Am from the loaded organic phase. The objective of this work is to design a flowsheet for the Am stripping and Cm re-extraction steps to recover americium selectively from Cm and Ln, with TODGA as extractant and SO3-Ph-BTBP as complexing reagent. The test was implemented in August 2023 at FZ Jülich with trace amounts of americium and curium.

Abstract Climate change will impact wind and, therefore, wind power generation with largely unknown effects and magnitude. Climate models can provide insight and should be used for long-term power planning. In this work, we use Gaussian processes to predict power output given wind speeds from a global climate model. We validate the aggregated predictions from past climate model data with actual power generation, which supports using CMIP6 climate model data for multi-decadal wind power predictions and highlights the importance of being location-aware. We find that wind power projections for the two in-between climate scenarios, SSP2–4.5 and SSP3–7.0, closely align with actual wind power generation between 2015 and 2023. Our location-aware future predictions up to 2050 reveal only minor changes in yearly wind power generation. Our analysis also reveals larger uncertainty associated with Germany’s coastal areas in the North than Germany’s South, motivating wind power expansion in regions where the future wind is likely more reliable. Overall, our results indicate that wind energy will likely remain a reliable energy source.

NoSPF: Non-stationary Long-term Power Consumption Forecasting for Servers in Cloud Data Centers

Abstract Advances in remote and continuous health monitoring using the internet of things (IoT) have highlighted the critical need for uninterrupted and long-term power supplies for these devices. Consequently, the study of energy harvesting has gained paramount importance to provide energy solutions. Energy harvesting involves multiple stages to recover energy from the surrounding environment. This article focuses on the energy management stage, where harvested energy is adapted to power low power devices. Specifically, we propose a reconfigurable rectifier with cross-gate coupling and a low-dropout voltage regulator (LDO converter) using 0.18μm TSMC CMOS technology for a piezoelectric power harvesting system. This circuit handles voltage ranges from 0.5V to 3.3 V with power output in the order of microwatts (μW) and occupies an area of 0.00577702125 mm2 (5777.021 μm2).

The purpose of this study is to clarify the characteristics of the Japanese legislative process on the premise that the floor party acts as a major legislative actor through party preliminary review system. In addition, a case analysis was conducted on the security legislation reform bill on how the party's preliminary review system operated in Japan, which adopted the three-pronged separation type parliamentary cabinet system. The preliminary review of the Liberal Democratic Party is a structure in which only bills that have been bound by the party are passed, and under a long-term power with a one-party advantage, a bill that has passed the internal review body can be finally prepared so that the cabinet can facilitate policy implementation. The continuity of policy implementation was high in that it was reflected in the security legislation reform bill, which had been prepared as the party's policy before the Liberal Democratic Party came to power in 2012. This can be seen as improving the rationality of the legislative process of the parliament in terms of procedures.

With increasingly stringent maritime environmental regulations, hybrid fuel cell ships have garnered significant attention due to their advantages in low emissions and high efficiency. However, challenges related to the coordinated control of multi-energy systems and fuel cell degradation remain significant barriers to their practical implementation. This paper proposes an innovative multi-timescale energy management strategy that focuses on optimizing the lifespan decay synergy of fuel cells and lithium batteries. The study designs an attention-based CNN-LSTM hybrid model for power prediction and constructs a two-stage optimization framework: The first stage employs Model Predictive Control (MPC) for long-term power planning to optimize equivalent hydrogen consumption, while the second stage focuses on real-time power allocation considering both power source degradation and system operational efficiency. The simulation results demonstrate that compared to single-layer MPC and the Equivalent Consumption Minimization Strategy (ECMS), the proposed method exhibits significant advantages in reducing single-voyage costs, minimizing differences in power source degradation rates, and alleviating power source stress. The overall performance of this strategy approaches the global optimal solution obtained through Dynamic Programming, comprehensively validating its superiority in simultaneously optimizing system economics and durability.

The dual-carbon objective aspires to enhance China’s medium- and long-term green power trading and facilitate the low-carbon economic operation of park microgrids from both medium- and long-term and spot market perspectives. First, the integration of medium- and long-term green power trading with spot trading was meticulously analyzed, leading to the formulation of a power purchase strategy for park microgrid operators. Subsequently, a sophisticated Bayesian fuzzy learning method was employed to simulate the interaction between supply and demand, enabling the prediction of the price for bilaterally negotiated green power trading. Finally, a comprehensive multi-objective optimization model was established for the synergistic operation of park microgrid in the medium- and long-term green power and spot markets. This model astutely considers factors such as green power trading, distributed photovoltaic generation, medium- and long-term thermal power decomposition, energy storage systems, and power market dynamics while evaluating both economic and environmental benefits. The Levy-based improved bird-flocking algorithm was utilized to address the multi-faceted problem. Through rigorous computational analysis and simulation of the park’s operational processes, the results demonstrate the potential to optimize user power consumption structures, reduce power purchase costs, and promote the green and low-carbon transformation of the park.

Solar radiation data forecasting algorithms are important, especially in developing countries, as vast solar power plants cannot measure reliable and constant solar irradiance. The challenges of solar irradiance prediction may be resolved by machine learning using weather datasets. This study emphasises the daily and monthly global solar radiation data predictions of three locations, Pretoria, Bloemfontein, and Vuwani, at different provinces in South Africa with various solar radiation distributions. The study evaluated five different machine learning models. Forecasting models were established to evaluate global solar radiation, focusing on input data. The selected forecast models are centered on their ability to perform with time series data. These models use five years of data from meteorological parameters, such as global horizontal irradiance (GHI), relative humidity, wind speed and ambient temperature between 1 January 2018 and 31 December 2022. The datasets from these meteorological parameters are utilised for training and testing the employed algorithms, which are examined using five statistical metrics. Moreover, the inconsistency of the solar irradiance time series was equally assessed using the clearness index. The results from this study demonstrate that the R2 value recording 0.866 datasets in Bloemfontein of random forest algorithm presents the highest performance during the training processes for all models studied, while the random tree in Vuwani showed the lowest performance of R2 of 0.210 with other algorithms in testing processes. Additionally, the maximum solar radiation was found in December for both Pretoria and Bloemfontein, recorded as 5.347 and 5.844 kWh/m2/day, respectively, while it was 4.692 kWh/m2/day at Vuwani in January. Similarly, the average clearness index of 0.605, 0.657 and 0.533 are obtained at Pretoria, Bloemfontein, and Vuwani, respectively. Among the three sites under study, the solar radiation and clearness index are higher in Bloemfontein. Therefore, the proposed algorithms could be used conveniently for short- and long-term solar power plants in South Africa.