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ObjectiveThis study aims to clinically and genetically assess 30 unrelated consanguineous Pakistani families from various ethnic backgrounds, all exhibiting features of neurodevelopmental disorders (NDDs).MethodsWe conducted clinical, genetic, biochemical, and molecular analyses on 30 consanguineous families with NDDs enrolled from various regions of Pakistan. The likely molecular causes of primary microcephaly and NDDs were identified. Detailed clinical investigations and molecular diagnoses were performed using whole exome sequencing (WES) of the proband, followed by Sanger sequencing for validation and segregation in the available family members of the affected families.ResultsWES identified likely disease-causing homozygous variants in 30 unrelated consanguineous families. Six families presented newly described variants in known NDD-related genes: ABAT (c.1439 T > G; p.Phe480Cys) [OMIM613163], SLC12A6 (c.2865_2865insT; p.Glu955Asnfs*5) [OMIM 218000], SHANK3 (c.1305-3_1,305-2delTT; p.Gln29-_Gly305del) [OMIM 606232], BCKDK (c.356_356insC; p.Gly119Alafs*24) [OMIM 614923], DDHD2 (c.2065G > T; p.Asp689Tyr) [OMIM 615033], ERCC2 (c.1255G > A; p.Glu419Lys) [OMIM 610756]. Additionally, 12 families had previously reported disease-causing variants associated with different types of NDDs: ATRX (c.109C > T; p.Arg37*) [OMIM 309580], GPR56 [ADGRG1] (c.1423C > T; p.Arg475*) [OMIM 606854], NAGLU (c.1694G > A; p.Arg565Gln) [OMIM 252920], DOLK (c.3G > A; p.Met1Ile) [OMIM 610768], GPT2 (c.815C > T; p.Ser272Leu) [OMIM 616281], DYNC1I2 (c.607 + 1G > A; p.?) [OMIM 618492], FBXL3 (c.885delT; p.Leu295Phefs25*) [OMIM 606220], LINGO1 (c.869G > A; p.Arg290His) [OMIM 618103], and ASPM (c.3978G > A; Trp1326*, c.9557C > G; p.Ser3186*, c.6994C > T; p.Arg2332*) [OMIM 608716]. All the identified variants showed segregation compatible with autosomal recessive inheritance.ConclusionIn the present study, we observed a high frequency of ASPM variants in the genetic analysis of 30 consanguineous families exhibiting features of NDDs, particularly those associated with autosomal recessive primary microcephaly. These findings contribute to studies on genotype–phenotype correlation, genetic counseling for families, and a deeper understanding of human brain function and development.

Bearings are critical components in modern manufacturing, yet they are prone to failures in induction machines. Detecting these faults early can reduce repair costs. To achieve efficient and accurate fault detection, we explore vibration-based analysis. Traditional methods rely on manual feature extraction, which is time-consuming. In contrast, our work leverages deep learning, particularly convolutional neural networks, to automatically extract fault features from raw data. We investigate various image sizes (16 × 16, 32 × 32, 64 × 64, 128 × 128, 256 × 256) and their performance in bearing fault diagnosis. Our convolutional neural networks-based approach is compared to traditional methods such as support vector machine, nearest neighbors, and artificial neural networks. Results demonstrate the superior performance of our data-driven fault diagnosis using convolutional neural networks.

Abstract This study investigates mechanical properties, durability performance, non-destructive testing (NDT) characteristics, environmental impact evaluation, and advanced machine learning (ML) modelling techniques employed in the analysis of high-strength self-compacting concrete (HSSCC) incorporating varying supplementary cementitious materials (SCMs) to develop sustainable building construction. The findings from the fresh characteristics test indicate that mixes’ optimal flowability and passing qualities can be achieved using different concentrations of marble powder (MP) alongside a consistent amount of silica fume (SF) and fly ash (FA). Moreover, the incorporation of 10% MP along with 10% FA and 20% SF in HSSCC significantly improved the compressive strength by 14.68%, while the splitting tensile strength increased by 15.59% compared to the reference mix at 56 days. While random forest (RF), gradient boosting (GB), and their ensemble models exhibit strong coefficient correlation (R2) values, the GB model demonstrates more precision, indicating reliable predicted outcomes of the mechanical properties. Following subsequent testing, it has been demonstrated that incorporating SCMs improves the NDT properties of HSSCC and enhances its durability. The finer MP, SF, and FA particles enhanced microstructural performance by minimizing voids and cracks while improving the C–H–S bond. As noticed by its lower CO2-eq per MPa for SCMs, the HSSCC mix with up to 15% MP inclusion increased mechanical strength while reducing the environmental footprint, making it an eco-friendly concrete alternative.

Since the world's population is rising continuously, more cultivable land is being utilized for their dwellings. As a result, the amount of food supply is decreasing day by day. In order to address the food shortage, a proper plan and technological breakthroughs is must. Tomato is a kind of vegetable which has the healthy ingredients and essential for our daily food list. The proposed system suggests an IoT based tomato cultivation and pest management system, with the help of deep learning methods. In the IoT implementation, camera module and moisture sensor are used to collect images of tomato plant, soil condition respectively. Based on the moisture content, the water pump will supply the water when it necessary. Besides, the real-time images of tomato leaf will be sent to the server to identify and classify natural enemies like various insect species. In the proposed system seven types of pests are identified with the help of ten deep learning models like InceptionV3, Xception, InceptionResNetV2, MobileNet, MobileNetV2, MobileNetV3Large, MobileNetV3Small, DenseNet121, DenseNet169, DenseNet201. This study has trained with leaves and insects separately to identify whether an image from a tomato plant is insectoid or not. 458 images of pests and 912 images of leaves are utilized in the proposed architecture. The accuracy of classifying insects or leaves using DenseNet201 is 100 ​%. The highest accuracy of 94 ​% is obtained to classify the different insects using the DenseNet201 model.

The innovative utilization of sugarcane bagasse ash (SCBA) in producing lightweight concrete (LWC) needs to be explored experimentally and quantification with machine learning approaches. Therefore, this study aims to investigate fresh, mechanical and microstructural characteristics and machine learning modeling of incorporating SCBA as a partial substitute of cement. In this investigation, OPC was replaced partially by the weight of SCBA in the proportion of 0 %, 5 %, 10 %, 15 %, and 20 %. The influences of SCBA on the fresh characteristics of concrete were investigated through tests of the slump, compacting factor, Kelly ball, k-slump, density, and air content. In addition, for hardened properties compressive, splitting tensile, flexural strength, and static modulus of elasticity were tested at 7 and 28 days. Furthermore, a microstructural characteristic was carried out for varying content of SCBA. Different machine learning (ML) predictive models were performed utilizing artificial neural network (ANN) and random forest (RF), to predict the fresh and mechanical attributes of the LWC. It was noticed that the workability value of fresh concrete was enhanced almost by 40 % to 50 % with the incorporation of SCBA content. The hardened test results revealed that a mixture having SCBA up to 10 % content SCBA resulted in nearly 10 % to 30 % better performance to satisfy the requirement of LWC. Regarding the sustainability assessment, the less embodied CO2 (eCO2) is produced in SCBA mixes, whereas it is also economically beneficial to produce SCBA mixes. Models' efficacy was evaluated including mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2). The maximum R2 and minimum RMSE came across for RF (0.989 and 1.393, respectively) which provides the well estimation of the model.

Abstract Multi‐walled carbon nanotubes (MWCNTs) were successfully synthesized and functionalized by chemical vapour deposition and acid reflux methods, respectively. Chitosan (CTS) was prepared by a chemical extraction method from waste prawn shells. Various weight fractions of functionalized multi‐walled carbon nanotubes (f‐MWCNTs) have been used as reinforcing agent in CTS biopolymer matrix. Fourier transform infrared spectroscopy analysis was done, which confirms the presence of absorption bands of the various functional groups of chitin, CTS, and MWCNTs. Raman spectra revealed the quality of MWCNTs, the extent of their functionalization, and the quality of nanocomposites. The X‐ray diffraction analysis showed the distinctive peaks for f‐MWCNTs’ and also revealed the formation of CTS/f‐MWCNTs nanocomposites. Transmission Electron Microscopy (TEM) analysis also exhibited that the CTS/f‐MWCNTs nanoparticles have a well‐defined crystalline structure. The highest coercivity and magnetization (Ms) of the CTS/5%f‐MWCNTs nanocomposite are 602 Oe and 0.1202 emu/g, respectively that have been enhanced by 3.83 and 5.27 times compared to the pure CTS respectively. It showed that the conductivity is getting higher with the addition of f‐MWCNTs in the CTS matrix. CTS/5% f‐MWCNTs composites exhibit the highest conductivity than other composites and the conductivity of CTS/5% f‐MWCNTs composite is 4.0×10−4 S/m.

This document is a correction notice for a journal article titled "Role of L-Carnitine supplementation on rate of weight gain and biomarkers of Environmental Enteric Dysfunction in children with severe acute malnutrition: A protocol for a double-blinded randomized controlled trial." The correction addresses an error in the equation in the Sample size and power subsection of the Materials and methods section. The corrected equation is provided in the document. The authors of the article are Jinat Alam, Md. Ridwan Islam, Shah Mohammad Fahim, Md. Amran Gazi, and Tahmeed Ahmed. [Extracted from the article]

This research aims to assess the rheological and mechanical characteristics of Self-compacting concrete (SCC) incorporating waste tire rubber aggregates (WRTA) as an interim substitute for coarse aggregates. However, the standard experimental modeling approach has significant obstacles when it comes to overcoming the nonlinearity and environmental susceptibility of concrete parts. Therefore, linear regression (LR) and extreme gradient boosting (XGBoost) were used as two standard single machine learning (ML) models to predict the aforementioned rubberized SCC features. In this study, conventional coarse aggregates were supplanted with WRTA at 0%, 5%, 10%, and 20% to uncover the optimal proportion of coarse aggregates substituting rubber. To find the optimum amount of WRTA to use as a substitute, the study follows the impacts of rubber on the self-compacting rubberized concrete's (SCRC) rheological and mechanical characteristics. The consequences on fresh properties were investigated by the slump flow, J-ring, and V-funnel tests, while compressive and splitting tensile strengths tests were conducted to assess mechanical properties. Increasing WRTA test outputs indicated a deterioration in workability and hardened qualities. While a 10% swapping ratio is deemed feasible for producing SCRC, optimal results were achieved by reducing environmental impacts and efficiently managing a significant volume of rubber tire waste with a 5% substitution of rubber within the coarse aggregates. The research findings indicated a noticeable decrease in fresh properties as the WRTA content increased. Notably, after 28 days, a 10% WRTA substitution led to a 34% reduction in compressive strength and a 28% decrease in splitting tensile strength, satisfying ACI standards. Furthermore, XGBoost demonstrated superior predictive performance with the highest R2 values, outperforming the LR model and affirming its efficacy in delivering more accurate predictions.