Search Results (256)

Shallow wetlands retain metals and metalloids from industrial, mining, agricultural, urban, and atmospheric sources; however, shallow water columns, active sediment–water exchange, and recurrent wetting–drying also favor remobilization. This critical narrative review evaluates physical, chemical, biological, and ecological engineering approaches for metal-contaminated shallow wetlands, focusing on the hydrological and biogeochemical conditions governing performance. Evidence was differentiated among natural or semi-natural wetlands, engineered wetland analogues, and transferable studies of contaminated sediments, soils, or wastewaters. Physical interventions rapidly control localized sediment inventories but can disturb habitats and redistribute particles. Chemical amendments reduce porewater concentrations and bioavailability, although durability depends on pH, redox conditions, dissolved organic matter, and competing ions. Biological and ecological engineering approaches can support ecological function recovery, but performance depends on plant traits, microbial processes, hydroperiod, and maintenance. No intervention was consistently superior across site conditions and outcome domains. Within this framework, sustainability is evaluated through durable risk reduction, ecological function recovery, life-cycle feasibility, responsible residual management, and accountable long-term stewardship. The reviewed mechanistic and conceptual evidence supports considering site-specific treatment trains, although direct comparative field evidence demonstrating their superiority over individual interventions remains limited. Future studies should prioritize hydrologically realistic field validation, standardized flux and bioavailability endpoints, mixed-contaminant scenarios, life-cycle assessment, and explicit measurement of ecological function recovery. Full article

Background: Retained totally implantable venous access ports (TIVAPs) pose a clinical management challenge after completion of intravenous anticancer therapy, particularly when future treatment needs remain uncertain. We aimed to characterize longitudinal post-treatment TIVAP trajectories after a day-90 conditional landmark, with elective removal prespecified as the primary first event. Methods: This retrospective cohort study included patients who were alive and event-free, retained the original TIVAP, had no documented TIVAP-related indication requiring removal, and had documented clinical and TIVAP status at day 90. Elective removal was the primary event; TIVAP reactivation, complication-related removal, and network-documented death were competing events. Results: Among 1406 patients, the 12-month cumulative incidence was 34.3% (95% confidence interval [CI], 31.4–37.1) for elective removal, 10.1% (95% CI, 8.2–11.9) for reactivation, 4.1% (95% CI, 3.0–5.3) for complication-related removal, and 1.0% (95% CI, 0.5–1.6) for network-documented death. During 1164.9 patient-years, 9978 maintenance-related patient-date encounters were recorded. In the assessed subcohort (n = 1319), 69.2% of assessments indicated willingness to choose a TIVAP again for similar future treatment, 64.4% indicated willingness to recommend TIVAP use, and 26.0% recorded a preference for earlier removal. Conclusions: Retained TIVAPs followed heterogeneous post-treatment courses, including elective removal, subsequent reuse, and continued retention requiring maintenance. These findings describe observed management patterns; they do not establish the comparative effectiveness of retention versus removal or identify an optimal removal time, and they support a prospective evaluation of structured reassessment pathways. Full article

Smart industries base their development, maintenance, and operation on technologies compatible with what is known as Industry 4.0. In this context, the education sector needs the implementation of new learning schemes and environments for conceptual and practical learning, thereby supporting students’ training to help in the industrial digital transformation. The main contributions of this work are a learning environment for robotics that integrates software applications that quantitatively measure the student’s learning process; a competence framework focused on learning robotics, ROS background, and developing pickup/delivery tasks very common in the Industry 4.0 context; and a learning path implemented in step-by-step guided documentation that considers the competence framework proposed and progressively teaches ROS concepts. This work also describes the development of the learning environment and the software tools that support the student’s learning process. Two pilot tests were performed on iBotAcademy considering twenty students of the Universidad del Valle. The iBotAcademy mobile robotics and the manipulator robotics learning paths achieved an average of 82% positive answers, and 99% positive answers, respectively. The average coefficients of variation of these tests were 16.4% and 8.3%, showing high data consistency. Then, the iBotAcademy learning environment helps to train students for the industrial digital transformation. Full article

Dirofilaria immitis, also known as dog heartworm, is a filarial nematode of medical and veterinary importance. It is primarily transmitted by mosquitoes of the genera Aedes, Anopheles, Culex, and Mansonia, including the invasive Aedes albopictus. While primarily a veterinary pathogen, this parasite occasionally causes cutaneous or pulmonary dirofilariasis in humans. The aim of this study was to investigate the status of D. immitis in Ae. albopictus, a competent invasive vector, and to assess its prevalence in the mid-Atlantic United States. Aedes albopictus specimens were collected from various urban and suburban localities in Suffolk, Virginia, during 2023. Genomic DNA extracted from the mosquitoes was screened for D. immitis using diagnostic genes in PCR assays, and the resulting products were sequenced to confirm the presence of this parasite’s DNA. Additionally, the host choice of Ae. albopictus was identified using PCR assays targeting the mitochondrial cytochrome b gene and sequencing. Dirofilaria immitis was detected in 9.2% (53/579) of the tested mosquito specimens, comprising 12.1% (46/379) of blood-fed and 3.5% (7/200) of non-blood-fed mosquitoes. Aedes albopictus predominantly obtained their blood meals from domestic cats and Virginia opossums. Among the D. immitis-positive specimens, the majority had obtained their blood meals from domestic cats and white-tailed deer. Our study provides evidence of the potential role of Ae. albopictus in the maintenance and transmission of D. immitis in this region and highlights the utility of xenosurveillance for tracking pathogen circulation in animal populations. Full article

This case study investigates specific language phenomena and dominance patterns among second-generation speakers of Sri Lankan origin in Naples within the framework of the HELLO Campania Project. While second generations are typically assumed to be dominant in the societal majority language, the Sri Lankan community—characterized by exceptionally high rates of heritage language maintenance—offers a critical testing ground for this assumption. Drawing on a corpus of sociolinguistic interviews with first- and second-generation speakers, the analysis focuses on two morphosyntactic variables that function as indicators of acquisitional development in Italian as L2: verbal morphology and the realization of definite and indefinite articles. Verbal forms were classified along a four-stage developmental scale derived from research on Italian interlanguage, while article use was evaluated through a three-level accuracy scale. Descriptive and inferential statistics reveal a clear asymmetry within the second generation specifically in terms of gender variable. Male speakers display near-native competence consistent with the expected dominance shift toward Italian. In contrast, several female speakers exhibit persistent features—such as infinitive overextension, auxiliary omission in perfective constructions, and residual article omission—patterns more typical of first stages of second-language acquisition than of heritage-speaker dominance. These findings suggest that, in contexts of strong heritage language maintenance and dense ethnic social networks, second-generation speakers may experience incomplete acquisition of the majority language rather than incomplete acquisition of the heritage language. Methodologically, the study demonstrates the importance of integrating perceptual self-reports with production data. Theoretically, it challenges universalist assumptions about second-generation dominance and underscores the need for community-specific, migration-scenario-based analyses in heritage language research. Full article

According to recent estimates, France is home to approximately 700,000 individuals of Turkish origin, encompassing first-, second-, and third-generation immigrants. Scholars have extensively investigated the language practices, maintenance, and shift, as well as the cultural practices and identity construction, of Turkish migrant families in France, employing both sociolinguistic and psycholinguistic approaches. However, the majority of these studies have examined family members in isolation, focusing separately on young children, adolescents, or parents. The present study aims to investigate language use, choice, and maintenance, as well as identity construction, across three generations of Turkish immigrants in France. Data were collected from two Turkish families, one in the Rouen region and the other in the Paris region: the first family consists of a father, his daughter, and his granddaughter, while the second family includes a mother, her daughter, and her granddaughter. To do this, we used questionnaires and semi-structured interviews tailored to each of the three generations. The data were subsequently analyzed through content analysis. The paper addresses the following interconnected research questions: (i) What intergenerational differences exist within the same family regarding language use and choice, as well as linguistic competence in L1 (Turkish) and L2 (French)? (ii) How do bilinguals’ language choices and usage relate to the construction of family members’ identities? (iii) Is there evidence of language attrition among third-generation immigrants? (iv) Is there evidence of an ongoing language shift toward L2 within Turkish families? The findings indicate robust maintenance of Turkish, even among the third generation. While the first generation predominantly uses Turkish, both the second and third generations identify themselves as bilingual and exhibit positive attitudes toward both languages. Full article

Battery energy storage systems can reduce the operating cost of active distribution networks while supporting voltage control through their power electronic converters. This paper develops an application-specific multi-operator Differential Evolution (DE) framework for the coordinated active and reactive power scheduling of distributed battery energy storage systems in radial distribution networks with photovoltaic generation. The optimization model minimizes the daily operating cost associated with conventional energy supply, photovoltaic and storage operation and maintenance, and battery degradation. Candidate schedules encode hourly active and reactive power references for three storage converters, producing a 144 dimensional decision vector for a 24 h horizon. Each candidate is repaired to satisfy active power, state of charge, terminal energy, and converter apparent power limits before being evaluated through an alternating current power flow based on matrix successive approximations. The search framework generates three competing trial schedules per target individual by combining established best-guided, random, and current-to-random DE mutation families with a discrete parameter pool, a common feasibility-repair operator, and greedy selection after AC network evaluation. The method is tested on modified 33-node and 69-node active distribution networks and compared with AJAYA, genetic algorithm, multiverse optimizer, and particle swarm optimization. In the deterministic 33-node case, Differential Evolution obtains the lowest best cost, USD 6846.206, and the largest best cost reduction, 2.1838 percent. The scenario study performs separate deterministic optimizations for pre-generated operating realizations and is therefore interpreted as a scenario-conditioned sensitivity assessment rather than as stochastic or robust optimization of one here-and-now schedule. In this assessment, DE achieves the largest average savings: 2.3487 percent in the 33-node network and 2.9314 percent in the 69-node network. Voltage magnitudes, branch loading, converter ratings, and cyclic state of charge constraints are satisfied in all evaluated cases. The results identify the proposed framework as a competitive day-ahead solver within the evaluated cases, while no claim of global optimality or universal superiority over alternative optimizers is made. Full article

The digital transformation associated with Industry 4.0 has increased the demand for engineers with competencies in condition monitoring, the Internet of Things (IoT), and artificial intelligence (AI). However, evidence on educational methodologies integrating these technologies within a competency-based assessment framework remains limited. This study developed and preliminarily evaluated a competency-based educational methodology and characterized the technical, digital, and analytical competency profiles of Mechanical Engineering students following participation in smart maintenance learning activities. A descriptive–exploratory case study was conducted with 31 students enrolled in an Industrial Maintenance course. The methodology integrated vibration analysis, infrared thermography, acoustic monitoring, IoT-based remote monitoring, and ChatGPT-assisted fault diagnosis. Competencies were assessed using an analytic rubric applied by six faculty evaluators, while students’ perceptions were examined through a 22-item questionnaire. The Global Competency Index for Smart Maintenance (GCIIM) was 72.86%, indicating a moderate overall competency level. Critical evaluation of artificial intelligence achieved the highest score, whereas fault diagnosis, technical communication, and technological adaptability showed the lowest performance. Students reported a favorable overall perception score (75.04%) across the questionnaire dimensions. These findings provide preliminary evidence of the feasibility of an integrated competency-based framework for smart maintenance education and its potential to support the responsible incorporation of digital technologies and artificial intelligence into engineering curricula. Full article

Predictive maintenance of electric vehicle (EV) fleets requires accurate estimation of Remaining Useful Life (RUL), Time-to-Failure (TTF), and State-of-Health (SOH) from heterogeneous Internet of Things (IoT) telemetry. However, real-world degradation patterns are nonlinear, nonstationary, and highly imbalanced near failure. This study proposes Q-TRACNet, a temporal attention framework that combines causal maintenance-aware preprocessing, adaptive temporal condensation, residual refinement, learnable phase modulation, and hybrid Particle Swarm Optimization–Quantum-Guided Descent parameter tuning. The framework is evaluated on the EV-HLM-RUL dataset and three established prognostics benchmarks: NASA CMAPSS, PHM 2012, and XJTU-SY. Chronological training, validation, and testing partitions are used to preserve temporal causality. On EV-HLM-RUL, Q-TRACNet achieves an MAE of 9.8, an RMSE of 14.7, an $R^2$ of 0.979, and a Critical Degradation Awareness Index (CDAI) of 0.91. It reduces RMSE by 20.11% relative to the strongest competing baseline and achieves an NRMSE of 0.102 and a Kendall correlation of 0.89 ($p<{10}^{-4}$). Cross-dataset experiments demonstrate stable performance for RUL, TTF, and short- and long-horizon SOH prediction. Ablation and sensitivity analyses further confirm the contributions of the temporal and attention components and the stability of degradation-aware evaluation. Q-TRACNet also provides lower training cost and inference latency than competing architectures, supporting practical maintenance planning, inspection prioritization, and resource allocation in connected EV fleets. Full article

This article examines heritage language transmission within Chinese migrant families in Italy, focussing on the role of Family Language Policy and the interplay between standard (Putonghua) and non-standard local varieties (fangyan). Drawing on semi-structured interviews with six families and the primary school teachers of their children (aged 6–8), the study adopts a multi-perspective design to explore how reported language practices, beliefs, and attitudes interact across family and school domains. Findings indicate a predominant shift towards Putonghua as the primary heritage language, often at the expense of fangyan, which tends to be marginalised by parents or reported as being retained only as passive competence among children. This shift reflects practical considerations and evaluative orientations related to prestige, usefulness, and standardisation. Variation across families shows that alternative orientations persist, with one family valuing fangyan for intergenerational continuity. Teachers’ perspectives further suggest a limited awareness of internal linguistic diversity within the student population. The convergence of family-driven shifts and school-based perceptions may contribute to processes of increasing standardisation in the representation and transmission of heritage varieties. This article contributes to research on heritage language maintenance by offering contextually situated insights into how the interplay between social actors and contextual factors may shape linguistic diversity within diasporic communities. Full article

In response to rapid shifts toward electrification, digitalization, and sustainability in the automotive industry, this study developed a sustainability-oriented, evidence-based competency framework for automotive maintenance technicians. Traditional competency frameworks, often derived from manufacturer manuals or curricula, overlook tacit knowledge from real-world maintenance practices, leading to gaps in diagnostic effectiveness, service quality, and resource efficiency. To address this limitation, 8500 maintenance records from 67 service centers (2022–2025) were subjected to quantitative content analysis to identify preliminary competency indicators across five vehicle systems. A three-round Delphi survey involving 24 senior automotive experts was subsequently conducted to validate and prioritize these indicators on the basis of mean importance scores and coefficients of variation (≤0.20). The final framework comprised 39 competencies, such as diagnostic proficiency, electronic system integration, system-level troubleshooting, and technical documentation application. Beyond traditional mechanical skills, cross-system diagnostic capability and digital tool proficiency have become essential competencies for modern electric vehicles. By transforming tacit maintenance knowledge into measurable indicators, the developed framework can contribute to supporting workforce sustainability, enhancing repair accuracy, reducing unnecessary part replacement, and improving resource efficiency. It can also inform vocational education, industry certification, and human capital development aligned with Sustainable Development Goals 8, 9, and 12. Full article

Ensuring the reliability and operational efficiency of critical scientific equipment is a central challenge in high-stakes research environments such as nuclear physics laboratories and particle accelerator facilities. Unexpected failures entail significant financial cost and prolonged interruptions to experimental programmes. We present a predictive maintenance (PdM) framework built around a two-layer Vanilla Long Short-Term Memory (LSTM) network trained on multivariate sensor streams collected at NRF-iThemba LABS between January 2021 and December 2023. Four channels, namely supply voltage, vibration velocity, differential pressure, and rotational speed, were recorded at 5 $\min$ intervals using a suite of industrial-grade transducers (power quality analyser, IEPE accelerometers, differential pressure transmitters, and proximity encoders) feeding a multi-channel data-acquisition chassis via OPC-UA, yielding a time-synchronised dataset of 315,360 observations. A normalised failure score converts the binary classifier output into a continuous, interpretable health indicator that supports tiered scheduling of maintenance. The Vanilla LSTM achieved a test-set F1-score of $75\%$ and an area under the receiver-operating-characteristic curve (AUC) of $0.856$, outperforming five competing architectures (PCA/$T^2$, Random Forest, Deep Neural Network, LSTM Autoencoder, and Bidirectional LSTM Autoencoder), and delivered a mean failure lead time of $(42.3\pm 7.2)$$\mathrm{h}$, exceeding the 36 $\mathrm{h}$ engineering requirement for proactive maintenance scheduling. Full article

2,4-Dichlorophenoxyacetic acid (2,4-D), originally developed as a synthetic auxinic herbicide, is the most widely used chemical inducer of somatic embryogenesis (SE) in plants. Despite extensive use of 2,4-D in plant regeneration, the systems-level regulatory mechanisms connecting hormonal signaling, metabolic reprogramming, translational control, and embryogenic competence remain poorly resolved. Here, we hypothesize that TOR signaling functions as an integrative molecular hub coordinating transcriptional, metabolic, and developmental reprogramming during somatic embryogenesis induction. To investigate the molecular regulatory landscape associated with 2,4-D-induced SE, we performed a systems-level analysis integrating publicly available transcriptomic data from Arabidopsis thaliana with high-confidence protein–protein interaction (PPI) network analyses using STRING v12.0 (confidence score ≥ 0.900). Using a previously published transcriptomic dataset, we identified 1927 upregulated genes associated with SE induction, which were organized into 34 functional modules related to transcriptional regulation, translation metabolism, hormone signaling and cellular homeostasis. Within this interactome, TARGET OF RAPAMYCIN (TOR) kinase emerged as an integrative regulatory hub associated with multiple pathways involved in embryogenic reprogramming. Network analyses revealed three major TOR-associated regulatory axes: (1) the TOR–FKBP12–RPS6A axis, associated with ribosome biogenesis and translational regulation; (2) the TOR–CBP20 axis, connected with transcriptional reprogramming; SE master regulators (LEC1, LEC2, and FUS3); and lipid, sterol, brassinosteroid (BR), and auxin-associated pathways; and (3) the TOR–TAP46 axis, linked with one-carbon metabolism, nucleotide biosynthesis, DNA replication and repair, and genome-stability pathways. Additionally, the network contained 411 embryo-lethal (EMBL) genes distributed across multiple regulatory modules, reinforcing the biological relevance of the identified interactome and highlighting the importance of coordinated developmental, metabolic, and transcriptional regulation during embryogenesis induction. These findings support a systems-level TOR-associated regulatory framework involved in the integration of transcriptional, translational, metabolic, hormonal, and genome-maintenance pathways during embryogenesis. This interactome model provides a foundation for functional studies aimed at dissecting the molecular mechanisms underlying SE and identifying candidate targets to improve regeneration and biotechnological application and crop genetic engineering. Collectively, this study proposes a mechanistic framework in which TOR signaling integrates developmental, metabolic, translational, and genome-stability pathways to orchestrate embryogenic competence, providing candidate molecular targets for improving plant regeneration and genome engineering platforms. Full article

The stabilization of public infrastructure on active volcanic slopes presents significant geotechnical challenges, particularly in coastal regions exposed to extreme hydrological stressors. This paper presents a forensic diagnosis and the structural rehabilitation of the Porto da Cruz Cemetery (Madeira Island, Portugal), which suffered severe progressive failure following localized, shallow-founded interventions in 2004. Historical inclinometer data (2015–2022) revealed continuous deep-seated creep within the volcanic colluvium (Geotechnical Zone 2–ZG2) at rates up to 0.17 mm/day, triggered by basal fluvial undercutting. To mitigate these kinematic drivers, a systemic “Toe-to-Crest” stabilization paradigm was implemented. Following the hydraulic confinement of the slope’s lower boundary, a high-capacity deep foundation network—comprising 26 m rock-socketed micropiles and 600 kN active multi-strand anchors—was executed to bypass the failure plane and encastre directly into the competent basaltic bedrock (Geotechnical Zone 1–ZG1). The structural performance was validated through rigorous load testing and a real-time robotic Structural Health Monitoring (SHM) system. Post-construction telemetry confirmed absolute kinematic stabilization, maintained continuously throughout the critical execution phases and subsequent monitoring period (2024–2025). By integrating deep bedrock anchoring, pore-pressure mitigation, and digital telemetry, this case study validates the economic and geomechanical superiority of systemic subsurface bypass over reactive surface maintenance. Ultimately, it establishes a scalable, climate-adaptive engineering blueprint for safeguarding critical coastal heritage across Macaronesia against escalating environmental multi-hazards. Full article

The supervisory control and data acquisition (SCADA) data are increasingly used for wind turbine anomaly detection, but purely data-driven methods may be limited by weak physical interpretability, class imbalance, and reduced generalization under changing wind-farm operating conditions. Although the Extreme Gradient Boosting (XGBoost) is effective for structured nonlinear classification, its use in SCADA-based anomaly detection remains affected by label quality, probability calibration, and cross-farm transferability. This paper validates a physics-informed XGBoost decision layer using residual-based indicators, including power-curve residuals, gearbox and generator thermal residuals, rotor-speed variance, active-power ratio, and wind-speed fluctuation. Comprehensive Anomaly Detection Benchmark for Wind Turbine SCADA Data (CARE) logbook labels are used as the reference labels, while 2σ, 3σ, and 4σ residual thresholds are evaluated as competing rule-based detectors. The decision layer is trained and internally tested using event-grouped chronological splits from Wind Farm A and externally evaluated on unseen Wind Farms B and C. The results show physically interpretable anomaly detection behavior, although performance varies across validation settings. Under external Farm A to Farm B/C transfer, XGBoost achieved row-level F1-scores of 0.6296 and 0.6551, respectively. Shapley additive explanations (SHAPs) link anomaly predictions mainly to thermal, power-conversion, and operating-context features. The findings support the proposed decision layer as an interpretable benchmark-validation framework, while showing that additional maintenance-log validation is required before definitive component-level fault-diagnosis claims can be made. Full article