Browse Articles

Municipal solid waste management forecasting using neural networks at discharge point scale

Abstract Urbanisation and population growth continue to accelerate waste generation, posing serious environmental and logistical challenges for the management of Municipal Solid Waste (MSW) management. The present study proposes a predictive framework for forecasting the behaviour of individual Discharge Points (DPs), with the view to enhancing decision-making in urban waste management. The necessity for localised predictions that extend beyond the scope of aggregated waste indicators is identified by research. Furthermore, it addresses the requirement for finer predictive granularity, which is capable of capturing the dynamic variations observed across DPs. The findings underscore the potential of data-driven approaches to facilitate more efficient, scalable, and intelligent waste collection planning in urban contexts by the incorporation of contextual and temporal information. By enabling accurate short-term forecasts, the proposed approach facilitates the transition from reactive to proactive collection planning, reducing operational cost and environmental footprints. Overall, the research contributes to advancing data-driven strategies for sustainable MSW management and demonstrates the potential of AI-based predictive model to support intelligent and scalable urban waste collection systems.

Correction: Validation of the Mentalization Scale (MentS) in francophone control and clinical samples

Performance of wall mesh encased stone columns using recycled rubber concrete and asphalt aggregates for sustainable geotechnics

Nucleolar protein 6 as a potential oncogenic factor in colorectal cancer

Background Colorectal cancer (CRC) is a common malignancy of the digestive tract associated with high mortality rates and significant invasive properties. Despite advancements in research, a comprehensive understanding of the regulatory mechanisms underlying CRC remains elusive. Objective This study aimed to investigate the potential role of nucleolar protein 6 (NOL6) and its related genes as novel biomarkers for cell proliferation in CRC. The findings of this study could significantly contribute to early diagnosis and more effective therapeutic strategies for CRC. Methods Human CRC cell line HCT116 was cultured under standard conditions. Quantitative real-time polymerase chain reaction and immunohistochemistry analysis were used to measure NOL6 expression levels in CRC tissues. Cell proliferation was assessed using the MTT assay, Celigo cell count assay, and colony formation assays, while flow cytometry was employed to evaluate cell apoptosis. Additionally, a transwell migration assay was performed to evaluate CRC cell migration and invasion. Comprehensive proteomic and transcriptomic analyses were performed to identify the downstream genes and pathways affected by NOL6 knockdown. The expression of these genes was further validated by Western blotting. Xenograft mouse models were used to determine the effects of NOL6 on CRC in vivo . Tandem mass tags (TMT)-labeled quantitative proteomic technology and bioinformatic analysis were employed to identify the functional pathway and proteins regulated by NOL6. Results The Cancer Genome Atlas data analysis revealed a significant upregulation of NOL6 in CRC cells compared with adjacent normal cells. In HCT116 cells, downregulation of NOL6 was associated with decreased proliferation and colony formation, as well as increased apoptosis. Additionally, NOL6 knockdown resulted in a decrease in the weight and volume of tumors in nude mice, suggesting its role in tumorigenesis. TMT and Western blot analyses revealed that NOL6 knockdown suppressed MCM3 and MCM7 expression. Conclusion This study demonstrated that NOL6 functions as an oncogene that facilitates CRC progression, suggesting its potential role as a therapeutic target for CRC management.

Integrated assessment of tool wear, chip morphology, surface ıntegrity and energy consumption in sustainable milling of Inconel 718

Research on the threshold of the supply and demand of ecosystem services

The ecological threshold has not yet formed a unified definition, and there is no definition for “the threshold of the supply and demand of ecosystem services ( Tr SD )”, leading to no limitation of the negative impact of production and life behavior on the supply and demand of ecosystem services. This study defined and set Tr SD , and took Urumqi as an example to carry out a case study. Firstly, the concept of Tr SD was elaborated referred to multiple definitions of the ecological threshold based on “the difference between the supply and demand of ecosystem services ( ES r )”. Then, the geographical simulation and optimization system- future land use simulation (GeoSOS-FLUS) software was used to simulate future land use. After that, the Land Use and Land Cover (LULC) matrix model was applied to calculate ES r . Finally, the Tr SD was determined via the inflection point analysis of ES r . This study concludes that the proposed Tr SD and its systematic calculation method are innovative and rational. The results can be used for ecosystem service management and ecological valuation, which helps the sustainability progress of the global.

Chemical activation of kaolin-based clay bricks as a sustainable route to enhanced mechanical and thermophysical properties

Abstract This work examines the usage of various acid activators, including hydrochloric acid (HCl), sulphuric acid (H 2 SO 4 ), phosphoric acid (H 3 PO 4 ), and a combination of them, to improve the thermophysical properties of fired clay-based composite bricks modified-kaolin. To enhance these materials’ insulating capabilities, the main focus is on reducing their diffusivity, specific heat capacity, and thermal conductivity. Whereas, fibrous clays’ surface and catalytic properties were enhanced chemically via the addition of acid-activated kaolinite clay. The mechanical, thermophysical, morphological, shrinkage, density, porosity, microstructure, and shrinkage of each clay–kaolin(acid) composite were thoroughly examined, and the thermal conductivity performance was maximized. All of the peaks’ intensities in the XRD pattern increased in comparison to the untreated peak when varying acid types were added to the kaolin matrix. In the meantime, the addition of these activators caused the compositions’ apparent porosity (i.e., 29.15–29.47%) and compressive strength (i.e., 11.59–12.33 kg/cm 2 ). The results demonstrate that treatment with these acids reduces thermal conductivity (i.e., 0.46–0.44 W/mk), and diffusivity, attributed to the increased porosity and altered microstructure of the bricks. Moreover, combining all three acids (H 2 SO 4 /HCl/H 3 PO 4 ) resulted in the most significant improvements, yielding a composite with superior insulation capabilities. The aforementioned observations are attributed to the formation of two key mineral phases within the fired bricks: mullite and diopside. Mullite strengthens the bonding within the aluminosilicate framework, thereby enhancing the ceramic network and promoting a denser and more mechanically stable microstructure without causing a significant increase in porosity. Meanwhile, diopside also contributes to strength development and is widely recognized for its role in insulation ceramics due to its excellent thermal stability and chemical corrosion resistance.

Correction: Diagnostic performance of eNose technology in detecting colorectal cancer recurrence: A prospective evaluation

Blockchain-enabled identity management for IoT: a multi-layered defense against adversarial AI

Abstract The growing deployment of the Internet of Things (IoT), especially in critical infrastructure, has increased the need for identity systems that are scalable and robust against attacks. However, existing centralized systems have fundamental weaknesses, especially where adversaries use artificial intelligence (AI)-based techniques, such as generative spoofing, model poisoning, and deepfakes to create fake identities. In this paper, we present a novel blockchain-based IoT security system that combines decentralized identity verification, zero-knowledge proofs, Byzantine-resistant federated learning, and formal verification of smart contracts. The proposed architecture eliminates single points of trust, allows device registration while preserving privacy, and provides defense against AI-driven attacks through formally modeled state transitions. Experimental results show that this method shows significant improvements over previous frameworks, including a 48% reduction in false acceptance rate during GAN-based spoofing and speedup the ZKP verification. This work provides a blockchain-enabled identity management system for IoT to encounter AI-based threats and maintain a balance between performance and security with the help of adversarial simulation, symbolic execution, and threshold cryptography.

Non-destructive DNA extraction for recovering mitochondrial genomes from museum grasshopper specimens

Museum collections of grasshoppers contain valuable genetic data for evolutionary, ecological, and systematic studies, but destructive sampling and complex molecular techniques often limit their use. This study presents a simple, non-destructive DNA extraction protocol designed for dried grasshopper specimens, effectively balancing DNA yield with morphological preservation. We tested this method on specimens aged 6–43 years, exploring various lysis conditions to optimize DNA recovery while maintaining specimen integrity. The protocol successfully extracted sufficient DNA to assemble mitochondrial genomes for most samples using cost-effective low-coverage shotgun sequencing. DNA from younger specimens produced the best results, while older samples (over 40 years) showed some challenges, though post-collection damage did not significantly affect mitochondrial sequence assembly. Additionally, we provide practical bioinformatics recommendations for processing short reads from dried specimens. Requiring minimal molecular expertise and relying on standard sequencing services, this accessible method is well-suited for widespread adoption. By unlocking genetic insights from museum collections, it offers new opportunities to deepen our understanding of grasshopper evolution and ecological dynamics.

Enhanced generalized normal distribution optimizer with Gaussian distribution repair method and cauchy reverse learning for features selection

Abstract The presence of noisy, redundant, and irrelevant features in high-dimensional datasets significantly degrades the performance of classification models. Feature selection is a critical pre-processing step to mitigate this issue by identifying an optimal feature subset. While the Generalized Normal Distribution Optimization (GNDO) algorithm has shown promise in various domains, its efficacy for feature selection is hampered by premature convergence and an imbalance between exploration and exploitation. This paper proposes a Binary Adaptive GNDO (BAGNDO) framework to overcome these limitations. BAGNDO integrates three key strategies: an Adaptive Cauchy Reverse Learning (ACRL) mechanism to enhance population diversity, an Elite Pool Strategy to balance the search process, and a Gaussian Distribution-based Worst-solution Repair (GDWR) method to improve exploitation. The performance of BAGNDO was rigorously evaluated against nine state-of-the-art metaheuristic algorithms on 18 UCI benchmark datasets. The results demonstrate the superior efficacy of BAGNDO, which achieved the highest classification accuracy with the most compact feature subsets in 14 out of 18 datasets. Statistical analysis, including the Wilcoxon signed-rank and Friedman tests, confirmed that BAGNDO’s performance is significantly better, establishing it as a robust and efficient solution for wrapper-based feature selection.

Factors influencing SARS-CoV-2 IgG test sensitivity: A Bayesian analysis of seroconversion and seroreversion by time since infection, test, age and disease severity

Background Antibody testing is commonly used to assess past exposure to pathogens, but the interpretation is complex. We quantified test-specific SARS-CoV-2 seroconversion and seroreversion by time since PCR-confirmed infection, age and disease severity. Methods We combined Belgian data from laboratory SARS-CoV-2 testing, prescriptions, contact tracing and hospital surveillance collected between March 2020 and June 2021 with data from published longitudinal studies on Wantai and EuroImmun IgG serological tests. We used a hierarchical Bayesian model to estimate time-varying sensitivity of serological tests following PCR-confirmed infection. The model employed a scaled Weibull-bi-exponential distribution. We accounted for disease severity (distinguishing between asymptomatic, symptomatic, and hospitalized cases), age (i.e., age groups 18–49, 50–64, and 65–74 years) and serological test used. Results We included 44,262 serological test results: 10,864 obtained from published studies, 33,398 from Belgian laboratories. Seroconversion occurred during the six weeks following a PCR-confirmed infection. Age, disease severity and the test used strongly influenced seroconversion rates and the rate of the subsequent seroreversion. For the EuroImmun test, 82% (95% Credible Interval (CrI): 80%−84%) of symptomatic individuals in the youngest age group seroconverted, compared to 95% (CrI: 95%−96%) for the Wantai test. Seroconversion was associated with hospitalization, (OR = 8.17 (CrI: 5.56–13.72), compared to asymptomatic infection) and older age (OR = 1.65 (CrI: 1.41–1.97), compared to 18–49 year-olds). Slower seroreversion was associated with older age, hospitalization and the Wantai test. At 50 weeks, seropositivity among symptomatic 18–49 year-olds was 64% (CrI: 58%−70%) for the EuroImmun test and 95% (CrI: 94%−96%) for the Wantai test. Conclusion These findings highlight the need for test-specific, time-varying sensitivity adjustments in seroprevalence studies. Such adjustments are crucial for translating seroprevalence results to cumulative incidence estimates.

Antibacterial activity of essential oils from Brocchia cinerea, Artemisia campestris and Origanum vulgare growing in Algeria against antibiotic-resistant foodborne pathogens

Distribution of visuo-attentional resources while reading multiple words

Recent studies have investigated the role of semantic processing in the parafovea using the Rapid Parallel Visual Presentation (RPVP) paradigm, which involves the simultaneous presentation of two words, one in the fovea (W1) and one in the parafovea (W2). Results have shown that both accuracy and response times are influenced by the semantic relatedness between the words. These findings suggest that semantic information can be extracted from the parafovea and in parallel with the processing of the foveal word. In the present study, we aimed to provide further evidence of parallel semantic processing and to gain deeper insight into the availability and spatial distribution of attentional resources when semantic relatedness is present. Two experiments were conducted. In both, the first part replicated the previous RPVP setup: two words were simultaneously presented and participants were required to read them aloud. Frequency and semantic relatedness between the two words were manipulated. Results replicated previous findings. Crucially, each experiment included a second task: as participants began reading the two words, a probe (an asterisk) could appear to the right of the parafoveal word (Experiment 1) or above the foveal word (Experiment 2). Probe detection times and accuracy were equally facilitated at both positions when the two words was of high-frequency and semantically related. In contrast, when W1 was of low-frequency, neither accuracy nor reaction times in probe detection benefited from parafoveal processing, suggesting that an increased load on the foveal word hinders parafoveal processing. Finally, the same probe detection results were obtained – both in terms of mean reaction times and accuracy – across the two spatial positions, indicating an even distribution of attentional resources across foveal and parafoveal words.

A computationally efficient approach to quantum state reconstruction using robust classical shadows

Correction: ACEF score as a predictor of new-onset atrial fibrillation in patients with ST-elevation myocardial infarction: A retrospective cohort study

High-flow nasal cannula versus noninvasive ventilation in patients with hypoxemic respiratory failure: a prospective cohort study

Harvesting easter eggs: An exploratory study of enjoying transnarrative media

Transnarrative storytelling, or fragmented narratives, has been undertheorized yet is increasingly more common in entertainment. An exploratory study guided by entertainment enjoyment theories explored potential predictors of enjoyment from transnarrative stories. A retrospective survey (n = 956) utilized both close- and open-ended measures and found parasocial relationships and fan behaviors (e.g., internet searches, discussions) were positively associated with intrinsic rewards and enjoyment. Intrinsic rewards were also positively associated with enjoyment above and beyond PSR and fan behaviors. Emerging themes from open-ended questions suggest that easter eggs are discovered by audiences when characters, objects, events/actions, and other forms of entertainment appear. Lastly, participants who found easter eggs described their responses as excited, happy, and full of pride. Implications include the necessity for additional research on transnarrative media processing.

Altered resting state EEG microstate dynamics in acute concussion in adolescents

Abstract Concussion is a global health concern; however, the neurophysiological underpinnings remain poorly understood, with a lack of clinically relevant, objective brain-based approaches. We investigated the potential of EEG microstate analysis to characterize alterations in brain activity post-concussion. We applied a modified k-means clustering algorithm to multi-channel resting-state EEG data, comparing participants within 2 weeks post-concussion to age- and sex-matched healthy controls. EEG topographical maps were classified into seven clusters, with each cluster represented by canonical microstates (A–G). Average duration, occurrence rate, and time coverage for each microstate were extracted and compared between groups. Multiple correlation analyses were performed between microstate measures and symptom severity within the concussed group. The concussed group showed a statistically significant reduction in the duration of microstate E and the duration, occurrence, and time coverage of microstate G compared to controls. The occurrence rate and time coverage of microstate C were significantly higher in the concussed group. There was a positive correlation between symptom severity and the duration and occurrence of microstate E; however, this did not reach statistical significance. Together, these findings indicate that acute concussion disrupts the dynamic interactions of large-scale brain networks and suggest that EEG microstate analysis applied to resting-state data may provide a potential biomarker of injury.

The sustainability of public health programs following donor transition: A comparative case study of HIV services and maternal and newborn care in Uganda

Although there is emerging evidence on the impact of donor transition on health programs, there is little research comparing sustainability outcomes across health programs. We sought to compare drivers of health program sustainability concerning maternal and newborn care in Western Uganda after the end of the ‘Saving Mothers Giving Life’(SMGL) project and HIV services in eastern Uganda following loss of PEPFAR support. Methods We report qualitative findings from a larger mixed-methods study. In-depth interviews were held with Ministry of Health officials (n = 11), district health teams (n = 27), facility in-charges (n = 39) and representatives of donor-implementing organizations (n = 22). Data were collected in eight districts in Western and Eastern Uganda. Data were analyzed by thematic approach based on the five themes proposed under the Integrated Sustainability Framework (ISF). Results Our case studies identified several enablers and hindrances to the sustainment of public health gains across HIV and Maternal and Newborn Health (MNH). The recipient government appeared to assign a higher political priority to MNH relative to HIV following donor transition. MNH attracted multiple external funders after the end of SMGL support. In terms of donor transition processes, the MNH intervention was perceived as a ‘terminal’ project, while PEPFAR support was perceived as more ‘open-ended’. In contrast to districts in Eastern Uganda, which lost PEPFAR support, internal ‘program champions’ were identified in districts in Western Uganda. Differences in disease control approaches were identified; HIV was described as more ‘capital intensive’ with more ‘recurrent’ needs compared to MNH programming. The expanded MNH workforce (such as nurses and midwives) was transitioned to the public sector payroll, while PEPFAR-salaried officials were not. Participants perceived the SMGL project on MNH to have been more embedded in the local health system while PEPFAR support was perceived as more ‘vertical’. Conclusions Our analysis suggests that variations in sustainability outcomes cross the two focus projects stem from differences in donor aid delivery mechanisms, transition processes and domestic political priorities. Our study suggests that donor transition is not a ‘one size fits all’ phenomenon regarding health programs, which has implications for planning for donor transition in Uganda and similar settings.