Identification of the G(3900) Structure as the P-Wave DD[over ¯]^{*}/D[over ¯]D^{*} Resonance.

The BESIII Collaboration recently performed a precise measurement of the e^{+}e^{-}→DD[over ¯] Born cross sections, and confirmed the G(3900) structure reported by BABAR and Belle with high significance. We identify the G(3900) as the first P-wave DD[over ¯]^{*}/D[over ¯]D^{*} molecular resonance. The experimental and theoretical identification of the P-wave dimeson state holds paramount importance in enhancing our comprehension of the nonperturbative QCD and few-body physics.

Modelling Euphrates river water quality index based on field measured data in Al-Diwaniyah City, Iraq.

Pollution monitoring in surface water using field observational procedure is a challenging matter as it is time consuming, and needs a lot of efforts. This study addresses the challenge of efficiently monitoring and predicting water pollution using a GIS-based artificial neural network (ANN) to detect heavy metal (HM) pollution in surface water and effect of wastewater required discharge on the Euphrates River in Al-Diwaniyah City, Iraq. The study established using 40 water sampling stations and incorporates Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-OES) to assess HM levels.

Comprehensive geological analysis and evaluation of the feasibility of renovating the Wanshunchang gas reservoir into a gas storage facility.

As many oil and gas reservoirs approach depletion stages in the future, alongside growing energy storage demands, constructing gas storage facilities becomes critical for ensuring a stable natural gas supply. Consequently, a comprehensive geological analysis is essential to evaluate the feasibility of converting depleted gas reservoirs into gas storage facilities. The W gas reservoir in the Sichuan Basin, China, is nearing depletion and presents potential for conversion into a gas storage facility.

A stable open-shell peri-hexacene with remarkable diradical character.

[n]Peri-acenes ([n]PA) have attracted great interest as promising candidates for nanoelectronics and spintronics. However, the synthesis of large [n]PA (n > 4) is extremely challenging due to their intrinsic open-shell radical character and high reactivity. Herein, we report the successful synthesis and isolation of a derivative (1) of peri-hexacene in crystalline form.

"Cation-Recognition" Effect of 2D Nanochannels in Graphene Oxide Membranes Intercalated with Ionic Liquid for High Desalination Performance.

Water and ion transport in nanochannels is crucial for membrane-based technology in biological systems. 2D materials, especially graphene oxide (GO), the most frequently used as the starting material, are ideal building blocks for developing synthetic membranes. However, the selective exclusion of small ions while maintaining in a pressured filtration process remains a challenge for GO membranes.

Albizia Procera-Derived Nitrogen-Doped Carbon: A Versatile Material for Energy Conversion, Storage, and Environmental Applications.

This review explores the diverse applications of nitrogen-doped carbon derived from Albizia procera, known as white siris. Native to the Indian subcontinent and tropical Asia, this species thrives in varied conditions, contributing to sustainable development. The nitrogen-rich leaves of Albizia procera are an excellent source for synthesizing nitrogen-doped carbon, which possesses remarkable properties for advanced technologies.

Catalytic and Noncatalytic in Situ Hydrogen Production from Heavy Oil: A Review of Experimental Studies.

Hydrogen (H) offers a less carbon-intensive energy production method than natural gas. The potential of utilizing hydrogen at a large scale within the future energy mix to fuel the world opens the door to investigating hydrogen production from heavy and extra-heavy oil reservoirs. Various reaction mechanisms are involved in the in situ combustion gasification of heavy oil to produce sustainable and low carbon intensive hydrogen.

Enhancing lesion detection in liver and kidney CT scans via lesion mask selection from two models: A main model and a model focused on small lesions.

Automated segmentation and detection of tumors in CT scans of the liver and kidney have a significant potential in assisting clinicians with cancer diagnosis and treatment planning. However, current approaches, including state-of-the-art deep learning ones, still face many challenges. Many tumors are not detected by these approaches when tested on public datasets for tumor detection and segmentation such as the Kidney Tumor Segmentation Challenge (KiTS) and the Liver tumor segmentation challenge (LiTS).

Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning.

Identifying and differentiating human activities is crucial for effectively preventing the threats posed by environmental pollution to aquatic ecosystems and human health. Machine learning (ML) is a powerful analytical tool for tracking human impacts on river ecosystems based on high-through datasets. This study employed an ML framework and 16S rRNA sequencing data to reveal microbial dynamics and trace human activities across China.

Numerical investigation of performance and exergy analysis in parabolic trough solar collectors.

This numerical investigation examines the performance and exergy analysis of parabolic trough solar collectors, focusing on the substitution of the conventional circular absorber with a rhombus-shaped absorber. By evaluating the thermal and fluid dynamics properties, this study aims to identify improvements in overall system performance and efficiency. This numerical study conducts a comprehensive thermal analysis of parabolic trough solar collectors by comparing a rhombus-shaped absorber with a conventional circular absorber.

Catalytic oxidation upcycling of polyethylene terephthalate to commodity carboxylic acids.

Catalytic upcycling of polyethylene terephthalate (PET) into high-value oxygenated products is a fascinating process, yet it remains challenging. Here, we present a one-step tandem strategy to realize the thermal catalytic oxidation upcycling of PET to terephthalic acid (TPA) and high-value glycolic acid (GA) instead of ethylene glycol (EG). By using the Au/NiO with rich oxygen vacancies as catalyst, we successfully accelerate the hydrolysis of PET, accompanied by obtaining 99% TPA yield and 87.

MM-DRPNet: A multimodal dynamic radial partitioning network for enhanced protein-ligand binding affinity prediction.

Accurate prediction of drug-target binding affinity remains a fundamental challenge in contemporary drug discovery. Despite significant advances in computational methods for protein-ligand binding affinity prediction, current approaches still face substantial limitations in prediction accuracy. Moreover, the prevalent methodologies often overlook critical three-dimensional (3D) structural information, thereby constraining their practical utility in computer-aided drug design (CADD).

Optomechanical energy enhanced BF-QEPAS for fast and sensitive gas sensing.

Traditional beat frequency quartz-enhanced photoacoustic spectroscopy (BF-QEPAS) are limited by short energy accumulation times and the necessity of a decay period, leading to weaker signals and longer measurement cycles. Herein, we present a novel optomechanical energy-enhanced (OEE-) BF-QEPAS technique for fast and sensitive gas sensing. Our approach employs periodic pulse-width modulation (PWM) of the laser signal with an optimized duty cycle, maintaining the quartz tuning fork's (QTF) output at a stable steady-state level by applying stimulus signals at each half-period and allowing free vibration in alternate half-periods to minimize energy dissipation.

Predicting student self-efficacy in Muslim societies using machine learning algorithms.

Introduction: Self-efficacy is a critical determinant of students' academic success and overall life outcomes. Despite its recognized importance, research on predictors of self-efficacy using machine learning models remains limited, particularly within Muslim societies. This study addresses this gap by leveraging advanced machine learning techniques to analyze key factors influencing students' self-efficacy.

Experimental study on ultrasonic reduction of irreducible water saturation in low permeability reservoir.

Irreducible water saturation is an important factor affecting the development effect of low permeability reservoir. Using the self-developed ultrasonic generator, kerosene was used as simulated oil, the natural low-permeability siltstone cores with different physical properties in Zhongyuan Oilfield were selected for indoor oil displacement experiment, and the effect of ultrasonic action on the saturation of irreducible water in low-permeability reservoirs was evaluated. It was found that ultrasound can further reduce the saturation of irreducible water on the basis of oil flooding.

Temporal trends and predictive modeling of air pollutants in Delhi: a comparative study of artificial intelligence models.

Air pollution monitoring and modeling are the most important focus of climate and environment decision-making organizations. The development of new methods for air quality prediction is one of the best strategies for understanding weather contamination. In this research, different air quality parameters were forecasted, including Carbon Monoxide (CO), Nitrogen Monoxide (NO), Nitrogen Dioxide (NO), Ozone (O), Sulphur Dioxide (SO), Fine Particles Matter (PM), Coarse Particles Matter (PM), and Ammonia (NH).

Global big data laboratory experiment, integrated with kernel-based algorithm with an improved nonlinear ensemble for compressive strength modeling.

With the continuous clamor for a reduction in embodied carbon in cement, rapid solution to climate change, and reduction to resource depletion, studies into substitute binders become crucial. These cementitious binders can potentially lessen our reliance on cement as the only concrete binder while also improving concrete functional properties. Finer particles used in cement microstructure densify the pore structure of concrete and enhance its performance properties.

Carbon Felts Uniformly Modified with Bismuth Nanoparticles for Efficient Vanadium Redox Flow Batteries.

The integration of intermittent renewable energy sources into the energy supply has driven the need for large-scale energy storage technologies. Vanadium redox flow batteries (VRFBs) are considered promising due to their long lifespan, high safety, and flexible design. However, the graphite felt (GF) electrode, a critical component of VRFBs, faces challenges due to the scarcity of active sites, leading to low electrochemical activity.

Integrated SERS-Microfluidic Sensor Based on Nano-Micro Hierarchical Cactus-like Array Substrates for the Early Diagnosis of Prostate Cancer.

The detection and analysis of cancer cell exosomes with high sensitivity and precision are pivotal for the early diagnosis and treatment strategies of prostate cancer. To this end, a microfluidic chip, equipped with a cactus-like array substrate (CAS) based on surface-enhanced Raman spectroscopy (SERS) was designed and fabricated for the detection of exosome concentrations in Lymph Node Carcinoma of the Prostate (LNCaP). Double layers of polystyrene (PS) microspheres were self-assembled onto a polyethylene terephthalate (PET) film to form an ordered cactus-like nanoarray for detection and analysis.

Estimation of Spacecraft Angular Velocity Based on the Optical Flow of Star Images Using an Optimized Kalman Filter.

Biomimetic vision is a promising method for efficient navigation and perception, showing great potential in modern navigation systems. Optical flow information, which comes from changes in an image on an organism's retina as it moves relative to objects, is crucial in this process. Similarly, the star sensor is a critical component to obtain the optical flow for attitude measurement using sequences of star images.

Preparation and Performance Evaluation of CO Foam Gel Fracturing Fluid.

The utilization of CO foam gel fracturing fluid offers several significant advantages, including minimal reservoir damage, reduced water consumption during application, enhanced cleaning efficiency, and additional beneficial properties. However, several current CO foam gel fracturing fluid systems face challenges, such as complex preparation processes and insufficient viscosity, which limit their proppant transport capacity. To address these issues, this work develops a novel CO foam gel fracturing fluid system characterized by simple preparation and robust foam stability.