Integrating heat pumps into large-scale electricity-to-heat industrial processes has proven highly successful in enhancing the utilisation of renewable energy and contributing to carbon emission reductions. However, most studies focus on overall system performance, overlooking the detailed thermal behaviour of the heat pump itself. This limits the adaptability of heat pumps in dynamic industrial settings. This work proposes an equation-oriented framework that enables flexible integration of thermodynamically detailed heat pump models into industrial heat recovery systems. A superstructure-based optimisation model is developed to minimise energy costs and enhance efficiency, considering process constraints, network layout, and heat pump performance. The model dynamically optimises heat pump operation and placement to enhance waste heat recovery and overall system integration. Moreover, the approach supports the integration of low-grade utilities to further improve the energy efficiency. The proposed framework is validated through an industrial-scale case study of a crude oil distillation process. Life cycle assessment is conducted to quantify potential environmental and economic benefits. Results show that integrating heat pumps into the system recovered 50.52 % of low-pressure steam, reducing the total operating cost and annual cost by 12.88 % and 12.42 %. Additionally, total net carbon emissions decreased by 28.70 %. Lower electricity prices increase heat pump use and economic benefits but also amplify rebound effects. Furthermore, although high-temperature heat pumps operating above 150 °C tend to increase capital expenditures, they unlock greater energy efficiency, thereby accelerating the industrial decarbonisation process.

This study investigates the optimization of a co-generation system involving multiple steam boilers and turbines, aiming to minimize CO2 emissions and energy consumption while maintaining reliable energy delivery. A hybrid Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) method is implemented within a Multi-Objective Mixed-Integer Nonlinear Programming (MOO-MINLP) framework. The approach effectively captures the nonlinear behavior of efficiency and operational constraints. The results show a reduction of up to 10 % in CO2 emissions and over 35 % in energy savings compared to GA-only approaches. Maximizing biomass usage at Extreme Point A achieves the lowest emissions (554.29 kg) and an energy cost of 4253.69 GJ, while minimizing energy consumption at Extreme Point C leads to 3532.67 GJ but higher emissions (708.86 tons). This study demonstrates the hybrid GA-SQP method's potential to optimize both CO2 emissions and energy consumption, offering decision-makers a balanced approach between cost and environmental impact. The results underscore the significance of fuel allocation, especially biomass, in reducing emissions despite lower efficiency, presenting a cost-effective and sustainable solution for co-generation system optimization.

Enhancement in energy recovery is always an essential element that requires academic spotlights to ensure its capability to contribute towards carbon neutrality. Recent works have extended to cover multi-solution heat exchanger networks (HEN) synthesis instead of generating a single best solution, which is not guaranteed to be practical. Nevertheless, owing to the technical challenges of synthesising all feasible networks, none of the existing works attempts to comprehensively elucidate how network topologies affect the network cost. To address this gap, P-HENS, a graph theoretic-based HEN synthesis tool, was utilised to generate the set of all heat exchanger networks with minimum utility consumption. Its effectiveness is demonstrated through an illustrative case study, which eventually generates more than 45,000 HENs. The impacts of structural variables on the cost, including the number of exchangers and the stream pairings, were analysed. The cost range of the networks was identified, revealing cost differences of 30 % despite minimum utility consumption or 15 % despite the minimum number of exchangers. Key stream pairs required to meet maximum energy recovery and influence cost were identified, leading to recommendations for improving solution searches. The solution set and the insight from this work are available to the research community for further analysis, offering valuable insights to enhance energy integration in the industry.

Healthy soil is essential for life on Earth, valued for its ability to sustain productivity, provide ecosystem services, support biodiversity, socioeconomic structure, food security, and promote environmental health. However, climate-induced changes, such as extreme weather events, shifting precipitation patterns, and rising temperatures, can disrupt essential soil processes. Climate change, combined with unsustainable soil management practices, can accelerate soil degradation, loss soil organic matter, reduce soil moisture retention, intensify erosion, disrupt nutrient cycling, and increase greenhouse gas emission. An increase in temperature of 1 °C is estimated to increase pest incidence by 10–25 % and reduce major crop yields by up to 7.4 %. Enhancing soil health strengthens plant resilience, suppresses disease development, and safeguards agroecosystems against the adverse effects of climate extremes. The growing recognition of the central role of soil in both agricultural and environmental sustainability has therefore driven interest in holistic strategies that integrate advanced agronomic practices, innovative technologies, and enabling policy frameworks to sustainably manage and restore soil health. This review examines recent advances in soil management strategies, highlighting the integration of interdisciplinary approaches to strengthen soil health as a basis for climate change resilience and increased crop productivity. Our synthesis emphasizes the importance of tailoring agricultural management practices such as soil amendments, diverse cropping systems, beneficial microbes, conservation agriculture, precision agriculture, and innovative technologies to specific soil and environmental contexts. By adopting these strategies through an interdisciplinary approach, we can improve soil productivity, sustain agroecosystem functions, and mitigate negative environmental impacts, ensuring the capacity of soil to meet the demands of a changing world.

BACKGROUND: Individuals with NREM parasomnias exhibit abnormal slow-wave activity and fragmented sleep. Sleep-state dissociation is the prevailing concept of NREM parasomnia-episodes; typically emerging from N3/N2 stages of NREM sleep's first cycle at the turning-point of deep sleep and arousal. While these relations provide a frame to understand these conditions, their mechanism and brain-topography remain unclear. METHODS: We performed a systematic search of the literature (1/01/2015-20/06/2024) on brain-topographies and morphological changes based on neurophysiological and imaging studies in patients with NREM parasomnias. RESULTS: It was shown that immediately preceding clinical episodes, the EEG spectral power of delta and theta frequency-bands increased in parallel with its reduction in the cingulate, motor, and premotor/supplementary motor cortices. Far from clinical episodes, in NREM and REM sleep as well as in wakefulness, a cortico-cortical sleep-state dissociation occurred, too. In addition, the partial arousals of episodes evolved from 'deeper' sleep with lower-amplitude slow waves, compared to episode-free arousals of the same people with NREM parasomnias. A single MR-morphology study revealed decreased grey-matter volume in the left dorsal posterior cingulate and mid-cingulate cortices in patients with mixed NREM parasomnias. CONCLUSION: Based on recent research, the state-dissociation evidenced in clinical episodes might characterize each vigilance state of people with NREM parasomnias, even outside the episodes, making sleep-wake dissociation a trait-like core feature of NREM parasomnias. The anterior cingulo-frontal regions seem to have central roles. PROSPERO registration ID: CRD42024552562.

Background: Stroke incidence rises with age. A stroke can severely affect walking ability, requiring therapy. Robot-assisted walking therapy (ROB) has been advocated as one form of walking rehabilitation in stroke patients. However, its comparative efficacy remains controversial and three-group comparisons are scant. We compared the effects of ROB, walking training therapy without a robot (WTT) and standard treatment therapy (STT) on clinical and mobility outcomes in acute ischemic stroke patients. Methods: Individuals (n = 45, 71 % males, age 64.4y ±6.34), who have recently experienced an ischemic stroke, were randomized to ROB, WTT or STT. Clinical and mobility outcomes were assessed before and after each intervention (3 weeks, 5 sessions/week) and after 5 weeks of no-intervention follow-up. Results: Outcomes did not differ between groups at baseline (p > 0.05). Modified Rankin Scale (primary outcome), improved (p < 0.05) after ROB and WTT vs. STT. These improvements were retained relative to baseline (p < 0.05) after follow-up. Barthel index, Berg Balance Scale, 10-m walking speed, the distance while walking with and without the robot for six minutes, and center pressure velocity in standing improved most after ROB (all p < 0.001), exceeding the changes after WTT which in turn were greater than the changes after STT (p ≤ 0.040). Conclusion: Older adults shortly after an ischemic stroke can quickly learn to walk with a soft robot and retain substantial clinical and mobility improvements at follow-up.

The dynamic study of the Supercritical Carbon Dioxide (SCO2) Brayton cycle has received extensive attention from the industry in recent years. While various dynamic operating conditions occur intermittently within the system, some commonly used control methods are unable to adapt effectively to those situations. In this study, a dynamic model of the SCO2 Brayton cycle coupled with a printed circuit heat exchanger with embedded PCM, a storage tank, and a proportional-integral-derivative (PID) controller was developed and validated with the model, and then the control effects of the various control models were compared in terms of their control effectiveness under three typical variable operating conditions (periodic temperature fluctuation, load reduction and recovery, and reduced flow rate). In addition, the stability assessment of the SCO2 Brayton cycle was modeled. Compared to the basic SCO2 Brayton cycle, the PCM-PCHE reduces the amplitude of the total efficiency fluctuations by 44.8 %, and the integrated layout covering the printed circuit heat exchanger with embedded PCM, storage tank, and PID controller shows the best stability. Controlling the extraction ratio with a PID controller contributes more to the stability of the SCO2 Brayton cycle than controlling the condensate flow rate with a PID controller. In contrast, the printed circuit heat exchanger with an embedded PCM contributes more to the stability of the SCO2 Brayton cycle concerning the storage tank. Overall, the total control layout reduced the instability by about 40 % compared to the initial recompression layout, indicating that the PCM, storage tank, and PID controller greatly improved the stability of the SCO2 Brayton cycle.

Abstract: This study assesses the performance of four nature-inspired optimization algorithms—Dynamic Differential Annealed Optimization (DDAO), Flower Pollination Algorithm (FPA), Firefly Algorithm (FF), and Particle Swarm Optimization (PSO) for achieving optimal space truss design. The aim is to minimize the structural weight of three benchmark trusses (10-bar, 25-bar, and 72-bar) while meeting stress and displacement constraints. The key contribution of this work is the first systematic evaluation of FPA in space truss optimization, demonstrating its greater effectiveness in obtaining optimal or near-optimal solutions with faster convergence and higher stability compared to PSO and FF. The results also highlight the limitations of DDAO in handling constrained engineering problems. Findings confirm that FPA and FF are highly effective for structural optimization, offering robust solutions with minimal computational cost. These insights contribute to advancing metaheuristic-based structural design, supporting the adoption of FPA in large-scale optimization problems.

Background: Adequate nutrient intake is an essential part of athletes’ preparation, which results in more effective training adaptation, recovery, better body composition ratios, and improved sports performance. Inadequate nutrient intake or lack of knowledge can lead to disordered eating and eating disorders, the prevalence of which is higher among athletes than in the general population. In most cases, these unwanted outcomes occur because athletes do not seek the help of a qualified dietitian. According to a 2019 report by the International Olympic Committee, disordered eating and eating disorders’ point prevalence ranges from 6% to 45% in women and 0% to 19% in men. In Hungary, no athlete sample has been tested with screening questionnaires for eating disorders, and the development of reliable measurement tools for identifying this problem is also necessary. Objective: The aim of this study is to present the Hungarian version of the DESA-6 questionnaire (DESA-6H) for screening disordered eating among athletes and to investigate its convergent validity (CV), as well as to evaluate athletes’ nutritional behaviors based on comparative study results. Methods: The DESA-6H questionnaire was tested on elite athletes (n = 131) and recreational athletes (n = 123) aged 14 and older. Kendall’s tau-b correlation was used to establish the CV between the DESA-6H and the total EAT-26 score, as well as between the DESA-6H and the three subscales of the EAT-26. The same method was used to test the potential linear relationship between eating behavior questionnaires and weekly training hours. Chi-square tests were conducted to test the relationships between nominal variables. Differences between groups based on age and sport participation levels were analyzed using Kruskal-Wallis H tests, and post-hoc analyses were conducted using Mann-Whitney U tests. Results: The CV analysis showed a significant positive correlation between the DESA-6H and the total EAT-26 score (τb = 0.49, p < 0.001), the EAT-26 Dieting subscale (τb = 0.53, p < 0.001), and the EAT-26 Bulimia subscale (τb = 0.39, p < 0.001). In the total sample, 20.4% scored above the threshold on the DESA-6H, while 40.1% scored above the threshold on the EAT-26. Among sports categories, the highest prevalence was found in aesthetic, weight-dependent, and endurance sports. A significant difference was observed in the DESA-6H scores based on gender (U = 4948.50, Z = –5.210, p < 0.001) and sports participation level (U = 6847.50, Z = –2.123, p = 0.034), with large (Cohen’s d = 0.68) and small ( Cohen’s d = 0.26) effect sizes. The Mann-Whitney U-test showed no significant difference between the 14–17-year-old and 18+ age groups in terms of DESA-6H scores (U = 6082.00, Z = –1.017, p = 0.309). No statistically significant differences were found between the two age groups on the EAT-26 scores either (U = 6490.00, Z = –0.220, p = 0.826). Overall, the statistical tests consistently supported the convergent validity of the DESA-6H when compared with the EAT-26. Conclusions: Based on the results of our research, it can be concluded that the DESA-6H questionnaire is a reliable tool for screening disordered eating among Hungarian athletes. Its application can help in the early detection and prevention of eating disorders.