Research and application of a novel selective stacking ensemble model based on error compensation and parameter optimization for AQI prediction.

With the ongoing process of industrialization, the issue of declining air quality is increasingly becoming a critical concern. Accurate prediction of the Air Quality Index (AQI), considered as an all-inclusive measure representing the extent of pollutants present in the atmosphere, is of paramount importance. This study introduces a novel methodology that combines stacking ensemble and error correction to improve AQI prediction. Additionally, the reptile search algorithm (RSA) is employed for optimizing model parameters. In this study, four distinct regional AQI data containing a collection of 34864 data samples are collected. Initially, we perform cross-validation on ten commonly used single models to obtain prediction results. Then, based on evaluation indices, five models are selected for ensemble. The results of the study show that the model proposed in this paper achieves an improvement of around 10% in terms of accuracy when compared to the conventional model. Thus, the model introduced in this study offers a more scientifically grounded approach in tackling air pollution.

Utilizing sludge-based activated carbon for targeted leachate mitigation in wastewater treatment.

Activated carbon (AC) based adsorbents derived from waste sludge were utilized to remediate mixed contaminants in wastewater as an integrated waste-to-resource approach promoting a paradigm shift in management of refuse sludge and wastewater. This review specifically focuses on the remediation of constituents of landfill leachate by sludge-based activated carbon (SBAC). The adsorption effectiveness of SBAC for the exclusion of leachate characters including heavy metals, phenols, dyes, phosphates, and phosphorus were explored with regard to modifiers such as pH, temperature, properties of the adsorbent including functional groups, initial doses of absorbent and adsorbate, and duration of exposure to note the impact of each parameter on the efficiency of adsorption of the sludge adsorbent. Through the works of various researchers, it was noted that the properties of the adsorbent, pH and temperature impact the working of SBACs. The pH of the adsorbent by influencing the functional groups. Temperature was expected to have a paramount effect on the adsorption efficiency of the SBACs. The importance of the regeneration and recycling of the adsorbents as well as their leachability is highlighted. Sludge based activated carbon is recommended as a timely, resource-efficient, and sustainable approach for the remediation of wastewater.

Catalytic Ozonation of Reactive Black 5 in Aqueous Solution Using Iron-Loaded Dead Leaf Ash for Wastewater Remediation.

In the current study, iron-loaded dead leaf ash (Fe-DLA) was used as a novel catalyst in the heterogeneous catalytic ozonation process (HCOP) for textile wastewater containing Reactive Black 5 (RB-5). The research demonstrates a significant boost in removal efficiency, reaching 98.76% with 1.0 g/min O3 and 0.5 g/L catalyst dose, by investigating key variables such as pH, ozone and catalyst doses, initial concentration, and the presence of scavengers in 1 L wastewater. The addition of tert-butyl alcohol (TBA) reduced RB-5 elimination, indicating the involvement of OH radicals. Catalyst reusability decreased slightly (2.05% in the second run; 4.35% in the third), which was attributed to iron leaching. A comparison of single ozonation (Fe-DLA) adsorption and catalytic ozonation processes (Fe-DLA/O3) revealed that the combined process improved dye degradation by 25%, with removal rates ranking as Fe-DLA adsorption O3 Fe-DLA/O3, with an impressive 76.44% COD removal. These results strongly support RB-5 removal using Fe-DLA and HCOP at a basic pH, highlighting the catalyst's utility in practical wastewater treatment.

Improving Water-Based Drilling Mud Performance Using Biopolymer Gum: Integrating Experimental and Machine Learning Techniques.

Drilling through shale formations can be expensive and time-consuming due to the instability of the wellbore. Further, there is a need to develop inhibitors that are environmentally friendly. Our study discovered a cost-effective solution to this problem using Gum Arabic (ArG). We evaluated the inhibition potential of an ArG clay swelling inhibitor and fluid loss controller in water-based mud (WBM) by conducting a linear swelling test, capillary suction timer test, and zeta potential, fluid loss, and rheology tests. Our results displayed a significant reduction in linear swelling of bentonite clay (Na-Ben) by up to 36.1% at a concentration of 1.0 wt. % ArG. The capillary suction timer (CST) showed that capillary suction time also increased with the increase in the concentration of ArG, which indicates the fluid-loss-controlling potential of ArG. Adding ArG to the drilling mud prominently decreased fluid loss by up to 50%. Further, ArG reduced the shear stresses of the base mud, showing its inhibition and friction-reducing effect. These findings suggest that ArG is a strong candidate for an alternate green swelling inhibitor and fluid loss controller in WBM. Introducing this new green additive could significantly reduce non-productive time and costs associated with wellbore instability while drilling. Further, a dynamic linear swelling model, based on machine learning (ML), was created to forecast the linear swelling capacity of clay samples treated with ArG. The ML model proposed demonstrates exceptional accuracy (R2 score = 0.998 on testing) in predicting the swelling properties of ArG in drilling mud.

Photodynamic Therapy in Adult Intra-axial Brain Tumours.

The management of high-grade gliomas is challenging considering their infiltrative nature, involvement of the eloquent cortex, and high recurrence rate. Photodynamic therapy (PDT) is an emerging modality that selectively destroys tumour cells while preserving normal brain tissue. Its safety, and the concurrent use with surgery, radiation, and chemotherapy, is some of its appealing tenets. Here, we present a review of the literature regarding the mechanism, safety, and efficacy of PDT.

Evolution of RANO in Assessing Brain Tumour Outcomes.

Assessing treatment response is extremely important in management of brain tumours. Response assessment in neuro-oncology (RANO) was introduced in 2008 for the purpose of making recommendations for it by addressing and countering the limitations in previously reported response criteriae. Subsequently, multiple RANO working groups have been formed to cater to different tumour types and to update their previous recommendations to counter the limitations in their criteria. Herein we have a summarized list of RANO criteria for adult brain tumours.

RAPNO efforts in Brain Tumour Outcomes.

Assessing treatment efficacy for brain tumours has evolved since its inception with the introduction of MacDonald's criteria, which pioneered the utility of imaging to determine an objective and quantifiable response to treatment. This criterion failed to distinguish pseudo response or progression from progression and did not account for non-enhancing disease therefore; the response assessment in neuro-oncology (RANO) working group was established to account for these limitations. Since, its commencement it has worked to determine response assessment for multiple tumours. As paediatric tumours exhibit heterogeneous and variable-enhancing characteristics, the response assessment in paediatric neuro-oncology (RAPNO) working group was formed to create separate criteria. Six response criteria have been published to date, and the article summarizes them.

Prognostic Value of BRAF Mutation in Glioblastoma.

With advances in molecular genetics, exploring targetable mutations for treating glioblastoma (GBM) patients, has become a centre of interest in modern day neuropathology. BRAF mutation has been extensively reported in several brain tumours. Recent studies report identification of BRAF mutation in GBM patients, especially isocitrate dehydrogenase wildtype glioblastomas (IDH-WT GBM), and its potential role in patient outcomes. Here we discuss the existing literature on the prognostic value of BRAF mutation in GBM.

Role of Machine Learning in Liquid Biopsy of Brain Tumours.

Liquid biopsy has multiple benefits and is used extensively in other fields of oncology, but its role in neuro-oncology has been limited so far. Multiple tumour-derived materials like circulating tumour cells (CTCs), tumour-educated platelets (TEPs), cell-free DNA (cfDNA), circulating tumour DNA (ctDNA), and miRNA are studied in CSF, blood (plasma, serum) or urine. Large and complex amounts of data from liquid biopsy can be simplified by machine learning using various algorithms. By using this technique, we can diagnose brain tumours and differentiate low versus highgrade glioma and true progression from pseudo-progression. The potential of liquid biopsy in brain tumours has not been extensively studied, but it has a bright future in the coming years. Here, we present a literature review on the role of machine learning in liquid biopsy of brain tumours.

The role of 18F-FDG PET/CT in Neurolymphomatosis: A Comprehensive Imaging Approach.

Neurolymphomatosis (NL) is an uncommon and rare neurologic disorder characterised by extranodal lymphoma, where the tumour cells invade the cranial nerves, nerve plexus, nerve root, spinal nerve roots, trunk nerves or peripheral nerves. MRI is the modality of choice, but is often challenging in detection of early recurrence, assessing residual disease and response evaluation. 18FFDG PET/CT has superior diagnostic performance compared with body CT in the evaluation of NL. 18F-FDG PET-CT is helpful in evaluation of disease extent and potential to guide biopsy. 18F-FDG PETCT is a highly sensitive technique for early localisation of NL than MRI or CT alone. Besides diagnostic and prognostic value in NL, it might be very helpful in response assessment.

Quality of life in survivors of pediatric medulloblastoma: a systematic review and meta-analysis.

INTRODUCTION: Medulloblastoma (MB) is the most common malignant pediatric brain tumor. The mainstay of treatment is maximum surgical resection and craniospinal radiation, which may be followed by chemotherapy. The debilitating effect of the tumor and the intensive treatment approaches in MB lead to long-term neuropsychological, physical, and chronic medical problems. We conducted a systematic review to assess the quality of life (QoL) in the long-term survivors of MB and the factors leading to compromised QoL. METHODS: We utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for our review. A comprehensive literature search was performed using PubMed, Cochrane Library, Digital Commons Network, and Wiley Online Library databases to search for articles having quality of life, medulloblastoma, and pediatric survivors in title or abstract. We removed duplicates and screened through titles, and full texts. Twelve articles were included in our study. Articles using and reporting all domains of PaedsQL were included in the meta-analysis. The PaedsQL scores of survivors and their caregivers were compared. Subgroup analysis was conducted for craniospinal and proton radiotherapy groups. RESULTS: As compared to other posterior fossa tumors, MB survivors have the lowest QoL scores. There is a difference in the perception of QoL of survivors between caregivers and survivors themselves with survivors rating themselves higher in several domains. The overall PaedsQL scores were significantly different for both groups (p < 0.001). Subgroup analysis showed that the difference between those who were treated with craniospinal or proton radiation was not significant (p = 0.76). For the subscales, physical (p = 0.005), psychosocial (p = 0.0003), and school (p = 0.03) perceptions were significantly different for the survivors and their caregivers; however, psychosocial (p = 0.80) and emotional (p = 0.93) scales were not different for the survivors or caregivers. Patient characteristics related to a worse QoL included disease severity, metastatic disease, lesser family income, smaller current ventricle size, need for permanent hydrocephalus treatment, and lesser age at diagnosis. CONCLUSION: An analysis of various studies, using different measures of QoL, concludes that QoL is compromised in all pediatric survivors of MB; however, the perception of QoL of the survivors is better than objective or caretaker-rated QoL.

Quality of life in children operated for spina bifida; low- and middle-income country perspective.

INTRODUCTION: Spina bifida is a potentially disabling congenital condition and affects the quality of life (QOL). We aimed to assess clinical outcomes and QOL in children who underwent spina bifida repair at our hospital. METHODS: This was a retrospective cohort study on children who underwent spina bifida repair at our hospital over 10 years. Phone calls were made to parents of the children, and the Health Utility Index Mark 3 (HUI 3) score was used to assess QoL, and degree of disability. Demographics and clinical data were obtained from the medical chart review. Statistical analysis was done using SPSS (version 21). RESULTS: Eighty children with a median age of 1.1 months (IQR 0.03-2.0) at the time of presentation, were included in this study. The mean follow-up period was 6.04 ± 2.54 years and the median HUI-3 score was 0.64 (IQR: 0.40 - 0.96) on a scale of 0 (dead) to 1 (perfectly healthy). Based on the severity of disability, 12 (23.1%) children had mild disability, 4 (7.7%) had moderate disability, and 23 (44.2%) had severe disability. Factors including a leaking spina bifida and paraplegia at presentation; radiological findings of hydrocephalus and Chiari malformation, were associated with a significantly low QOL. Children who required CSF diversion (EVD/ VP shunt) during the repair or at a later stage also had significantly low QOL. CONCLUSION: In LMIC, children with myelomeningocele (MMC) born with lower limb weakness, hydrocephalus, Chiari malformation, and those presenting with leaking MMC, have a significantly low QoL at a mean follow-up of 6 years.

Acupuncture Alleviates CUMS-Induced Depression-Like Behaviors by Restoring Prefrontal Cortex Neuroplasticity.

Purpose: To explore the therapeutic efficiency of acupuncture and the related molecular mechanism of neural plasticity in depression. Methods: Chronic unpredictable mild stress- (CUMS-) induced rats were established for the depression animal model. There were a total of four rat groups, including the control group, the CUMS group, the CUMS+acupuncture group, and the CUMS+fluoxetine group. The acupuncture group and the fluoxetine group were given a 3-week treatment after the modeling intervention. The researcher performed the open-field, elevated plus maze, and sucrose preference tests to evaluate depressive behaviors. The number of nerve cells, dendrites' length, and the prefrontal cortex's spine density were detected using Golgi staining. The prefrontal cortex expression, such as BDNF, PSD95, SYN, and PKMZ protein, was detected using the western blot and RT-PCR. Results: Acupuncture could alleviate depressive-like behaviors and promote the recovery of the neural plasticity functions in the prefrontal cortex, showing the increasing cell numbers, prolonging the length of the dendrites, and enhancing the spine density. The neural plasticity-related proteins in the prefrontal cortex, including BDNF, PSD95, SYN, and PKMZ, were all downregulated in the CUMS-induced group; however, these effects could be partly reversed after being treated by acupuncture and fluoxetine (P < 0.05). Conclusion: Acupuncture can ameliorate depressive-like behaviors by promoting the recovery of neural plasticity functions and neural plasticity-related protein upregulation in the prefrontal cortex of CUMS-induced depressed rats. Our study provides new insights into the antidepressant approach, and further studies are warranted to elucidate the mechanisms of acupuncture involved in depression treatment.

Exploring the 'January effect' at a university hospital in Pakistan: a retrospective cohort study investigating the impact of trainee turnover on patient care quality outcomes.

OBJECTIVE: There are reports of a potential rise in a teaching hospital's morbidity and mortality rates during the trainee turnover period, i.e., with the induction of new residents and house staffs, and the changeover of clinical teams. The published literature displays mixed reports on this topic with lack of reproducible observations. The current study was conducted to explore existence of any such phenomenon (January effect) in Pakistan. METHODS: This retrospective cohort study was conducted at Aga Khan University Hospital, Karachi, Pakistan. Five-year (2013-2018) record of all the patients in all age groups related to these outcomes was retrieved and recorded in specifically designed questionnaire. Different outcome measures were used as indicators of patient care and change in these outcomes at the time of new induction was related to possible January effect. RESULTS: During the five-year study period, more than 1100 new trainees were inducted into the post graduate medical education program (average of 237 per year) with more than 22,000 inpatient admissions (average of 45,469 per year). Some patterns were observed in frequencies of surgical site infections, medication errors, sentinel events, patient complaints, and adverse drug reactions. However, these were not consistently reproducible and could not be directly attributed to the trainee turnover. All other indicators did not show any pattern and were considered inconclusive. No effect of overlap was observed. CONCLUSIONS: Inconsistency in the patient care quality indicators do not favor existence of January effect in our study. Further research is recommended to establish our results.

Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning.

The recent advancements in the Internet of Things have made it converge towards critical infrastructure automation, opening a new paradigm referred to as the Industrial Internet of Things (IIoT). In the IIoT, different connected devices can send huge amounts of data to other devices back and forth for a better decision-making process. In such use cases, the role of supervisory control and data acquisition (SCADA) has been studied by many researchers in recent years for robust supervisory control management. Nevertheless, for better sustainability of these applications, reliable data exchange is crucial in this domain. To ensure the privacy and integrity of the data shared between the connected devices, access control can be used as the front-line security mechanism for these systems. However, the role engineering and assignment propagation in access control is still a tedious process as its manually performed by network administrators. In this study, we explored the potential of supervised machine learning to automate role engineering for fine-grained access control in Industrial Internet of Things (IIoT) settings. We propose a mapping framework to employ a fine-tuned multilayer feedforward artificial neural network (ANN) and extreme learning machine (ELM) for role engineering in the SCADA-enabled IIoT environment to ensure privacy and user access rights to resources. For the application of machine learning, a thorough comparison between these two algorithms is also presented in terms of their effectiveness and performance. Extensive experiments demonstrated the significant performance of the proposed scheme, which is promising for future research to automate the role assignment in the IIoT domain.

Hybrid Deep Learning and Discrete Wavelet Transform-Based ECG Biometric Recognition for Arrhythmic Patients and Healthy Controls.

The intrinsic and liveness detection behavior of electrocardiogram (ECG) signals has made it an emerging biometric modality for the researcher with several applications including forensic, surveillance and security. The main challenge is the low recognition performance with datasets of large populations, including healthy and heart-disease patients, with a short interval of an ECG signal. This research proposes a novel method with the feature-level fusion of the discrete wavelet transform and a one-dimensional convolutional recurrent neural network (1D-CRNN). ECG signals were preprocessed by removing high-frequency powerline interference, followed by a low-pass filter with a cutoff frequency of 1.5 Hz for physiological noises and by baseline drift removal. The preprocessed signal is segmented with PQRST peaks, while the segmented signals are passed through Coiflets' 5 Discrete Wavelet Transform for conventional feature extraction. The 1D-CRNN with two long short-term memory (LSTM) layers followed by three 1D convolutional layers was applied for deep learning-based feature extraction. These combinations of features result in biometric recognition accuracies of 80.64%, 98.81% and 99.62% for the ECG-ID, MIT-BIH and NSR-DB datasets, respectively. At the same time, 98.24% is achieved when combining all of these datasets. This research also compares conventional feature extraction, deep learning-based feature extraction and a combination of these for performance enhancement, compared to transfer learning approaches such as VGG-19, ResNet-152 and Inception-v3 with a small segment of ECG data.

Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction.

Intelligent transportation systems (ITSs) have become an indispensable component of modern global technological development, as they play a massive role in the accurate statistical estimation of vehicles or individuals commuting to a particular transportation facility at a given time. This provides the perfect backdrop for designing and engineering an adequate infrastructural capacity for transportation analyses. However, traffic prediction remains a daunting task due to the non-Euclidean and complex distribution of road networks and the topological constraints of urbanized road networks. To solve this challenge, this paper presents a traffic forecasting model which combines a graph convolutional network, a gated recurrent unit, and a multi-head attention mechanism to simultaneously capture and incorporate the spatio-temporal dependence and dynamic variation in the topological sequence of traffic data effectively. By achieving 91.8% accuracy on the Los Angeles highway traffic (Los-loop) test data for 15-min traffic prediction and an R2 score of 85% on the Shenzhen City (SZ-taxi) test dataset for 15- and 30-min predictions, the proposed model demonstrated that it can learn the global spatial variation and the dynamic temporal sequence of traffic data over time. This has resulted in state-of-the-art traffic forecasting for the SZ-taxi and Los-loop datasets.

mTORC2 Is Activated under Hypoxia and Could Support Chronic Myeloid Leukemia Stem Cells.

Hypoxia is a critical condition that governs survival, self-renewal, quiescence, metabolic shift and refractoriness to leukemic stem cell (LSC) therapy. The present study aims to investigate the hypoxia-driven regulation of the mammalian Target of the Rapamycin-2 (mTORC2) complex to unravel it as a novel potential target in chronic myeloid leukemia (CML) therapeutic strategies. After inducing hypoxia in a CML cell line model, we investigated the activities of mTORC1 and mTORC2. Surprisingly, we detected a significant activation of mTORC2 at the expense of mTORC1, accompanied by the nuclear localization of the main substrate phospho-Akt (Ser473). Moreover, the Gene Ontology analysis of CML patients' CD34+ cells showed enrichment in the mTORC2 signature, further strengthening our data. The deregulation of mTOR complexes highlights how hypoxia could be crucial in CML development. In conclusion, we propose a mechanism by which CML cells residing under a low-oxygen tension, i.e., in the leukemia quiescent LSCs, singularly regulate the mTORC2 and its downstream effectors.

A Magnetic Surfactant Having One Degree of Unsaturation in the Hydrophobic Tail as a Shale Swelling Inhibitor.

One of the foremost causes of wellbore instability during drilling operations is shale swelling and hydration induced by the interaction of clay with water-based mud (WBM). Recently, the use of surfactants has received great interest for preventing shale swelling, bit-balling problems, and providing lubricity. Herein, a novel synthesized magnetic surfactant was investigated for its performance as a shale swelling inhibitor in drilling mud. The conventional WBM and magnetic surfactant mixed WBM (MS-WBM) were formulated and characterized using Fourier Transform Infrared (FTIR) and Thermogravimetric analyzer (TGA). Subsequently, the performance of 0.4 wt% magnetic surfactant as shale swelling and clay hydration inhibitor in drilling mud was investigated by conducting linear swelling and capillary suction timer (CST) tests. Afterward, the rheological and filtration properties of the MS-WBM were measured and compared to conventional WBM. Lastly, the swelling mechanism was investigated by conducting a scanning electron microscope (SEM), zeta potential measurement, and particle size distribution analysis of bentonite-based drilling mud. Experimental results revealed that the addition of 0.4 wt% magnetic surfactant to WBM caused a significant reduction (~30%) in linear swelling. SEM analysis, contact angle measurements, and XRD analysis confirmed that the presence of magnetic surfactant provides long-term swelling inhibition via hydrophobic interaction with the bentonite particles and intercalation into bentonite clay layers. Furthermore, the inhibition effect showed an increase in fluid loss and a decrease in rheological parameters of bentonite mixed mud. Overall, the use of magnetic surfactant exhibits sterling clay swelling inhibition potential and is hereby proffered for use as a drilling fluid additive.

Citrous Lime-A Functional Reductive Booster for Oil-Mediated Green Synthesis of Bioactive Silver Nanospheres for Healthcare Clothing Applications and Their Eco-Mapping with SDGs.

Silver nanoparticles (Ag-NPs) are most effective against pathogens and have widely been studied as antibacterial agents in commodity clothing, medical textile, and other hygiene products. However, prolonged utilization of silver and rapid mutation in bacterium stains has made them resistant to conventional silver agents. On the other hand, strict compliance against excessive utilization of toxic reagents and the current sustainability drive is forcing material synthesis toward green routes with extended functionality. In this study, we proposed an unprecedented chemical-free green synthesis of bioactive Ag-NPs without the incorporation of any chemicals. Cinnamon essential oil (ECO) was used as a bio-reducing agent with and without the mediation of lime extract. A rapid reaction completion with better shape and size control was observed in the vicinity of lime extract when incorporated into the reaction medium. The interaction of natural metabolites and citrus compounds with nanoparticles was established using Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy. The application of as-prepared nanoparticles on textiles encompasses extended bioactivity to treated fabric with infused easy-care performance. To the best of our knowledge, this is the first reported instance of utilizing bioactive silver nanoparticles as a functional finish, both as an antimicrobial and as for easy care in the absolute absence of toxic chemicals. The easy-care performance of fabric treated with lime-mediated nanoparticles was found to be 141O, which is around 26% better than bare cotton without any significant loss in fabric strength. Furthermore, to enlighten the sustainability of the process, the development traits were mapped with the United Nations Sustainable Development Goals (SDGs), which show significant influence on SDGs 3, 8, 9, and 14. With the effective suspension of microorganisms, added functionality, and eco-mapping with SDGs with the chemical-free synthesis of nanoparticles, widespread utilization can be found in various healthcare and hygiene products along with the fulfillment of sustainability needs.