Search Results (1,561)

Classification of island coastal landscapes is a challenge to incorporate both the terrestrial and the aquatic environment characteristics, and place biological diversity in a regional and insular context. The Coastal and Marine Ecological Classification Standard (CMECS) was developed for use in the United States and incorporates geomorphic data, substrate data, biological information, as well as water column characteristics. The CMECS framework was applied to the island of Great Exuma, The Bahamas. The classification used data from existing studies to include oceanographic data, seawater temperature, salinity, benthic invertebrate surveys, sediment analysis, marine plant surveys, and coastal geomorphology. The information generated is a multi-dimensional description of benthic and shoreline biotopes characterized by dominant species. Biotopes were both mapped and described in hierarchical classification schemes that captured unique components of diversity in the mosaic of coastal natural communities. Natural community classification into biotopes is a useful tool to quantify ecological landscapes as a basis to develop monitoring over time for biotic community response to climate change and human alteration of the coastal zone. Full article

To achieve rapid screening and semi-quantitative analysis of pesticide residues in mobile laboratories and on-site tea testing, a novel method based on thermal-assisted plasma ionization–time-of-flight mass spectrometry (TAPI-TOF/MS) has been developed for the detection of 20 pesticide residues, including insecticides and fungicides, in tea. This method eliminates the need for liquid chromatography, or column connections. Instead, it utilizes the high temperature of the sample inlet and stage to fully volatilize and inject the sample. By integrating TAPI-TOF/MS with an automated pesticide residue pretreatment instrument, the entire sample extraction process can be performed automatically. The analysis time for each sample has been reduced to 1.5 min, allowing for the processing of 60 samples per batch. An accurate mass spectrometry database has been established for screening and confirmation purposes. The software automatically matches the mass spectrometry database by analyzing the measured ion mass deviation, ion abundance ratio, and the relative contribution weight of each ion, generating a qualitative score ranging from 0 to 100. The lowest concentration yielding a qualitative score of ≥75 was defined as the screening limit, which ranged from 0.10 to 5.00 mg/kg for the 20 pesticides. Within their respective linear ranges, the method demonstrated good linearity with correlation coefficients (R²) ranging from 0.983 to 0.999. The average recovery rates (n = 5) of the target pesticides ranged from 70.6% to 117.0% at the set standard concentrations, with relative standard deviations (RSD) ranging from 1.7% to 13.1%. Using this method, 15 tea samples purchased from the Rizhao market in China were analyzed. Ten samples were found to contain residues of metalaxyl or pyraclostrobin, yielding a detection rate of 66.7%. This technology provides technical support for the rapid detection and quality control of multiple pesticide residues in tea, meeting the requirements for high-throughput and on-site analysis. Full article

The floating photovoltaic (FPV) power generation technology in water has made up for some of the shortcomings of traditional inland photovoltaics and has developed rapidly in the past decade, enabling truly sustainable solar energy exploitation. Multi-module hinged offshore floating photovoltaics (OFPV) are widely used in the sea. However, how to ensure the survival of OFPVs in extreme natural environments is the biggest challenge for the implementation of the project in the future. The focus of this paper is the hydrodynamic problems that multi-module OFPV structures may encounter under regular waves. The effects of column spacing and heave plates were analyzed for a single FPV platform in order to obtain the ideal single module. Furthermore, the motion responses and inter-module forces of each module are calculated within the overall OFPV system under regular waves to investigate the overall hydrodynamic characteristics. Qualitative and quantitative comparisons between single and multi-modules are made for a deep understanding of this structure to ensure its sustainability. The corresponding conclusions can provide scientific references for multi-module OFPVs and the sustainable utilization of energy. Full article

The increasing uncertainty of distributed energy resources (DERs) challenges the secure and resilient operation of low-voltage distribution networks (LVDNs). Flexible interconnection via power-electronic devices enables controllable links among LVDAs, supporting capacity expansion, reliability, load balancing, and renewable integration. This paper proposes a two-stage robust optimization framework for flexible interconnection planning in LVDNs. The first stage determines investment decisions on siting and sizing of interconnection lines, while the second stage schedules short-term operations under worst-case wind, solar, and load uncertainties. The bi-level problem is reformulated into a master–subproblem structure and solved using a column-and-constraint generation (CCG) algorithm combined with a distributed iterative method. Case studies on typical scenarios and a modified IEEE 33-bus system show that the proposed approach mitigates overloads and cross-area imbalances, improves voltage stability, and maintains high DER utilization. Although the robust plan incurs slightly higher costs, its advantages in reliability and renewable accommodation confirm its practical value for uncertainty-aware interconnection planning in future LVDNs. Case studies on typical scenarios and a modified IEEE 33-bus system demonstrate that under the highest uncertainty the proposed method reduces the voltage fluctuation index from 0.0093 to 0.0079, lowers the autonomy index from 0.0075 to 0.0019, and eliminates all overload events compared with stochastic planning. Even under the most adverse conditions, DER utilization remains above 84%. Although the robust plan increases daily operating costs by about $70, this moderate premium yields significant gains in reliability and renewable accommodation. In addition, the decomposition-based algorithm converges within only 39 s, confirming the practical efficiency of the proposed framework for uncertainty-aware interconnection planning in future LVDNs. Full article

The Public Opinion Survey of Human Attributes–Stuttering (POSHA–S) Database, intermittently updated, at the time of this report, contains 25,739 respondents from 45 countries with responses in 28 languages, representing 11 world regions. Among public and selected population samples, more than 600 self-identified stutterers are included. The Microsoft Excel database file features more than 150 columns of POSHA–S results. Some data, such as state/province and country of respondents, primary job or occupation, languages known, race, and religion, are included as text. Other demographic items and all attitude items are numerical data. The POSHA–S has check boxes or scales of 1–5 for other demographic variables and general ratings that compare stuttering to four other “anchor” attributes (intelligence, left-handedness, obesity, and mental illness). All subsequent stuttering attitude items are scored on a scale of 1–3, reflecting “no”, “not sure”, and “yes”, respectively. All scaled ratings are converted to a uniform −100 to +100 scale, with some item ratings inverted so that, uniformly, higher ratings reflect more positive attitudes and lower ratings reflect more negative attitudes. All respondents are classified according to population, a category within population, region or continent, country, language, and other distinctive features. Full article

Addressing the inefficiency, subjective errors, and limited adaptability of existing methods for surveying complex ancient structures, this study presents an intelligent hierarchical algorithm for generating line drawings guided by structured architectural features. Leveraging point cloud data, our approach integrates prior semantic and structural knowledge of ancient buildings to establish a multi-granularity feature extraction framework encompassing local geometric features (normal vectors, curvature, Simplified Point Feature Histograms-SPFH), component-level semantic features (utilizing enhanced PointNet++ segmentation and geometric graph matching for specialized elements), and structural relationships (adjacency analysis, hierarchical support inference). This framework autonomously achieves intelligent layer assignment, line type/width selection based on component semantics, vectorization optimization via orthogonal and hierarchical topological constraints, and the intelligent generation of sectional views and symbolic annotations. We implemented an algorithmic toolchain using the AutoCAD Python API (pyautocad version 0.5.0) within the AutoCAD 2023 environment. Validation on point cloud datasets from two representative ancient structures—Guanchang No. 11 (Luoyuan County, Fujian) and Li Tianda’s Residence (Langxi County, Anhui)—demonstrates the method’s effectiveness in accurately identifying key components (e.g., columns, beams, Dougong brackets), generating engineering-standard line drawings with significantly enhanced efficiency over traditional approaches, and robustly handling complex architectural geometries. This research delivers an efficient, reliable, and intelligent solution for digital preservation, restoration design, and information archiving of ancient architectural heritage. Full article

Event-based models (EBMs) are powerful tools for inferring probabilistic sequences of monotonic biomarker changes in progressive diseases, but their use is often hindered by data quality issues, high dimensionality, and limited interpretability. We introduce ReduXis, a streamlined pipeline that overcomes these challenges via three key innovations. First, upon dataset upload, ReduXis performs an automated data readiness assessment—verifying file formats, metadata completeness, column consistency, and measurement compatibility—while flagging preprocessing errors, such as improper scaling, and offering actionable feedback. Second, to prevent overfitting in high-dimensional spaces, ReduXis implements an ensemble voting-based feature selection strategy, combining gradient boosting, logistic regression, and random forest classifiers to identify a robust subset of biomarkers. Third, the pipeline generates interpretable outputs—subject-level staging and subtype assignments, comparative biomarker profiles across disease stages, and classification performance visualizations—facilitating transparency and downstream analysis. We validate ReduXis on three diverse cohorts: the Emory Healthy Brain Study (EHBS) cohort of patients with Alzheimer’s disease (AD), a Genomic Data Commons (GDC) cohort of transitional cell carcinoma (TCC) patients, and a GDC cohort of colorectal adenocarcinoma (CRAC) patients. Full article

In traditional Chinese timber architecture, “column reduction” (Jian Zhu Zao) and “column relocation” (Yi Zhu Zao) enhances spatial continuity, yet often produces bending-dominated, material-intensive frames. This study develops a generative frame system that encodes raised beam logic into a parametric line-model workflow and couples it with simulation-based optimization. Informed by case analysis, the tool implements three lateral strategies—ridge-support revision, insertion of inclined members, and inclination of originally horizontal members—and one longitudinal strategy—longitudinal truss formation—whose use is governed by a user-defined historical authenticity parameter. Structural responses were evaluated using Karamba3D, and cross-section sizing was searched using Wallacei under gravity-dominant loading. The results indicate clearer load paths, greater axial-force participation, and reduced bending, yielding lower maximum displacements at comparable self-weight; moreover, the performance ranking aligns with the calibrated authenticity loss schedule, suggesting that the authenticity controller also acts as a practical proxy for expected stiffness gains. The framework improves design and modeling efficiency while offering quantitative decision support for culturally sensitive conservation and imitation design. Limitations include line-model idealization, simplified timber and joint behavior, gravity-only loading, and a modest historical corpus. The approach is extensible to other traditional systems via parameter and rule adaptation. Full article

The use of biogas in Indonesia, derived from livestock manure, palm oil waste, and organic waste, remains limited to household-scale applications due to its inefficiency in transportation and storage. This limitation arises from the presence of CO₂ and H₂O in raw biogas, which results in a lower methane content compared to natural gas. Furthermore, raw biogas is not suitable for storage in cylinders or long-distance distribution without purification. This research aims to address these challenges by developing biogas into Bio-Compressed Natural Gas (Bio-CNG), a high-methane-content fuel suitable for industrial applications and power generation. Bio-CNG is produced through biogas purification, primarily using the water scrubbing method, to achieve methane concentrations exceeding 92%, followed by compression to 120 Bar for compact storage and ease of transport. The study focuses on designing and testing an industrial-scale effective water scrubber system for biogas purification, thereby enabling the broader utilization of renewable biogas energy beyond local reactor sites. The development of the biogas purification and compression system begins with the system modeling and the detailed design, which are then followed by the hardware fabrication in industrial-scale scenarios. The purification and compression of biogas consist of two main components: the purification system and the biogas compression system. The core of the purification system is a scrubber, designed as a vertical column measuring 6 m in height and 0.5 m in diameter. The designed and fabricated system for industrial-scale biogas purification and compression was then tested. The results showed a linear correlation between scrubber operating pressure and methane and CO₂ content. Based on the results of the pressure and water flow rate variation tests, an operating pressure of 2 bar is recommended for the water scrubber, as this condition yielded the lowest specific energy consumption of 0.3 kWh/Nm³. Meanwhile, in the biogas compression system, the energy required is exponentially proportional to the pressure between 75 and 105 bar. Full article

The start-up process of water-distribution networks has been extensively investigated in recent years, particularly regarding the pressure surges that may occur during such transient events. In this context, researchers have concentrated on exploring physical formulations capable of describing the behaviour of the two interacting phases—water and air—typically resolved through numerical approaches. This paper presents an analytical solution to the nonlinear mathematical model governing the start-up of water pipelines containing a trapped air pocket. The model adopts the rigid water column approximation for the liquid phase and a polytropic gas law to account for the compressibility of the air. The resulting system can be formulated as a second-order nonlinear differential equation. The analytical approach consists of transforming the governing equation into a first-order linear ordinary differential equation, in which the square of the water front velocity is expressed as a function of the water column length. This transformation yields a closed-form solution expressed as a special integral series. The required integrals are evaluated using binomial expansions and incomplete gamma functions, enabling the derivation of a general solution valid within alternating intervals of monotonic motion. A practical application involving an 800 m pipeline is presented. Furthermore, the proposed solution is validated against experimental measurements, demonstrating the accuracy and effectiveness of the analytical approach in capturing the system’s transient behaviour. Full article

Methylene blue and safranin are dyes that may have harmful effects on both aquatic ecosystems and human health. This research aims to simulate an industrial-scale operational adsorption column for competitively removing these dyes from wastewater, employing Cedrela odorata L. as a bioadsorbent material. Aspen Adsorption (v.1) software simulated an industrial-scale packed-bed adsorption column under various configurations. Moreover, machine learning algorithms were applied to predict the results generated by Aspen, representing an advancement in the development of new strategies in this field. The kinetic model employed was the Linear Driving Force (LDF) model. Adsorption efficiencies of 96.1% were achieved for both methylene blue and safranin using the Langmuir–LDF model. The Freundlich–LDF model showed efficiencies of 94.8% for methylene blue and 96% for safranin. Meanwhile, the Langmuir–Freundlich–LDF model achieved up to 96.1% for methylene blue and 94.8% for safranin. This study demonstrated the feasibility of simulating the competitive adsorption of dyes in solution at an industrial scale using Cedrela odorata L. as a bioadsorbent. The application of LDF kinetic models and adsorption isotherms (Langmuir, Freundlich, and Langmuir–Freundlich) resulted in high adsorption efficiencies, highlighting the potential of this approach for the remediation of dye-contaminated effluents as a viable method for predicting the performance of full-scale packed columns. Machine learning algorithms were implemented in this research, obtaining R² higher than 0.996 for validation and testing stages for the responses of the model. Full article

Aiming at the uncertainty in load demand and wind-solar power output during multi-energy virtual power plant (VPP) scheduling, this paper proposes a robust optimal scheduling method incorporating incentive-based demand response (IDR). By integrating robust optimization theory, a ladder-type carbon trading mechanism, and IDR compensation strategies, a comprehensive scheduling model is established with the objective of minimizing the operational cost of the VPP. To enhance computational efficiency and adaptability, we propose a hybrid approach that combines the Column-and-Constraint Generation (C&CG) algorithm with Karush–Kuhn–Tucker (KKT) condition linearization to transform the robust optimization model into a tractable form. A robustness coefficient is introduced to ensure the adaptability of the scheduling scheme under various uncertain scenarios. The proposed framework enables the VPP to select the most economically and environmentally optimal dispatching strategy across different energy vectors. Extensive multi-scenario simulations are conducted to evaluate the performance of the model, demonstrating its significant advantages in enhancing system robustness, reducing carbon trading costs, and improving coordination among distributed energy resources. The results indicate that the proposed method effectively improves the risk resistance capability of multi-energy virtual power plants. Full article

The proportion of neutral and weakly alkaline high-sulfate mine water in China is over 50%, resulting in the problem of high treatment costs. Low-cost, sustainable, and non-secondary pollution remediation technologies for in situ application in underground coal mines have rarely been reported. Here, the mixed packed and layered packed SRB-PRB (sulfate-reducing bacteria-permeable reactive barrier) column experiments at a flow speed of 300 mL/d using low-cost corncob as a carbon source were conducted to simulate sulfate in situ remediation in goafs. The column experiments utilized the simulated weakly alkaline mine water, with an initial sulfate concentration of 1027.45 mg/L. The results showed that during the 40 d operation, the SO₄²⁻ removal kinetics included three stages: rapid reduction (0–6 d), stable reduction (6–16 d), and reduction attenuation (16–40 d). Corncob could provide a relatively long-term carbon source supply, with the maximum average removal efficiency of 65.5% for the mixed packed column and 56.6% for the layered packed column. A large number of complex organic-degrading bacteria were detected in both the effluent water samples and the solid packed media, while SRB became dominant only in the solid packed media. However, the low-abundance SRB could still maintain a high-efficiency SO₄²⁻ reduction, due to the supply of readily utilizable carbon sources provided by hydrolytic and fermentative bacteria. This indicated that the synergistic effect between SRB and these organic matter-degrading bacteria was the critical limiting factor for SO₄²⁻ removal. The microscopic characterizations of SEM-EDS (scanning electron microscopy and energy-dispersive spectroscopy) and FTIR (Fourier transform infrared spectroscopy) confirmed the damage of functional groups in corncobs and the generation of SO₄²⁻ removal products (i.e., FeS). The engineering application schemes of the SRB-PRB under both in-production and abandoned mining scenarios were proposed. Additionally, the material cost estimate results showed that the SRB-PRB could achieve in situ low-cost remediation (0.2–1.55 USD/m³) of the characteristic pollutant SO₄²⁻. These findings would benefit the engineering application of in situ microbial remediation technology for high-sulfate mine water. Full article

ELM is an innovative learning algorithm that minimizes output error by only finding optimal output weights. Meta-learning is composed of base ELMs and exhibits good generalization. To improve its performance further by introducing orthogonal constraints into the base ELMs and “top” ELM, we propose a novel Meta-ELM with orthogonal constraints (Meta-QEC-ELM). Because of the particularity of the Meta-ELM, its orthogonal constraint problem is the quadratic equality constraint problem—that is, a one-column Procrustes problem—and it can preserve much more information from feature space to output subspace. The experimental results show that the Meta-QEC-ELM is both effective and feasible. Full article

The crystal structure of caysichite-(Y) from the Ploskaya Mt (Kola Peninsula, Russia) has been refined to R₁ = 0.051 for 4472 unique observed reflections. The mineral is orthorhombic, Ccm2₁, a = 13.2693(3), b = 13.9455(4), c = 9.7384(2) Å, V = 1802.06(8) ų, Z = 4. There are two M sites predominantly occupied by Y, but also including Ca and other rare earth elements (REEs). Both M sites are coordinated by eight O atoms to form distorted bicapped trigonal prisms. The crystal structure is based upon a three-dimensional framework formed by columns of MO₈ polyhedra and (CO₃) groups and double-crankshaft chains of SiO₄ tetrahedra running parallel to the c-axis. The topology of linkage of MO₈ polyhedra understood in terms of the M–M links shorter than 5 Å corresponds to the M network with the paracelsian (pcl) topology. The channels in the network are occupied by double-crankshaft Si chains and H₂O groups. The new general chemical formula of a caysichite-(Y)-type mineral can be written as [Y_2+2x−y′Ca_{2−3x−y″}☐_x+y′+y″][Si₄O₁₀](HCO₃)_3y′+2y″(CO₃)_{3−3y′−2y″}·(4−z)H₂O, where z ~ 0.2; x ≤ 2/3; y′ ≤ 2/3; y″ ≤ 1; 3y′+2y″ ≤ 2. This general formula allows for several end-member formulas according to different x, y′ and y″ values: (Y₂Ca₂)[Si₄O₁₀](CO₃)₃·4H₂O (x = y′ = y″ = z = 0), (Y₂Ca☐)[Si₄O₁₀](HCO₃)₂(CO₃)·4H₂O (x = y′ = z = 0; y″ = 1), (Y_10/3☐_2/3)[Si₄O₁₀](CO₃)₃·4H₂O (y′ = y″ = z = 0; x = 2/3), Ca₂Y_4/3☐_2/3)[Si₄O₁₀](HCO₃)₂(CO₃)·4H₂O (x = y″ = z = 0; y′ = 2/3). The samples studied in this work have the compositions (REE_2.05Ca_1.87☐_0.18)[Si₄O₁₀](HCO₃)_0.11(CO₃)_2.89·3.8H₂O (x = 0.025, y′ = 0, y″ = 0.055) and (REE_2.25Ca_1.52☐_0.23)[Si₄O₁₀](HCO₃)_0.21(CO₃)_2.79·3.8H₂O (x = 0.125, y′ = 0, y″ = 0.115). The end-member formula most close to these compositions is (Y₂Ca₂)[Si₄O₁₀](CO₃)₃·4H₂O, which is different from the formula (Ca,Yb,Er)₄Y₄(Si₈O₂₀)(CO₃)₆(OH)·7H₂O currently adopted by the International Mineralogical Association but is generally identical to the formula (Y,Ca)₄Si₄O₁₀(CO₃)₃·4H₂O proposed in the original study of the mineral. In order to resolve the problem of the caysichite-(Y) formula, additional studies of materials from different localities (and, especially, one from the holotype locality) are needed. Full article