Search Results (278)

3,4-dihydroxybenzenesulfonyl-functionalized polyethyleneimine (PS), a novel polymeric chelator, was synthesized by conjugating 3,4-dihydroxybenzenesulfonyl (CAM) groups with branched polyethyleneimine (BPEI, MW = 600 Da) via N-acylation. PS demonstrated a high uranium adsorption capacity of 78.08% at a concentration of 4 mg/mL, accompanied by significant selectivity over competing ions such as Ca²⁺, Zn²⁺, and Cu²⁺. Notably, in competitive adsorption experiments, PS exhibited a uranium adsorption rate of 59.49%, which was 3.95 times higher than that of calcium (15.06%) in the Ca²⁺ system. Cytotoxicity assays revealed enhanced biocompatibility (IC₅₀ = 86.98 μg/mL), surpassing CaNa₃-DTPA 3.7-fold. In a uranium exposure model (200 μg/mL), PS significantly improved cell survival rates and reduced intracellular uranium levels by 77.37% (immediate administration) and 64.18% (delayed administration). These findings establish PS as a potent and safe polymeric chelator for uranium decorporation, offering a promising strategy for mitigating the hazards of radioactive materials. Full article

Products with complex structures are structurally intricate and involve multiple professional fields and engineering construction elements, making it difficult for a single contractor to independently develop and manufacture such complex structural products. Therefore, during the research, development, and production of complex products, collaboration between manufacturers and suppliers is essential to ensure the smooth completion of projects. In this process, a complex supply chain network is often formed to achieve collaborative cooperation among all project participants. Within such a complex supply chain network, issues such as delayed delivery, poor product quality, or low resource utilization by any participant may trigger the bullwhip effect. This, in turn, can negatively impact the delivery cycle, product cost, and quality of the entire complex product, causing it to lose favorable competitive positions such as quality advantages and delivery advantages in fierce market competition. Therefore, this paper firstly explores the mechanism of complex product manufacturing and the supply network of complex product manufacturing, in order to grasp the inherent structure of complex product manufacturing with a focus on identifying symmetrical properties among supply chain nodes. Secondly, a complex product supply chain network model is constructed with the Graphical Evaluation and Review Technique (GERT), incorporating symmetry constraints to reflect balanced resource allocation and mutual dependencies among symmetrical nodes. Then, from the perspective of supply chain, we focus on identifying the shortcomings of supply chain suppliers and optimizing the management cost of the whole supply chain in order to improve the quality of complex products, delivery level, and cost saving level. This study constructs a Restricted Grey GERT (RG-GERT) network model with constrained outputs, integrates moment-generating functions and Mason’s Formula to derive transfer functions, and employs a hybrid algorithm (genetic algorithm combined with non-linear programming) to solve the multi-objective optimization problem (MOOP) for joint optimization of delivery time, quality, and cost. Empirical analysis is conducted using simulated data from Y Company’s aerospace equipment supply chain, covering interval parameters such as delivery time [5–30 days], cost [40,000–640,000 CNY], and quality [0.85–1.0], validated with industry-specific constraints. Empirical analysis using Y Company’s aerospace supply chain data shows that the model achieves a maximum customer satisfaction of 0.96, with resource utilization efficiency of inefficient suppliers improved by 15–20% (p < 0.05) after secondary optimization. Key contributions include (1) integrating symmetry analysis to simplify network modeling; (2) extending GERT with grey parameters for non-probabilistic uncertainty; (3) developing a two-stage optimization framework linking customer satisfaction and resource efficiency. Full article

This paper presents the multi-triangular ring and self-routing protocol (MTRSRP), which is a new decentralized strategy designed to boost throughput and network efficiency in multiring scatternets. MTRSRP comprises two primary phases: leader election and scatternet formation, which collaborate to establish an effective multi-triangular ring topology. In the leader election phase, nodes exchange broadcast messages to gather neighbor information and elect coordinators through a competitive process. The scatternet formation phase determines the optimal number of rings based on the coordinator’s collected node information and predefined rules. The master nodes then send unicast connection requests to establish piconets within the scatternet, following a predefined role table. Intra- and inter-bridge nodes were activated to interconnect the piconets, creating a cohesive multi-triangular ring scatternet. Additionally, MTRSRP incorporates a self-routing addressing scheme within the triangular ring architecture, optimizing packet transmission paths and reducing overhead by utilizing master/slave relationships established during scatternet formation. Simulation results indicate that MTRSRP with dual-bridge connectivity outperforms the cluster-based on-demand routing protocol and Bluetooth low-energy mesh schemes in key network transmission performance metrics such as the transmission rate, packet delay, and delivery ratio. In summary, MTRSRP significantly enhances throughput, optimizes routing paths, and improves network efficiency in multi-ring scatternets through its multi-triangular ring topology and self-routing capabilities. Full article

False cleavers (Galium spurium L.) is a broadleaf weed species that affects wheat productivity because of its strong competition for resources. It has developed resistance to acetolactate synthase (ALS) inhibitors, such as sulfonylureas and triazolopyrimidines, which are herbicides widely used in durum wheat. Integrated weed management programs can contribute to the control of this species and delay the evolution of herbicide resistance. Thus, a two-year field experiment was conducted to evaluate the effects of sowing time, variety, and herbicides on crop yield, density, and dry weight of a false cleavers population with resistance to ALS inhibitors. In both growing seasons, a split-split-plot design was used with three replicates. The sowing date was chosen as the main plot factor, durum wheat varieties as the subplot factor, and herbicides as the sub-subplot factor. The herbicide treatments were: (1) metsulfuron-methyl/bensulfuron-methyl (4/50 g a.i. ha⁻¹), (2) aminopyralid/florasulam (9.9/4.95 g a.i. ha⁻¹), (3) pyroxsulam and florasulam/2,4-D (18.75 + 4.725/225 g a.i. ha⁻¹), (4) 2,4-D/bromoxynil (633.15/601.2 g a.i. ha⁻¹), non-treated control, and hand-weeded control for the first season, while in the second season one more herbicide treatment (halauxifen-methyl/florasulam, 5.6/5.15 g a.i. ha⁻¹) was added. Herbicide application was performed on 10 March 2021 and 28 March 2022, when the crop was at the end of tillering and the beginning of stem elongation. The results showed that the density of false cleavers was not affected by the variety or sowing time. However, its dry weight was 17.3–23.4% higher in early sowing (16 November in 2020 and 8 November 2021) than in late sowing (24 December 2020 and 2 December 2021). Among the herbicides tested, 2,4-D/bromoxynil and halauxifen-methyl/florasulam effectively controlled false cleavers, showing greater efficacy in late sowing (>88%), which ultimately led to a higher yield. In conclusion, our two-year findings demonstrate that delayed sowing as part of an integrated weed management strategy can contribute to controlling resistant populations of false cleavers to ALS-inhibiting herbicides without affecting the quantity and quality of durum wheat yield in areas with a Mediterranean climate. Full article

This study aimed to analyse the spatiotemporal evolution of phytoplankton community dynamics and its underlying mechanisms in the Liaoning section of the Liao River Basin in 2010, 2015, and 2020. Phytoplankton species diversity increased significantly, with an increase from three phyla and 31 species in 2010 to six phyla and 74 species in 2020. Concurrent increases in α-diversity indicated continuous improvements in habitat heterogeneity. The community structure shifted from a diatom-dominated assemblage to a green algae–diatom co-dominated configuration, contributing to an enhanced water purification capacity. The upstream agricultural zone (Tieling section) had elevated biomass and low diversity, indicating persistent non-point-source pollution stress. The midstream urban–industrial zone (Shenyang–Anshan section) emerged as a phytoplankton diversity hotspot, likely due to expanding niche availability in response to point-source pollution control. The downstream wetland zone (Panjin section) exhibited significant biomass decline and delayed diversity recovery, shaped by the dual pressures of resource competition and habitat filtering. The driving mechanism of community succession shifted from nutrient-dominated factors (NH₃-N, TN) to redox-sensitive factors (DO, pH). These findings support a ‘zoned–graded–staged’ ecological restoration strategy for the Liao River Basin and inform the use of phytoplankton as bioindicators in watershed monitoring networks. Full article

Regulating lamprey populations is crucial for maintaining ecological equilibrium. However, the unique sex determination process of lampreys is constrained by multiple factors, complicating intuitive analysis of population dynamics and their impact on the natural environment. This study employed a two-species competition mechanism to elucidate the factors influencing sex ratios and their mechanistic effects on lamprey population size. Using the Lotka–Volterra equations, we investigated how sex ratios affect trophic levels both upstream and downstream of lampreys in the food web. A logistic population growth model was applied to assess the impact of sex ratio variations on symbiotic parasitic species, while the Analytic Hierarchy Process (AHP) was utilized to explore the dynamic relationship between sex ratio changes and ecosystem stability. To validate model efficacy, we manipulated temperature and food availability under controlled disturbance conditions, analyzing temporal variations in lamprey population size across different disturbance intensities to evaluate model sensitivity. The findings indicate that the variable sex ratio’s benefit is in facilitating the lampreys’ population’s enhanced adaptation to environmental shifts. The coexisting species exhibit a similar pattern of population alteration as the lampreys, albeit with a minor delay. A definitive link between the quantity of lampreys and the parasitic species is absent. A male ratio of 0.6 optimally contributes to the ecosystem’s equilibrium. Over time, the configuration of our model’s parameters proves to be sensible. This research provides robust theoretical support for developing scientific strategies to regulate lamprey populations. Full article

Parkinson’s disease is one of the neurodegenerative conditions that has seen a significant increase in prevalence in recent decades. The lack of specific screening tests and notable disease biomarkers, combined with the strain on healthcare systems, leads to delayed detection of the disease, which worsens its progression. The development of diagnostic support tools can support early detection and facilitate timely intervention. The ability of Deep Learning algorithms to identify complex features from clinical data has proven to be a promising approach in various medical domains as support tools. In this study, we present an investigation of different architectures based on Gated Recurrent Neural Networks to assess their effectiveness in identifying subjects with Parkinson’s disease from gait records. Models with Long-Short term Memory (LSTM) and Gated Recurrent Unit (GRU) layers were evaluated. Performance results reach competitive effectiveness values with the current state-of-the-art accuracy (up to 93.75% (average ± SD: 86 ± 5%)), simplifying computational complexity, which represents an advance in the implementation of executable screening and diagnostic support tools in systems with few computational resources in wearable devices. Full article

The Architecture, Engineering, and Construction (AEC) industry is plagued by persistent challenges such as low productivity, cost overruns, and frequent project delays. Integrated Project Delivery (IPD) has emerged as a potential solution, offering collaborative approaches to improve project outcomes. This study proposes a two-tiered methodology for evaluating the disruptiveness of innovations in the AEC industry, with a particular focus on IPD as a disruptive innovation. In the first tier, a multidimensional framework is developed to systematically assess the disruptiveness of innovations in the AEC sector. This framework, informed by a thorough literature review and disruptive innovation theory, includes dimensions such as business models, processes, and anticipated outcomes. The second tier applies the framework by analyzing the disruptiveness of IPD. The assessment draws on data from three comprehensive studies, including ethnographic research, interviews, and focus groups, which examine IPD’s impact on different stakeholder groups such as clients, consultants, and contractors. Findings reveal that IPD has the potential to significantly disrupt traditional business models, processes, and project outcomes, particularly at the project level. Notable disruptive characteristics include shifts in collaboration dynamics, redefined project financing models, and improved efficiency. However, several barriers hinder IPD adoption, including resistance to change and misalignment with conventional contractual structures. Expert interviews support these results, indicating that IPD represents a fundamental shift in the AEC industry. This research contributes to the existing body of knowledge by offering a structured framework for assessing the disruptiveness of AEC innovations and demonstrating its practical application. In this way, AEC organizations, projects, and practitioners can better strategize for the adoption of any new disruptive innovation and thus pursue a strategic advantage in the highly competitive industry market. Full article

This research work seeks to introduce eco-friendly, low-carbon, and high-octane biofuel gasoline production using a synergistic approach. Four types of high-octane gasoline, including SynergyFuel-92, SynergyFuel-95, SynergyFuel-98, and SynergyFuel-100, were generated, emphasizing the deliberate combination of petroleum-derived gasoline fractions using reformate, isomerate, and delayed coking (DC) naphtha with octane-boosting compounds—bio-methanol and bio-ethanol. A set of tests have been performed to examine the effects of antiknock properties, density, oxidation stability, distillation range characteristics, hydrocarbon composition, vapor pressure, and the volatility index on gasoline blends. The experimental results indicated that the gasoline blends made from biofuel (SynergyFuel-92, -95, -98, and 100) showed adherence to important fuel quality criteria in the USA, Europe, and China. These blends had good characteristics, such as low quantities of benzene and sulfur, regulated levels of olefins and aromatics, and good distillation qualities. By fulfilling these strict regulations, Synergy Fuel is positioned as a competitive and eco-friendly substitute for traditional gasoline. The results reported that SynergyFuel-100 demonstrated the strongest hot-fuel-handling qualities and resistance to vapor lock among all the mentioned Synergy Fuels. Finally, the emergence of eco-friendly, low-carbon, and high-octane biofuel gasoline production with synergistic benefits is a big step in the direction of sustainable transportation. Full article

Youth sport participation provides undeniable physical, emotional, and social benefits. However, the current landscape of pediatric athletics has shifted toward early sports specialization (ESS), year-round training, and heightened competitive pressures. This has led to an increased prevalence of overuse-related traumatic injuries in adolescent patients, as well as increased risk of worsening mental health due to burnout, depression, suicide, and general psychological distress. There are numerous innovations and solutions aimed at addressing the increased risk of injury associated with current sporting trends, such as neuromuscular training programs, delayed specialization, promotion of free play, and pediatric specific surgical techniques mindful of future growth, such as those seen for anterior cruciate ligament reconstruction (ACL-R). However, the social factors associated with an injury remain problematic and are not adequately addressed; these include social isolation, depression, anxiety, and academic decline. Sport psychology is a promising solution to address many risk factors associated with poor performance, address the challenges associated with injury, and increase return-to-play in adolescent sports medicine. Integrating sport psychology into pediatric sports medicine offers the ability to directly address the emotional and cognitive demands of injury and recovery. Emphasizing mental health support and redefining success in youth sports—prioritizing enjoyment, personal growth, and long-term health over scholarships and professional aspirations—are key steps in preserving the overall benefits of pediatric sport participation. Yet sport psychology remains often underutilized and has been slow to gain traction, particularly in youth sports. This editorial serves to highlight the current state of mental health advocacy in pediatric sports medicine and how sport psychology can help young athletes manage the mental stress of high-performance athletics and mitigate the detrimental effect of injury and delayed return to sport. Full article

With the advent of Big Data, data mining techniques have become crucial for improving decision-making across diverse sectors, yet their employment demands significant resources and time. Time is critical in industrial contexts, as delays can lead to increased costs, missed opportunities, and reduced competitive advantage. To address this, systems for analyzing data can help prototype data mining pipelines, mitigating the risks of failure and resource wastage, especially when experimenting with novel techniques. Moreover, business experts often lack deep technical expertise and need robust support to validate their pipeline designs quickly. This paper presents Rainfall, a novel framework for rapidly prototyping data mining pipelines, developed through collaborative projects with industry. The framework’s requirements stem from a combination of literature review findings, iterative industry engagement, and analysis of existing tools. Rainfall enables the visual programming, execution, monitoring, and management of data mining pipelines, lowering the barrier for non-technical users. Pipelines are composed of configurable nodes that encapsulate functionalities from popular libraries or custom user-defined code, fostering experimentation. The framework is evaluated through a case study and SWOT analysis with INGKA, a large-scale industry partner, alongside usability testing with real users and validation against scenarios from the literature. The paper then underscores the value of industry–academia collaboration in bridging theoretical innovation with practical application. Full article

This paper analyses Poland’s participation in implementing European climate policy within the framework of the National Energy and Climate Plan (NECP), looking toward 2040. It assesses the feasibility of Poland’s commitments to the European Union’s decarbonisation targets, particularly with regard to transitioning from fossil fuels to renewable energy sources and nuclear power. The study highlights the challenges related to the speed of the energy transition, the security of electricity supply, and the competitiveness of the national economy. The study also assesses the energy mix scenarios proposed in the NECP, taking into account historical energy consumption data, economic and demographic projections, and expert analyses of energy security. It also critically examines the risks of delayed investment in nuclear and offshore wind, the potential shortfall in renewable energy infrastructure, and the need for transitional solutions, including coal and gas generation. An alternative scenario is proposed to mitigate potential energy supply shortfalls between 2035 and 2040, highlighting the role of energy storage, strategic reserves, and the maintenance of certain fossil fuel capacities. Poland’s energy policy should prioritize flexibility and synchronization with EU objectives, while ensuring economic stability and technological feasibility. The analysis underlines that the sustainable development of the national energy system requires not only alignment with European climate goals, but also a long-term balance between environmental responsibility, energy affordability, and security. Strengthening the sustainability dimension in energy policy decisions—by integrating resilience, renewability, and social acceptance—is essential to ensure a just and enduring energy transition. Full article

Pollen allergy has emerged as a critical global health challenge. Proactive pollen monitoring is imperative for safeguarding susceptible populations through timely preventive interventions. Current manual detection methods suffer from inherent limitations: notably, suboptimal accuracy and delayed response times, which hinder effective allergy management. Therefore, we present an automated pollen concentration detection system integrated with a novel GGD-YOLOv8n model (Ghost-generalized-FPN-DualConv-YOLOv8), which was specifically designed for allergenic pollen species identification. The methodological advancements comprise three components: (1) combining the C2f convolution in Backbone with the G-Ghost module, this module generates features through half-convolution operations and half-symmetric linear operations, enhancing the extraction and expression capabilities of detailed feature information. (2) The conventional neck network is replaced with a GFPN architecture, facilitating cross-scale feature aggregation and refinement. (3) Standard convolutional layers are substituted with DualConv, thereby reducing model complexity by 22.6% (parameters) and 22% GFLOPs (computational load) while maintaining competitive detection accuracy. This systematic optimization enables efficient deployment on edge computing platforms with stringent resource constraints. The experimental validation substantiates that the proposed methodology outperforms the baseline YOLOv8n model, attaining a 5.4% increase in classification accuracy accompanied by a 4.7% enhancement in mAP@50 metrics. When implemented on Jetson Nano embedded platforms, the system demonstrates computational efficiency with an inference latency of 364.9 ms per image frame, equating to a 22.5% reduction in processing time compared to conventional implementations. The empirical results conclusively validate the dual superiority in detecting precision and operational efficacy when executing microscopic pollen image analysis on resource-constrained edge computing devices; they establish a feasible algorithm framework for automated pollen concentration monitoring systems. Full article

This paper proposes a distributed cloud–edge collaborative scheduling method to address the oversight of network transmission delay in traditional task scheduling, a critical factor that frequently leads to degraded execution efficiency. A holistic framework is introduced that dynamically models transmission delays, designs a decentralized scheduling algorithm, and optimizes resource competition through a two-dimensional matching mechanism. The framework integrates real-time network status monitoring to adjust task allocation, enabling edge nodes to independently optimize local queues and avoid single-point failures. A delay-aware scheduling algorithm is developed to balance task computing requirements and network latency, transforming three-dimensional resource matching into a two-dimensional problem to resolve conflicts in shared resource allocation. Simulation results verify that the method significantly reduces task execution time and queue backlogs compared with benchmark algorithms, demonstrating improved adaptability in dynamic network environments. This study offers a novel approach to enhancing resource utilization and system efficiency in distributed cloud–edge systems. Full article

Background/Objectives: Pressure to stay lean may lead adolescent athletes to dietary restraint and disordered eating. Lack of nutrition awareness can also contribute to suboptimal dietary habits, increasing the risk of eating disorders and Relative Energy Deficiency in Sport [RED-S], though evidence in competitive athletes is limited. This study explored eating disorder symptoms and RED-S knowledge in adolescent artistic gymnasts. Methods: Eighty-four female artistic gymnasts, thirty-nine international and national level gymnasts [high-level; 14 [14, 15] y] and forty-five recreational and club level gymnasts [low-level; 14 [13, 15] y] completed the Eating Disorder Examination Questionnaire [EDE-Q 6.0], the RED-S knowledge Questionnaire and provided training details. Results: Seventeen gymnasts (20.2%) scored above the cutoff point on the EDE-Q. In addition, high-level gymnasts scored higher than low-level on EDE-Q [2.21 ± 1.37 (35.9%) vs. 1.19 ± 0.79 (6.7%), respectively; p < 0.001] and on its subscales: Restraint, Eating Concerns, Weight Concerns, and Shape Concerns [p < 0.001 to 0.009], thus indicating more severe disordered eating symptoms. No group differences were found in binge eating and compensatory behaviors. An important percentage of gymnasts reported at least one episode of binge eating and excessive training [39.3–58.3%], while four gymnasts reported self-induced vomiting. RED-S knowledge did not differ between groups. On average, gymnasts were unaware of correct answers related to RED-S [51.5%], its definitions [79.8–92.9%], and its association with menstrual disturbances and bone health [54.8–86.9%]. However, gymnasts reported better awareness of the impact of food restriction on illness and performance [47.6–84.5%]. Conclusions: Elite artistic gymnasts exhibited a higher prevalence of eating disorder symptoms than lower-level peers. Gymnasts at all levels demonstrated limited knowledge of the effects of RED-S on menstrual and bone health. Failure to recognize these risks may influence gymnasts’ eating behaviors and delay RED-S detection and management. Full article