Abstract: The increasing prevalence of peri-implantitis has led to a growing clinical use of implantoplasty, a procedure involving intraoral machining of the dental implant sur-face to remove biofilm. The absence of standardized clinical protocols may contribute to premature fatigue failure of dental implants. The present study aimed to determine the influence of machining depth on the cyclic mechanical behavior of dental im-plants. A total of 250 commercially pure Grade 3 titanium dental implants were dis-tributed into four groups according to machining depth: untreated (original), 0.2 mm, 0.4 mm, and 0.6 mm wall reduction. The implant system featured an internal connec-tion with a thread height of 0.4 mm. Finite element analysis was performed for each machining depth to evaluate Von Mises stress distribution and to simulate fatigue be-havior. The numerical models were validated through experimental fatigue testing using a servohydraulic MTS Bionix testing machine under ISO 14801:2016 standard conditions. Fractographic analysis was conducted by scanning electron microscopy. The results revealed that maximum Von Mises stresses were concentrated at the junc-tion between the implant thread and the implant body. The fatigue limit of the un-treated implants was approximately 400 N. Implants subjected to 0.4 mm machining exhibited a fatigue limit of 350 N, whereas lower fatigue limits were observed for 0.2 mm (290 N) and 0.6 mm (180 N) reductions. These findings demonstrate the signifi-cant mechanical effect of thread removal. At higher applied loads, fracture occurred in the coronal region of the implant, whereas at lower loads failure shifted to the im-plant–abutment connection. Finite element predictions showed high agreement with experimental results. The findings highlight a clinically relevant criterion: implanto-plasty depth should not exceed the original thread height, as excessive wall reduction markedly compromises fatigue resistance and long-term mechanical reliability.

Abstract: In the marine navigation environment, static obstacles such as shallow waters, islands, and restricted zones coexist with dynamic threats like typhoons. Rapidly planning safe, shortest routes is crucial for ensuring vessel and personnel safety while enhancing navigation efficiency. However, existing path planning algorithms face limitations when addressing dynamic threats like typhoons, struggling to achieve an effective balance between efficiency and effectiveness. To address this, this study proposes an improved Time-Dynamic Theta algorithm (TDM-Theta*) based on the Theta algorithm. By incorporating wave height as a key constraint, it comprehensively evaluates the actual impact of dynamic marine environments on routes, thereby efficiently planning safe, shortest paths that proactively avoid typhoon impacts. Through testing and analysis of eight case studies across three typical scenarios, this algorithm demonstrates high efficiency and effectiveness in planning the shortest safe routes across diverse operational environments. The research findings provide theoretical foundations and methodological support for intelligent planning of safe vessel routes.

Abstract: This paper provides a rigorous examination of eight fundamental architectural deficiencies that render the Linux kernel unsuitable for deployment in safety-critical avionics. These deficiencies include inadequate temporal determinism, the absence of physical memory isolation, driver-induced contamination of global kernel state, an excessively large and unbounded Trusted Computing Base (TCB), open and nondeterministic system semantics, insufficient inter rocess fault containment, unstable kernel behavior due to continuous patching, and a highly complex toolchain that imposes prohibitive DO-330 qualification burdens. Through a technical and standards-aligned analysis, this paper demonstrates that Linux cannot satisfy the determinism, verifiability, isolation, and lifecycle stability required for airworthiness certification, making it inherently incompatible with certifiable airborne platforms.

Abstract: This paper introduces a novel theoretical framework for classifying Autonomous Mobile Robots (AMRs) into three hierarchical layers: Perception, Cognition, and Operation. Unlike prior hardware-centric taxonomies, our approach, grounded in a structured review of seminal works, foundational methodologies, and state-of-the-art advances, explicitly integrates locomotion mechanisms (wheeled, legged), application domains (industrial, agricultural), and autonomy levels with navigation strategies. The framework unifies terrestrial navigation techniques into a cohesive taxonomy, clarifying modular boundaries and interdependencies. Serving as both a conceptual guide and educational tool, it empowers researchers to evaluate trade-offs in sensor configurations, decision-making algorithms, and trajectory execution under real-world constraints. A comparative analysis positions this framework against established navigation architectures, highlighting its role as a high-level reference design for modular implementations. By bridging theoretical principles with system optimization, the framework enhances interoperability across robotic platforms. Ultimately, this work delivers a practical design atlas, structuring the end-to-end pipeline of autonomous navigation to guide researchers and practitioners in selecting algorithms suited to their specific robotic platforms and mission requirements.

Abstract: The environmental impacts from fossil fuel use have accelerated the global transition to sustainable energy sources. Hydrogen has become a promising alternative due to its high energy density and clean combustion. However, hydrogen production streams are frequently contaminated with methane, which needs efficient, durable, and cost-effective purification technologies such as Pressure Swing Adsorption (PSA). The present study provides a comparative evaluation of biomass-derived activated carbons and a commercial activated carbon for hydrogen–methane separation. High surface-area activated carbons were synthesized from sustainable pine and birch precursors via chemical activation using potassium hydroxide (KOH, impregnation ra-tio 3:1) at 800 °C. Their adsorption performance was systematically assessed in a fixed-bed PSA system operating at pressures of 25, 35, and 50 bar, with a gas mixture of hydrogen-methane, where methane feed concentrations was ranging from 10 to 30 vol%. The biomass-derived activated carbons showed well-developed textural characteristics, with specific surface areas up to 1416 m² g⁻¹, exceeding that of the commercial reference material (1023 m² g⁻¹). This improved pore structure was reflected in their adsorption behavior at an operating pressure of 50 bar, the birch-derived carbon achieved a me-thane uptake of 10.5 mol kg⁻¹, more than twice the capacity measured for the commercial adsorbent of 5.30 mol kg⁻¹. Beyond initial adsorption capacity, the study emphasizes operational durability and reusability. Cyclic adsorption–desorption experiments, supported by Raman spectroscopy, revealed pronounced structural degradation in the commercial activated carbon under repeated operational stress, as indicated by an increase in the ID/IG ratio from 1.08 to 1.24. In contrast, the biomass-derived activated carbons preserved their morphological integrity and adsorp-tion efficiency over successive cycles. These findings demonstrate that pine- and birch-derived activated carbons are not only sustainable alternatives but also operationally stable adsorbents capable for hydrogen purification pro-cesses.

Abstract: Nerolidol (NER) is a sesquiterpene alcohol with recognized antimicrobial potential, whose applications as pure substance are limited by hydrophobicity, instability, and cytotoxicity. Invasomes, i.e. liposomes with terpene ingredients, offer a strategy to improve its delivery; however, the NER loading limits compatible with vesicle integrity are still unclear. Here, Nerolidol-loaded invasomes were produced using a controlled simil-microfluidic coaxial injection process. As a preliminary step, unloaded liposomes were fabricated to consolidate operating conditions and ensure their reproducible colloidal properties. Thereafter, formulations with progressively decreasing nominal NER loads were investigated to evaluate vesicle size, polydispersity, ζ-potential, encapsulation efficiency, effective loading, and stability. High nominal loads promoted turbidity, size increase (by agglomeration coalescence phenomena), and structural instability, whereas formulations containing approximately 1–2% NER achieved nearly complete encapsulation, Z-average ≈ 300 nm, |ζ| > 30 mV, and satisfactory physical stability. Antimicrobial and cytotoxic profiles of representative formulations, previously evaluated in an independent study, confirmed biological activity. Overall, this work identifies a realistic loading window for Nerolidol invasomes and highlights the suitability of the simil-microfluidic approach to obtain scalable, well-controlled formulations, providing a rational basis for their future biological assessment. Nerolidol invasomes systems indeed can be considered a promising versatile platform for antimicrobial applications, including prospective use in animal feed.

Abstract: In architecture and construction, it is common to use acrylic products with a high flammable content, from lacquers to improve the curing of concrete and mortar to resins that offer protection, sealing, flexibility and elasticity properties. The drying process of the treated surface involves the formation of volatile organic compound (VOC) vapours. To prevent these from degenerating into a potentially dangerous flammable atmosphere, a procedure is presented that establishes the maximum application yield for solvent-based products, providing equations that relate the maximum application surface area and minimum drying time to the air velocity available in the work area. The results are provided for both indoor and outdoor applications. A maximum application speed is established to prevent the generation of areas classified as fire or explosion hazards: 1.5 m²/h indoors and 1 m²/h outdoors. When this is carried out at an ambient temperature of 20°C, and above 40°C, it is not possible to apply varnishes without generating a flammable atmosphere.

Abstract: This paper proposes a formal method and associated techniques for completing the ISO/IEC/IEEE Standard 15288 technical process 6.4.2 – stakeholder needs and requirements definition within the 15288-SysML grid framework. The paper is a companion work to Engineering systems with standards and digital models: Development of a 15288-SysML Grid, which describes an engineering design method that supports the tenets of the Industry 4.0 paradigm. The formal method presented here is grounded using established constructs from systems science; specifically, the systems principles of hierarchy, emergence, requisite parsimony, minimum critical specification, and requisite saliency. The application of accepted principles ensures that stakeholders are able to objectively specify measurable criterion that can satisfy stakeholder needs and capabilities. The method uses: (1) international standards for systems (e.g., ISO/IEC/IEEE 15288); (2) adopts the four fundamental aspects of system design supported by model-based systems engineering (MBSE); (3) invokes the international standard for the systems modeling language (SysML); and (4) adopts a hierarchical requirements tree that specifies Mission, Goals, Objectives, and Sub-objectives (MGOS) to provide the stakeholder-analysis process for the articulation of system-level engineering requirements. Utilization of the MGOS is intended to have a positive impact on the system design process by ensuring reproducibility, replicability, transparency, and generalization.

Abstract: This data descriptor provides a standardized and reproducible subsystem-level represen-tation of the NREL wind turbine gearbox condition monitoring benchmarking dataset. The released records are derived from Healthy (H1–H10) and Damaged (D1–D10) meas-urement files and include subsystem-level standardized indices (KHI_HS, KHI_IMS, KHI_PL) together with a calibrated 0–1 Gearbox Health Index (GHI). The indices are gen-erated using a fully specified and deterministic feature extraction and aggregation work-flow based on established vibration indicators and healthy-referenced normalization. The Zenodo deposit contains machine-readable CSV tables intended to support transparent benchmarking across supervised classification and anomaly detection studies. The pro-posed GHI is introduced as an interpretable and reproducible reference baseline rather than an optimized diagnostic model. Technical validation demonstrates condition-level separability within the analyzed dataset while emphasizing the descriptive nature of the index. By releasing structured derived records and a documented regeneration procedure, this work enables implementation-independent comparison of gearbox condition moni-toring approaches and supports reproducible evaluation of alternative health index formulations.

Abstract: This paper analyzes the phenomenon of output voltage collapse under step load perturbations in dual bridge converters where an extremum seeking control (ESC) optimization algorithm is employed. Although ESC is an effective online duty-cycle optimization method under steady-state power transfer conditions, it can result in severe output voltage degradation during large-signal transients. This degradation is primarily caused by two factors: the reduced power transfer capability associated with the optimized duty-cycles, and the limited dynamic capability of the ESC structure to rapidly adjust the duty-cycles. To overcome this limitation, an enhanced extremum seeking control (EESC) structure is proposed, which enables fast output voltage reference tracking under dynamic operating conditions, while preserving ESC’s capability for online duty-cycle optimization to minimize losses and improve efficiency. The proposed method extends the applicability of ESC from steady-state optimization to large-signal dynamic scenarios. Comparative experimental results on a dual active half-bridge (DAHB) converter reveal the output voltage collapse associated with conventional ESC structure and verify the high efficiency and absence of dynamic voltage collapse achieved by the proposed EESC structure.

Abstract: Non-invasive assessment of tissue water content is clinically relevant for edema detection, fluid management, and monitoring of local inflammation. In the short-wave infrared (SWIR), water exhibits strong absorption near 1450 nm with a secondary band near 1650 nm, enabling hydration-sensitive reflectance measurements. However, many SWIR systems rely on spectrometers or high-power broadband sources, limiting translation to compact or wearable platforms. We present a compact SWIR diffuse-reflectance probe built from small-form-factor components using four discrete LEDs (1450 nm and 1650 nm) and a single photodetector to acquire spatially resolved measurements at two source–detector separations (4.5 mm and 7 mm). Probe-geometry-matched Monte Carlo simulations were used to generate lookup tables relating reduced scattering to same-wavelength spatial ratios. 11 A diffusion-based forward model was then used to perform a calibration-anchored water-fraction consistency analysis. Eight gelatin–Intralipid phantoms spanning two scattering conditions and formulation-defined water fractions were evaluated. Spatial-ratio signatures were repeatable and monotonic with nominal water fraction, yielding a mean absolute percent error of 1.55% and a maximum absolute percent error of 3.33% under absorption-consistent conditions. These results demonstrate the feasibility of compact SWIR ratio sensing for controlled hydration changes in tissue-mimicking phantoms and provide a modeling framework for future extension to unknown or in vivo samples.

Abstract: The paper deals with comparative analysis of machined surfaces by classic electrical discharge machining (EDM) and hybrid ultrasonic EDM of CoCr alloys, using computer vision aimed at emphasizing the advantages of this hybrid technology. The analysis revealed generally the superior stability of EDM+US process against classic EDM explained by better evacuation of debris from the working gap due to ultrasonically induced cavitation. This key phenomenon also contributed to the enhancement of machining rate by removing the material in liquid state and also the in solid state from the microgeometry peaks but also reducing the surface roughness if the power on the ultrasonic chain is optimzed.

Abstract: Determining which sensor modalities carry genuine discriminative signal for CNC monitoring—and how many can be removed before performance degrades—is a practical question that prior work rarely answers quantitatively under leakage-resistant evaluation. We address this through a systematic cross-validated ablation study on a 9-class CNC toolpath and condition classification task, combining three toolpath strategies (adaptive, face, pocket) with three conditions (air-cutting, active-cutting, and damaged-spindle). Using 120 operation files from a desktop CNC mill with six consistently active sensors (17 channels per sensor) plus 8 machine-level electrical features, we evaluate six model families across 690 cross-validated runs spanning five cumulative feature-ablation levels (110–56 features) and ten temporal resolutions. To handle the fusion challenge, we introduce MM-DTAE-LSTM, a multi-modal denoising temporal attention encoder with unidirectional LSTM-based classification that combines learned modality gates, cross-modal attention, and a self-supervised denoising objective. Key findings on this single-machine, single-material dataset: (1) MM-DTAE-LSTM reaches 96.3% ± 4.7% accuracy at a 98-feature configuration (excluding proximity and pressure), leading all baselines by 3.1–5.2 points, though differences are not statistically significant at n=5 folds; (2) reducing the feature set by 49% (to accelerometer, gyroscope, temperature, RMS audio, and electrical channels) retains 92.5% ± 8.3% for the encoder while XGBoost drops to 84.4%, a loss of 10.7 points from its full-feature peak; (3) at full features, baselines are competitive (Random Forest: 95.6%, XGBoost: 95.1%); and (4) one-way ANOVA reveals that pressure channels encode session-level barometric confounds (F > 2,000) rather than machining dynamics, explaining baseline degradation when confound-prone channels are removed. These results suggest that core inertial, acoustic, and machine-level modalities may be sufficient for effective CNC operation classification on similar platforms, providing sensor-selection and temporal-configuration guidance for cost-effective monitoring deployments. Generalization to industrial machines, diverse materials, and production environments requires further validation.

Abstract: Microplastics (MPs) are emerging pollutants of global concern, posing significant ecological and human health risks. They are frequently detected in stormwater systems, with urban runoff serving as a major transport pathway into the environment. Green storm-water infrastructure, particularly bioretention systems (BRS), offers a promising approach to mitigate these risks by filtering and retaining various contaminants. However, the occurrence of MPs in BRS and their capacity in retaining these pollutants remain largely unexplored in the literature, despite being critical for stormwater management and water quality protection. Therefore, this study examines the occurrence, vertical distribution, and retention of MPs within field-installed BRS, emphasizing their role in reducing MPs transport. Field samples were collected at depths of 2, 12, and 24 inches below the surface and processed in the laboratory for MP detection and quantification. The results revealed an average concentration of 1,095 particles per kg of dried sediment, with fragments (microplastics shape) accounting for 78.54% of total MPs. Although no clear vertical distribution pattern was observed, MPs accumulated predominantly at 24 inches, indicating their transport through the media and the retention capacity of BRS (surface and mid layer) in capturing microplastics from stormwater environments. Integrating BRS into urban stormwater infrastructure provides dual benefits: improved stormwater management and reduced plastic pollution. This study highlights the importance of optimizing bioretention design and media composition to improve the removal efficiency of MPs.

Abstract: This study extends the application of the 3M&I (human, Material/Machine, Money, and Information resources) body science framework to explore the wisdom law of body wealth and energy in additive-multiplicative systems, integrating both artificial and natural bodies. The research formalizes the mechanisms underlying body intelligence and survival through look-ahead and nano-transforming schemes grounded in a static-stable system perspective, modeled across two- and three-dimensional spaces. We propose refinements to Matsui’s original motion equations by introducing an ad-renewable formulation (ME) and a generalized version, MW = ZL, to capture both cumulative and progressive dynamics in body schemes. A ternary/pair-map framework is developed to address three-body systems and dualism-based challenges, extending beyond traditional two-body models. The framework also incorporates a wealth-additive scheme, reinterpreting the flow retention concept to prioritize energy/wealth maximization over cost minimization. Finally, we introduce Chameleon’s objective function—defined as (revenue × lead time) / departure—as a metric for optimizing marginal diversity within sustainable development contexts. The proposed model offers a theoretical foundation for predictivity versus sustainability processing and integrates body system modeling with broader eco-entropy and sustainability goals.

Abstract: Effective management of reservoir water for irrigation is crucial in arid regions prone to drought and water shortages. However, evaporation losses from reservoirs remain poorly understood. Direct measurements typically quantify evaporation only at the measurement site rather than across the entire reservoir. This study introduces the Turbulent Exchange Approach for Reservoir Evaporation Estimation (TEAREE). The TEAREE is a simple model that integrates a bulk aerodynamic formulation with Landsat 8–9 satellite water-surface temperature data and meteorological observations to estimate spatially distributed daily reservoir evaporation. The TEAREE model was first evaluated at Elephant Butte and Caballo reservoirs in New Mexico, USA, and subsequently applied across multiple reservoirs with diverse climatic conditions to demonstrate its applicability for estimating open-water evaporation. Daily evaporation was obtained by upscaling satellite overpass-time evaporation estimates using the daily-to-instantaneous vapor pressure deficit ratio (k_e) and wind speed. The model performed strongly across 12 lakes (R² = 0.91–0.99; RMSE = 0.27–0.85 mm/day) compared with the bulk aerodynamic (B_AER) method. Comparison with eddy covariance (EC) evaporation also showed good agreement. Monte Carlo analysis indicated moderate uncertainty associated with k_e variability, supporting the operational use of a constant k_e = 0.95 for daily upscaling.

Abstract:

This study investigates the influence of selected technical and technological parameters on cutting forces and power consumption during the milling of medium-density fibreboards. The main objective was to experimentally measure orthogonal cutting force components (F_x, F_y, F_z) and electrical power consumption under varying spindle speeds (14 000, 16 000 and 18 000 rpm), feed speed (6, 8 and 10 m/min), and milling strategies (conventional and climb), and to evaluate the suitability of the obtained data for predictive modelling. Cutting forces were measured using a Kistler 9257B piezoelectric dynamometer, and power consumption was recorded by a three-phase power quality analyser. Statistical analysis confirmed significant effects of machining parameters on force components, total cutting force, and power consumption. Spindle speed showed the strongest influence on total cutting force and power consumption, while milling strategy predominantly affected F_x and F_y components. Power consumption increased with increasing spindle speed. Based on the measured data, several machine learning models were developed to predict the total cutting force. After model comparison using RMSE, R², training time, and model size, a Fine Tree model was identified as the most suitable, achieving high prediction accuracy without signs of overfitting. The results confirm that experimentally obtained force and energy data are suitable for reliable predictive modelling in CNC milling of MDF.

Abstract: The objective of this study was to produce, characterize, and optimize modified potato starch derived from locally sourced potatoes, and to evaluate the physicochemical properties of native, cross-linked, acetylated, and dual cross-linked–acetylated potato starches as disintegrants for tablet formulation. Starch modification was performed through cross-linking and acetylation using sodium hexametaphosphate (SHMP) and acetic anhydride (AA) as modifying agents, respectively. Native and modified potato starches were characterized using Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), rapid visco analysis (RVA), and X-ray diffraction (XRD). The key modification parameters investigated included reaction temperature, reaction time, pH, concentration of the modifying agent (AA), and concentration of the NaOH catalyst. Based on preliminary experiments, reaction temperature (40, 60, and 80 °C), modifying agent concentration (10, 20, and 30%), and reaction time (40, 55, and 70 min) were selected as the primary variables. Process optimization for dual crosslinked-acetylated potato starch was carried out using response surface methodology based on a Box-Behnken experimental design, with acetyl content as the response variable. The optimized modification conditions were a reaction temperature of 40.22 °C, a reaction time of 69.85 min, and an acetic anhydride concentration of 21.92% (w/w). Under these optimized conditions, an acetyl content of 1.32 ± 0.077% was obtained. Tablets formulated using the dual crosslinked-acetylated potato starch as a disintegrant exhibited a disintegration time of 29.2 ± 0.29 min, a disintegration efficiency ratio of 500 ± 0.99 N min⁻¹, a crushing strength of 92.35 ± 0.86 N, and friability of 0.63 ± 0.08% (w/w). The modified starch was employed as a disintegrant in tablet formulations containing 10% paracetamol as the active pharmaceutical ingredient, magnesium stearate (10%) as a lubricant, and suitable fillers.

Abstract: The city of Baghdad is witnessing a continuous increase in traffic and urbanization, which has led to frequent traffic jams and deterioration of the urban environment and quality of life due to pollution and the waste of time and energy. Hence, it has become necessary to adopt integrated planning concepts that regulate land uses, promote transport efficiency, and support sustainable urban development. This research aims to investigate the concept of transport-oriented development (TOD) and explore its applicability in the city of Baghdad, focusing on identifying obstacles and challenges that may face the implementation of this concept in the local context, whether related to transport infrastructure, urban planning, or community participation, to provide an analytical framework that can be relied upon in the development of effective strategies to promote sustainable transport and integrated urban development. The BRT bus rapid transit system is an essential part of the Comprehensive Development Plan for Baghdad 2030, aiming to improve mass transit and reduce congestion on major streets such as Palestine Street by providing fast, efficient transportation that connects the city's neighborhoods and encourages walking and the use of sustainable transport. The project supports sustainable urban development by integrating the principles of TOD, increasing residential and commercial density around the stations, and adopting an integrative methodology that analyzes the relationships among transport, land uses, and urban density to provide a scientific framework to support planning and future decision-making.

Abstract: The Colorado River and its tributaries housed in the Colorado River Basin (CRB) are the primary source of water to the western United States and the Republic of Mexico. The river system is under intense stress due to prolonged drought and anthropogenic activities which have worsened its water quality. Total dissolved solids (TDS) and total suspended solids (TSS) are two water quality parameters (WQPs) that are crucial to the sustainability of the river system. These parameters are noted to have caused varied severity to the sustenance of the basin’s water. Monitoring of these WQPs has been conventionally conducted using field and laboratory analysis which are cost and labor-intensive. This study utilized a novel method to effectively develop machine learning (ML) models to estimate TDS and TSS concentrations in the CRB by utilizing the potential of optically sensitive multispectral Sentinel 2 A/B Multispectral Scanners (MSI) and Landsat 8 Operational Land Imager (OLI) remote sensing (RS) data retrieved from the Google Earth Engine (GEE) and in situ measurements collected from 2013-2022. Several standalone models such as linear regressions (LR), ridge regressions (Ridge), lasso regressions (Lasso), and k-nearest neighbor (KNN), and ensemble methods including the gradient boosting machines (GBM), random forest (RF), adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), and bagging were applied for the accurate estimation of TDS and TSS. Results found ensemble models like the XGBoost as the most optimal model estimating TDS using images from both Sentinel-2 MSI and Landsat 8 OLI with performance on the external validation dataset derived as 0.99, 26.52 mg/L, and 19.19 mg/L, respectively for R², RMSE, and MAE for Sentinel-2 images. The XGBoost yielded R², RMSE, and MAE of 0.97, 35.82 mg/L, and 27.90 mg/L, respectively. The AdaBoost was found to be best model for TSS estimations with values of 0.92, 29.48 mg/L, and 24.64 mg/L, respectively, for R², RMSE, and MAE for the Sentinel-2 image on the external validation dataset. The RF model was found to be the optimal model for TSS estimations with the Landsat 8 OLI with reported R², RMSE, and MAE of 0.90, 32.80 mg/L, and 22.91 mg/L, respectively on the external validation dataset. These findings show great potential of utilizing RS data to produce cost-efficient spatiotemporal changes on the WQPs which has an important implication for the continuous management of the limited water resources. Further study should consider the effect of land use land cover, runoff, and other climatic data to understand the complexity and dynamics of these parameters on TDS and TSS in the river.