Search Results (10,028)

This article introduces a cost-effective, IoT-enabled flexible energy management system (EMS) for residential photovoltaic (PV) microgrids with battery storage, implemented on an ESP32 microcontroller. The proposed system achieves indirect control over commercial household inverters by altering wattmeter readings and utilizing Modbus communication, thereby avoiding expensive hardware modifications. A significant contribution of this work is enabling the injection of energy from the Battery Energy Storage System (BESS) into the grid, a capability often restricted by commercial inverters. Real-world experimentation validated robust performance of the proposed system, demonstrating its ability to dynamically manage energy flows, achieve minimal tracking errors, and optimize energy usage in response to both flexibility market signals and electricity prices. This approach provides a practical and accessible solution for prosumers to actively participate in energy trading and flexibility markets using widely available technology. Full article

Microbial fuel cells (MFCs) represent a sustainable alternative for wastewater treatment by simultaneously removing organic pollutants and generating energy. In this work, ceramic membranes were fabricated from low-cost locally available clays and tested as separators in MFCs. The systems achieved chemical oxygen demand (COD) removal efficiencies of up to 95%, comparable to those obtained with conventional Nafion membranes. In terms of energy performance, the ceramic membranes maintained open-circuit voltages of 0.80 ± 0.05 V during batch operation with voltage generation cycles ranging from 6 to 3 days, and delivered power densities between 140 and 180 mW/m² under closed-circuit conditions. These values were very similar to those obtained with Nafion. The ceramic membranes maintained consistent COD removal performance during successive batch feeding cycles, confirming their stability under repeated operation. Overall, these results highlight the potential of ceramic materials as cost-effective and robust alternatives for large-scale wastewater treatment using MFC technology. Full article

Traffic accidents remain a major global burden, causing mortality, disability, and socio-economic losses that hinder sustainable development. Beyond human suffering, crashes place long-term pressures on health systems, labor markets, and national economies, disproportionately impacting low- and middle-income countries. Estimating the true societal costs of accidents is therefore essential for designing effective, equitable, and sustainable road safety policies. This study applies the Willingness-to-Pay (WTP) method to evaluate the external costs of traffic-related deaths and injuries in Türkiye between 2008 and 2018. By incorporating material and immaterial losses, the WTP framework captures a broader spectrum of impacts than traditional approaches, offering valuable insights into the scale of welfare losses and the value of risk reduction. The findings reveal that external costs rose substantially over the decade, from 1.63% to 2.72% of national Gross Domestic Product (GDP), underscoring that economic losses from road crashes are growing faster than the economy. These results highlight the need for systematic interventions that integrate road safety into national sustainability agendas, including safer infrastructure, behavioral programs, advanced vehicle technologies, and efficient emergency response systems. The evidence presented strengthens the case for prioritizing traffic safety as a fundamental component of sustainable transport and public health strategies. Full article

The growing importance of environmental technologies in a circular economy requires the use of tools that allow a realistic assessment of their economic efficiency. Classical investment indicators, such as NPV or IRR, are proving inadequate in the case of installations whose main objective is not to maximise profit but to reduce waste and emissions. There is a lack of tools in the literature that would allow for an unambiguous assessment of the unit cost of waste treatment, taking into account the life cycle of the installation and market conditions. This study aims to assess the feasibility of using the Levelised Cost of Waste (LCOW) indicator, modelled on the Levelised Cost of Energy (LCOE) from the energy sector, as a planning and decision-making tool in the waste management sector. In this study, an LCOW calculation model was developed and applied to analyse textile waste pyrolysis technology. Simulations were conducted for three plant scales (1000, 5000, and 10,000 Mg/year), and a sensitivity analysis was performed to examine the relationship between the LCOW and by-product prices, energy costs, capital expenditures, and CO₂ emissions. The results confirm that the LCOW is a helpful tool for determining tariffs, identifying subsidy thresholds and comparing technology options. Its application is particularly well suited to small-scale environmental investments where classical approaches fail. Full article

Proposed process intensification in the literature claims relevant savings in operational cost through optimization of the energy required to operate a typical solvent-based CO₂ capture facility, meanwhile granting the same capture performance. However, the techno-economic assessment for these proposed designs is not well developed and not fairly compared using a detailed and standardized cost evaluation technique that follows the association for the advancement of cost engineering (ACEE) class 4 costing methodology. This limitation makes it difficult and less viable to decide which solution is more cost-effective in consideration of the integration market with coal or natural gas combined cycle power plants. This work suggests a standardized methodology for cost evaluation and ultimately aids in formulating an accurate and high-fidelity guideline for industrial deployment of the proposed technologies, covering analysis on the flue gas compression (FGC) and lean vapor compression (LVC) configurations. Design, simulation, sensitivity analysis, and optimization are conducted initially to build a baseline design that closely represents an existing commercial design, such as Cansolv and Petra Nova technologies. The energy saving from the two configurations is analyzed in parallel to the investment cost, levelized cost of electricity (LCOE), and the CO₂ avoided cost. It was found that FGC improved the capture performance of the baseline design, but at the same time raised the cost of operation and investment by a higher magnitude, making the CO₂ avoided cost $98.2/tonneCO₂, which is $16 higher than that of the baseline design. Meanwhile, LVC has been defined as an attractive configuration for lowering the CO₂ avoided cost. Full article

Glass fiber (GF) reinforced unsaturated polyester resin (UP) composites are used in cured-in-place pipe (CIPP) rehabilitation technology of drainage systems due to their low cost and excellent force chemical properties. However, the weak interfacial compatibility between GF and the polymer matrix limits the stress transfer efficiency. Herein, a strategy of a polyhydric boehmite (AlOOH) layer coated on GF (GF-AlOOH) was developed for improving the mechanical properties of UP composites, and the enhancement effects of the coating process were analyzed. The AlOOH-modified GFs significantly improved the flexural and tensile strengths of the modified composites by 41.21% and 21.05%, respectively. Moreover, the enhancement mechanism was explored by analyzing the surface chemical structure of GF-AlOOHs. The nano-AlOOH was grafted on the GF surface by O=Al–OH. Meanwhile, the increase in the mechanical properties of UP/GF-AlOOH was mainly attributed to the combined effect of mechanical interlocking interaction, covalent bonding and hydrogen bonding, which improved the interfacial adhesion between GF and UP. In summary, this work provides effective guidance for achieving high-quality interfaces in GF composites and offers important insights into designing durable and cost-effective materials for CIPP rehabilitation and broader infrastructure applications. Full article

Glioblastoma (GB) is an extremely aggressive tumor for which effective therapy is still in its infancy. Although several candidate therapeutics have been identified in functional preclinical assays, clinical trials have not supported their effectiveness in GB patients. The poor clinical efficacy of the treatments can be attributed to the insufficient mimicry of GB in patients by the preclinical models used. In this review article, we provide a comprehensive overview of the available GB preclinical models, which are classified according to their origin (animal or human), species, type and modeling strategy (two- or three-dimensional cell culture, in vivo grafting or in silico modeling). Moreover, the article compares developing cutting-edge technologies, including GB-derived organoids, bioprinting, microfluidic devices, and their multimodal integration in GB-on-chip systems, which aim to replicate the GB microenvironment with high precision. In silico and in vivo approaches are also reviewed, including zebrafish transplantation models. The costs, benefits, applications and clinical relevance of each model system and/or modeling strategy are discussed in detail and compared. We highlight that the most appropriate, or combination of, GB preclinical models must be selected (or even customized) based on the specific aims and constraints of each study. Finally, to improve the reliability and translational relevance of GB research, we propose a practical roadmap that addresses critical challenges in preclinical assay development, ranging from short-term adjustments to long-term strategic planning. Full article

With the rapid growth of electricity demand in industrial parks and the increasing penetration of renewable energy, vehicle-to-grid (V2G) technology has become an important enabler for mitigating grid stress while improving charging economy. This paper proposes a multi-objective rolling linear-programming-model-based predictive control (LP-MPC) method for coordinated electric vehicle (EV) scheduling in industrial park microgrids. The model explicitly considers transformer capacity limits, EV state-of-charge (SOC) dynamics, bidirectional charging/discharging constraints, and photovoltaic (PV) generation uncertainty. By solving a linear programming problem in a receding horizon framework, the approach simultaneously achieves load peak shaving, valley filling, and EV revenue maximization with real-time feasibility. A simulation study involving 300 EVs, 100 kW PV, and a 1000 kW transformer over 24 h with 5-min intervals demonstrates that the proposed LP-MPC outperforms greedy and heuristic load-leveling strategies in peak load reduction, load variance minimization, and charging cost savings while meeting all SOC terminal requirements. These results validate the effectiveness, robustness, and economic benefits of the proposed method for V2G-enabled industrial park microgrids. Full article

The growing emphasis on controlling sulfur-containing compounds in fuel oils has driven the development of numerous desulfurization technologies. Among these, catalytic oxidative desulfurization (CODS) has garnered considerable research interest due to its exceptional capability to efficiently remove refractory sulfur compounds, particularly dibenzothiophene (DBT), under relatively mild reaction conditions. However, the widespread application of CODS has been hindered by the high cost and complex preparation processes of the catalysts. To enhance the practical potential of CODS, in this study, a novel Zr@AC catalyst was developed by a facile “solution impregnation + high-temperature calcination” strategy, where zirconium species were effectively supported on activated carbon. Experimental results demonstrated that under optimized conditions of 0.1 g catalyst dosage, 2.0 O/S ratio, reaction temperature 100 °C and reaction time 50 min, the Zr@AC-mediated CODS system achieved a remarkable desulfurization efficiency of 97.24% for DBT removal. The removal efficiency of DBT increased by 9.0% compared with non-catalytic systems. The characterization techniques revealed that the Zr@AC catalyst possesses a hierarchically rough surface morphology, high specific surface area, abundant active sites, and distinctive Zr-O functional groups. Kinetic analysis indicated that the oxidation process follows second-order reaction kinetics. Furthermore, the catalyst maintained over 95% desulfurization efficiency after five consecutive regeneration cycles, confirming that the prepared catalyst has the exceptional recyclability and operational stability. Full article

Hemodialysis (HD) technology, pivotal in managing end-stage kidney disease, has witnessed significant advancements. Yet, the high cost of novel equipment often restricts its usage in resource-limited settings. This study introduces a two-filter adaptation to conventional HD machines, aimed at enhancing toxin removal while maintaining cost-effectiveness. Using a benchtop experimental setup, the performance of the adapted system was compared with that of standard HD. The results demonstrated that the two-filter system improved urea clearance rates by 54% compared with standard HD, without increasing albumin loss or causing additional hemolysis. In a pilot study of four HD patients, the modified setup achieved a higher single-pool Kt/V (1.82) and urea-reduction ratio (80%). These findings underscore the potential of this adaptation to enhance HD machine efficiency without additional patient risks, thereby offering a feasible solution for improving access to advanced renal therapies in under-resourced areas. Further clinical trials with larger populations are warranted to validate these benefits and evaluate middle-molecule clearance for comparison with hemodiafiltration (HDF). Full article

Metal-organic frameworks (MOFs), a class of crystalline porous materials featuring a high specific surface area, tunable pore structures, and functional surfaces, exhibit remarkable potential in fluorescent sensing. This review systematically summarizes recent advances in the construction strategies, sensing mechanisms, and applications of MOF-based fluorescent sensors. It begins by highlighting the diverse degradation pathways that MOFs encounter in practical applications, including hydrolysis, acid/base attack, ligand displacement by coordinating anions, photodegradation, redox processes, and biofouling, followed by a detailed discussion of corresponding stabilization strategies. Subsequently, the review elaborates on the construction of sensors based on individual MOFs and their composites with metal nanomaterials, MOF-on-MOF heterostructures, covalent organic frameworks (COFs), quantum dots (QDs), and fluorescent dyes, emphasizing the synergistic effects of composite structures in enhancing sensor performance. Furthermore, key sensing mechanisms such as fluorescence quenching, fluorescence enhancement, Stokes shift, and multi-mechanism coupling are thoroughly examined, with examples provided of their application in detecting biological analytes, environmental pollutants, and food contaminants. Finally, future directions for MOF-based fluorescent sensors in food safety, environmental monitoring, and clinical diagnostics are outlined, pointing to the development of high-performance, low-cost MOFs; the integration of multi-technology platforms; and the construction of intelligent sensing systems as key to enabling their practical deployment and commercialization. Full article

Background and Objectives: Dynamic nutrient profiling represents a paradigm shift in personalized nutrition, integrating real-time nutritional assessment with individualized dietary recommendations through advanced algorithmic approaches, biomarker integration, and artificial intelligence. This comprehensive systematic review and meta-analysis examines the current state of dynamic nutrient profiling methodologies for personalized diet planning, evaluating their effectiveness, methodological quality, and clinical outcomes. Methods: Following PRISMA 2020 guidelines, we conducted a comprehensive search of electronic databases (PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and Google Scholar) from inception to December 2024. The protocol was prospectively registered in PROSPERO (Registration: CRD42024512893). Studies were systematically screened using predefined inclusion criteria, quality was assessed using validated tools (RoB 2, ROBINS-I, Newcastle–Ottawa Scale), and data were extracted using standardized forms. Random-effects meta-analyses were performed where appropriate, with heterogeneity assessed using I² statistics. Publication bias was evaluated using funnel plots and Egger’s test. Results: From 2847 initially identified records plus 156 from additional sources, 117 studies met the inclusion criteria after removing 391 duplicates and systematic screening, representing 45,672 participants across 28 countries. Studies employed various methodological approaches: algorithmic-based profiling systems (76 studies), biomarker-integrated approaches (45 studies), and AI-enhanced personalized nutrition platforms (23 studies), with some studies utilizing multiple methodologies. Meta-analysis revealed significant improvements in dietary quality measures (standardized mean difference: 1.24, 95% CI: 0.89–1.59, p < 0.001), dietary adherence (risk ratio: 1.34, 95% CI: 1.18–1.52, p < 0.001), and clinical outcomes including weight reduction (mean difference: −2.8 kg, 95% CI: −4.2 to −1.4, p < 0.001) and improved cardiovascular risk markers. Substantial heterogeneity was observed across studies (I² = 78–92%), attributed to methodological diversity and population characteristics. AI-enhanced systems demonstrated superior effectiveness (SMD = 1.67) compared to traditional algorithmic approaches (SMD = 1.08). However, current evidence is constrained by practical limitations, including the technological accessibility of dynamic profiling systems and equity concerns in vulnerable populations. Additionally, the evidence base shows geographical concentration, with most studies conducted in high-income countries, underscoring the need for research in diverse global settings. These findings have significant implications for shaping public health policies and clinical guidelines aimed at integrating personalized nutrition into healthcare systems and addressing dietary disparities at the population level. Conclusions: Dynamic nutrient profiling demonstrates significant promise for advancing personalized nutrition interventions, with robust evidence supporting improved nutritional and clinical outcomes. However, methodological standardization, long-term validation studies exceeding six months, and comprehensive cost-effectiveness analyses remain critical research priorities. The integration of artificial intelligence and multi-omics data represents the future direction of this rapidly evolving field. Full article

The utilization of oil sludge for the creation of value-added petroleum products represents an important research direction, as certain processing routes do not incur the additional costs that are associated with more complex refining operations. The selection of the most appropriate treatment method is therefore critical for achieving cost-effective processing outcomes. The economic feasibility of a particular technology is largely determined by the physical–chemical properties and potential toxicity of oil sludge, and thus, it is essential to comprehensively characterize and assess the toxicity of this substance. In this study, the physical–chemical composition and principal characteristics of oil sludge obtained from a Kazakhstan oil company were examined. To clean the oil sludge, an alkaline solution was used as a surfactant with a solid–liquid ratio of 1:3. The solid content in the sludge was reduced from 23% to 0.76%. The results revealed that the hydrocarbon fraction of the oil sludge was predominantly composed of heavy fractions. In addition, the effects of thermal parameters on treatment efficiency were found to contribute to the secondary products present in high oil fractions. Treatment with inert gases improved processing efficiency rates by over 57%. The most efficient results included the pyrolysis of cleaned oil sludge with minimum solid residues (5.8% under CO₂) and maximum gas products (37.8% under N₂). Full article

This paper systematically reviewed studies on the integration of Artificial Intelligence (AI) into infrastructure management to support sustainable urban planning across three primary domains: predictive maintenance and energy optimization, traffic and mobility systems, and public participation with ethical considerations. Findings from thirty peer-reviewed studies underscore how AI-driven models enhance operational efficiency, sustainability, and governance in smart cities. Effective management of AI-driven smart infrastructure can transform urban planning by optimizing resources efficiency and predictive maintenance, including 15% energy savings, 25–30% cost reductions, 25% congestion reduction, and 18% decrease in travel times. Similarly, participatory digital twins and citizen-centric approaches are found to enhance public participation and help address ethical issues. The findings further reveal that AI-based predictive maintenance frameworks improve system reliability, while deep learning and hybrid models achieve up to 92% accuracy in traffic forecasting. Nonetheless, obstacles to equitable implementation, including the digital divide, privacy infringements, and algorithmic bias, persist. Establishing ethical and participatory frameworks, anchored in responsible AI governance, is therefore vital to promote transparency, accountability, and inclusivity. This study demonstrates that AI-enabled smart infrastructure management strengthens urban planning by enhancing efficiency, sustainability, and social responsiveness. It concludes that achieving sustainable and socially accepted smart cities depends on striking a balance between technological innovation, ethical responsibility, and inclusive governance. Full article

From the interdisciplinary perspective of industrial economics and behavioral finance, this study establishes a noncooperative-cooperative biform game model between new energy enterprises and traditional energy enterprises. In this model, sales price is considered the non-cooperative strategy, while R&D expenses borne forms the basis of cooperative alliances. The Shapley value is applied to allocate profits, and numerical analysis is conducted to analyze the impact of factors, such as government subsidies and competitive intensity, on optimal strategies. The findings reveal the following: (1) Government subsidies effectively increase energy sales volume, promote technological advancements in new energy enterprises, and reduce the traditional energy enterprises’ proportion of R&D expenses borne. Moderate increases in competitive intensity are conducive to expanding market size, thereby enhancing both energy sales volume and profits. (2) Reasonably increasing the executive risk preference of energy enterprises encourages traditional energy enterprises to bear a higher proportion of R&D expenses and stimulates new energy enterprises to improve their production level, leading to increased sales value of energy. (3) Rising investment and production costs result in a higher proportion of R&D expenses borne for new energy enterprises. Consequently, the shrinking of new energy value reduces their profits, while the profits of traditional energy enterprises increase correspondingly. Full article