Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants.

The escalation of global urbanization and industrial expansion has resulted in an increase in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of titanium dioxide (TiO2) in photocatalytic degradation through traditional methods is resource-intensive and complex due to the detailed photocatalyst structures and the wide range of contaminants. Therefore in this study, recent advancements in machine learning (ML) are used to offer data-driven approach using thirteen machine learning techniques namely XG Boost (XGB), decision tree (DT), lasso Regression (LR2), support vector regression (SVR), adaBoost (AB), voting Regressor (VR), CatBoost (CB), K-Nearest Neighbors (KNN), gradient boost (GB), random Forest (RF), artificial neural network (ANN), ridge regression (RR), linear regression (LR1) to address the problem of estimation of TiO2 photocatalytic degradation rate of air contaminants. The models are developed using literature data and different methodical tools are used to evaluate the developed ML models. XGB, DT and LR2 models have high R2 values of 0.93, 0.926 and 0.926 in training and 0.936, 0.924 and 0.924 in test phase. While ANN, RR and LR models have lowest R2 values of 0.70, 0.56 and 0.40 in training and 0.62, 0.63 and 0.31 in test phase respectively. XGB, DT and LR2 have low MAE and RMSE values of 0.450 min-1/cm2, 0.494 min-1/cm2 and 0.49 min-1/cm2 for RMSE and 0.263 min-1/cm2, 0.285 min-1/cm2 and 0.29 min-1/cm2 for MAE in test stage. XGB, DT, and LR2 have 93% percent errors within 20% error range in training phase. XGB has 92% and DT, and LR2 have 94% errors with 20% range in test phase. XGB, DT, LR2 models remained the highest performing models and XGB is the most robust and effective in predictions. Feature importances reveal the role of input parameters in prediction made by developed ML models. Dosage, humidity, UV light intensity remain important experimental factors. This study will impact positively in providing efficient models to estimate photocatalytic degradation rate of air contaminants using TiO2.

Numerical and artificial intelligence based investigation on the development of design guidelines for pultruded GFRP RHS profiles subjected to web crippling.

This article presents a numerical and artificial intelligence (AI) based investigation on the web crippling performance of pultruded glass fiber reinforced polymers' (GFRP) rectangular hollow section (RHS) profiles subjected to interior-one-flange (IOF) loading conditions. To achieve the desired research objectives, a finite element based computational model was developed using one of the popular simulating software ABAQUS CAE. This model was then validated by utilizing the results reported in experimental investigation-based article of Chen and Wang. Once the finite element model was validated, an extensive parametric study was conducted to investigate the aforementioned phenomenon on the basis of which a comprehensive, universal, and coherent database was assembled. This database was then used to formulate the design guidelines for the web crippling design of pultruded GFRP RHS profiles by employing AI based gene expression programming (GEP). Based on the findings of numerical investigation, the web crippling capacity of abovementioned structural profiles subjected to IOF loading conditions was found to be directly related to that of section thickness and bearing length whereas inversely related to that of section width, section height, section's corner radii, and profile length. On the basis of the findings of AI based investigation, the modified design rules proposed by this research were found to be accurately predicting the web crippling capacity of aforesaid structural profiles. This research is a significant contribution to the literature on the development of design guidelines for pultruded GFRP RHS profiles subjected to web crippling, however, there is still a lot to be done in this regard before getting to the ultimate conclusions.

Development of correlations between various engineering rockmass classification systems using railway tunnel data in Garhwal Himalaya, India.

Engineering rockmass classifications are an integral part of design, support and excavation procedures of tunnels, mines, and other underground structures. These classifications are directly linked to ground reaction and support requirements. Various classification systems are in practice and are still evolving. As different classifications serve different purposes, it is imperative to establish inter-correlatability between them. The rating systems and engineering judgements influence the assignment of ratings owing to cognition. To understand the existing correlation between different classification systems, the existing correlations were evaluated with the help of data of 34 locations along a 618-m-long railway tunnel in the Garhwal Himalaya of India and new correlations were developed between different rock classifications. The analysis indicates that certain correlations, such as RMR-Q, RMR-RMi, RMi-Q, and RSR-Q, are comparable to the previously established relationships, while others, such as RSR-RMR, RCR-Qn, and GSI-RMR, show weak correlations. These deviations in published correlations may be due to individual parameters of estimation or measurement errors. Further, incompatible classification systems exhibited low correlations. Thus, the study highlights a need to revisit existing correlations, particularly for rockmass conditions that are extremely complex, and the predictability of existing correlations exhibit high variations. In addition to augmenting the existing database, new correlations for metamorphic rocks in the Himalayan region have been developed and presented that can serve as a guide for future rock engineering projects in such formations and aid in developing appropriate excavation and rock support methodologies.

Comparative study of eco-friendly wire mesh configurations to enhance sustainability in reinforced concrete structures.

Recent and past studies mainly focus on reducing the dead weight of structure; therefore, they considered lightweight aggregate concrete (LWAC) which reduces the dead weight but also affects the strength parameters. Therefore, the current study aims to use varied steel wire meshes to investigate the effects of LWAC on mechanical properties. Three types of steel wire mesh are used such as hexagonal (chicken), welded square, and expanded metal mesh, in various layers and orientations in LWAC. Numerous mechanical characteristics were examined, including energy absorption (EA), compressive strength (CS), and flexural strength (FS). A total of ninety prisms and thirty-three cubes were made. For the FS test, forty-five 100 × 100 × 500 mm prism samples were poured, thirty-three 150 × 150 × 150 mm cube samples were made, and forty-five 400 × 300 × 75 mm EA specimens were costed for fourteen days of curing. The experimental findings demonstrate that the FS was enhanced by adding additional forces that spread the forces over the section. One layer of chicken, welded, and expanded metal mesh enhances the FS by 52.96%, 23.76%, and 22.2%, respectively. In comparison to the remaining layers, the FS in a single-layer hexagonal wire mesh has the maximum strength, 29.49 MPa. The hexagonal wire mesh with a single layer had the greatest CS, measuring 36.56 MPa. When all three types of meshes are combined, the CS does not vary in this way and is estimated to be 29.79 MPa. In the combination of three layers, the chicken and expanded wire mesh had the most energy recorded prior to final failure, which was 1425.6 and 1108.7 J, whereas it was found the highest 752.3 J for welded square wire mesh. The energy absorption for the first layer with hexagonal wire mesh increased by 82.81% prior to the crack and by 88.34% prior to the ultimate failure. Overall, it was determined and suggested that hexagonal wire mesh works better than expanded and welded wire meshes.

Innovative Solutions for High-Performance Silicon Anodes in Lithium-Ion Batteries: Overcoming Challenges and Real-World Applications.

Silicon (Si) has emerged as a potent anode material for lithium-ion batteries (LIBs), but faces challenges like low electrical conductivity and significant volume changes during lithiation/delithiation, leading to material pulverization and capacity degradation. Recent research on nanostructured Si aims to mitigate volume expansion and enhance electrochemical performance, yet still grapples with issues like pulverization, unstable solid electrolyte interface (SEI) growth, and interparticle resistance. This review delves into innovative strategies for optimizing Si anodes' electrochemical performance via structural engineering, focusing on the synthesis of Si/C composites, engineering multidimensional nanostructures, and applying non-carbonaceous coatings. Forming a stable SEI is vital to prevent electrolyte decomposition and enhance Li+ transport, thereby stabilizing the Si anode interface and boosting cycling Coulombic efficiency. We also examine groundbreaking advancements such as self-healing polymers and advanced prelithiation methods to improve initial Coulombic efficiency and combat capacity loss. Our review uniquely provides a detailed examination of these strategies in real-world applications, moving beyond theoretical discussions. It offers a critical analysis of these approaches in terms of performance enhancement, scalability, and commercial feasibility. In conclusion, this review presents a comprehensive view and a forward-looking perspective on designing robust, high-performance Si-based anodes the next generation of LIBs.

Robot-Assisted Video Endoscopic Inguinal Lymph Node Dissection for Penile Cancer: An Indian Multicenter Experience.

Objective: To report outcomes of multicenter series of penile cancer patients undergoing robot-assisted video endoscopic inguinal lymph node dissection (RA-VEIL). Materials and Methods: In this retrospective analysis from 3 tertiary care centers in India, consecutive intermediate-/high-risk carcinoma penis (CaP) patients with nonpalpable inguinal lymphadenopathy and/or nonbulky (<3 cm) mobile inguinal lymphadenopathy undergoing RA-VEIL were included. Patients with matted/bulky (>3 cm) and fixed lymphadenopathy were excluded. Demographic, clinical, and intraoperative data were recorded. Perioperative complications were graded by the Clavien-Dindo classification (CDC). The International Society of Lymphology (ISL) {0-III} grading was used for the assessment of lymphedema. Incidence and pattern of recurrences were assessed on follow-up. Results: From January 1, 2011, to September 30, 2023, 115 patients (230 groins) underwent bilateral RA-VEIL for CaP. The median age of the cohort was 60 (50-69) years. Clinically palpable (either unilateral or bilateral) inguinal lymphadenopathy was seen in 54 patients (47%). The "per groin" median operative time was 120 (100-140) minutes with median lymph node yield of 12 (9-16). No complications were recorded in 87.8% groins operated, with major complications (CDC 3) seen in 2.6% groins. At a median follow-up of 13.5 months, 13 patients had documented recurrences and there were 10 cancer-related deaths. No port-site recurrences were observed. No/minimal lymphedema (ISL 0/I) was seen in 94% legs. Conclusion: RA-VEIL demonstrates safety and oncologic efficacy in penile cancer patients presenting with clinically nonpalpable and/or nonbulky inguinal lymphadenopathy, with favorable functional outcomes.

Computational prediction of workability and mechanical properties of bentonite plastic concrete using multi-expression programming.

Bentonite plastic concrete (BPC) demonstrated promising potential for remedial cut-off wall construction to mitigate dam seepage, as it fulfills essential criteria for strength, stiffness, and permeability. High workability and consistency are essential attributes for BPC because it is poured into trenches using a tremie pipe, emphasizing the importance of accurately predicting the slump of BPC. In addition, prediction models offer valuable tools to estimate various strength parameters, enabling adjustments to BPC mixing designs to optimize project construction, leading to cost and time savings. Therefore, this study explores the multi-expression programming (MEP) technique to predict the key characteristics of BPC, such as slump, compressive strength (fc), and elastic modulus (Ec). In the present study, 158, 169, and 111 data points were collected from the experimental studies for the slump, fc, and Ec, respectively. The dataset was divided into three sets: 70% for training, 15% for testing, and another 15% for model validation. The MEP models exhibited excellent accuracy with a correlation coefficient (R) of 0.9999 for slump, 0.9831 for fc, and 0.9300 for Ec. Furthermore, the comparative analysis between MEP models and conventional linear and non-linear regression models revealed remarkable precision in the predictions of the proposed MEP models, surpassing the accuracy of traditional regression methods. SHapley Additive exPlanation analysis indicated that water, cement, and bentonite exert significant influence on slump, with water having the greatest impact on compressive strength, while curing time and cement exhibit a higher influence on elastic modulus. In summary, the application of machine learning algorithms offers the capability to deliver prompt and precise early estimates of BPC properties, thus optimizing the efficiency of construction and design processes.

Effect of rock properties on wear and cutting performance of multi blade circular saw with iron based multi-layer diamond segments.

This study is an attempt for comprehensive, combining experimental data with advanced analytical techniques and machine learning for a thorough understanding of the factors influencing the wear and cutting performance of multi-blade diamond disc cutters on granite blocks. A series of sawing experiments were performed to evaluate the wear and cutting performance of multi blade diamond disc cutters with varying diameters in the processing of large-sized granite blocks. The multi-layer diamond segments comprising the Iron (Fe) based metal matrix were brazed on the sawing blades. The segment's wear was studied through micrographs and data obtained from the Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive X-ray (EDS). Granite rock samples of nine varieties were tested in the laboratory to determine the quantitative rock parameters. The contribution of individual rock parameters and their combined effects on wear and cutting performance of multi blade saw were correlated using statistical machine learning methods. Moreover, predictive models were developed to estimate the wear and cutting rate based on the most significant rock properties. The point load strength index, uniaxial compressive strength, and deformability, Cerchar abrasivity index, and Cerchar hardness index were found to be the significant variables affecting the sawing performance.

Estimation of compressive strength of waste concrete utilizing fly ash/slag in concrete with interpretable approaches: optimization and graphical user interface (GUI).

Geo-polymer concrete has a significant influence on the environmental condition and thus its use in the civil industry leads to a decrease in carbon dioxide (CO2) emission. However, problems lie with its mixed design and casting in the field. This study utilizes supervised artificial-based machine learning algorithms (MLAs) to anticipate the mechanical characteristic of fly ash/slag-based geopolymer concrete (FASBGPC) by utilizing AdaBoost and Bagging on MLPNN to make an ensemble model with 156 data points. The data consist of GGBS (kg/m3), Alkaline activator (kg/m3), Fly ash (kg/m3), SP dosage (kg/m3), NaOH Molarity, Aggregate (kg/m3), Temperature (°C) and compressive strength as output parameter. Python programming is utilized in Anaconda Navigator using Spyder version 5.0 to predict the mechanical response. Statistical measures and validation of data are done by splitting the dataset into 80/20 percent and K-Fold CV is employed to check the accurateness of the model by using MAE, RMSE, and R2. Statistical analysis relies on errors, and tests against external indicators help determine how well models function in terms of robustness. The most important factor in compressive strength measurements is examined using permutation characteristics. The result reveals that ANN with AdaBoost is outclassed by giving maximum enhancement with R2 = 0.914 and shows the least error with statistical and external validations. Shapley analysis shows that GGBS, NaOH Molarity, and temperature are the most influential parameter that has significant content in making FASBGPC. Thus, ensemble methods are suitable for constructing prediction models because of their strong and reliable performance. Furthermore, the graphical user interface (GUI) is generated through the process of training a model that forecasts the desired outcome values when the corresponding inputs are provided. It streamlines the process and provides a useful tool for applying the model's abilities in the field of civil engineering.

Surgical and oncological outcomes of robot-assisted versus laparoscopic radical nephroureterectomy for upper-tract urothelial carcinoma: A single-center comparative analysis.

Introduction: We retrospectively compared surgical and oncological outcomes of robot-assisted (RA) radical nephroureterectomy (RNU) in patients of upper-tract urothelial carcinoma with a cohort of patients who underwent the same procedure using a laparoscopic approach. Methods: Data of 63 consecutive patients who underwent RNU with bladder cuff excision (BCE) from 2011 to 2022 at a single tertiary care institution was retrospectively retrieved from the electronically maintained institutional database. Twenty-six cases underwent RNU with a laparoscopic approach, whereas 37 were done by RA approach. Demographic, clinical, surgical, and pathologic details and survival analyses were reported and compared. The tetrafecta of RNU, which include the performance of a BCE, lymphadenectomy, no positive surgical margin, and no major surgical complication, was also reviewed. Results: The mean age and body mass index of the robotic and laparoscopic groups were 61.5 years versus 62.7 years and 23.8 versus 24.9 kg/m2, respectively (P = 0.710 and 0.309). The Charlson Comorbidity Index and upper-tract tumor site distribution were comparable between the groups. There was no significant difference in the distribution of T stage, N stage, presence of multifocality, or lymphovascular invasion between the two groups. Although the rate of concomitant carcinoma in situ was higher in laparoscopic cohort, 42.8% versus 10.8% in robotic cohort (P = 0.004). The laparoscopic group had higher blood transfusion rates (50 vs. 13.5%, P = 0.002) and longer median hospital stays (7 vs. 4 days, P = 0.000). The median follow-up time was 21.5 versus 27 months in the laparoscopic and robotic groups. The RA group was significantly better in the achievement of the tetrafecta outcomes. The 5-year urinary bladder recurrence-free survival (UB RFS) and elsewhere RFS between the laparoscopic and robotic cohorts were 65% versus 72% and 56% versus 70%, respectively (P = 0.510 and 0.190). The laparoscopic cohort had worse 5-year cancer-specific survival and overall survival (64% vs. 90% and 58% vs. 74%, P = 0.04 and 0.08). Conclusion: The robotic approach to RNU and BCE has significantly lower transfusion rates, lower hospital stays, and significantly better cancer-specific survival rates.

Prostate cancer detection using magnetic resonance imaging-transrectal ultrasound fusion biopsy: A retrospective cohort study.

Introduction: Transrectal ultrasound (TRUS) guided systematic prostate biopsy is conventionally used for the diagnosis of carcinoma prostate (CaP). However, magnetic resonance imaging (MRI) guided biopsies have been shown to have superior diagnostic performance. MRI-TRUS fusion biopsy improves the detection by combining the systematic and the targeted biopsies (TB). In this study, we evaluated the role of fusion biopsy in the detection of CaP as well as clinically significant carcinoma prostate (CsCaP). Methods: In this retrospective study, the patients who underwent fusion biopsy from January 2016 to July 2022 were evaluated. Patients underwent multiparametric MRI and the suspicious lesions were reported as per the Prostate Imaging Reporting and Data System (PIRADS) version 2. The clinical, imaging, and biopsy parameters were recorded and evaluated. Results: A total of 330 patients with PIRADS ≥3 underwent MRI-TRUS fusion biopsy and prostate cancer was detected in 187 patients (56.67%). With an increase in the PIRADS score, there was a significant rise in the detection of CaP (P < 0.001) and CsCaP (P < 0.0000001). Prostatitis was observed in 13%-18.1% of the patients with a lesion on MRI irrespective of the PIRADS score. The systematic and TB were comparable for the detection of CaP (P = 0.88) and CsCaP (P = 0.26). With a prostate-specific antigen density (PSAD) cutoff of 0.15 ng/mL/cc and 0.22 ng/mL/cc, biopsy could be safely avoided in 14.2% and 20.3% of the patients, missing only 0.3% of CaP and 0.9% of CsCaP, respectively. Different subgroups based on PSA levels, prostate volume, lesion dimension, and PIRADS score did not show a significant difference between the systematic and the targeted cores for the detection of CsCaP. Conclusion: This single center study of MRI-TRUS fusion prostate biopsy shows that in men with clinical suspicion of prostate cancer a pre-biopsy MRI and MRI-TRUS fusion combined systematic and targeted prostate biopsy improves the detection of prostate cancer and CsCaP. Patients with a PIRADS 3 lesion with a PSA density <0.22 can safely avoid prostate biopsy, without a significant risk of missing clinically significant prostate cancer.

The Dissection of Nitrogen Response Traits Using Drone Phenotyping and Dynamic Phenotypic Analysis to Explore N Responsiveness and Associated Genetic Loci in Wheat.

Inefficient nitrogen (N) utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers, redundant plant growth, greenhouse gases, long-lasting toxicity in ecosystem, and even effect on human health, indicating the importance to optimize N applications in cropping systems. Here, we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions. Powered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties. Then, we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season, which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes (i.e., phenotypic changes) during N response. After that, we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores (NECS), based on which we classified the varieties into 4 N responsiveness (i.e., N-dependent yield increase) groups. The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies. Finally, we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes, helping us explore genetic components underlying N responsiveness in wheat. In summary, we believe that our work demonstrates valuable advances in N response-related plant research, which could have major implications for improving N sustainability in wheat breeding and production.

Safety and efficacy of paromomycin/miltefosine/liposomal amphotericin B combinations for the treatment of post-kala-azar dermal leishmaniasis in Sudan: A phase II, open label, randomized, parallel arm study.

BACKGROUND: Treatment for post-kala-azar dermal leishmaniasis (PKDL) in Sudan is currently recommended only for patients with persistent or severe disease, mainly because of the limitations of current therapies, namely toxicity and long hospitalization. We assessed the safety and efficacy of miltefosine combined with paromomycin and liposomal amphotericin B (LAmB) for the treatment of PKDL in Sudan. METHODOLOGY/PRINCIPAL FINDINGS: An open-label, phase II, randomized, parallel-arm, non-comparative trial was conducted in patients with persistent (stable or progressive disease for ≥ 6 months) or grade 3 PKDL, aged 6 to ≤ 60 years in Sudan. The median age was 9.0 years (IQR 7.0-10.0y) and 87% of patients were ≤12 years old. Patients were randomly assigned to either daily intra-muscular paromomycin (20mg/kg, 14 days) plus oral miltefosine (allometric dose, 42 days)-PM/MF-or LAmB (total dose of 20mg/kg, administered in four injections in week one) and oral miltefosine (allometric dose, 28 days)-LAmB/MF. The primary endpoint was a definitive cure at 12 months after treatment onset, defined as clinical cure (100% lesion resolution) and no additional PKDL treatment between end of therapy and 12-month follow-up assessment. 104/110 patients completed the trial. Definitive cure at 12 months was achieved in 54/55 (98.2%, 95% CI 90.3-100) and 44/55 (80.0%, 95% CI 70.2-91.9) of patients in the PM/MF and AmB/MF arms, respectively, in the mITT set (all randomized patients receiving at least one dose of treatment; in case of error of treatment allocation, the actual treatment received was used in the analysis). No SAEs or deaths were reported, and most AEs were mild or moderate. At least one adverse drug reaction (ADR) was reported in 13/55 (23.6%) patients in PM/MF arm and 28/55 (50.9%) in LAmB/MF arm, the most frequent being miltefosine-related vomiting and nausea, and LAmB-related hypokalaemia; no ocular or auditory ADRs were reported. CONCLUSIONS/SIGNIFICANCE: The PM/MF regimen requires shorter hospitalization than the currently recommended 60-90-day treatment, and is safe and highly efficacious, even for patients with moderate and severe PKDL. It can be administered at primary health care facilities, with LAmB/MF as a good alternative. For future VL elimination, we need new, safe oral therapies for all patients with PKDL. TRIAL REGISTRATION: ClinicalTrials.gov NCT03399955, https://clinicaltrials.gov/study/NCT03399955 ClinicalTrials.gov ClinicalTrials.gov.

Cutaneous Radiation-Associated Angiosarcoma After Treatment of Carcinoma Penis: First Report from Tertiary Cancer Centre of North India.

Cutaneous radiation-associated angiosarcoma (cRAA) is a rare and aggressive secondary cutaneous angiosarcoma (cAS) with poor survival. cRAA has been mostly reported in breast carcinoma patients. Owing to its rarity, there is scanty literature available and no treatment guidelines. To the best of our knowledge, this is the first report of cRAA after multimodality treatment of carcinoma penis. A sixty-eight-year-old gentleman, a known case of carcinoma penis, underwent total penectomy with perineal urethrostomy and bilateral radical inguinopelvic lymph node dissection 6 years ago. He received adjuvant radiotherapy to the pelvis and bilateral groin. He presented with a bleeding plaque-like lesion with ulceration over the left lower abdomen (within previous radiation field) which rapidly progressed in size over the past 2 months. On examination, the lesion bled profusely on touch. Contrast MRI was suggestive of lobulated exophytic enhancing cutaneous lesion free from underlying muscle. Wedge biopsy was suggestive of cutaneous angiosarcoma. He underwent wide local excision with local perforator flap reconstruction from the right lower abdomen. Histopathology was suggestive of cutaneous angiosarcoma which showed immunoexpression of CD31, ERG1, cMYC suggestive of cRAA. cRAA is a very aggressive disease with 5-year survival of 15-34%. To the best of our knowledge, this is the first ever reported case of cRAA of lower abdomen after multimodality management of carcinoma penis. It masquerades with other benign and less aggressive radiation-induced skin lesions. cMYC immunoexpression is specific for secondary cAS. Wide local resection with negative margin provides the best outcome.

Assessment of the ground vibration during blasting in mining projects using different computational approaches.

The investigation compares the conventional, advanced machine, deep, and hybrid learning models to introduce an optimum computational model to assess the ground vibrations during blasting in mining projects. The long short-term memory (LSTM), artificial neural network (ANN), least square support vector machine (LSSVM), ensemble tree (ET), decision tree (DT), Gaussian process regression (GPR), support vector machine (SVM), and multilinear regression (MLR) models are employed using 162 data points. For the first time, the blackhole-optimized LSTM model has been used to predict the ground vibrations during blasting. Fifteen performance metrics have been implemented to measure the prediction capabilities of computational models. The study concludes that the blackhole optimized-LSTM model PPV11 is highly capable of predicting ground vibration. Model PPV11 has assessed ground vibrations with RMSE = 0.0181 mm/s, MAE = 0.0067 mm/s, R = 0.9951, a20 = 96.88, IOA = 0.9719, IOS = 0.0356 in testing. Furthermore, this study reveals that the prediction accuracy of hybrid models is less affected by multicollinearity because of the optimization algorithm. The external cross-validation and literature validation confirm the prediction capabilities of model PPV11. The ANOVA and Z tests reject the null hypothesis for actual ground vibration, and the Anderson-Darling test rejects the null hypothesis for predicted ground vibration. This study also concludes that the GPR and LSSVM models overfit because of moderate to problematic multicollinearity in assessing ground vibration during blasting.

Experimental study on the eco-friendly plastic-sand paver blocks by utilising plastic waste and basalt fibers.

Plastic waste poses a significant hazard to the environment as a result of its high production rates, which endanger both the environment and its inhabitants. Similarly, another concern is the production of cement, which accounts for roughly 8% of global CO2 emissions. Thus, recycling plastic waste as a replacement for cementitious materials may be a more effective strategy for waste minimisation and cement elimination. Therefore, in this study, plastic waste (low-density polyethylene) is utilised in the production of plastic sand paver blocks without the use of cement. In addition to this, basalt fibers which is a green industrial material is also added in the production of eco-friendly plastic sand paver blocks to satisfy the standard of ASTM C902-15 of 20 N/mm2 for the light traffic. In order to make the paver blocks, the LDPE waste plastic was melted outside in the open air and then combined with sand. Variations were made to the ratio of LDPE to sand, the proportion of basalt fibers, and sand particle size. Paver blocks were evaluated for their compressive strength, water absorption, and at different temperatures. Including 0.5% percent basalt fiber of length 4 mm gives us the best result by enhancing compressive strength by 20.5% and decreasing water absorption by 50.5%. The best results were obtained with a ratio of 30:70 LDPE to sand, while the finest sand provides the greatest compressive strength. Moreover, the temperature effect was also studied from 0 to 60 °C, and the basalt fibers incorporated in plastic paver blocks showed only a 20% decrease in compressive strength at 60 °C. This research has produced eco-friendly paver blocks by removing cement and replacing it with plastic waste, which will benefit the environment, save money, reduce carbon dioxide emissions, and be suitable for low-traffic areas, all of which contribute to sustainable development.

Robot-assisted complex urinary tract reconstruction using intestinal segments: redefining the paradigm.

Complex urinary tract reconstruction has significantly advanced with the increasing use of robot-assisted procedures. Robotic surgery aims to achieve the same outcomes as open surgery while minimizing morbidity by causing less blood loss, faster postoperative recovery, and reducing complications. This article shares our technique, challenges encountered, and experience of robot-assisted complex urinary tract reconstruction using intestinal segments. Between January 2020 to March 2022, 6 patients who underwent robot-assisted complex urinary tract reconstruction using intestinal segments at our centre were retrospectively reviewed. Demographic, clinical, and operative data were recorded. Patients underwent renal function tests, blood gas analysis, and radiographic imaging in the follow-up. Symptomatic and radiologic relief were the criteria for success. Out of 6 cases, three patients underwent ileal ureter replacement, two combined ileal ureter with augmentation ileo-cystoplasty and one augmentation ileo-cystoplasty alone. The mean age, estimated blood loss, length of hospital stay, and follow-up period were 32.6 years, 110 ± 13.1 mL, 7.0 ± 1.1 days, and 11.3 months, respectively. The indications for surgery were either benign ureteral stricture following lithotripsy or sequelae of genitourinary tuberculosis. No intra-operative complications were found. Clavien-Dindo grade-II and Grade-IIIa were found in three and one patient, respectively. During follow-up, none had compromised renal function or acidosis. Robot-assisted complex urinary tract reconstruction using intestinal segments is safe and offers the advantages of minimally invasive techniques. Techniques demonstrated in this article make these reconstructions feasible with good surgical and clinical outcomes.