Techno Press
Tp_Editing System.E (TES.E)
Login Search
You logged in as...

anr
 
CONTENTS
Volume 21, Number 3, September 2026
 

Abstract
Acid Mine Drainage (AMD) can impose severe acidity and elevated metal loads on stream networks, yet copper behavior at confluence zones remains difficult to predict, as dilution, Potential of Hydrogen (pH) recovery, sorption, mineral precipitation, colloid formation, and sediment retention occur simultaneously. This study presents an integrated computational assessment of copper fate in a stream confluence affected by AMD. The analysis distinguishes site scale observations from process interpretation, defines the modeling domain, and treats the one dimensional assumption as a reach averaged approximation rather than a full hydraulic representation. Ten monitoring sites were evaluated across acid inflow, neutral inflow, junction core, near field plume, and downstream recovery zones. Acid inflow waters showed pH values as low as 2.64 and dissolved copper concentrations up to 8156 micrograms per liter, whereas DS02 reached pH 6.57 and dissolved copper of 286 micrograms per liter. Conservative dilution alone did not account for copper attenuation, as net copper removal increased from 6.1 percent at the junction core to 49.0 percent downstream. Sediment and BCR results indicated substantial copper storage in the bed material, with mobility declining from 40.6 percent to 10.5 percent along the acid source to recovery gradient. Speciation results showed a decline in free copper ion and a concurrent increase in colloid bound and organically complexed copper. A staged modeling framework indicated that a hybrid reactive transport formulation performed better than conservative, equilibrium, and sorption corrected alternatives. Overall, the interpretation suggests that confluence zones can reduce dissolved copper exposure while maintaining persistent particulate and sediment reservoirs that remain important for risk assessment and remediation design. In addition, the study incorporates a Metal Organic Framework (MOF) adsorption benchmark as an engineered remediation layer for interpreting copper retention under acidic to circumneutral mixing conditions. The MOF component is treated as a comparative sorbent rather than a field measured mineral phase, allowing natural attenuation by Iron (Fe) and Aluminum (Al) precipitates, organic films, and aged flocs to be evaluated against a high surface area, chemically tunable adsorption framework.

Key Words
acid mine drainage; copper attenuation; copper speciation; engineered sorbent remediation; metal organic framework (MOF); MOF adsorption; reactive transport modeling; sediment geochemistry; stream confluence

Address
Zhenghui Li: College of Chemical and Material Engineering, Quzhou University, Quzhou 324000, China

Refka Ghodhbani: Center for Scientific Research and Entrepreneurship, Northern Border University, Arar 73213, Saudi Arabia

Samia Elattar: Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

H. Elhosiny Ali: Department of Physics, Faculty of Science, King Khalid University, P.O. Box 9004, Abha, Saudi Arabia/ Physics Department, Faculty of Science, Zagazig University, 44519 Zagazig, Egypt

Aseel Smerat: Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India/ Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan

José Escorcia-Gutierrez: Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla 080002, Colombia


Abstract
In this study, a palladium-based magnetic nanocatalyst with the structure Fe3O4@SiO2–NH2–Pd was synthesized and fully characterized in a stepwise manner. The successful formation of a core-shell structure, functionalization of a surface with amine groups, and uniform immobilization of palladium nanoparticles were confirmed by various analyses including FT-IR, XRD, SEM, TEM, EDX, VSM, BET, XPS and ICP-OES. The results showed that the catalyst had a suitable specific surface area (86 m2/g), uniform Pd distribution (3–6 nm), and significant magnetic property (42 emu/g), which enabled its easy separation. Then the efficiency of this catalyst in the carbonylation-cyclization reaction of aromatic alkynes in the production of isoquinolone derivatives was studied. The best reaction conditions were taking of green solvent (EtOH/H2O), temperature of 90 oC and mild pressure of CO which resulted in high yields of between 75-90. It was also demonstrated that the catalyst could be recovered and reused in several cycles without any significant loss of activity. These results indicate the high efficiency of this catalytic system in the synthesis of heterocyclic compounds of pharmaceutical importance.

Key Words
anti-inflammatory compounds; antiviral activity; control; Fe3O4@SiO2–NH2–Pd; green synthesis; hybrid material; magnetic nanocatalyst; nanocatalyst

Address
Xu Xu: Department of Physical Education, Hebei Agricultural University, Baoding 071000, Hebei, China

Min Zheng: School of Management, Wuhan Donghu University, Wuhan 430212, Hubei, China

Mostafa Habibi: Technical Sciences, Chennai, India/ Department of Mechanical Engineering, Faculty of Engineering, Haliç University, Istanbul, Turkey/ Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, India


Abstract
In this paper we study the propagation of thermal waves in hybrid composite structures for advanced sports helmet systems. The safety and comfort of the athlete are strongly dependent on the effective heat dissipation and the superior mechanical properties. Multi scale reinforcements are embedded in a polymer matrix to achieve lightweight protection and improved thermal transport. Thermo mechanical properties are obtained using homogenization techniques based on the Halpin–Tsai model and rule of mixtures. Realistic thermal loading conditions; uniform, linear, and sinusoidal are considered to reflect temperature variations during intense athletic activity. The structural interaction between the composite shell and helmet padding is modeled using a variable Winkler–Pasternak foundation. Different higher-order shear deformation theories and Hamilton's principle are employed to derive the governing equations and obtain analytical solutions. Firstly, the accuracy of outcomes is validated and then some numerical examples are provided to show the sensitivity of wave propagation to various parameters. The effects of carbon nanotubes content, carbon fibers volume fraction, wave number, foundation stiffness and its various types, higher-order theories and thermal loading patterns on wave frequency and phase velocity are studied. The results provide guidance for improving heat management and mechanical response of next generation sports helmet designs.

Key Words
higher-order theories; multi-scale hybrid composites; sports helmet systems; thermal loading; wave propagation

Address
Yueqiang Dai: Minnan Science and Technology College, Nan'an 362332, Fujian, China/ Faculty of Sports Science and Technology, Bangkok Thonburi University, 16/10 Taweewattana, Bangkok, Thailand

Xiang Xu: The ministry of Junti, Jiangxi Agricultural University, Nanchang 330045, Jiangxi, China

Ying Zhao: School of Physical Education and Health, Sanming University, Sanming 365004, Fujian, China

Mostafa Habibi: Technical Sciences, Chennai, India/ Department of Mechanical Engineering, Faculty of Engineering, Haliç University, Istanbul, Turkey/ Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, India


Abstract
Artificial Intelligence (AI) based intelligent campuses require more than digital portals, since reliable student services also depend on resilient classrooms, laboratories, residences, mobility routes, emergency facilities, and utility assets. This study develops a multidisciplinary framework that connects a unified student service community with nano enabled campus infrastructure indicators and uses chloride resistance and high strain rate impact response as a material scale demonstration. The framework integrates student services across campus functions. It also supports a student one-stop management system. Four nano SiO2 modified steel fiber reinforced concrete mixtures were investigated, with nano SiO2 incorporated at 2.0 percent of the total binder mass. The reported nano SiO2 characteristics included a primary particle size of 12 ± 3 nm, a purity of 99.8 percent, and a Brunauer Emmett Teller (BET) specific surface area of 200 ± 25 m2/g. The experimental program covered 0, 30, 90, 180, and 270 exposure cycles in a 5.0 percent NaCl solution. Each 24 h cycle consisted of 11 h of immersion, 10 h of drying at 60 ± 2 °C, and 3 h allocated to filling, drainage, ventilation, and cooling. Quasi static compression and chloride profile measurements were conducted using three specimens for each condition. Split Hopkinson Pressure Bar (SHPB) tests were performed at nominal impact velocities of 5.4, 6.7, and 7.8 m/s, corresponding to mean strain rates of 58 ± 4, 87 ± 6, and 119 ± 8 s-1, respectively. After 270 exposure cycles, chloride penetration depth ranged from 22 to 28 mm. At 7.8 m/s, CF exhibited the largest increase in dynamic compressive strength relative to the corresponding unexposed condition, reaching 25.2 percent. CFS exhibited the largest increase in energy absorption, reaching 123.0 percent at 5.4 m/s. The maximum SHPB force mismatch was 7.4 percent, remaining below the adopted equilibrium limit of 10 percent. The proposed campus framework treats material condition indicators as inputs to an AI assisted asset service layer supporting predictive maintenance, service continuity planning, risk based room allocation, and integrated emergency response. Principal challenges include sensor interoperability, model governance, privacy protection, limited sample size, and the need for direct validation of nanoscale and corrosion mechanisms.

Key Words
intelligent campus; unified student services; AI; nano enabled infrastructure; chloride resistance; steel fiber reinforced concrete; high strain rate response

Address
Jianguo Zhao: Yangzhou Polytechnic Institute, Yangzhou, Jiangsu, 225127, China

Samia Elattar: Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh 11671, Saudi Arabia

Hamiden Abd El-Wahed Khalifa: Department of Operations and Management Research, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt

Aseel Smerat: Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India/ Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan

Yasmin Khairy: Central Labs, King Khalid University, AlQura'a, P.O. Box 960, Abha, Saudi Arabia

José Escorcia-Gutierrez: Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, 080002, Colombia



Abstract
The identification of circulating tumour cells (CTCs) is now emerging as one of the most promising approaches to the diagnosis, prognosis and monitoring of cancer. Traditional methods of detection are not always sensitive enough and take too much time to process, and are not always as effective in complex biological environments. This work introduces a novel nanosensor system with deep learning capabilities for the detection of circulating tumor cells (CTC) from blood, combining the use of nanomaterials with sophisticated AI algorithms. The development of a detailed synthetic dataset of 480 samples aimed to cover as many realistic variations of nanosensor characteristics, physiological parameters of blood, expression of the biomarkers, and conditions of the signals' acquisition. A total of fourteen input variables were used, such as the size of the gold nanoparticles, concentration of graphene oxide, content of magnetic nanoparticles, surface functionalization density, antibody binding efficiency, blood flow rate, blood temperature, blood pH, and the number of blood cells, and electrical conductivity, the level of noise in the sensor, and the ratio of the measured signal to noise. The proposed framework is based on a deep neural network that is able to model the complex, nonlinear relationships between sensor characteristics and diagnostic performance. To enhance the prediction accuracy and the generalization ability of the models, data preprocessing, feature normalization, and model optimization were conducted. Multiple statistical parameters such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to evaluate the performance. The developed model is able to show an excellent prediction of the nanosensor performance and able to identify the dominant parameters affecting the nanosensor performance. Antibody binding efficiency, target biomarker expression, signal-to-noise ratio and surface functionalization density are the most important factors to consider for accurate detection of CTCs, according to sensitivity analysis. The deep learning based nanosensor platform will enable intelligent optimization of biosensors using efficient computational solution and will facilitate the development of real-time, accurate, and fast cancer diagnostic systems for precision medicine applications.

Key Words
artificial intelligence; cancer diagnosis; circulating tumor cells; deep learning; nanosensors

Address
Lin Li: Department of Hematology, The Second Hospital of Hebei Medical University, Shijiazhuang City, Hebei Province, China/ Department of Blood and Marrow Transplantation, Tianjin Cancer Hospital Airport Hospital, Tianjin City, China

Li Qian: Department of Hematology, Tianjin Medical University Cancer Institute and Hospital, Tianjin City, China

Li Nan, Chen Lin: Department of Blood and Marrow Transplantation, Tianjin Cancer Hospital Airport Hospital, Tianjin City, China

Zhang Jingyu: Department of Hematology, The Second Hospital of Hebei Medical University, Shijiazhuang City,
Hebei Province, China


Abstract
Nanocellulose (NC) is a promising sustainable material for water treatment, yet its lack of intrinsic antimicrobial activity limits its efficacy in disinfection. In this study, high-performance antibacterial nanocomposite filters were developed via a two-stage process: hydrothermal acid hydrolysis of bleached hardwood kraft pulp (BHKP) followed by centrifugal spray-drying with silver nanoparticles (AgNPs). Structural characterization (XRD and FTIR) confirmed that the treatment effectively refined the NC matrix, increasing the crystallinity index from 68.5% to 77.3% while preserving the Cellulose I structure. The antibacterial performance was evaluated under dynamic filtration conditions against Escherichia coli and Bacillus subtilis. Results demonstrated that a minimum AgNPs loading of 90 ppm was required to achieve 100% bacterial removal. Interestingly, pristine NC alone removed 99.54% of B. subtilis through physical entrapment but showed limited effect on E. coli, highlighting a synergistic mechanism where the nanocellulose network provides size-exclusion retention while AgNPs deliver potent bactericidal activity. This scalable and eco-friendly fabrication strategy offers a sustainable solution for decentralized water disinfection, particularly in resource-limited regions.

Key Words
antibacterial filtration; nanocellulose; nanocomposite materials; silver nanoparticles; water disinfection

Address
Nguyen Hoang Chung, Le Quang Dien: Hanoi University of Science and Technology, 1 Dai Co Viet, Bach Mai, Hanoi, Vietnam

Phan Thi Hong Thao: Institute of Biology, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet,
Nghia Do, Hanoi, Vietnam


Abstract
Early and correct diagnosis is crucial because a late diagnosis and/or imperfect management can have a profound impact on the prognosis of cerebrovascular diseases. In this study, a novel, multimodal, clinical data driven Nano-AI integrated system is developed to predict, detect and monitor cerebrovascular diseases in real-time. The framework involves demographic parameters, cardiovascular risk factors, physiological parameters, biochemical parameters, neurological scores, imaging parameters – MRI lesion volume, perfusion delay and vessel narrowing. To illustrate the proposed analytical framework, a set of 487 simulated observations was created. The outputs from the Nano-AI system include cerebrovascular disease status, disease subtype, disease prediction probability, therapeutic response and real-time monitoring risk. The inputs of the multimodal data were statistically and visually described, and the association between clinical characteristics and AI predicted outputs were assessed. The results show that there are major differences between clinical, laboratory, neurological and imaging variables and advocate for integration of multimodal information. In the prediction-probability analysis, the continuously varying nature of the disease risk allows risk assessment to be viewed, and the ROC analysis demonstrates the potential to evaluate the discriminatory power of the proposed prediction model. Further, risks and therapeutic responses monitored reveal the potential for monitoring beyond the initial diagnosis using AI. Correlation analysis helps to show significant associations between selected clinical and imaging parameters and AI prediction probability. In general, the proposed Nano-AI framework serves as a computational paradigm to connect real-time nanoscale sensing with multimodal clinical intelligence for enabling early detection of cerebrovascular risk, personalized monitoring and therapeutic decision making. Data presented here is simulated and must be confirmed in future with prospective multimodal clinical data before use in the clinical environment.

Key Words
artificial intelligence; cerebrovascular diseases; detection; monitoring; nano-AI integrated systems

Address
Ma Zhenkai, Guan Min, Meng Shuhui, Wu Liheng, Zhou Zhilong, Zhou Tengfei, Zhu Liangfu: Department of Cerebrovascular disease, Henan Provincial People's Hospital, Zhengzhou, Henan, China/ Department of Neurosurgery, Zhengzhou University People's Hospital, Zhengzhou, Henan, China


Abstract
The increasing generation of industrial and construction waste has encouraged the development of sustainable and economical materials for pavement engineering. This study investigates the application of nano-sized waste materials as modifiers in asphalt mixtures to improve pavement performance while reducing environmental impacts and production costs. Waste-derived nanoparticles, obtained from materials such as glass, fly ash, silica-rich residues, and construction debris, are incorporated into asphalt binder at appropriate proportions. Their effects on the physical, rheological, mechanical, and durability properties of asphalt are evaluated through conventional binder tests and mixture performance assessments. The results are expected to show that nano waste additives can enhance binder stiffness, adhesion, resistance to rutting, fatigue cracking, moisture damage, and thermal deterioration because of their high surface area and filler activity. At appropriate dosages, these materials may also reduce the demand for virgin mineral fillers and costly commercial nanomaterials. However, excessive addition can increase viscosity and reduce workability, highlighting the need for dosage optimization. The proposed approach supports circular-economy principles by converting waste into a value-added pavement material, reducing landfill disposal, conserving natural resources, and lowering the environmental footprint of road construction. Overall, nano waste-modified asphalt offers a promising pathway toward durable, low-cost, and environmentally friendly pavement systems.

Key Words
asphalt pavement; circular economy; environmental assessment; life-cycle cost; nanomaterials; nano-waste materials; reclaimed asphalt pavement; waste valorisation

Address
Yong Liu: School of Intelligent Construction, Luzhou vocational and technical College, Luzhou 646000, China

Shanqin Chen: China Communications 2ND Navigational Bureau 2ND Engineering CO., LTD. China

Masoud Ghasemi: Department of Civil Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia

Ibrahim Albaijan: Mechanical Engineering Department, College of Engineering at Al-Kharj, Prince Sattam Bin Abdulaziz University, Al Kharj, 16273, Saudi Arabia

Wenyan Ma: School of civil engineering, Chongqing Vocational Institute of Engineering, Chongqing, 402260, China

Samia Elattar: Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

Firas Daghistani: Department of Civil Engineering, College of Engineering, University of Business and Technology,
Jeddah 21361, Saudi Arabia

H. Elhosiny Ali: Department of Physics, Faculty of Science, King Khalid University, P.O. Box 9004, Abha, Saudi Arabia


Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2026 Techno-Press ALL RIGHTS RESERVED.
P.O. Box 33, Yuseong, Daejeon 34186 Korea.
General Inquiries: info@techno-press.com / Journal Administration: admin@techno-press.com