Search Results (28)

Bulk carriers play a critical role in global dry bulk transportation, and their safe operation is closely related to commodity supply chains, port continuity, and maritime governance. However, bulk carrier accidents are unevenly distributed across maritime space, and existing maritime blackspot studies are often limited by single-scale density estimation, unconstrained planar smoothing, and insufficient consideration of temporal persistence. These limitations make it difficult to distinguish robust accident-prone waters from scale-sensitive or temporally unstable hotspots. To address this problem, this study proposes a constrained multiscale consensus framework for identifying and interpreting global bulk carrier accident blackspots. The framework first screens and standardizes global maritime accident records to extract valid bulk carrier accident samples. It then constructs an ocean-constrained equal-area analysis grid and estimates severity-weighted accident intensity under multiple Gaussian smoothing bandwidths. Scale-specific hotspots are further extracted through threshold-based segmentation and minimum-area filtering, and a consensus persistence rule is developed to classify core, secondary, and transition blackspots. Finally, threshold sensitivity analysis, bootstrap resampling, time-window comparison, lifecycle classification, accident-type stratification, and severity-weighted versus frequency-only comparison are conducted to evaluate the robustness and interpretability of the identified blackspots. Based on 38,139 raw accident records, the empirical analysis retained 1441 cleaned bulk carrier accidents from 2015 to 2023 and identified 87 core consensus blackspots, covering approximately 8.06 million km² and containing 863 accidents. These blackspots are mainly concentrated in major coastal shipping regions, and the proposed framework provides a reproducible and geographically constrained basis for global maritime blackspot identification. Full article

Marine pollution monitoring increasingly relies on Biological Early Warning Systems (BEWSs), which use living organisms as continuous, integrative sentinels of water quality. The moon jellyfish Aurelia aurita is a sensitive but under-exploited candidate for this role. We present a computer-vision BEWS pipeline that is unsupervised at inference time and operates without labelled pollution-response data, converting side-view aquarium video of single A. aurita medusae into a binary pollution alarm. Per-frame YOLO bounding-box detections are reduced to a continuous bell-area signal and a centroid trajectory, from which eleven pulsation, kinematic, and detection-quality features are extracted on 60 s sliding windows. A per-animal baseline is fitted on a clean-water baseline (recommended ≥15 min), and a two-layer detector—fast outlier detection on the mean absolute z-score with a k-of-N rule, plus one-sided CUSUM (cumulative sum) accumulation—flags any sustained deviation. Validation on six adult medusae exposed to diesel-WAF detected all six animals (95% CI 54–100%) and produced no false alarms in 203 clean-window opportunities (exact 95% upper bound 1.8%; rule-of-three estimate ≈1.5%). First-alarm latencies ranged from 1.0 to 23.7 min, and the observed responses were described as three descriptive patterns in this pilot dataset: sharp step-change, slow drift, and mixed. The deployed anomaly scoring step contains no neural-network weights, runs in under 300 lines of Python, and is designed for field-portable use in settings where a stationary side-view camera can be positioned alongside an aquarium, although field validation remains required. Per-animal anomaly detection accommodates the strong inter-individual variability of the diesel-WAF response that limits supervised clean-versus-polluted classification at this sample size. Full article

A massive number of early-constructed small-to-medium-span bridges are collectively entering an “aging” phase in China. Meanwhile, vast amounts of unstructured bottom-level inspection texts remain underutilized. To address them, this paper proposes a data governance method. Large Language Models were leveraged to process unstructured defect data from 18,238 real-world bridges nationwide. The data were structurally cleaned and mapped into discrete features, revealing multidimensional vulnerabilities. On this basis, the Stable Contrastive Pattern Risk (SCPR) intelligent decision-making model was developed. The results demonstrate that, following robust filtration, 6 nationwide common risk rules were extracted from 2064 initial candidate combinations. These rules converge into three core risk patterns: the heavy-duty aging pattern, the substructure-dominated pattern, and the over-water small-span low-seismic-design pattern. Guided by these robust rules and specific damage enrichment characteristics, risk stratification and differentiated management strategies were further formulated for Class III bridges. This research facilitates a paradigm shift in bridge maintenance. It moves from reactive, post-event symptom characterization toward data-driven, proactive early warnings. This shift provides a substantive scientific foundation for optimizing resource allocation and enabling precise investment decisions at the road network level. Full article

Background: Open defecation (OD) has remained a threat to the attainment of SDG 6 (sanitation and hygiene). This study measured the level of exposure to the “Clean Nigeria, Use the Toilet” campaign against open defecation, determined the level of public knowledge about open defecation-related harms and diseases, ascertained the public attitude towards open defecation, and established the prevailing defecation practices and the perceived barriers to toilet usage in Ebonyi state, the most prevalent OD state in Nigeria. Methods: The study employed a survey design, using a structured questionnaire for data collection. The multi-stage sampling technique was employed in selecting the respondents from two randomly selected Local Government Areas (LGAs) in the state. Analysis was conducted using 384 valid responses. Results: The results were presented in simple percentage frequency tables and interpreted through the descriptive method, while the Chi-Square test was used to analyse the formulated hypotheses, using the decision rule of p < 0.05. The findings show a high level of awareness of the campaign against open defecation, through the radio and community engagements by environmental activists/NGOs, even though regular access to such information was limited. The results also showed inadequate knowledge of the public health implications of open defecation, whereas good knowledge of environmental consequences was reported. The study found favourable attitudes toward OD practice and persistent open defecation, and major barriers to toilet usage include the high cost of toilet construction, lack of access to toilet facilities, poor sanitation and management of available toilets, and perceived risks of contracting infection from public toilets. However, the Chi-Square values showed that the SBCC campaign was significantly associated with knowledge, attitude, and practice (p < 0.05). Conclusions: The study concluded that localised, culturally relevant and socio-demographically targeted communication interventions, grassroot advocacy, community watch, and neighbourhood taskforce on open defecation, in addition to the provision of aids for the construction of modern toilets with water facilities, are required to combat open defecation in Ebonyi and related contexts in Nigeria. Full article

Direction-of-arrival (DOA) estimation in shallow-water underwater acoustics is challenged by coherent multipath and impulsive disturbances, which jointly cause covariance rank deficiency and outlier-driven subspace distortion. This paper proposes an embedded robust covariance-construction mechanism for coherent-plus-impulsive DOA estimation. The mechanism is implemented as mixture-correntropy-weighted simplified spatial smoothing (SS–MCC), in which snapshot reliability is enforced during subarray covariance accumulation rather than after decorrelation. A two-kernel residual-based weighting rule suppresses strongly contaminated snapshots while retaining moderately perturbed but informative snapshots. Under a controlled narrowband uniform linear array benchmark with fully coherent two-arrival multipath and Bernoulli–Gaussian impulsive noise, SS–MCC yields more stable DOA behavior than MUSIC, SS-MUSIC, and FLOM-MUSIC, especially in low-SNR, high-impulsiveness, and near-threshold regimes, although absolute strict recovery remains limited in the hardest cases. All-trial strict correct-two-peak statistics and ablation results show that the gain mainly comes from embedded covariance cleaning rather than post-processing or parameter tuning. A measured-noise-injected benchmark using NOAA–Navy SanctSound FK01 underwater recordings further confirms the same qualitative robustness trend under real noise waveforms, while remaining a semi-realistic noise-injection check rather than measured-array sea-trial validation. A simplified DOA- assisted MVDR benchmark indicates that improved covariance robustness can also support more favorable beamforming-oriented trends. The results provide controlled benchmark evidence that reliability-aware covariance construction can stabilize subspace extraction under joint coherent multipath and impulsive contamination; validation under wideband propagation, model mismatch, partial coherence, and measured array data remains future work. Full article

Surface anomalies such as dust accumulation and bird droppings on photovoltaic (PV) panels can significantly reduce their energy production and lead to inefficient maintenance decisions. This paper proposes a vision-based deep learning framework for the automatic detection of PV panel surface conditions and validates the detected anomalies using real inverter-level energy production data. Unlike conventional studies focusing solely on detection performance, the proposed approach introduces a unified and physically interpretable framework that directly links image-based anomaly detection with inverter-level energy performance and decision-oriented PV maintenance. An EfficientNetB3-based model is trained using a two-stage transfer learning strategy on a publicly available Kaggle dataset and evaluated using standard classification metrics. The trained model is then deployed and validated at a 1 MW solar power plant located at Karaman, Türkiye. Classification results obtained from field images are systematically linked with inverter-associated hourly energy production measurements. Following panel cleaning and natural rainfall, an approximately 12.5% increase in inverter-level hourly energy production is observed for the analyzed PV group (120 panels, ~270 Wp), corresponding to an increase from 23.2 to 26.1 kWh. In addition, the study introduces an energy–water–sustainability-aware cleaning decision framework tailored for arid and semi-arid regions where water scarcity and deep groundwater extraction present critical constraints. The framework defines a quantitative decision rule in which panel cleaning is performed only when the expected recoverable energy exceeds the energy cost of water extraction and cleaning. Overall, the proposed approach enables accurate surface anomaly detection while supporting sustainability-aware, resource-efficient and data-driven maintenance decisions for PV power plant operation. Full article

As the global energy transition accelerates, clean energy development has surged. However, accurately modeling correlations and uncertainties of hydro, wind, and photovoltaic energy remains challenging in long-term scheduling for energy complementarity. This study employs Latin hypercube sampling and Cholesky decomposition to capture the temporal correlations of water runoff, wind, and photovoltaic resources. It generates numerous scenarios for uncertainty simulation. The scenario set is reduced based on probability distance while maintaining a high-fidelity approximation. A stochastic dual-objective model is proposed for long-term multi-energy complementary system scheduling (LMCS), aiming to maximize expected revenue considering carbon emission costs while ensuring minimum power output guarantees. An evolutionary algorithm—namely, an orthogonal multi-population evolutionary (OMPE) algorithm based on orthogonal design and a multi-population search framework—is introduced, along with constraint-handling strategies. Three annual-regulation hydropower stations in the Hongshui River Basin serve as a case study. The experimental results indicate that generated scenarios capture temporal characteristics with high accuracy. The proposed algorithm efficiently solves the LMCS problem, achieving average increases of 5.46% and 3.89% in revenue and minimal output compared to benchmarks. The validation results demonstrate that orthogonalization-based initialization, recombination operators, and dominance rules significantly enhance OMPE performance. Sensitivity analysis indicates that economic efficiency and risk trade-offs can be adjusted by varying scenario numbers. Full article

Wastewater treatment is one of the major challenges in the reuse of water as a natural resource. Cleaning of water depends on analyzing and treating the water for the pollutants that have a significant impact on the quality of the water. Detecting and analyzing the surges of these pollutants well before the recycling process is needed to make intelligent decisions for water cleaning. The dynamic changes in pollutants need constant monitoring and effective planning with appropriate treatment strategies. We propose an edge-computing-based smart framework that captures data from sensors, including ultraviolet, electrochemical, and microfluidic, along with other significant sensor streams. The edge devices send the data from the cluster of sensors to a centralized server that segments anomalies, analyzes the data and suggests the treatment plan that is required, which includes aeration, dosing adjustments, and other treatment plans. A logic layer is designed at the server level to process the real-time data from the sensor clusters and identify the discharge of nutrients, metals, and emerging contaminants in the water that affect the quality. The platform can make decisions on water treatments using its monitoring, prediction, diagnosis, and mitigation measures in a feedback loop. A rule-based Large Language Model (LLM) agent is attached to the server to evaluate data and trigger required actions. A streamlined data pipeline is used to harmonize sensor intervals, flag calibration drift, and store curated features in a local time-series database to run ad hoc analyses even during critical conditions. A user dashboard has also been designed as part of the system to show the recommendations and actions taken. The proposed system acts as an AI-enabled system that makes smart decisions on water treatment, providing an effective cleaning process to improve sustainability. Full article

Arsenic contamination demands innovative, sustainable remediation. This study presents a fuzzy approach for synthesizing a magnetic biochar nanocomposite from pecan shell agricultural waste for efficient arsenic removal. Using a Multi-Input Fuzzy Rules Emulated Network (MiFREN), a systematic investigation of the synthesis process revealed that precursor type (biochar), Fe:precursor ratio (1:1), and iron salt type were the most significant parameters governing material crystallinity and adsorption performance, while particle size and N₂ atmosphere had a minimal effect. The MiFREN-identified optimal material, the magnetic biochar composite (FS7), achieved > 90% arsenic removal, outperforming the least efficient sample by 50.61%. Kinetic analysis confirmed chemisorption on a heterogeneous surface (q_e = 12.74 mg/g). Regeneration studies using 0.1 M NaOH demonstrated high stability, with FS7 retaining > 70% removal capacity over six cycles. Desorption occurs via ion exchange and electrostatic repulsion, with post-use analysis confirming structural integrity and resistance to oxidation. Application to real groundwater from the La Laguna region proved highly effective; FS7 maintained selectivity despite competing ions like ${Na}^{+}$, ${Cl}^{-},$ and ${SO}_4^{2-}$. By integrating AI-driven optimization with reusability and real contaminated water, this research establishes a scalable framework for transforming agricultural waste into a high-performance adsorbent, supporting global Clean Water and Sanitation goals. Full article

The environmental sustainability of cleaning materials used in heritage conservation remains poorly quantified despite growing attention to the replacement of hazardous petroleum-based solvents with bio-based alternatives. This study applies a comprehensive Life Cycle Assessment (LCIA) to compare conventional solvents with innovative bio-based formulations, including Fatty Acid Methyl Esters (FAMEs), Deep Eutectic Solvents (DES), and aqueous or organogel systems used for cleaning painted surfaces. Following ISO 14040/14044 standards and using the Ecoinvent v3.8 database with the EF 3.1 impact method, three functional units were adopted to reflect material and system-level scales. Results demonstrate that water-rich systems, such as agar gels and emulsified organogels, yield significantly lower climate and toxicity impacts (up to 85–90% reduction) compared with petroleum-based benchmarks, while FAME and DES exhibit outcomes highly dependent on allocation rules and baseline datasets. When including application materials, cotton wipes dominate total environmental burdens, emphasizing that system design outweighs solvent substitution in improving sustainability. The study provides reproducible data and methodological insights for integrating LCIA into conservation decision-making, contributing to the transition toward evidence-based and environmentally responsible heritage practices. Full article

Background: Typhoid fever (TF) is a systemic infection caused by Salmonella enterica serovar Typhi, typically associated with regions where sanitation and access to clean water are inadequate. Although rare in non-endemic countries, TF remains a diagnostic consideration in travelers returning from endemic areas with febrile illness. Case report: We present the case of an 18-year-old female who developed TF following recent travel to Nigeria. The initial clinical presentation, including fever, dysuria, and abdominal pain, led to a misdiagnosis of acute pyelonephritis. Malaria, arboviral infections, acute viral hepatitis, and parasitic diseases were systematically ruled out through clinical evaluation, serological testing, and parasitological analysis. The clinical course was marked by fever, abdominal pain, somnolence, and hematological and hepatic abnormalities. Blood cultures confirmed the diagnosis, with the isolate verified and serotyped by the National Center of Infectious and Parasitic Diseases. Targeted antimicrobial treatment with ceftriaxone and levofloxacin resulted in full recovery, with no evidence of relapse or chronic carriage over a three-month follow-up period. Conclusions: This case highlights the critical importance of a structured differential diagnostic approach and microbiological confirmation in febrile patients with relevant travel history. In non-endemic settings, where TF may be underrecognized, early recognition, pathogen identification, and appropriate antimicrobial therapy remain essential to favorable outcomes and public health safety. Full article

Yangzhi jingshui 楊枝淨水 (willow twigs and clean water) are part of one of the most popular rituals used in Chinese Buddhist practices. In order to preserve dental health and eliminate bad odors, the Vinaya texts specify rules on chewing willow twigs as a form of tooth brushing in one’s daily facial washing process. Willow twigs are also frequently employed in Esoteric (mijiao 密教) rituals, where they are accompanied by spells to create intricate ceremonies that have the power to heal illnesses, ward off bad luck, and bring about happiness and tranquility. For the development of this ritual in China, the usage of yangzhi jingshui was not originally connected to any particular deity, but later on, the ritual became primarily linked to Avalokitêśvara (Guanyin, 觀音), who was believed to use them as crucial tools for healing and saving lives. The symbolic meaning of using willow and water has been thoroughly discussed by Master Zhiyi 智顗 (538–597) and then has since developed into the more complete Repentance Practice of Guanyin (Guanyin chanfa 觀音懺法). Using yangzhi jingshui to save people as well as trees is also an important aspect described in Buddhist biographies and Chinese novels, such as Gaoseng zhuan 高僧傳 [The Biographies of Eminent Monks], Song Gaoseng Zhuan 宋高僧傳 [Biographies of Eminent Monks in the Song Dynasty], and stories of collected in Taiping guangji 太平廣記 [Extensive Records of the Taiping (xingguo) Period], Lunhui Xingshi 醒世輪回 [Reincarnation Stories to Awaken the World], and Xiyou ji 西遊記 [Journey to the West], which all demonstrate the rich cultural significance of this ceremony. Through the narratives of monks, the worship of Yangliu Guanyin, and its portrayal in the literature, yangzhi jingshui evolved from a cleansing tool in scriptures to a ritual object in Esoteric Buddhist healing ceremonies, ultimately becoming a common Buddhist practice. While new elements were added over time, its core themes of healing and purification have remained consistent. Full article

Most people in the world now live in urban areas and their shared quest for better cities is embodied in several Sustainable Development Goals of the United Nations. These indicate that successful cities need jobs, adequate housing stock, effective governance, and other support systems. At the most basic level, they need a basket of core public works services like clean water and efficient transit, among others. These must be provided to improve public trust in government by addressing equity and affordability while also improving operational and cost efficiency. These targets are moving as transitions are occurring from stove-piped to integrated services, even while social contracts between government and the private sector are also shifting. Essential tools to improve cities include urban planning and infrastructure development, but applying them effectively faces challenges like climate change, inequality, social disorder, and even armed conflicts. This paper focuses on seven core public works services for drinking water, wastewater, stormwater, trash collection, mass transit, streets and traffic control, and disaster management. It reviews how these have evolved in the US, how they are organized under the federalism system, and how the goal of integrated management is being pursued. Challenges to integrated approaches include increasing responsibilities but lack of funding, political stress, and rule-driven and internally oriented management. Methods for performance assessment are explained under legacy systems based on methods like indicators and benchmarking applied to public works systems. Current methods focus on regulatory targets and the details; information has been shallow and not always timely. This paper projects how the performance assessment of core public works systems can be broadened to address goals like those of the SDGs and assesses why it is difficult to rate major systems. Examples of the activities of NGOs are given and an example of how progress toward SDG6 is included to show why performance management of integrated management applied to linked systems is needed. Performance dashboards with open government are currently the most common pathways, but emerging methods based on data analytics and visualization offer new possibilities. Reviewing the status of public works management shows that it is an important branch of the field of public administration, and it can be presented as a professional field with its own identity. The findings will support educators and researchers as well as provide policy insights into public works and stakeholder engagement. Full article

Clean water is vital for a sustainable environment, human wellness, and welfare, supporting life and contributing to a healthier environment. Fuzzy-logic-based techniques are quite effective at dealing with uncertainty about environmental issues. This study proposes two methodologies for assessing water quality based on Mamdani and Sugeno fuzzy systems, focusing on water’s physiochemical attributes, as these provide essential indicators of water’s chemical composition and potential health impacts. The goal is to evaluate water quality using a single numerical value which indicates total water quality at a specific location and time. This study utilizes data from the Acea Group and employs the Mamdani fuzzy inference system combined with various defuzzification techniques as well as the Sugeno fuzzy system with the weighted average defuzzification technique. The suggested model comprises three fuzzy middle models along with one ultimate fuzzy model. Each model has three input variables and 27 fuzzy rules, using a dataset of nine key factors to rate water quality for drinking purposes. This methodology is a suitable and alternative tool for effective water-management plans. Results show a final water quality score of 85.4% with Mamdani (centroid defuzzification) and 83.5% with Sugeno (weighted average defuzzification), indicating excellent drinking water quality in Tivoli, Italy. Water quality evaluation is vital for sustainability, ensuring clean resources, protecting biodiversity, and promoting long-term environmental health. Intermediate model evaluations for the Mamdani approach with centroid defuzzification showed amounts of 72.4%, 83.4%, and 92.5% for the first, second, and third fuzzy models, respectively. For the Sugeno method, the corresponding amounts were 76.2%, 83.5%, and 92.5%. These results show the precision of both fuzzy systems in capturing nuanced water quality variations. This study aims to develop fuzzy logic methodologies for evaluating drinking water quality using a single numerical index, ensuring a comprehensive and scalable tool for water management. Full article

The cavitation water jet cleaning and coating removal technique represents an innovative sustainable method for cleaning and removing coatings, with the nozzle serving as a crucial component of this technology. Developing an artificially submerged nozzle with a reliable structure and excellent cavitation performance is essential for enhancing cavitation water jets’ cleaning and coating removal efficacy in an atmosphere environment (non-submerged state). This study is based on the shear flow cavitation mechanism of an angular nozzle, the resonance principle of an organ pipe, and the jet pump principle. A dual-nozzle co-current cavitation water jet nozzle structure was designed and manufactured. The impact of the nozzle’s inlet pressure on the vapor volume percentage, as well as the axial and radial velocities inside the flow field, were examined utilizing ANSYS Fluent software with the CFD method. The dynamic change rule of the cavitation cloud is derived by analyzing the picture of the cavitation cloud in the nozzle’s outflow field utilizing pseudo-color imaging techniques. The results show that the maximum vapor volume percentage is more significant than 95% for different inlet pressures in the internal nozzle. The changes that occur in the cavitation cloud exhibit notable regularity, including the four stages of cavitation, which are inception, development, shedding, and collapse. A change period is 1.5 ms, which proves that the homemade co-current cavitation water jet nozzle can achieve good cavitation effects. Full article