Search Results (14,938)

Background and aim: Advanced Therapy Medicinal Products (ATMPs) are innovative drugs based on genes, tissues, or cells that target rare and severe diseases. ATMPs have shown promising clinical outcomes but are associated with high costs, raising questions about cost-effectiveness. Hence, this systematic review aims to analyze the cost-effectiveness and cost-utility profiles of the European Medicines Agency-authorized ATMPs for treating rare diseases. Methods: A systematic review was conducted following PRISMA guidelines. Studies were identified by searching PubMed, Embase, Web of Science, and ProQuest scientific databases. Economic evaluations reporting incremental cost-effectiveness/utility ratios (ICERs/ICURs) for ATMPs were included. Costs were standardized to 2023 Euros, and a cost-effectiveness plane was constructed to evaluate the results against willingness-to-pay (WTP) thresholds of EUR 50,000, EUR 100,000, and EUR 150,000 per QALY, as part of a sensitivity analysis. Results: A total of 61 studies met the inclusion criteria. ATMPs for rare blood diseases, such as tisagenlecleucel and axicabtagene ciloleucel, were found to be cost-effective in a majority of studies, with incremental QALYs ranging from 1.5 to 10 per patient over lifetime horizon. Tisagenlecleucel demonstrated a positive cost-effectiveness profile in the treatment of acute lymphoblastic leukemia (58%), while axicabtagene ciloleucel showed a positive profile in the treatment of diffuse large B-cell lymphoma (85%). Onasemnogene abeparvovec for spinal muscular atrophy (SMA) showed uncertain cost-effectiveness results, and voretigene neparvovec for retinal diseases was not cost-effective in 40% of studies, with incremental QALYs around 1.3 and high costs exceeding the WTP threshold set. Conclusions: ATMPs in treating rare diseases show promising economic potential, but cost-effectiveness varies across indications. Policymakers must balance innovation with system sustainability, using refined models and the long-term impact on patient outcomes. Full article

Continuous and accurate biomass measurement is a critical enabler for control, decision making, and optimization in modern plant production systems. It supports the development of plant growth models for advanced control strategies like model predictive control, and enables responsive, data-driven, and plant state-dependent cultivation. Traditional biomass measurement methods, such as destructive sampling, are time-consuming and unsuitable for high-frequency monitoring. In contrast, image-based estimation using computer vision and deep learning requires frequent retraining and is sensitive to changes in lighting or plant morphology. This work introduces a low-cost, load-cell-based biomass monitoring system tailored for vertical farming applications. The system operates at the level of individual growing trays, offering a valuable middle ground between impractical plant-level sensing and overly coarse rack-level measurements. Tray-level data allow localized control actions, such as adjusting light spectrum and intensity per tray, thereby enhancing the utility of controllable LED systems. This granularity supports layer-specific optimization and anomaly detection, which are not feasible with rack-level feedback. The biomass sensor is easily scalable and can be retrofitted, addressing common challenges such as mechanical noise and thermal drift. It offers a practical and robust solution for biomass monitoring in dynamic, growing environments, enabling finer control and smarter decision making in both commercial and research-oriented vertical farming systems. The developed sensor was tested and validated against manual harvest data, demonstrating high agreement with actual plant biomass and confirming its suitability for integration into vertical farming systems. Full article

The high cost of hydrogen production is the primary factor limiting the development of the hydrogen energy industry chain. Additionally, due to the inefficiency of hydrogen production by water electrolysis technology, the development of high-performance catalysts is an effective means of producing low-cost hydrogen. In water electrolysis technology, the electrocatalytic activity of the electrode affects the kinetics of the oxygen evolution reaction (OER) and the hydrogen evolution rate. This study utilizes the liquid phase co-precipitation method to synthesize three types of Prussian blue analog (PBA) electrocatalytic materials: Fe/PBA(Fe₄[Fe(CN)₆]₃), Fe-Mn/PBA((Fe, Mn)₃[Fe(CN)₆]₂·nH₂O), and Fe-Mn-Co/PBA((Mn, Co, Fe)₃^II[Fe^III(CN)₆]₂·nH₂O). X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses show that Fe-Mn-Co/PBA has a smaller particle size and higher crystallinity, and its grain boundary defects provide more active sites for electrochemical reactions. The electrochemical test shows that Fe-Mn-Co/PBA exhibits the best electrochemical performance. The overpotential of the oxygen evolution reaction (OER) under 1 M alkaline electrolyte at 10/50 mA·cm⁻² is 270/350 mV, with a Tafel slope of 48 mV·dec⁻¹, and stable electrocatalytic activity is maintained at 5 mA·cm⁻². All of these are attributed to the synergistic effect of Fe, Mn, and Co metal ions, grain refinement, and the generation of grain boundary defects and internal stresses. Full article

Intelligent driving technology is expected to reshape urban transportation, but its promotion is hindered by user acceptance challenges and diverse technical routes. This study proposes a comprehensive user acceptance evaluation framework for intelligent driving from the perspective of value engineering (VE). The novelty of this framework lies in three aspects: (1) It unifies behavioral theory and utility theory under the value engineering framework, and it extracts key indicators such as safety, travel efficiency, trust, comfort, and cost, thus addressing the issue of the lack of integration between subjective and objective factors in previous studies. (2) It establishes a systematic mapping mechanism from technical solutions to evaluation indicators, filling the gap of insufficient targeting at different technical routes in the existing literature. (3) It quantifies acceptance differences via VE’s core formula of V = F/C, overcoming the ambiguity of non-technical evaluation in prior research. A case study comparing single-vehicle intelligence vs. collaborative intelligence and different sensor combinations (vision-only, map fusion, and lidar fusion) shows that collaborative intelligence and vision-based solutions offer higher comprehensive acceptance due to balanced functionality and cost. This framework guides enterprises in technical strategy planning and assists governments in formulating industrial policies by quantifying acceptance differences across technical routes. Full article

Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO₂ emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO₂ emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article

The plant-based oil industry contributes significantly to food waste/by-products in the form of underutilized biomass, including oil pomace, cake/meal, seeds, peels, wastewater, etc. These waste/by-products contain a significant quantity of nutritious and bioactive compounds (phenolics, lignans, flavonoids, dietary fiber, proteins, and essential minerals) with proven health-promoting effects. The utilization of them as natural, cost-effective, and food-grade functional ingredients in novel food formulations holds considerable potential. This review highlights the potential of waste/by-products generated during plant-based oil processing as a promising source of bioactive compounds and covers systematic research, including recent studies focusing on innovative extraction and processing techniques. It also sheds light on their promising potential for valorization as food ingredients, with a focus on specific examples of food fortification. Furthermore, the potential for value creation in the food industry is emphasized, taking into account associated challenges and limitations, as well as future perspectives. Overall, the current information suggests that the valorization of plant-based oil industry waste and by-products for use in the food industry could substantially reduce malnutrition and poverty, generate favorable health outcomes, mitigate environmental concerns, and enhance economic profit in a sustainable way by developing health-promoting, environmentally sustainable food systems. Full article

The utilization of Unmanned Aerial Vehicles (UAVs) in missions such as reconnaissance and surveillance has grown rapidly, underscoring the need for efficient path planning algorithms that ensure both optimality and collision avoidance. The A-star algorithm is widely used for global path planning due to its ability to generate optimal routes; however, its high computational cost makes it unsuitable for real-time applications, particularly in unknown or dynamic environments. For local path planning, the Artificial Potential Field (APF) algorithm enables real-time navigation by attracting the UAV toward the target while repelling it from obstacles. Despite its efficiency, APF suffers from local minima and limited performance in dynamic settings. To address these challenges, this paper proposes the A-star-Guided Potential Field (AGPF) algorithm, which integrates the strengths of A-star and APF to achieve robust performance in both global and local path planning. The AGPF algorithm was validated through simulations conducted in the Robot Operating System (ROS) environment. Simulation results demonstrate that AGPF produces smoother and more optimal paths than A-star, while avoiding the local minima issues inherent in APF. Furthermore, AGPF effectively handles moving and previously unknown obstacles by generating real-time avoidance trajectories, demonstrating strong adaptability in dynamic and uncertain environments. Full article

Biodiesel, as one of the alternatives to fossil fuels, faces significant challenges in large-scale industrial production due to its high production costs. In addition to raw material costs, catalyst costs are also a critical factor that cannot be overlooked. This review summarizes various methods for preparing biodiesel catalysts from solid waste. These methods not only enhance the utilization rate of waste but also reduce the production costs and environmental impact of biodiesel. Finally, the limitations of waste-based catalysts and future research directions are discussed. Research indicates that solid waste can serve as a catalyst carrier or active material for biodiesel production. Methods such as high-temperature calcination, impregnation, and coprecipitation facilitate structural modifications to the catalyst and the formation of active sites. The doping of metal ions not only alters the catalyst’s acid-base properties but also forms stable metal bonds with functional groups on the carrier, thereby maintaining catalyst stability. The application of microwave-assisted and ultrasound-assisted methods reduces reaction parameters, making biodiesel production more economical and sustainable. Overall, this study provides a scientific basis for the reuse of solid waste and ecological protection, emphasizes the development potential of waste-based catalysts in biodiesel production, and offers unique insights for innovation in this field, thereby accelerating the commercialization of biodiesel. Full article

This paper presents a comprehensive review of the environmentally friendly management and reutilization of electronic waste (e-waste) plastics in flexible pavement construction. The discussion begins with an overview of e-waste management challenges and outlines key recycling approaches for converting plastic waste into asphalt-compatible materials. This review then discusses the types of e-waste plastics used for asphalt modification, their incorporation methods, and compatibility challenges. Physical and chemical treatment techniques, including the use of free radical initiators, are then explored for improving dispersion and performance. Additionally, in situations where advanced pretreatment methods are not applicable due to cost, safety, or technical constraints, the application of alternative approaches, such as the use of low-cost complementary additives, is discussed as a practical solution to enhance compatibility and performance. Finally, the influence of e-waste plastics on the conventional and rheological properties of asphalt binders, as well as the performance of asphalt mixtures, is also evaluated. Findings indicate that e-waste plastics, when combined with appropriate pretreatment methods and complementary additives, can enhance workability, cold-weather cracking resistance, high-temperature anti-rutting performance, and resistance against moisture-induced damage while also offering environmental and economic benefits. This review highlights the potential of e-waste plastics as sustainable asphalt modifiers and provides insights across the full utilization pathway, from recovery to in-field performance. Full article

Sources of renewable energy have attracted considerable attention. Their expanded use will have a substantial impact on both the cost of energy production and climate change. Solar energy is one efficient and safe option; however, solar energy harvesting sites, irrespective of the location, can impact the ecosystem. This experimental study explores the energy available inside and outside of novel miniature energy harvesting cages by measuring light intensity and power generated. Varying light intensity outside the cage has been utilized to study the remaining energy inside the cage of a flexible design, where the heights of the harvesting panels are parameters. Cages are built from custom photovoltaic panels arranged in a staircase manner to provide access to growing plants. The balance between power generation and biological development is investigated. Two different structures are presented to explore the variation of illumination intensity inside the cages. The experimental results show a substantial reduction in energy inside the cages. The experimental results showed up to 24% reduction in illumination inside the cages in winter. The reduction is even larger in summer, up to 57%. The results from the models provide a framework to study the possible impact on a biological system residing inside the cages, paving the way for practical farming with sustainable energy harvesting. Full article

The lateral flow immunoassay (LFIA) is a widely utilized, rapid diagnostic technique characterized by its short analysis duration, cost efficiency, visual result interpretation, portability and suitability for point-of-care applications. However, conventional LFIAs have limited sensitivity, a challenge that can be overcome by the introduction of gold nanoparticles, which provide enhanced sensitivity and selectivity (compared, for example, to latex beads or carbon nanoparticles) for the detection of target analytes, due to their optical properties, chemical stability and ease of functionalization. In this work, gold nanoparticle-based LFIAs are developed for the detection of aflatoxin B1, and the relative performance of different morphology particles is evaluated. LFIA using gold nano-labels allowed for aflatoxin B1 detection over a range of 0.01 ng/mL–100 ng/mL. Compared to spherical gold nanoparticles and gold nano-flowers, star-shaped gold nanoparticles show increased antibody binding efficiency of 86% due to their greater surface area. Gold nano-stars demonstrated the highest sensitivity, achieving a limit of detection of 0.01ng/mL, surpassing the performance of both spherical gold nanoparticles and gold nano-flowers. The use of star-shaped particles as nano-labels has demonstrated a five-fold improvement in sensitivity, underscoring the potential of integrating diverse nanostructures into LFIA for significantly improving analyte detection. Moreover, the robustness and feasibility of gold nano-stars employed as labels in LFIA was assessed in detecting aflatoxin B1 in a wheat matrix. Improved sensitivity with gold nano-stars holds promise for applications in food safety monitoring, public health diagnostics and rapid point-of-care diagnostics. This work opens the pathway for further development of LFIA utilizing novel nanostructures to achieve unparallel precision in diagnostics and sensing. Full article

This paper presents a comprehensive analysis comparing the implementation of electric and diesel buses for public transport services in the low-density area of the City of Jastrebarsko in Croatia. It utilizes a multidimensional approach and incorporates direct and indirect costs, such as vehicle acquisition, operation, charging, maintenance, and environmental impact costs during the lifecycle of the buses. The results show that, despite the higher initial investment in electric buses, these vehicles offer savings, especially when coupled with significantly reduced emissions of pollutants, which decreases indirect costs. However, local contexts differ, leading to a need to revise whether or not a municipality can finance the procurement and operations of such a fleet. The paper utilizes a robust methodological framework, integrating a proposal based on real-world data and demand and combining it with predictive analytics to forecast long-term benefits. The findings of the paper support the introduction of buses as a sustainable solution for Jastrebarsko, which provides insights for public transport planners, urban planners, and policymakers, with a discussion about the specific issues regarding the introduction, procurement, and operations of buses of different propulsion in a low-density area. Full article

Cervical cancer remains a major health burden among women in sub-Saharan Africa, where screening is often limited. Persistent high-risk human papillomavirus (HR-HPV) infection is the principal cause, highlighting the need for accurate molecular diagnostics. This cross-sectional study evaluated the analytical performance of one mRNA assay, APTIMA^® HPV assay (APTIMA mRNA), and two DNA-based assays, the Abbott RealTime High Risk HPV assay (Abbott DNA) and Seegene Allplex™ II HPV28 assay (Seegene DNA), in 527 cervical samples from a South African tertiary hospital, focusing on 14 shared HR-HPV genotypes. Seegene DNA yielded the highest detection rate (53.7%), followed by Abbott DNA (48.2%) and APTIMA mRNA (45.2%). APTIMA mRNA showed a strong agreement with Abbott DNA (87.9%, κ = 0.80), 89.9% sensitivity, 91.2% NPV, and the highest accuracy (AUC = 0.8804 vs. 0.8681). The agreement between APTIMA mRNA and Seegene DNA was moderate (83.4%, κ = 0.70), reflecting target differences. Many DNA-positive/mRNA-negative cases likely represent transient infections, though some may be latent with reactivation potential, warranting a follow-up. In resource-constrained settings, prioritizing transcriptionally active infections through mRNA testing may enhance screening efficiency and reduce burden. Scalable, cost-effective assays with strong clinical utility are essential for broadening access and improving cervical cancer prevention. Further studies should assess the integration of mRNA testing into longitudinal screening algorithms. Full article

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article

The logistics industry faces challenges from evolving consumer expectations, technological advances, sustainability demands, and market disruptions. Logistics collaboration is in theory perceived as one of the most promising solutions to solve these issues, but here are still a lot of challenges that needs to be better understood and addressed. While vertical collaboration among supply chain actors is well advanced, horizontal collaboration among competing service providers remains under-explored. This study developed a novel methodology based on the COM-B behaviour-change framework to better understand the main challenges, opportunities, capabilities and drivers that would motivate competing companies to exploit the potential of horizontal logistics collaboration. A survey was designed and conducted among 71 logistics service providers in Slovenia, chosen for its fragmented market and low willingness to collaborate. Statistical analysis reveals cost reduction (M = 4.21/5) and improved vehicle utilization (M = 4.29/5) as the primary motivators. On the other hand, maintaining company reputation (M = 4.64/5), fair resource sharing (M = 4.20/5), and transparency of logistics processes (M = 4.17/5) all persist as key enabling conditions. These findings underscore the pivotal role of behavioural drivers and suggest strategies that combine economic incentives with targeted trust-building measures. Future research should employ experimental designs in diverse national contexts and integrate vertical–horizontal approaches to validate causal pathways and advance theory. Full article