Search Results (1,123)

Open-pit polymetallic mines commonly encounter challenges such as the asynchrony between mining and processing operations, large fluctuations in ore supply structure, and high haulage volumes, which lead to increased transportation costs and instability in processing plant feed grades. To address these issues, this study, driven by practical production requirements, proposes a two-stage hybrid optimization strategy that combines the global search capability of NSGA-III with the local intensification of particle swarm optimization (PSO), aiming to achieve the coordinated optimization of transportation cost minimization and plant feed grade maximization under constraints imposed by ore supply boundaries and processing plant capacity. To further identify the most suitable solution from the resulting Pareto-optimal set, the VIKOR multi-criteria decision-making method is employed to evaluate and select a blending scheme with optimal balance under the dual objectives of cost and grade. The effectiveness of the proposed approach is validated using a real-world production case, with experimental results showing that the optimized blending scheme achieves a cost reduction of more than 9%, while the gold grades of oxide and sulfide ores are increased to 2.40–3.16 g/t and 2.14–2.17 g/t, respectively, leading to a significant improvement in the overall plant feed grade. Compared with the actual weekly blending plan used in practice, the proposed method enables a comprehensive optimization of transportation cost, feed grade, and ore supply structure within a unified framework. Full article

Attention-deficit/hyperactivity disorder (ADHD), brain–derived neurotrophic factor (BDNF), and enuresis are interconnected in several ways, primarily through their potential links to neurodevelopmental factors and brain function. ADHD is considered a neurobehavioral and neuropsychiatric condition characterized by numerous comorbidities, and it represents one of the most frequently encountered neuropsychiatric disorders in clinical practice. Enuresis constitutes a subgroup of intermittent incontinence occurring during sleep that can be further subdivided into monosymptomatic (MNE) and non-monosymptomatic enuresis (NMNE). BDNF plays a crucial role in neurodevelopment, including neuronal growth, proliferation, survival, differentiation, and synaptic plasticity. This narrative review synthesized available literature identified through a systematic search of PubMed/MEDLINE, Science Direct, and Scopus databases (January 2000–December 2025). However, the evidence base is heterogeneous, and findings regarding BDNF in ADHD are inconsistent. Studies examining BDNF in enuresis often involve urinary BDNF, which reflects local bladder production rather than central BDNF activity. Further research is needed to clarify the specific roles of BDNF in the development and manifestation of these conditions and to fully elucidate the complex relationship between BDNF, ADHD, and enuresis. Full article

To address the high hardware costs associated with hyperspectral imaging in precision agriculture, spectral reconstruction (SR) is emerging as a feasible solution for obtaining hyperspectral images. However, existing methods, mainly including CNN and Transformer, face a notable dilemma: convolutional neural networks (CNNs) are limited by their local receptive fields, while Transformers encounter the problem of quadratic computational complexity. Effectively balancing computational efficiency with the capture of long-range spatial dependencies remains a significant challenge. To this end, this study proposes FGA-Mamba (Frequency-Gradient Attention Mamba), a novel reconstruction network based on the Mamba architecture. This network introduces a Frequency-Visual State Space (F-VSS) module, which combines the linear long-range modeling capability of state space models (SSMs) with a frequency-domain self-calibration mechanism to enhance global structural consistency by explicitly modulating frequency features. In addition, we designed an Enhanced Gradient Attention Module (EGAM). This module optimizes local feature representation through a gradient-aware mechanism, effectively compensating for the loss of spatial details. Experimental results on 3 datasets shows that FGA-Mamba have significant improvement in both quantitative and qualitative metrics. Moreover, the high consistency observed in vegetation index (VI) calculations confirms its potential for practical agricultural application. Full article

Australian regional communities are actively seeking development pathways that generate local economic value while maintaining environmental and cultural integrity. In this context, GeoRegions have emerged in Australia as a community-led approach for recognising and interpreting geoheritage and associated abiotic–biotic–cultural (ABC) values through geotourism and geoeducation. The GeoRegion concept remains intentionally operationally flexible, but for regional communities encountering a myriad of barriers to sustainable geotourism implementation, any uncertainty for proponents about what constitutes an implementable GeoRegion and what resources and governance arrangements are required for credible and sustained delivery requires resolution. This study developed a stakeholder-informed conceptual model to clarify the practical ‘building blocks’ of GeoRegion establishment and the conditions under which GeoRegions can contribute to sustainability-oriented regional development. Using a design thinking framing and semi-structured interviews with thirteen expert participants, we used semantic discourse analysis to identify the factors perceived as essential to GeoRegion viability and legitimacy. We found that participants expected GeoRegions to be geologically centred, but their perceived value and long-term durability depend on (i) genuine community support and locally legitimate narratives (including Indigenous knowledge where appropriate), (ii) capable champions or coordinating groups, (iii) sustained resourcing for interpretation and visitor readiness, and (iv) a facilitative and not prescriptive role for government. Participants emphasised that GeoRegions should never be constrained by land tenure but cautioned that competing land uses, access logistics and uneven capacity across regions were highly influential in the delineation of feasible boundaries and management intensity. Our GeoRegion model differentiates core inputs (community mandate, knowledge co-production, geoheritage significance, human capacity and funding) from expected outputs (interpretive materials, geoeducation, geotourism, economic development, conservation outcomes and strengthened place identity), and we identify feedback that can either reinforce or erode sustainability outcomes over time. We argue that GeoRegions can provide a low-risk, scalable mechanism for geoconservation-informed regional development, particularly where formal protected-area tools or geopark ambitions are politically or economically constrained, provided that supporting governance and resourcing are treated as essential design requirements. Full article

Background: Pediatric kidney failure, whether acute or chronic, constitutes a major public health issue because of its impact on survival, linear growth, neurocognitive development, and long-term quality of life. While high-income countries have markedly improved outcomes through early diagnosis, advanced dialysis technologies, and kidney transplantation, management remains limited in low- and middle-income countries, particularly in the Maghreb region. Objective: This review aims to provide an updated synthesis of pediatric kidney failure, with emphasis on renal replacement therapy modalities and the specific challenges encountered in resource-limited contexts, particularly in Algeria. Methods and Content: We successively address the pathophysiological and clinical bases of pediatric acute kidney injury and chronic kidney disease, followed by a discussion of available therapeutic strategies: peritoneal dialysis, intermittent hemodialysis, continuous renal replacement therapy, and pediatric kidney transplantation. Particular attention is given to organizational constraints, actual availability of modalities, limited access to consumables and immunosuppressive therapies, and the specificities of pediatric kidney care in the Maghreb region in comparison with international recommendations. Perspectives: Improving outcomes for children with kidney failure in Maghreb countries requires a multidimensional approach integrating early screening, strengthening peritoneal dialysis programs, structured development of pediatric kidney transplantation, and enhanced regional and international collaboration. Reinforcing local research capacity and participation in international registries are essential steps toward reducing disparities in care and adapting global guidelines to local realities. Full article

This paper introduces a new explicit integration method for second-order ordinary differential equations (ODEs) commonly encountered in engineering applications. Traditionally, these problems are solved either by reformulating them as first-order systems to apply one-step methods such as Runge–Kutta schemes, or by using direct second-order approaches widely adopted in linear dynamics, including the generalized-α, central difference, and Newmark methods. The proposed method is derived from a Taylor series expansion truncated at the third derivative, resulting in a fully explicit algorithm that requires only one function evaluation per time step. Similar to Newmark’s formulation, it includes adjustable parameters that allow the user to balance accuracy and stability. For a specific parameter choice, the method exhibits convergence and stability properties comparable to those of the central difference scheme. An important advantage is that it remains explicit even when nonlinearities depend on first-derivative terms. The paper presents a theoretical analysis covering stability, local truncation error, spectral properties, numerical damping, and period elongation. The method is validated through four test cases from multibody dynamics, including linear and nonlinear problems. Results demonstrate that the Explicit Integration Grade 3 (EIG-3) method achieves accuracy comparable to existing explicit second-order integrators while significantly reducing computational cost, particularly in nonlinear applications. Full article

The easily defanged myth that baby rattlesnakes (genera Crotalus and Sistrurus) are more dangerous than adults has persisted in North America despite all evidence to the contrary. The most often cited reason for the babies-more-dangerous (BMD) myth is the venom-dump (VD) hypothesis: babies, in contrast to adults, cannot control how much venom they expend, and therefore inject all of it when biting. We undertook three approaches to explore the origin, transmission, and prevalence of this myth and its most frequent explanation. First, we examined historical newspaper accounts. From 130 newspaper stories mentioning the relative danger of baby rattlesnakes, we identified a timeline in which (1) most stories prior to 1969 were factually correct; (2) the BMD myth and VD hypothesis likely originated in the mid-to-late 1960s and became entrenched in California, especially, from 1970 to 1999; (3) factually incorrect statements subsequently prevailed throughout North America from 2000 to 2014; and (4) factually correct stories regained prominence with apparent effective messaging success from 2015 onward. We further learned that general information stories about rattlesnakes, more often citing subject experts like university professors, were much more likely to provide accurate information than local snakebite stories, which more often cited health professionals (e.g., physicians, veterinarians, pharmacists) and emergency responders (e.g., police and fire officers) who frequently supplied misinformation. Second, we surveyed familiarity with the BMD myth and VD hypothesis among 53 university classrooms (including one high school) representing 3751 students across 29 states within the United States. Consistent with the California media’s outsized influence on misinformation transmission, familiarity with the myth was greatest in the southwestern states (52.6%) and declined moving north and east, with the least familiarity in the northeastern states (16.4%). Third, a small survey of 75 emergency responders and health professionals from Southern California revealed that a whopping 73.3% actually believed the BMD myth. Numerous organizations generally regarded as authoritative further amplified the misinformation, especially on the internet, where some content persists to this day. Unfortunately, belief in the BMD myth and VD hypothesis can lead to negative consequences, including misinformed risk-taking by those encountering snakes, unwarranted fear among snakebite victims, and inappropriate care delivered by misinformed or patient/family-pressured medical professionals. Our findings target health professionals and emergency responders as priority audiences for education. Full article

Nested Named Entity Recognition (nested NER) frequently encounters challenges like boundary conflicts, complications in modeling long-distance dependencies, and inadequate representation of deep nested semantics resulting from overlapping spans and hierarchical inclusion relationships of entities. This research presents a multi-scale heterogeneous graph attention network to facilitate end-to-end recognition of nested entities through the collaborative modeling of structure and semantics. The model initially presents the structural integration mechanism, which consolidates the hierarchical restrictions of the syntactic tree and the inter-word relationships of the dependency tree within a singular heterogeneous graph space. It subsequently generates 1/2/3-hop multi-scale subgraphs and employs multi-scale subgraph attention to adaptively integrate information from various structural receptive fields, harmonizing the local cues of shallow entities with the global dependencies of deep entities. The experimental findings on the ACE2004, ACE2005, and GENIA benchmark datasets indicate that the proposed method surpasses several robust baselines regarding overall performance and nested entity recognition, particularly exhibiting notable advantages in identifying long entities and low-frequency entities. We further evaluate MHGAT on KBP2017 and GermEval2014 to validate generalization across datasets and languages. Full article

Precise assessment of rock brittleness is a prerequisite for effective wellbore integrity and successful reservoir stimulation in drilling programs. To achieve precise prediction of rock brittleness index (BI), this study proposes an improved optimization algorithm for an artificial gorilla troops optimizer (GTO), called a Bernoulli Differential Evolution Gorilla Troops Optimizer (BDEGTO). In the BDEGTO, Bernoulli mapping is introduced during the population initialization process, and the differential evolution is embedded after the exploration stage of the GTO. These modifications effectively address the early-stage optimization weaknesses and the susceptibility to local optima that are commonly encountered in a traditional GTO. To evaluate the performance of the BDEGTO, comparisons are made with other optimization algorithms based on 91 datasets from 32 rock types. The results demonstrate the significant advantages of the BDEGTO over other algorithms. Furthermore, the BDEGTO is applied to the optimization process of Least Squares Boosting (LSB), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM). A comparison is made with Support Vector Regression (SVR), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) algorithms for predicting rock brittleness based on input parameters such as P-wave velocity (V_p), point load index (Is₅₀), and unit weight (UW). The findings indicate that BDEGTO-XGB achieves the best prediction performance for BI. Additionally, through SHapley Additive exPlanations (SHAP) analysis, it is determined that among the three input parameters, Is₅₀ has the most significant influence. These research results provide valuable guidance for the brittleness assessment of similar rocks. Full article

Understanding local population density and spatial habitat use is essential for wildlife conservation in fragmented mountainous landscapes. This study examined the habitat use patterns of Capra aegagrus in the mountainous regions of Batman, Türkiye, using Kernel Density Estimation (KDE) and spatial regression modeling. Significant spatial autocorrelation (Moran’s I = 0.799, p < 0.001) justified the use of a Spatial Error Model (AIC = −254.59). Built land proportion had a strong negative effect, with a 10% increase associated with a 31% decline in KDE intensity. Elevation also showed a modest negative association with habitat use intensity, whereas slope and bare land proportion were positively associated. The southern stratum exhibited higher relative encounter intensity, and the spatial autoregressive parameter (λ = 0.92) indicated strong spatial structuring. To complement spatial habitat analysis with demographic estimates, population density was assessed using Line Transect Distance Sampling in the northern and southern sub-regions. The estimated local density was 6.47 individuals/km² (95% CI: 4.11–10.16), with overlapping confidence intervals between sub-regions. The variation in detection probability and encounter rate contributed the most to overall uncertainty. Because the surveys were restricted to accessible mountainous terrain, estimates represent local ecological density rather than province-wide abundance. Together, these results provide a spatially explicit baseline linking relative habitat use patterns with locally derived density estimates to support future monitoring and conservation planning. Full article

The inherent randomness, high volatility, and non-stationarity of photovoltaic (PV) power generation pose substantial threats to the stability of modern power grids. Developing high-precision forecasting models is essential for grid operation, yet conventional architectures often encounter a performance bottleneck: they struggle to simultaneously achieve high computational efficiency for long-range dependency modeling and robust perception for local, abrupt fluctuations. To address these limitations, this paper proposes HDCF-Mamba, a novel forecasting framework that resolves the feature distribution gap between long-range trends and short-term volatility. The core innovation lies in the Heterogeneous Dual-branch Cross-Fusion (HDCF) mechanism, which enables the synergetic integration of a Mamba-based global branch and a Multi-Kernel Filter Unit-based multi-scale local branch. Specifically, we integrate the Mamba Selective State Space Mechanism into the global branch to efficiently capture long-term dependencies with $O\left(L\right)$ linear complexity, fundamentally overcoming the quadratic computational bottleneck of Transformers. Meanwhile, the Multi-Scale Feature Extraction Module (MSFEM) acts as a local compensator to capture high-frequency power fluctuations caused by transient weather changes. Unlike simple hybrid models that rely on linear addition, our HDCF design utilizes a temporal concatenation mechanism to ensure non-linear alignment of these heterogeneous features. Extensive experiments on four real-world PV operational datasets (including publicly available benchmark datasets and actual photovoltaic power station monitoring data: ECD-PV, LSP-PV, APS-PV, and PSB-PV) demonstrate that HDCF-Mamba consistently outperforms state-of-the-art models, achieving a reduction in Mean Absolute Error (MAE) of up to 11.4% compared to iTransformer and 8% compared to SCINet, while maintaining superior computational efficiency. Full article

The existing methods for solving non-linear equations encounter convergence issues and computing constraints, especially when used in fractional-order or complex non-linear problems. This study develops a higher-order fractional technique for solving non-linear equations based on the Caputo fractional derivative. The proposed method uses a fractional framework to improve local convergence and stability while ensuring high efficiency in every iteration step. Local convergence analysis using generalized Taylor series expansion reveals that the order of the new fractional scheme for solving non-linear equations is $5¢+1$, where $¢\in$ (0,1] represents the Caputo fractional order, determining the memory depth of the Caputo fractional derivative. The performance of the method is further investigated using a variety of non-linear problems from engineering optimization and applied sciences, such as engineering control systems, computational chemistry, thermodynamics models, and operations research, such as inventory optimization. Analyzing the key performance metrics, such as dynamical analysis, percentage convergence, residual error, and computation time, confirms the advantages of the developed method over the state-of-the-art. This study provides a solid framework for higher-order fractional iterative approaches, paving the way for advanced applications of non-linear problems. Full article

In August 2023, a large forest fire burned more than 60% of the Dadia–Lefkimi–Soufli Forest National Park in northeastern Greece, following another large fire in 2022. To quantify the effects of these fires on local herpetofauna, we analyzed community composition, abundance, and diversity before and after the 2023 event. Standardized visual encounter surveys were conducted across 29 sites between 2015 and 2024, spanning burned and unburned areas. Species richness, abundance, and diversity metrics, together with Bray–Curtis community dissimilarities, were compared across sampling periods and fire-severity classes. Amphibian assemblages showed high postfire persistence, with 82% of regional species still detected and no significant changes in diversity indices, likely reflecting the buffering role of perennial streams and other hydrologically stable refugia. In contrast, reptile communities showed clear compositional shifts and experienced severe declines: overall reptile species richness decreased to 30% of prefire levels and diversity indices dropped significantly. Tortoises (i.e., Testudo graeca, T. hermanni) declined by nearly 90% relative to prefire estimates, indicating high vulnerability of low-mobility, long-lived species. Snakes were not detected in any burned sites, whereas only a few small-bodied lizards and the freshwater turtle Mauremys rivulata persisted locally. These findings demonstrate that extreme, landscape-scale fires can restructure reptile communities in Mediterranean forests, particularly where long-term habitat change and drought had already reduced population resilience. The study underscores the need for targeted postfire restoration, conservation planning for slow-dispersing taxa, and long-term biodiversity monitoring under increasingly frequent fire regimes. Full article

In the context of cross-Strait integrated development, agricultural cooperation policies between the Chinese mainland and Taiwan are intended to serve as key instruments for integration. However, these policies frequently encounter an implementation dilemma in which higher-level authorities actively promote policy goals while grassroots governments respond primarily through symbolic actions. Existing studies have largely explained this phenomenon from static perspectives, such as resource constraints or individual motivation, but have paid insufficient attention to how defensive compliance and distorted feedback interact to sustain systemic implementation failure. To address this gap, this study adopts political systems theory and conceptualizes policy implementation as a dynamic process involving input, conversion, output, and feedback. Using a comparative case study of two counties, supported by semi-structured interviews, participant observation, and document analysis, the study examines how local governments process politically sensitive policy mandates under conditions of high political pressure and resource mismatch. The findings show that contradictory inputs create strong risk-avoidance incentives, leading local governments to adopt defensive compliance strategies during the conversion stage. Through symbolic implementation, resource diversion, and responsibility shifting, policies are translated into formally compliant but substantively hollow outputs. These symbolic outputs generate distorted feedback that conceals implementation failures and prevents higher-level authorities from making corrective adjustments, thereby trapping the policy system in a state of suspended implementation and apparent stability. Theoretically, this study extends political systems theory by revealing how defensive compliance and feedback distortion function as adaptive mechanisms that sustain system persistence while undermining substantive policy performance. Practically, it provides important insights for enhancing governance effectiveness and preventing systemic implementation failure in politically sensitive policy domains. Full article

An HPO (Hunter–Prey Optimizer)-optimized Bidirectional LSTM (HPO-BiLSTM) model is introduced to address the challenges in predicting gas concentration within coal mining working faces. This study aims to adaptively adjust the key hyperparameters (such as learning rate and number of hidden layer units) of the BiLSTM network through intelligent optimization algorithms. While the BiLSTM architecture inherently mitigates gradient vanishing and exploding problems through its gating mechanisms, the proposed HPO method focuses on addressing the inefficiency of manual parameter tuning and the risk of trapping in local optima that traditional methods encounter when dealing with nonlinear and non-stationary gas concentration time series. The experiment utilized the actual methane monitoring data from the 15117 working face of Jishazhuang Coal Mine in Jinzhong City, Shanxi Province (with a sampling interval of 2 min). The proposed HPO-BiLSTM model was compared with baseline models such as LSTM, BiLSTM, GA-BiLSTM, and PSO-BiLSTM in terms of performance. This study systematically compares the performance of LSTM, BiLSTM, and BiLSTM models optimized with GA, PSO, and HPO. Results demonstrate that all optimized models outperform the baselines, with HPO-BiLSTM achieving the best overall performance. It attained the lowest RMSE and highest R² across the training, validation, and test sets, showcasing superior fitting and generalization capabilities. Furthermore, HPO-BiLSTM converged to the lowest loss value (0.00062) in only 15 iterations, demonstrating significantly greater efficiency and stability than both GA-BiLSTM (loss 0.00072, 25 iterations) and PSO-BiLSTM (loss 0.00071, 30 iterations). The experiments confirm that the HPO algorithm effectively configures BiLSTM hyperparameters, mitigates overfitting, and provides a more accurate and robust solution for gas concentration prediction in coal mines. Full article