Search Results (11,377)

The conventional OLSR protocol faces substantial challenges in highly dynamic and interference-intensive UAV environments, including high mobility, frequent topology changes, and insufficient adaptability to electromagnetic interference. This paper proposes a cross-layer improved OLSR protocol, OLSR-LCN, that integrates three evaluation metrics—link lifetime (LL), channel interference index (CII), and node load (NL)—to enhance communication stability and network performance. The proposed protocol extends the OLSR control message structure and employs enhanced MPR selection and routing path computation algorithms. LL prediction enables proactive selection of stable communication paths, while the CII helps avoid heavily interfered nodes during MPR selection. Additionally, the NL metric facilitates load balancing and prevents premature node failure due to resource exhaustion. Simulation results demonstrate that across different UAV flight speeds and network scales, OLSR-LCN protocol consistently outperforms both the OLSR and the position-based OLSR in terms of end-to-end delay, packet loss rate, and network efficiency. The cross-layer optimization approach effectively addresses frequent link disruptions, interference, and load imbalance in dynamic environments, providing a robust solution for reliable communication in complex FANETs. Full article

High alkalinity facilitates copper–sulfur flotation separation but also leads to issues such as high reagent consumption, pipeline scaling, and gold loss in tailings. The ore from a copper mine in Serbia contains 2.86% copper, 1.64 g/t gold, and 20.39% sulfur, with copper occurring mainly in covellite and enargite. To achieve efficient separation and recovery of copper–sulfur, a systematic study was conducted using micro-flotation, Scanning Electron Microscopy (SEM), X-ray photoelectron spectroscopy (XPS) and contact angle analysis to investigate the inhibition and activation patterns of pyrite under low and high alkalinity conditions. The results indicate that the combined use of hydrogen peroxide and lime as inhibitor enables efficient separation of pyrite and covellite under low-alkalinity conditions. This effect is attributed to its ability to enhance oxidation of the pyrite surface, which generates more hydrophilic substances. Under low-alkalinity conditions (slurry pH = 10) regulated with hydrogen peroxide and lime in a covellite flotation cycle, and under acidic conditions (slurry pH = 6) in the pyrite flotation cycle, satisfactory results are obtained in both flotation cycles in comparison with industrial data. The copper flotation index was similar, but pyrite and gold recovery increased by 2.3% and ~4%, respectively, over those using lime alone. This process reduced the activator dosage required for pyrite activation substantially, while improving gold recovery. Results demonstrate an efficient method for copper–sulfur separation and recovery, providing theoretical guidance or industrial production processes. Full article

Entangled photons are essential for photonic quantum technologies. Their generation typically relies on spontaneous parametric down-conversion, but conventional nonlinear crystals are bulky and hard to integrate on chips. Rhombohedral-stacked MoS${}_2$ combines a high refractive index, large second-order nonlinearity, and flexibility for heterogeneous integration, making it a promising platform for integrated quantum photonics. However, the typical thin-film form of 3R-MoS${}_2$ restricts the effective nonlinear interaction length, limiting entanglement generation efficiency in practical devices. To overcome this, phase-matching strategies in integrated waveguides are required but have so far remained undeveloped. Here, we introduce a waveguide-integrated 3R-MoS${}_2$ platform with periodic grooves to achieve quasi-phase matching, enhancing down-conversion efficiency. Leveraging ${\chi }^{\left(2\right)}$ tensor symmetries and orthogonal waveguide modes, the design efficiently generates entangled photons, providing a compact, scalable route toward 2D-material-based integrated quantum photonic circuits. Full article

With regard to low-voltage (LV) distribution networks, the quality of distributed electricity can be compromised by the level of phase load imbalance. Consequently, numerous phase load balancing (PLB) algorithms have been proposed in the specialized literature. However, those models have been focused on the quality of the solution obtained rather than performance, which leads to reduced practical applicability for the distribution network (reduced scalability, slow convergence, and a higher computational cost). Furthermore, certain constraints regarding the electrical network and the switching operations of consumers must be integrated into the mathematical model. In this context, the proposed PLB algorithm represents an improved constrained greedy optimization (ICGO), capable of achieving fast convergence even on large datasets, with a lower computational cost. Three scenarios (30, 250, and 500 consumers), each with 20 distinct initial non-symmetries, were simulated. The results support the practical effectiveness and scalability of the ICGO: an absolute value of the neutral current below 0.63 A (99.53% relatively reduction), a current unbalance index below 0.1%, a small number of iterations (between 4 and 11 iterations), and an execution time between 0.00051 and 0.01149 s). Therefore, this research proposes an efficient PLB algorithm, with the possibility for its improvement in future work. Full article

Convolutional Neural Networks (CNNs) have excellent performance in various fields, such as machine learning, computer vision, and image recognition. With the development of CNNs, huge quantities of data in computing and transmission have placed significant pressure on circuit and architecture design, and RRAM-based computing-in-memory (CIM) is one of the promising solutions to alleviate this problem. However, because of the current deviation phenomenon and the resistance on/off ratio (R ratio) issue in RRAM, there is a trade-off problem between computational accuracy and computational efficiency for CIM. In this paper, we propose a layer-wise activated word-line (AWL) strategy to configure the appropriate number of AWLs for each layer. Based on the observed risk factors, we design a risk index to AWL mapping methodology. Meanwhile, based on the proposed quantization and current deviation error calculation methods, we design a CIM simulation framework to simulate the accuracy of CNNs in the inference stage. We evaluate our methodology on Cifar-10, VGG-8, and ResNet-18. The proposed methodology improves computational efficiency with only slight accuracy loss. In comparison with a fixed-AWL configuration, our methodology has better accuracy with a small resistance on/off ratio. For higher resistance on/off ratios, our methodology gets a significant improvement in computational efficiency in comparison with the baseline. On the exploration of different R ratios, experimental results show that our layer-wise AWL configuration methodology has a more flexible planning space and better computational efficiency. Full article

Automated valet parking (AVP) is a key component of autonomous driving systems. Its functionality and reliability need to be thoroughly tested before road application. Current testing technologies are limited by insufficient scenario coverage and lack of comprehensive evaluation indices. This study proposes an AVP testing and evaluation framework using OnSite (Open Naturalistic Simulation and Testing Environment) and Unity3D platforms. Through scenario construction based on field-collected data and model reconstruction, a testing scenario library is established, complying with industry standards. A simplified kinematic model, balancing simulation accuracy and operational efficiency, is applied to describe vehicle motion. A multidimensional evaluation system is developed with completion rate as a primary index and operation performance as a secondary index, which considers both parking efficiency and accuracy. Over 500 AVP algorithms are tested on the OnSite platform, and the testing results are evaluated through the Unity3D platform. The performance of the top 10 algorithms is analyzed. The evaluation platform is compared with CARLA simulation platform and field vehicle testing. This study finds that the framework provides an effective tool for AVP testing and evaluation; a variety of high-level AVP algorithms are developed, but their flexibility in complex dynamic scenarios has limitations. Future research should focus on exploring more sophisticated learning-based algorithms to enhance AVP adaptability and performance in complex dynamic environment. Full article

Investment in equity assets is characterized by high volatility, both in prices and returns, which poses a constant challenge for the efficient management of risk and profitability. In this context, investors continuously seek innovative strategies that enable them to maximize their returns within acceptable risk levels, in accordance with their investment profile. The purpose of this research is to develop a model with a high predictive capacity for equity asset returns through the application of artificial intelligence techniques that integrate genetic algorithms and neural networks. The methodology is framed within a technical analysis-based investment approach, using the Relative Strength Index as the main indicator. The results show that more than 58% of the predictions generated with the proposed methodology outperformed the results obtained through the traditional technical analysis approach. These findings suggest that the incorporation of genetic algorithms and neural networks constitutes an effective alternative for optimizing investment strategies in equity assets, by providing superior returns and more accurate predictions in most of the analyzed cases. Full article

Energy efficiency presents significant potential, especially for Western Balkan (WB) countries, if effectively addressed through energy efficiency measures. The building sector, which includes residential, commercial, and public buildings, is the most energy-intensive sector globally. Public buildings in the Western Balkan countries are characterized by poor energy efficiency performance. The average energy consumption in public buildings is anticipated to exceed double the European Union (EU) requirement, given that more than 60-70% of these structures were built over 60 years ago with no regard for energy efficiency. This study assesses the Public Building–Energy Efficiency Readiness Index (PB-EERI) to evaluate how legislative specificity, institutional capacity, financing mechanisms, renovation guidelines, energy market conditions, and societal awareness collectively influence the readiness of Western Balkan economies to enhance energy efficiency in public buildings. The index serves as an operational diagnostic to identify the presence of enabling conditions, determine the most significant gaps, and prioritize policy efforts accordingly. This study presents a novel approach by integrating, within a single transparent index, (i) the existence of energy laws, (ii) market feasibility, (iii) renovation needs of public buildings, and (iv) societal awareness. The awareness pillar is both central and novel. By utilizing harmonized Regional Cooperation Council (RCC) data, this article quantifies societal awareness, thereby ensuring that the index accurately reflects the importance of stakeholder comprehension in the success of renovating initiatives for public buildings. The theoretical framework derives from the application of composite indicators in numerous studies and reports to illustrate the status of energy or energy efficiency. The methodology for developing this indicator is derived from the Organization for Economic Co-operation and Development (OECD) Handbook on Constructing Composite Indicators. For the aggregation method, the summation of weighted and normalized sub-indicators was used. The PB-EERI reveals considerable regional variations, with total scores ranging from around 39 to 72% and concentrating around the mid-0.5s. The findings reveal systematic differences in most indicators’ performance. The legal framework indicator significantly influences variation between countries, together with market conditions and societal awareness. Energy efficiency in public buildings, praised as the “first fuel”, should be prioritized beyond mere compliance with EU regulations. The PB-EERI emphasizes that success relies more on the capacity to transform formal strategies into concrete renovation programs, quantifiable objectives, and higher awareness of society to ensure uptake of the renovation measures. Full article

This study presents a comprehensive review of the CLH index, a predictive tool developed to estimate the wear of tunnel boring machine (TBM) disc cutters operating in hard rock conditions. The CLH index provides a simplified, time-efficient, and cost-effective alternative to conventional wear prediction methods by employing a statistically derived empirical formula. The methodology is based on the identification and quantitative assessment of key rock properties that influence cutter wear. A detailed statistical analysis was conducted to validate the index, quantify potential errors, and determine confidence levels. As part of this review, updated reference tables are proposed to facilitate cutter wear estimation without the need for preliminary laboratory testing. These tables are derived from empirical data obtained at the Rock Mechanics Laboratory of the Higher Technical School of Mining and Energy Engineers (ETSIME-UPM), using operational records from TBM excavation in multiple Spanish high-speed railway tunnels, with a total length exceeding 120 km. The results confirm the reliability and practical applicability of the CLH index as a decision-support tool in TBM performance forecasting and maintenance planning. Full article

Background: Epigallocatechin-3-gallate (EGCG), a potent green tea polyphenol, possesses significant therapeutic potential, but its clinical application is limited by poor gastrointestinal stability and low oral bioavailability. To address this, a novel herbal nanomedicine-based delivery system was developed utilizing D-α-tocopheryl polyethylene glycol succinate (TPGS) and Poloxamer 407. Objectives: This study aims to develop and characterize EGCG-loaded TPGS/Poloxamer 407 micelles, evaluating their physicochemical properties, storage stability, in vitro drug release profile, in vivo oral bioavailability, and preliminary tolerability observation. Methods: The micelles were prepared using the film hydration method followed by lyophilization. Results: The optimized 2:2 TPGS-to-poloxamer 407 weight ratio yielded EGCG-loaded micelles, displaying a mean particle size of 15.4 nm, a polydispersity index (PDI) of 0.16, a zeta potential of −17.7 mV, an encapsulation efficiency of 82.7%, and a drug loading capacity of 7.6%. The critical micelle concentration (CMC) was determined to be 0.00125% w/v. Transmission electron microscopy (TEM) confirmed the micelles’ uniform spherical morphology. In vitro release studies demonstrated a sustained release profile in both simulated gastric and intestinal fluids. EGCG formulation remained stable for at least six months when stored at 4 °C. No adverse clinical signs were noted during the 28-day tolerability observation. In vivo pharmacokinetic evaluation in mice revealed a significant elevation in oral bioavailability, achieving a 2.27-fold increase in area under the curve (AUC) and a 1.8-fold increase in peak plasma concentration (Cmax) compared to free EGCG. Conclusions: Collectively, these findings underscore the potential of the TPGS/poloxamer 407-based micelle system as a promising oral delivery platform for EGCG, enhancing its stability and pharmacokinetic performance. Full article

Paclobutrazol (PBZ) is extensively used to modulate plant architecture in rice. However, its comprehensive effects on grain yield and aroma in aromatic rice have not been thoroughly investigated. This study used the local aromatic rice cultivars (Meixiangzhan 2 and Xiangyaxiangzhan) as experimental materials to evaluate the impacts of foliar-applied PBZ at three concentrations (0 (CK), 150 (T1), and 300 (T2) mg L⁻¹) on grain yield, photosynthetic characteristics, fragrance formation, and radiation use efficiency (RUE). Field experiments revealed that T1 significantly reduced the leaf area index (LAI) by 10.12% and intercepted photosynthetically active radiation (IPAR) by 10.74%, meanwhile significantly increasing SPAD values by 12.94% and net photosynthetic rate (Pn) by 9.95%, leading to improved RUE up to 25.21%. These changes contributed to a larger number of grains per panicle and increased 1000-grain weight, ultimately enhancing grain yield. In contrast, T2 resulted in a sharp reduction by 24.84% in IPAR and a significant decline in Pn by 10.07% during the late grain-filling stage, thus limiting the supply of photosynthetic assimilates, eventually reducing grain yield. PBZ application also significantly elevated 2-acetyl-1-pyrroline (2-AP) content by 28.74% under T1 and 17.51% under T2, compared to the control. The increase in 2-AP was mainly associated with elevated levels of key precursors, including proline, Δ1-pyrroline-5-carboxylic acid, and Δ1-pyrroline. In spite of differences in traits between cultivars, the traits responded to PBZ in the same pattern. These results indicate that foliar application of PBZ at 150 mg L⁻¹ can effectively improve both yield and aroma of aromatic rice, offering a promising cultivation strategy for high-quality aromatic rice production. Full article

Digital finance, representing the deep integration of finance and technology, has become a critical enabler of sustainable industrial transformation. Focusing on resource-based enterprises (RBEs)—key actors in transitioning towards sustainable practices—this study investigates how digital finance development fosters new quality productive forces (NQPFs), a core driver of high-quality, sustainable development. Utilizing panel data from Chinese A-share listed RBEs (2008–2022), we measure NQPF using the entropy method and gauge regional digital finance development with the Peking University Digital Financial Inclusion Index (DFII). Empirical analysis employing two-way fixed effects and panel threshold regression models provides robust evidence that digital finance significantly enhances NQPFs within RBEs. Crucially, mechanism analysis identifies three fundamental pathways underpinning sustainability: (1) mitigating financial constraints; (2) facilitating technological innovation and transformation; (3) strengthening green transition awareness. Furthermore, the impact of digital finance exhibits synergistic enhancement alongside increasing environmental regulation intensity and improved financial resource allocation efficiency. Heterogeneity analysis reveals that the effect is more pronounced in regions with lower marketization, within state-owned enterprises, and among RBEs in recession stages. Collectively, these findings offer significant implications for policymakers and industry practitioners aiming to strategically leverage digital finance to accelerate the sustainable transformation of resource-intensive industries, thereby contributing directly to environmentally sustainable and resilient economic development. Full article

Neurorehabilitation has become increasingly data-enabled, yet the conditions that most strongly modulate recovery, sleep consolidation, circadian alignment, medication ecology, and social–environmental context are rarely captured or acted upon. This opinion paper argues that an exposome perspective, defined as the cumulative pattern of external and internal exposures and their biological imprints across the life course, is not ancillary to rehabilitation but foundational to making therapy learnable, timely, and equitable. We propose a pragmatic model that centers on a minimal exposure dataset collected in minutes and interpreted at the point of care. Two clinical exemplars illustrate feasibility and utility. First, sleep and circadian rhythms: brief actigraphy and standardized reporting can make daily alertness windows visible, allowing teams to align high-intensity sessions to receptive states and to justify environmental adjustments as clinical interventions. Second, anticholinergic burden: a simple, trackable index can be integrated with functional goals to guide deprescribing and optimize cognitive availability for training. Implementation hinges less on new infrastructure than on workflow design: a short intake that surfaces high-yield exposures; embedding targets, e.g., sleep efficiency thresholds or anticholinergic load reductions, into plans of care; enabling secure import of device data; and training staff to interpret rhythm metrics and burden scores. We outline a parallel research agenda comprising pragmatic trials of bundled, exposure-informed care; longitudinal cohorts with time-stamped exposure streams; and causal methods suited to time-varying confounding, all under explicit equity and ethics safeguards. By measuring a few modifiable exposures and linking them to routine decisions, neurorehabilitation can convert context from a source of unexplained variance into actionable levers that improve outcomes and narrow unjust gaps in recovery. Full article

This study systematically investigates settlement sites that record living patterns of ancient humans, aiming to reveal the interactive mechanisms of human–environment relationships. The core issues of landscape archeology research are the surface spatial structure, human spatial cognition, and social practice activities. This article takes the Han Dynasty settlement site in Sanyangzhuang, Neihuang County, Anyang City, Henan Province, as a typical case. It comprehensively uses ArcGIS 10.8 spatial analysis and remote sensing image interpretation techniques to construct spatial distribution models of elevation, slope, and aspect in the study area, and analyzes the process of the Yellow River’s ancient course changes. A regional historical geographic information system was constructed by integrating multiple data sources, including archeological excavation reports, excavated artifacts, and historical documents. At the same time, the sequences of temperature and dry–wet index changes in the study area during the Qin and Han dynasties were quantitatively reconstructed, and a climate evolution map for this period was created based on ancient climate proxy indicators. Drawing on three dimensions of settlement morphology, architectural spatial organization, and agricultural technology systems, this paper provides a deep analysis of the site’s spatial cognitive logic and the ecological wisdom it embodies. The results show the following: (1) The Sanyangzhuang Han Dynasty settlement site reflects the efficient utilization strategy and environmental adaptation mechanism of ancient settlements for land resources, presenting typical scattered characteristics. Its formation mechanism is closely related to the evolution of social systems in the Western Han Dynasty. (2) In terms of site selection, settlements consider practicality and ceremony, which can not only meet basic living needs, but also divide internal functional zones based on the meaning implied by the orientation of the constellations. (3) The widespread use of iron farming tools has promoted the innovation of cultivation techniques, and the implementation of the substitution method has formed an ecological regulation system to cope with seasonal climate change while ensuring agricultural yield. The above results comprehensively reflect three types of ecological wisdom: “ecological adaptation wisdom of integrating homestead and farmland”, “spatial cognitive wisdom of analogy, heaven, law, and earth”, and “agricultural technology wisdom adapted to the times”. This study not only deepens our understanding of the cultural value of the Han Dynasty settlement site in Sanyangzhuang, but also provides a new theoretical perspective, an important paradigm reference, and a methodological reference for the study of ancient settlement ecological wisdom. Full article

Generative Artificial Intelligence (GAI) technologies are revolutionizing productivity and creativity across educational and engineering contexts. This study addresses a critical gap by examining the key factors influencing the successful integration of GAI tools to enhance faculty and student productivity, with a focus on higher education and its role in advancing sustainable development. Specifically, it investigates challenges, opportunities, and essential conditions for effective GAI adoption that support not only academic excellence but also the preparation of engineers capable of addressing global sustainability challenges in line with the United Nations Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 12 (Responsible Consumption and Production). A preliminary literature review identified significant factors requiring attention, further refined through interviews with 14 students and 13 faculty members, and expanded upon via a survey involving 54 students and 42 faculty members. Participants rated the significance of various factors on a five-point Likert scale, allowing for the calculation of the Relative Importance Index (RII). The findings reveal that while compliance with ethical standards and bias mitigation emerged as the most significant concerns, mid-level considerations such as institutional support, training, and explainability are critical for fostering GAI adoption in sustainable learning environments. Foundational elements, including robust technical infrastructure, data security, and scalability, are vital for long-term success and alignment with responsible and sustainable innovation. Notably, this study highlights a divergence in perspectives between faculty and students regarding GAI’s impact on productivity, with faculty emphasizing ethical considerations and students focusing on efficiency gains. This study offers a comprehensive set of considerations and insights for guiding GAI integration in educational and engineering settings. It emphasizes the need for multidisciplinary collaboration, continuous training, and strong governance to balance innovation, responsibility, and sustainability. The findings advance theoretical understanding and provide practical insights for academia, policymakers, and technology developers aiming to harness GAI’s full potential in fostering sustainable engineering education and development. Full article