This study focuses on the Lujiang Alum Mine, analyzing sources of acid mine drainage (AMD) generated during remediation activities. A numerical model of groundwater flow was constructed to simulate and predict the causes of AMD under the influence of remediation measures. Concurrently, hydrogen and oxygen stable-isotope-tracing techniques were employed to elucidate the pathways through which AMD occurred and the mechanisms underlying water acidification. A fully mixed model was established to quantify the rates of contribution from different water sources. The results indicate that the annual amount of acidic wastewater produced under the influence of disturbance via remediation is approximately 3.29 × 105 m3. The fully mixed model based on environmental isotopes further revealed that the discharge of water from the first branch of the +85 m adit serves as the primary cause of AMD during the wet, normal, and dry seasons, with a contribution exceeding 50%. This is followed by recharge from Tianchi Lake, accounting for approximately 20–30%. In contrast, the contributions from seepage water from the roof of the +85 m adit and water from the Xiaofanshan Inclined Shaft are relatively minor. Based on these findings, we propose targeted strategies for source prevention and end-of-pipe treatment of AMD in the mining area. This work provides scientific support for the ongoing ecological restoration project at the Lujiang Alum Mine and offers valuable insights for AMD management in similar mines.

Cloud-native environments introduce highly dynamic security challenges due to ephemeral workloads, distributed microservices, and continuous deployment practices. Conventional vulnerability management approaches, primarily reactive and severity-score driven, struggle to maintain adequate security posture under these conditions. This article presents an intelligent vulnerability management framework that leverages predictive threat intelligence and machine learning to enable continuous, risk-aware security assessment in cloud-native environments. The proposed framework integrates real-time vulnerability discovery with predictive analytics to estimate exploitation likelihood and prioritize remediation actions based on contextual risk. Vulnerability signals are correlated with threat intelligence indicators, environmental attributes, and historical exploitation patterns to generate actionable risk scores. These insights are embedded directly into infrastructure-as-code and GitOps workflows, enabling automated remediation and compliance enforcement without disrupting deployment velocity. Empirical evaluation across heterogeneous cloud-native workloads demonstrates a 73% reduction in mean time to remediation, an 89% improvement in effective vulnerability detection accuracy, and a 58% reduction in security alert volume compared to traditional reactive scanning approaches. False-positive rates were reduced from 12.7% to 3.2%, improving analyst efficiency and remediation focus. The results indicate that predictive threat intelligence can substantially enhance vulnerability management effectiveness while preserving operational scalability and system performance.

This paper aimed to investigate the extent of trace metal (TM) contamination of soils in areas adjacent to the bunkers of the Molotov Line in Poland and to assess reclamation activities on the extent of TM contamination of soils. The Molotov Line is a zone of Soviet fortifications constructed in 1940–41. Surface (0–20 cm) and subsurface (20–40 cm) soil samples were collected at four transects and distances from the bunkers. TMs (Cd, Cu, Mn, Pb, Zn, Ni, Cr and Bi), pH, texture, TOC, HA, base exchange capacity (BEC) and effective cation exchange capacity (ECEC) were determined in the investigated soils. Several indicators of contamination were used to analyse the degree of contamination: Igeo, pollution load index (PLI), pollution index (PI), CD, RI and top-bottom (TB) index. The conducted research has revealed that soils subjected to military pressure exhibit different properties from natural soils. The TM content in the 0–20 cm soil layers was higher than in the subsurface layers and several times higher than the geochemical background. This indicates that despite remediation efforts (ploughing and afforestation), there exists a clear geochemical record of military activities along the Molotov Line. Thanks to the contamination indices used in this study, it was found that soils affected by the past wartime activities may pose a real threat to health. The regularities presented in this study can provide a basis for action regarding the direction of remediation activities for areas with sensitive uses, such as military training grounds. The results presented here allow us to conclude that despite the remediation activities undertaken, there is a clear geochemical record of military activities on the Molotov Line.

Machine learning is a cornerstone of data science, enabling the analysis and prediction of complex data patterns. Among various algorithms, Naive Bayes is a popular probabilistic classifier based on Bayes' theorem, assuming strong independence among features. Its efficiency in handling large datasets, coupled with ease of implementation, makes it a valuable tool in data science workflows. The purpose of this study is to illustrate the research and development trends of Turkish business enterprises based on unit of measure (national currency or US dollars) and price basis measurements by the Naive Bayes algorithm. The result of classifying shows that economic activities show varied preferences for currency and price bases: agriculture, forestry, and fishing, construction, and sewerage, waste management, and remediation activities are split equally between national and US dollar currencies and price types. Manufacturing favors current prices; mining and quarrying prefer national currency and current prices, while services lean towards the US dollar and current prices. Business enterprises with all economic activities prioritize the national currency (67%) and constant prices (67%).

ABSTRACT This paper examines how communication strategies shape citizen trust in regulatory agencies following different types of regulatory failure. Using a survey experiment on a representative sample of 1568 Danish citizens, it tests the effects of five communicative strategies. The results indicate that the effectiveness of communicative responses in promoting citizen trust is highly context-sensitive. Overall, the results support earlier studies indicating that ‘silence’ is less effective than active communication. In addition, admitting responsibility does not appear crucial for fostering citizen trust, while active strategies involving a justification or a plan for remedial action seem to be the most effective.

This paper proposes the novel application of group counterfactual explanations to the problem of predicting students at risk of dropout. Our objective is to explain how to recover the largest possible number of students while minimizing effort and cost. Using group counterfactuals, instructors and institutions could recover large groups of students with minimal remedial actions. For testing, we used the well-known public educational Open University Learning Analytics Dataset (OULAD), which contains students’ clicks made throughout interactions with online courses. We modified and adapted the only existing algorithm for the generation of group counterfactuals, named GROUP-CF. We also used the Diverse Counterfactual Explanations (DiCE) individual counterfactual algorithm with the K-means clustering method and new options in discovering the most representative counterfactuals for a group of students. The results obtained are very promising; our approach can be successfully applied to recover 99.3% of students at risk of failing in a shorter time in comparison to traditional individual counterfactuals. Moreover, although a group counterfactual proposes to change a greater number of students’ features, the values are lighter and therefore seem easier to apply than the ones obtained with individual counterfactuals. This work opens up a new line of research in education.

Abstract This report presents the findings of a visual structural damage assessment conducted for an office building of the Dredging Corporation of India Limited in Navi Mumbai, India. The objective was to evaluate the structural integrity of the aged reinforced concrete framed building to ascertain its condition status and recommend urgent remedial actions. The methodology employed a comprehensive visual reconnaissance survey, supplemented by hammer tapping, to identify defects including concrete spalling, crack mapping, reinforcement corrosion, and water ingress. Results indicate severe structural deterioration in load-bearing elements, with a high probability of active corrosion and significant water damage, compromising the building's safety and stability. The significance of the assessment lies in its urgent call for immediate propping and comprehensive scientific repairs to prevent potential collapse, thereby safeguarding human life and outlining a critical methodology for assessing aging infrastructure in coastal urban environments.

Chronic hepatitis B is a serious liver infection, caused by the hepatitis B virus. Chronic hepatitis B further leads for the risk of liver function failure, liver cancer and serious scarring of the liver called cirrhosis. The objective of the current study was to investigate the prevalence of chronic hepatitis b virus infection and associated factors among pregnant women attending antenatal care at Felege Hiwot Comprehensive Specialized Hospital Ethiopia. Institutional based retrospective cross sectional study design was conducted at Felege Hiwot Comprehensive Specialized Hospital. To measure the prevalence of hepatitis B infection in the study area, testing of hepatitis B surface antigen (HBsAg) was conducted over time. The study was conducted among 221 pregnant women who have attended antenatal care. Systematic random sampling technique was conducted for the selection of participants in the current study. The overall prevalence of hepatitis B virus among pregnant women in the current study was 4.55%. Among 221 pregnant women taken as study participants, 49 of them who had history of medical illness were screened for hepatitis B surface antigen and among these 25 had positive result, yielding a sensitivity (95% confidence interval) of 80% (70 - 88.5) and a specificity of 96.8% (93.7 - 98.4) for Healgen and Determine; 91.6 - 96.5 for Advanced Quality). Among the predictors, history of blood transfusion [AOR = 11.5; 95%, CI: (8.67, 17.5), history of abortion (AOR = 3.47, 95% (1.29, 7.40), history of genital mutilation (AOR = 12.2, 95% CI: (9.30, 18.4)), history of tattoo (AOR = 6.04, 95% CI: (1.20, 10.0)), drinking alcohol (AOR = 1.52, 95% CI: (1.19, 4.36)), multiple sexual partner (AOR = 1.35, 95% CI: (1.09, 4.82)) and History of contamination with individuals with liver disease (AOR = 1,45; 95% CI: (1.23, 7.84)). The prevalence of hepatitis B virus was significantly affected by history of blood transfusion, history of alcohol consuming, history of contact an individual with liver disease, having multiple sexual partners, body tattoo and history of genital mutilation. The first step during ANC shall be screening of HBV infection for pregnant women under the follow up period to take remedial action. The current study recommends conducting health education campaigns to make awareness about HBV transmission and prevention among pregnant women.

In the paper are described the theoretical basis of safety criteria N-1 and remedial actions applied in operational planning in power systems. A description of one of the operational planning procedures is presente.The results of two examples of applying remedial actions to the grid models in the software tool Enterprise Transmission Network Analyzer 2.4 (eTNA) are presented.

Theoretical background: Implementing the Environmental Liability Directive (ELD) based on the “polluter pays” principle has increased the legal liability of entities using the environment for the imminent threat of environmental damage and environmental damage caused by their activities. Conclusions of the ELD evaluation assessment and Special Report prepared by the European Court of Auditors revealed several problems confirming a need for continuous monitoring and control of the implemented environmental law, usually performed by the state. Complying with legal requirements in the field of environmental protection is a principal-agent relationship, where based on the implicit social contract, the state (the principal) gives licences to operate and partly delegates the duty to protect the environment to the operator (the agent) and the agent in case of imminent threat of environmental damage or environmental damage is required to, inter alia, take preventive and remedial action. Purpose of the article: The paper mainly aims to examine, whether and to what extent standalone environmental insurance, also referred to as environmental impairment liability (EIL) insurance, may be used by authority (the principal) as an instrument of control of environmental damage in terms of the ELD caused by operators (the agent). Research methods: The research methodology involves deductive reasoning and is based on a qualitative analysis of the literature (mainly on agency theory and environmental insurance), the ELD, national reports for Member States from the CEE countries, including ELD cases, industry reports, and documents on the environmental insurance market in the EU-CEE countries, and a comparative analysis of the features of available insurance solutions for environmental damage. Main findings: The paper’s findings indicate that standalone environmental insurance, which supports the environmental risk management process and provides a cover for liabilities arising under the ELD, may be used to monitor the agency’s activity. However, despite the broader scope of standalone environmental insurance coverage and better alignment with ELD requirements than that of traditional property and casualty insurance, it is not a widely available and commonly used tool in selected CEE countries. A great deal of effort is therefore needed to develop these EIL insurance markets for cooperation between the government and the insurance market in this area.

Although the evaluation of Islamic Religious Education (PAI) learning has received attention in previous studies, research that specifically investigates the completeness and quality of evaluation components in Kurikulum Merdeka-based PAI teaching modules at the vocational high school level remains limited. This study aimed to analyze the completeness, appropriateness, and quality of learning evaluation components in PAI teaching modules developed by teachers at SMK Negeri 1 Sei Suka. A qualitative approach was employed using a case study design grounded in document analysis, involving 3–4 PAI teachers selected through purposive sampling. Data were collected through document analysis of PAI teaching modules and semi-structured interviews, and were then analyzed using content analysis and thematic analysis. The findings indicate that the PAI teaching modules generally include diagnostic, formative, and summative assessments and, for the most part, align with the principles of authentic assessment in Kurikulum Merdeka. However, variations in quality were still identified, particularly in the clarity of rubrics for assessing attitudes, the accuracy of assessment indicators, and the systematization of follow-up learning programs in the form of remedial and enrichment activities. These findings contribute to the development of learning evaluation theory and authentic assessment in Islamic education and broaden the understanding of PAI learning evaluation practices in vocational high schools. The study concludes that the systematic, measurable, and authentic development of learning evaluation components plays an important role in improving the quality of PAI instruction and recommends sustained support for teachers through training and mentoring in assessment development. The implications of this study include theoretical contributions to enriching the literature on PAI learning evaluation and practical implications for schools and teacher professional development, while also opening up opportunities for further research on the influence of evaluation quality on student learning outcomes and character formation.

Cloud infrastructures generate millions of telemetry events per second, making manual monitoring impractical. Traditional anomaly detection systems rely on static thresholds or supervised ML, which limits their ability to detect novel incidents. This study presents a generative AI framework that uses variational autoencoders and diffusion-based sequence models to learn normal cloud behavior and flag emerging anomalies. The model processes log streams, metrics, configuration changes and request traces from distributed microservices. Experiments were conducted on OpenStack clusters and AWS EC2 testbeds under both normal and fault-injected conditions. Results show a 29 percent improvement in anomaly detection accuracy and a 33 percent reduction in false alarms compared with LSTM- and PCA-based baselines. The system also predicts incident severity and suggests remediation actions through a reinforcement learning component trained on historical operational data. Deployment studies confirm that the model scales efficiently across multi-region cloud clusters and maintains sub-100 ms detection latency. The findings demonstrate that generative AI can significantly improve cloud observability and autonomous IT operations. Future work will focus on integrating cross-platform observability standards such as OpenTelemetry.

The clamping force for surgical head immobilization must be sufficiently large enough to prevent slippage but not to induce fracturing. Motivated by an unmet need to detect skull fractures during surgery to trigger remedial action, we sought to demonstrate a method of viable measurement and interpretation of acoustic emissions (AE) generated by a fracturing skull prior, during, and after a fracture. Escalating clamping force via skull pins affixed in an Integra Life Sciences Mayfield® skull clamp was applied to a formalin-fixed cadaveric head. Variable and pathologic skull thickness was simulated by drilling the skull to reduce thickness at pin locations on the frontal or temporal bone. AE was monitored via an in-house developed device. This method produced a mixture of "punch-out type" skull fractures with some occurrences of larger area "cave-in" type fractures. The recorded AE waveforms, amplitudes, and temporal patterns showed the skull can fail not only immediately upon application of clamping, but also in delayed manners seconds or minutes after force application. A stereotypical, escalating rate of event occurrence and amplitude was detectable prior to delayed fractures (foreshocks) up to a peak event correlating with macroscopic fracture. A stereotypical hyperbolic decay of event rates was detectable immediately post-fracture (aftershocks). Other acoustic sources like drilling were distinguishable. Skull fractures produce stereotypical AE, possibly identifiable intra-operatively. Detecting patterns indicative of impending or recent fracture may allow for immediate intervention, avoiding severe complications. This represents the first reported evidence of detectable foreshock and aftershock AE sequences from bone fracture and points to striking parallels with seismicity generated by earthquakes, thus enabling tools from geophysics to be applied to detect imminent and recent bone failure.

A petrochemical plant in the Middle East was experiencing caustic stress corrosion cracking (SCC) failures of carbon steel piping in a caustic loop removing a catalyst from the process stream. The rest of the loop, also carbon steel, did not experience any failures. A sister plant in the US was experiencing no failures at all. Electrochemical testing was undertaken to identify the probable cause of the SCC. The results showed that the caustic liquor was very reducing, and the polarization curves and open circuit potentials differed for the two plant liquors. A probable cause of the SCC was identified, and some options for remedial action were suggested.

Rethinking soil contamination assessment for urban brownfield management: from total hazard to bioavailability.

Background: Growth faltering and nutritional deficiencies are still playing significant roles in childhood morbidity in developing countries. Timely detection of clinical predictors is important towards avoiding long-term physical and developmental disability in children below the age of five years. Objectives: To identify clinical predictors of growth faltering and nutritional deficiency in children below the age of five years, who report to a tertiary care hospital. Methods: 160 children of the age 659 months were reviewed in the cross-sectional clinical study. A structured questionnaire was used to record demographic features, feeding behaviors, socioeconomic status, frequent infections, immunization history, and medical conditions to collect data. Anthropometry was done according to the WHO standards encompassing weight, height/length, and MUAC. When necessary, laboratory tests comprised of hemoglobin and ferritin, vitamin D, and zinc concentrations. Chi-square tests and logistic regression were used as the statistical analysis with the level of significance of p < 0.05. Results: 46.9 percent of the children had growth faltering with most of the abnormalities being stunting (27.5 percent). Nutritional deficiencies were revealed 58.1, iron deficiency anemia (36.2) and vitamin D deficiency (26.2) were the most common. Recurrent diarrhea (p = 0.001), recurrence respiratory infections (p = 0.002), poor dietary intake (p < 0.001), low birth weight (p = 0.004) and incomplete immunization (p = 0.001) were significant predictors of growth faltering. Conclusion: Nutritional and infectious factors play a major role in the growth faltering of young children, which is complicated by socioeconomic issues. Preventive measures and remedial actions should be integrated through early detection, better feeding habits, better immunization coverage, and promotion of healthy growth during early childhood to reduce malnutrition.

This research analyses the impact of the National Programme for Reducing School Dropout (NPRSD) on the academic performance of 8th form students, with emphasis on the results obtained in the subject of Romanian language and literature. The study is based on a pre-experimental design and was conducted in rural areas, involving two comparable educational units. The experimental group benefited from remedial and support activities within NPRSD, including additional preparation sessions, digital educational resources and a hot meal. The primary method of data collection was the analysis of school documents; the assessment tool was the National Evaluation mock exam, applied before and after the intervention. The results indicate a significant improvement in academic performance within the experimental group, reflected by an increase in the average marks and a reduction in the number of students with marks below the passing threshold. Statistical analysis highlighted a significant positive effect, supported by the t-test for paired groups and Cohen’s d-index.

Plundered: How Racist Policies Undermine Black Homeownership in America by Bernadette Atuahene (2025) is a comprehensive look at how local municipalities have used their lawful power to remove Black homeowners through unconstitutional property tax assessments and mass foreclosures. Atuahene documents how the City of Detroit has consistently illegally assessed residential properties (in violation of the Michigan Constitution) and thereby caused over one-third of all homes to be foreclosed upon between 2009-2015. It is these discriminatory policies impacting predominantly Black communities that create the core of her “predatory governance” model – the extraction of wealth from disadvantaged communities through the use of state authority in unjust ways. Atuahene places Detroit’s situation within a larger national scope of racialized displacement and homelessness and states that true justice will require redress and not simply reform. Atuahene calls for remedial action, including equitable assessments, accountability measures, and reparative housing action, making Plundered a required read for scholars, policy makers, and practitioners who are committed to housing justice and racial equity. This review outlines the book’s clarity, rigor, and lasting impact.

Investigate the increased incidence of Mycobacterium chelonae positive respiratory cultures in hospitalized patients. Apply the Healthcare-Associated Links in Transmission of Nontuberculous Mycobacteria (HALT NTM) toolkit to an outbreak investigation of M. chelonae. Quaternary-care pediatric hospital and medical center in the United States with a recently opened LEED-certified critical care tower. Adult and pediatric patients with M. chelonae positive respiratory cultures between June 2022 and January 2024. An epidemiological investigation involving clinical and laboratory practices, water management, building construction and renovation projects. Environmental sampling of air vents, water sources and endoscope reprocessing equipment was performed. M. chelonae isolates recovered from patients and the environment were analyzed using whole genome sequencing and compared for relatedness. Three clusters of matching environmental and patient isolates were identified. The most common environmental source of M. chelonae was ice/water dispensers with 40% positivity of sampled units. The critical care tower's water system performance and metrics were suboptimal, leading to four physical remediation activities along with a hyperchlorination treatment. Recent and ongoing construction along with the implementation of a LEED-certified, low-flow water system in a new critical care tower provided enhanced opportunities for M. chelonae exposure at point of use locations such as ice/water dispensers. More national infection prevention and control guidance is needed to address the infection risks from water sustainability efforts and construction activities in healthcare facilities.

Reliable mapping and delineation of contaminant plumes and accurate estimation of contaminant mass discharge (CMD) are critical for groundwater risk assessment and planning of remedial actions at contaminated sites. However, traditional interpolation methods are often challenged by low-density sampling resulting in improper plume delineation. This study introduces a probabilistic censoring method that enhances geostatistical interpolation by incorporating comparably cheap, high-resolution, but semi-quantitative data collected from direct push-probes in the subsurface. The method converts halogen-specific detector signals into binary presence-absence indicators, which are interpolated using indicator kriging to generate a probability field of contaminant distribution. The probability field is then used to censor a spatial concentration field derived from traditional groundwater sampling, retaining interpolated concentration values only in areas where contamination is likely. We apply the method to a site contaminated with chlorinated solvents using two datasets with different sampling densities. Results show that, using our new method, plume fringes became more clearly defined and the total area with low concentrations (<10 μg L-1) increased by 41-85%. CMD estimates were reduced by 13-18%, while relative uncertainty remained largely unchanged. The method integrates seamlessly with traditional interpolation methods and our censoring workflow can be applied to other forms of direct-push data (e.g., relative permeability). As such, the framework offers a useful method for incorporating semi-quantitative field measurements into concentration interpolation and CMD estimation at contaminated sites.