Catalytic and thermal fast pyrolysis of agricultural plastic waste: Comparison of BTEX and steam cracker feed production in a bubbling fluidized bed reactor - Elmeri Pienihäkkinen; Christian Lindfors; Roman Tschentscher; Stephan Kubowicz; Syed Farhan Hashmi; Muhammad Hassam Khan; Pouya Sirous-Rezaei (Fuel, 2026)

Engineering of novel N-TiO2/P-doped graphene oxide nanocomposite: Mechanistic insights into the photocatalytic removal of benzophenone-3 and H2 production - Wisal Ahmad; Suyeon Jeon; Roman Shah; Dilaram Khan; Syed Izaz Ali Shah; Muhammad Anwar; Raed Al-Juboori; Asad Ali; Noor S. Shah; Javed Ali Khan; Changseok Han (Chemical Engineering Journal, 2026)

Spatiotemporal variability of particulate organic carbon in relation to chlorophyll-a and sea surface temperature in the Arabian Gulf - Md Masudur Rahman; Roman Shults; Rajendran Sankaran; Wanchang Zhang; Mahfuzur Rahman; Muhammad Usman (Ocean Dynamics, 2026)

Deep learning-based classification of nitrate and nitrite concentrations from water samples using colorimetric test strip images - Muhammad Roman; Mazhar Sher; Chamika Kuruppuarachchi; Arshid Ali; Chulwoo Pack; Azlan Zahid; Ali Mirzakhani Nafchi (Environmental Monitoring and Assessment, 2026)
Accurate monitoring of nitrate and nitrite concentrations in water is essential for sustainable agriculture, safeguarding public health, and protecting aquatic ecosystems from nutrient pollution. Traditional methods for detecting nitrate and nitrite in water samples are precise but costly, complex, and time-consuming, limiting their practicality for frequent on-site testing. This research proposes deep learning-based computer vision techniques to classify nitrate and nitrite concentrations using images of colorimetric test strips. An RGB IMX219 camera was used to acquire images of colorimetric test strips under standardized, controlled illumination conditions to ensure consistent image quality. A total of 1938 nitrate images and 1190 nitrite images were collected before augmentation. After preprocessing and training-only data augmentation, both classical machine learning baselines based on hand-crafted color and texture features and deep learning models—including a multilayer perceptron (MLP) and convolutional neural networks (AlexNet, VGG16, ResNet18, and GoogLeNet)—were trained and evaluated using an independent test set and stratified fivefold cross-validation. For nitrate classification, ResNet18 and GoogLeNet achieved near-perfect 100% test accuracy, with mean cross-validation accuracy of 99.97% ± 0.04%, substantially outperforming classical baseline models based on hand-crafted color and texture features, which achieved at most 83.5% test accuracy. For nitrite classification, GoogLeNet achieved the strongest overall performance, with a test accuracy of 97.48% and a fivefold cross-validation accuracy of 95.22% ± 1.17%, substantially outperforming the best classical baseline model, which achieved a maximum test accuracy of 83.19%. These results demonstrate that deep CNN-based feature learning provides a significant performance advantage over simpler methods under controlled imaging conditions, supporting the suitability of the proposed system for rapid, image-based water quality assessment and motivating future evaluation under broader real-world deployment scenarios.

Development of an AI-Enabled Vision-Based On-Site Rapid Detection of Nitrates and Nitrites from Drainage Samples - Muhammad Roman; Azlan Zahid; Ali Mirzakhani Nafchi; Mazhar Sher (Water, Air, & Soil Pollution, 2026)
Plant growth is significantly impacted by the concentration of nutrients in the soil. Accurate and real-time measurement of nitrates and nitrites levels remains a significant challenge. Existing laboratory-based methods are expensive, time-consuming, and labor-intensive, highlighting the need for a rapid, on-site solution. This study proposes a rapid method for measuring nitrates and nitrite levels in water samples collected from tile drainage. We utilized Hach Nitrate and Nitrite test strips for image dataset collection as these test strips change color based on the concentration of nitrate and nitrite in the water samples. A purpose-built black box, equipped with an internal lighting arrangement for imaging test strips, was designed to collect images of the test strips. Unlike many existing smartphone-based colorimetric approaches, which are sensitive to ambient lighting variations and often rely on external calibration or offline analysis, the proposed system integrates a controlled illumination environment with real-time edge computation for robust on-site detection of nitrate and nitrite. An Nvidia Orin Nano module, connected with an IMX219 camera sensor, was used to capture images of the test strips. Image preprocessing was performed, followed by the implementation of a VGG16-based network for feature extraction. A dataset of approximately 3128 images spanning multiple nitrate and nitrite concentration levels was collected under controlled imaging conditions. Multiple machine learning models including logistic regression (LR), support vector machines (SVM), k-nearest neighbors (KNN), naïve Bayes (NB), and random forest (RF) were evaluated for classification. The nitrate detection using KNN achieved an accuracy of 99.96% training, 99.92% testing, and 99.79% cross-validation. For nitrite detection, the SVM model achieved accuracy of 99.02%, 98.04%, and 98.07% for training, testing, and cross-validation, respectively, demonstrating both systems' high reliability and practical applicability for real-time monitoring. The total system cost is approximately USD 300, highlighting the affordability and practicality of the proposed solution for on-site water quality monitoring. This technology can enable farmers, water quality researchers, and agronomists to efficiently monitor the levels of nitrates and nitrites in tile drainage samples, enabling data-driven decisions to maximize crop yields.

Performance Assessment of Diverse Spelt Wheat (Triticum spelta L.) Genotypes Across Three Years Seasons in Central Europe - Jaffar Iqbal; Naeem Ahmad; Muhammad Roman; Lukáš Hlisnikovský; Jiří Hermuth; Michal Hejcman (International Journal of Plant Production, 2026)
Spelt wheat ( Triticum spelta L.), known as an ancient cereal, is gaining popularity again due to its exceptional dietary value, adaptability to climate change, and low resource inputs. However, limited information is available about the diverse spelt lines agronomic and nutritional performance under fluctuating environments in central Europe. To determine agronomic and grain nutritional characteristics of 46 spelt wheat genotypes from the Czech Republic, Germany, and Switzerland, a 3-year field study (2021–2023) was carried-out at the Crop Research Institute, Czech University of Life Sciences, Prague. Our results showed highest genotypic variability among measured traits. Agronomic traits such as plant height, spike length, 1000 grain weight and grain yield were highest in genotype 69Z6.205, followed by 69Z6.204. Nutritional quality traits such as crude protein and starch content were greatest in genotypes 69Z6.205 (20.7% and 63.3%). Moreover, Zeleny sedimentation values were noted higher in 69Z6.205 genotype, compared to genotypes under study. Regarding years comparison, 2023 significantly improved the agronomic and quality traits followed by 2021 and 2022. The correlation and principal component analysis demonstrated that the grain yield, harvest index and quality factors (starch and Zeleny sedimentation value) are strongly associated with each other, thus indicating integrated adaptability potential to promote productivity and quality. The principal findings of the mantel correlation test are that the interannual climatic variability was significant contributor, shows trade-offs in total biomass production and partitioning efficiency to grain components, thereby revealing insights into traits performance over temporal scale. Overall, certain genotypes (particularly, 69Z6.205, 69Z6.204, 69Z6.148 and 69Z6.153) produced high yield and had good nutritional value, these lines could be considered for future breeding projects because of the nutrient-enhanced and climate-resilient characteristics. These findings suggest that spelt wheat has agronomic and nutritional potential as a viable substitute for bread wheat in central European agroecosystems.

Retraction Note: Novel acetamide derivatives of 2-aminobenzimidazole prevent inflammatory arthritis in rats via suppression of pro-inflammatory mediators - Aymun Madni Zubair; Muhammad Nasir Hayat Malik; Waqas Younis; Muhammad Atif Hayat Malik; Shah Jahan; Ishtiaq Ahmed; Alamgeer Yuchi; Muhammad Naveed Mushtaq; Romeeza Tahir; Muhammad Bilal Sarwar; Muhammad Roman; Ayaz Ali Khan; et al. (Inflammopharmacology, 2026)

Effects of biological and technical subsoil amelioration on root growth and crop performance in cereal crops - Muhammad Arslan; Roman Kemper; Thomas F. Döring; Oliver Schmittmann; Miriam Athmann (Soil and Tillage Research, 2026)

Review on sea water quality (SWQ) monitoring using satellite remote sensing techniques (SRST) - Md Masudur Rahman; Roman Shults; Surya Prakash Tiwari; Arfan Arshad; Muhammad Usman; Asif Raihan; Md Fatin Ishraque (Marine Pollution Bulletin, 2025)

Land subsidence and groundwater storage change assessment using InSAR and GRACE in the arid environment of Saudi Arabia - Esubalew Adem; Roman Shults; Muhammad Ukasha; Amro Elfeki; Faisal Alqahtani; Mohamed Elhag (Natural Hazards, 2024)

Dynamics of industrial hemp vegetative growth and metabolite accumulation in response to light intensity - Muhammad Roman; Mingyue Li; Jinzhan Yuan; Muzammal Rehman; Lijun Liu (South African Journal of Botany, 2024)

Antagonistic impact on cadmium stress in alfalfa supplemented with nano-zinc oxide and biochar via upregulating metal detoxification - Hafiz Abdul Kareem; Muhammad Adeel; Muhammad Azeem; Muhammad Arslan Ahmad; Noman Shakoor; Mahmood Ul Hassan; Sana Saleem; Annie Irshad; Junpeng Niu; Zhipeng Guo; Ćupina Branko; Roman Hołubowicz; et al. (Journal of Hazardous Materials, 2023)

RETRACTED ARTICLE: Novel acetamide derivatives of 2-aminobenzimidazole prevent inflammatory arthritis in rats via suppression of pro-inflammatory mediators - Aymun Madni Zubair; Muhammad Nasir Hayat Malik; Waqas Younis; Muhammad Atif Hayat Malik; Shah Jahan; Ishtiaq Ahmed; Alamgeer Yuchi; Muhammad Naveed Mushtaq; Romeeza Tahir; Muhammad Bilal Sarwar; Muhammad Roman; Ayaz Ali Khan; et al. (Inflammopharmacology, 2022)

Pharmacological evaluation of anti-arthritic potential of terpinen-4-ol using in vitro and in vivo assays - Sania Aslam; Waqas Younis; Muhammad Nasir Hayat Malik; Shah Jahan; Alamgeer; Ambreen Malik Uttra; Muhammad Usman Munir; Muhammad Roman (Inflammopharmacology, 2022)

Pollution characteristics and human health risk assessments of toxic metals and particle pollutants via soil and air using geoinformation in urbanized city of Pakistan - Khalid Mehmood; Yansong Bao; Roman Abbas; Saifullah; George P. Petropoulos; Hamaad Raza Ahmad; Muhammad Mohsin Abrar; Adnan Mustafa; Alwaseela Abdalla; Katia Lasaridi; Shah Fahad (Environmental Science and Pollution Research, 2021)

The Small Matter of a Red Ox, a Particularly Sensitive Pink Cat, and the Quest for the Yellow Stone of Wisdom - Muhammad Jawad Nasim; Polina Denezhkin; Muhammad Sarfraz; Roman Leontiev; Yannik Ney; Ammar Kharma; Sharoon Griffin; Muhammad Irfan Masood; Claus Jacob (Current Pharmacology Reports, 2018)