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The current research work was carried out to simulate monthly streamflow historical record using Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) at the Astore Basin, Gilgit-Baltistan, Pakistan. The performance of SWAT and ANN models was assessed during calibration (1985–2005) and validation (2006–2010) periods via statistical indicators such as coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and root-mean-square error (RMSE). R2, NSE, PBIAS, and RMSE values for SWAT (ANN with Architecture (2,27,1)) models during calibration are 0.80 (0.88), 0.73 (0.82), 15.7 (0.008), and 79.81 (70.34), respectively, while during validation, the corresponding values are 0.71 (0.86), 0.66 (0.95), 17.3 (0.10), and 106.26 (75.92). The results implied that the ANN model is superior to the SWAT model based on the statistical performance indicators. The SWAT results demonstrated an underestimation of the high flow and overestimation of the low flow. Comparatively, the ANN model performed very well in estimating the general and extreme flow conditions. The findings of this research highlighted its potential as a valuable tool for accurate streamflow forecasting and decision-making. The current study recommends that additional machine learning models may be compared with the SWAT model output to improve monthly streamflow predictions in the Astore Basin.

This study investigates changes in river flow patterns, in the Hunza Basin, Pakistan, attributed to climate change. Given the anticipated rise in extreme weather events, accurate streamflow predictions are increasingly vital. We assess three machine learning (ML) models – artificial neural network (ANN), recurrent neural network (RNN), and adaptive fuzzy neural inference system (ANFIS) – for streamflow prediction under the Coupled Model Intercomparison Project 6 (CMIP6) Shared Socioeconomic Pathways (SSPs), specifically SSP245 and SSP585. Four key performance indicators, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), guide the evaluation. These models employ monthly precipitation, maximum and minimum temperatures as inputs, and discharge as the output, spanning 1985–2014. The ANN model with a 3-10-1 architecture outperforms RNN and ANFIS, displaying lower MSE, RMSE, MAE, and higher R2 values for both training (MSE = 20417, RMSE = 142, MAE = 71, R2 = 0.94) and testing (MSE = 9348, RMSE = 96, MAE = 108, R2 = 0.92) datasets. Subsequently, the superior ANN model predicts streamflow up to 2100 using SSP245 and SSP585 scenarios. These results underscore the potential of ANN models for robust futuristic streamflow estimation, offering valuable insights for water resource management and planning.

This study presents a critical analysis of the environmental challenges regarding global environmental policies and current practices in China. The study provides imperative evidence about the current emission control strategies, environmental planning, legislation, policy instruments, and measures to provide a sustainable environment for the present and future generations. The study followed a well-defined analytical methodology to analyse the measures adopted to control emissions as a trade-balancing tool for the environment. The findings indicated that domestic as well as the international collaborations were effective in controlling the present problem of environmental pollution, and suggested a need for collaborative agreements to amend the Environmental Protection Law (EPL). The analytical findings determined that the proposed EPL considered SO2 or NO2 emissions while neglecting an important source of environmental pollution, i.e., CO2 emissions. The research findings also suggested a need for to accelerate efforts in a more professional, practical, and result-oriented manner to analyse the diverse nature of environmental issues. The research highlighted some of the obstacles to the successful implementation of EPL for current and future environmental challenges.

Streamflow forecasting is essential for planning, designing, and managing watershed systems. This research study investigates the use of artificial neural networks (ANN), recurrent neural networks (RNN), and adaptive neuro-fuzzy inference systems (ANFIS) for monthly streamflow forecasting in the Hunza River Basin of Pakistan. Different models were developed using precipitation, temperature, and discharge data. Two statistical performance indicators, i.e., root mean square error (RMSE) and coefficient of determination (R2), were used to assess the performance of machine learning techniques. Based on these performance indicators, the ANN model predicts monthly streamflow more accurately than the RNN and ANFIS models. To assess the performance of the ANN model, three architectures were used, namely 2-1-1, 2-2-1, and 2-3-1. The ANN architecture with a 2-3-1 configuration had higher R2 values of 0.9522 and 0.96998 for the training and testing phases, respectively. For each RNN architecture, three transfer functions were used, namely Tan-sig, Log-sig, and Purelin. The architecture with a 2-1-1 configuration based on tan-sig transfer function performed well in terms of R2 values, which were 0.7838 and 0.8439 for the training and testing phases, respectively. For the ANFIS model, the R2 values were 0.7023 and 0.7538 for both the training and testing phases, respectively. Overall, the findings suggest that the ANN model with a 2-3-1 architecture is the most effective for predicting monthly streamflow in the Hunza River Basin. This research can be helpful for planning, designing, and managing watershed systems, particularly in regions where streamflow forecasting is crucial for effective water resource management.

A delimited maritime border is an essential requirement for creating a secure and stable environment that facilitates further development. However, standing hostilities and competing interests among neighbouring states have made delimitation agreements difficult; consequently, critical maritime areas remain undefined, leading to disputes. The present study employs qualitative legal analysis and comparative case study methodology, drawing on primary sources including UNCLOS provisions, bilateral agreements, arbitral decisions, official state documents, and empirical evidence of state practice from 1994 to 2025. Using the East and South China Sea disputes as case studies, this research examines the complexities of law enforcement in contested maritime zones where overlapping claims create jurisdictional challenges. The study also highlights the probable consequences of violating Articles 74(3) and 83(3) of UNCLOS and how this impacts the States’ practices in the disputed areas. Through detailed analysis of these two critical Asian maritime disputes, the study demonstrates how the overlapping maritime claims result in outstanding diplomatic business for the states concerned, which gives rise to tension in inter-state relations or even disputes; therefore, the rules of international law should provide clear guidance for the conduct of the coastal states during the period while overlapping claims remain undelimited.

Concrete 3D printing, an innovative construction technology, offers reduced material waste, increased construction speed, and the ability to create complex and customized shapes that are challenging to achieve with traditional methods. This study delves into the unique fresh-state performance required for 3D printing concrete, discussing buildability, extrudability, and shape retention in terms of concrete rheology, which can be modified using admixtures. Currently most 3D printing concretes feature high cement contents, with little use of secondary cementitious materials. This leads to high embodied carbon, and addressing this is a fundamental objective of this work. The research identifies attapulgite, bentonite, and sepiolite clay as potential concrete admixtures to tailor concrete rheology. Eight low-carbon concrete mixes are designed to incorporate GGBS at a 50% replacement level and are used to measure the influence of each clay on the concrete rheology at varying dosages. A comprehensive rheological test protocol is designed and carried out on all mixes, together with other tests including slump-flow and compression strength. The objective is to determine the applicability of each clay in improving the printability of low-carbon concrete. The findings reveal that at a dosage of 0.5%, sepiolite was seen to improve static yield stress, dynamic yield stress, and rate of re-flocculation, resulting in improved printability. The addition of attapulgite and sepiolite at a dosage of 0.5% by mass of binder increased compressive strength significantly; bentonite had very little influence. These gains are not repeated at 1% clay content, indicating that there may be an optimum clay content. The results are considered encouraging and show the potential of these clays to enhance the performance of low-carbon concrete in 3D printing applications.

The present study is aimed at enhancing the solubility of rosuvastatin (RST) by designing betacyclodextrin/polyvinypyrrolidone-co-poly (2-acrylamide-2-methylpropane sulphonic acid) crosslinked hydrophilic nanogels in the presence of crosslinker methylene bisacrylamide through free-radical polymerization method. Various formulations were fabricated by blending different amounts of betacyclodextrin, polyvinylpyrrolidone, 2-acrylamide-2-methylpropane sulphonic acid, and methylene bisacrylamide. The developed chemically crosslinked nanogels were characterized by FTIR, SEM, PXRD, TGA, DSC, sol-gel analysis, zeta size, micromeritics properties, drug loading percentage, swelling, solubility, and release studies. The FTIR spectrum depicts the leading peaks of resultant functional groups of blended constituents while a fluffy and porous structure was observed through SEM images. Remarkable reduction in crystallinity of RST in developed nanogels revealed by PXRD. TGA and DSC demonstrate the good thermal stability of nanogels. The size analysis depicts the particle size of the developed nanogels in the range of 178.5 ±3.14 nm. Drug loading percentage, swelling, solubility, and release studies revealed high drug loading, solubilization, swelling, and drug release patterns at 6.8 pH paralleled to 1.2 pH. In vivo experiments on developed nanogels in comparison to marketed brands were examined and better results regarding pharmacokinetic parameters were observed. The compatibility and non-toxicity of fabricated nanogels to biological systems was supported by a toxicity study that was conducted on rabbits. Efficient fabrication, excellent physicochemical properties, improved dissolution, high solubilization, and nontoxic nanogels might be a capable approach for the oral administration of poorly water-soluble drugs.

According to the previous reports, the collagen triple helix repeat containing 1 (CTHRC1) causes tumorigenesis by modulating the tumor microenvironment, however, the evidence is limited to a few human cancer subtypes. In the current study, we analyzed and validated the CTHRC1 expression variations in 24 different human cancer tissues paired with normal tissues using publically available databases. We observed that CTHRC1 was overexpressed in all the 24 major subtypes of human cancers and its overexpression was significantly associated with the reduced overall survival (OS) duration of head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), Lung adenocarcinoma (LUAD), stomach adenocarcinoma (STAD), and Uterine corpus endometrial carcinoma (UCEC). This implies that CTHRC1 plays a significant role in the development and progression of these cancers. We further noticed that CTHRC1 was also overexpressed in HNSC, KIRC, LIHC, LUAD, STAD, and UCEC patients of different clinicopathological features. Pathways enrichment analysis revealed the involvement of CTHRC1 associated genes in seven diverse pathways. We also explored few interesting correlations between CTHRC1 expression and promoter methylation, genetic alterations, CNVs, CD8+ T immune cells infiltration, and tumor purity. In conclusion, CTHRC1 can serve as a shared diagnostic and prognostic biomarker in HNSC, KIRC, LIHC, LUAD, STAD, and UCEC patients of different clinicopathological features.

Two major families exist in ticks, the Argasidae and Ixodidae. The Argasidae comprise 2 sub-families, Argasinae and Ornithodorinae. The placement into subfamilies illuminate differences in morphological and molecular systematics and is important since it provides insight into evolutionary divergence within this family. It also identifies fundamental gaps in our understanding of argasid evolution that provide directions for future research. Molecular systematics based on mitochondrial genomics and 18S/28S ribosomal RNA confirmed the placement of various genera and subgenera into the Argasinae: Argas (including Argas and Persicargas), Navis, Ogadenus, Otobius lagophilus, Proknekalia, Secretargas and the Ornithodorinae: Alectorobius, Antricola (including Antricola and Parantricola), Carios, Chiropterargas, Nothoaspis, Ornithodoros (including Microargas, Ornamentum, Ornithodoros sensu strictu, Pavlovskyella), Otobius sensu strictu, Reticulinasus and Subparmatus. The position of Alveonasus remains controversial since traditional taxonomy placed it in the Ornithodorinae, while cladistic and limited molecular analysis placed it in the Argasinae. The current study aimed to resolve the systematic position of Alveonasus using mitochondrial genomic and 18S/28S ribosomal RNA systematics by sequencing the type species Alveonasus lahorensis from Pakistan. In addition, the mitochondrial genomes for Argas reflexus and Alectorobius kelleyi are reported from Germany and the USA, respectively. The systematic data unambiguously place Alveonasus in the Argasinae and also suggest that Alveonasus may be another paraphyletic genus.

Currently, the renewable energy sectors have dynamically revolved around targeting green turbulence, mainly due to increased customer environmental awareness. Therefore, this paper investigates green initiatives. The results show barriers and explain the strategies for adopting green renewable energy sources in Yemen. The political barrier has the highest weight of 0.191, while technical barrier sored the second highest weight of 0.181. The weights of managerial and information energy were found to be 0.18 and 0.17, respectively. Market barrier weighed the lowest score of 0.12, while economic barrier (0.15 weight) is the barrier to develop renewable energy road map. The research developed a comprehensive decision making framework to identify major barriers, sub-barriers, and develop plans for green energy in Yemen. Fuzzy analytical hierarchal process (FAHP) results indicate that the category of political obstacles is more important than other obstacles. Yemen has undergone power reforms and achieved better energy efficiency, compared to the countries that have applied imperfect. Economic efficiency in Yemen is the lowest among the considered barriers. Twenty-five percent of the considered barriers were identified with an alarming efficiency of 0.5%. The effects of FTOPSIS show that the planned explanation “developing research methods to achieve green innovation in renewable” energy is significant to address the obstacles to green innovation in renewable energy.