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Design of an IoT-Based Automatic Irrigation System for Chili Plants
Manual watering of chili plants makes it difficult for farmers to monitor soil moisture levels and ensure consistent, periodic watering. This study aims to design an Internet of Things (IoT)-based automatic irrigation system capable of real-time soil moisture monitoring and automated watering based on soil conditions. The prototyping method was employed, comprising the stages of communication, quick planning, quick design modeling, prototype construction, and deployment and feedback. The system was built using a soil moisture sensor, ESP32, relay, water pump, Firebase Realtime Database, and an Android application. Black Box Testing was conducted to verify that the system functions according to requirements. The results demonstrate that the system can read soil moisture levels, automatically control the water pump based on predefined thresholds, and transmit and display data in real-time via Firebase and the Android application. Testing also confirmed that all core system functions—including sensor readings, pump control, data transmission, and monitoring—operated correctly as designed. Thus, the developed IoT-based automatic irrigation system can assist in monitoring soil conditions and watering chili plants.
Copyright (c) 2026 Kurniawan D. Irianto, Muhammad Haris Rusnanda https://creativecommons.org/licenses/by/4.0
Sustainable Off-Road Machinery Design: Reducing Soil Damage through Terrain–Machine Interaction Modeling
Off-road machinery used in forestry, agriculture, land management, construction, and field logistics must operate across deformable, sloped, wet, rooted, and otherwise sensitive terrain. While such machines are essential for industrial productivity, they can also cause soil compaction, rutting, root damage, excessive slip, high contact pressure, increased fuel consumption, and long-term reduction of terrain productivity. Sustainable off-road machinery design therefore requires more than increased engine power, larger tires, or higher load capacity. It requires a systematic understanding of terrain–machine interaction. This article proposes an engineering framework for reducing soil damage through terrain–machine interaction modeling. The framework integrates soil bearing capacity, wheel–soil contact mechanics, root reinforcement, tire or track configuration, machine weight distribution, slip behavior, and mobility prediction into an applied design process for off-road machinery. Special attention is given to forestry machines operating on rooted soft soil and sloped terrain, where trafficability, productivity, operator safety, and environmental protection must be considered simultaneously. The article presents a practical modeling workflow, key engineering parameters, soil damage indicators, design strategies, an illustrative evaluation matrix, and future research directions. The proposed framework supports sustainable off-road machine development by connecting terramechanics, mechanical design, simulation-based engineering, sensor-assisted monitoring, and environmentally responsible machinery optimization.
Copyright (c) 2026 Abdurasul Pirnazarov https://creativecommons.org/licenses/by/4.0
Digital Twin and Simulation-Based Design of Agricultural Processing Equipment
Agricultural processing equipment plays a critical role in transforming raw agricultural materials into usable industrial inputs. In cotton processing, grain handling, seed cleaning, fiber preparation, and other post-harvest operations, machinery must process biological materials that are variable in moisture, size, density, contamination level, mechanical strength, and flow behavior. This variability creates engineering challenges related to throughput, cleaning efficiency, energy consumption, material loss, machine wear, equipment reliability, and product quality. This article proposes a digital twin and simulation-based design framework for agricultural processing equipment, with particular emphasis on cotton cleaning, cotton separation, and raw material handling machinery. The proposed framework connects physical equipment, sensor data, simulation models, process optimization, and design feedback into an integrated engineering system. Instead of relying only on physical prototyping or trial-and-error modification, engineers can use digital twin models to evaluate machine geometry, operating speed, material flow, cleaning performance, energy demand, and failure risk before or during equipment operation. The article presents a conceptual digital twin architecture, a simulation-based design workflow, key input and output parameters, performance indicators, an illustrative evaluation matrix, and future research directions. The framework is designed to support agricultural processing machinery modernization, industrial automation, predictive maintenance, and sustainable manufacturing. It is especially relevant to cotton processing equipment, where improved machinery design can reduce processing losses, improve raw material quality, increase energy efficiency, and support more resilient agricultural manufacturing systems.
RGB versus Early-Fusion RGB-D Glass Segmentation
Transparent glass is a persistent perception hazard for indoor mobile robots: RGB boundaries can be visually ambiguous, while commodity depth sensors often return missing or distorted measurements. This study isolates the effect of adding raw depth to a lightweight transformer segmenter. Two SegFormer B0 models were trained on identical partitions of the 3,009-image RGB-D Glass Surface Detection dataset using the same optimization and checkpoint-selection protocol: a three-channel RGB model and a four channel early-fusion RGB-D model. On the fixed 609-image test split, RGB outperformed RGB-D on seven of eight reported measures, reaching 0.9448 pixel accuracy, 0.8234 mean intersection over union, 0.7108 glass IoU, 0.8376 recall, and 0.8310 F1. RGB-D retained higher precision (0.8374 versus 0.8244). RGB also reduced mean inference latency by 10.7% (10.24 versus 11.47 ms) and increased throughput by 12.0% (97.63 versus 87.18 frames/s). The frozen checkpoints were then evaluated in 20 Pioneer P3-DX navigation runs. At the selected center-ratio threshold of 0.35, each model produced glass-triggered stops in both of its two glass runs (4/4 combined). Across all recorded thresholds, RGB triggered in 5/5 glass runs and RGB-D in 3/5; the independent distance safety stop terminated the remaining two RGB-D runs. Neither model produced a glass trigger in the ten non-glass runs. Under this controlled early-fusion design, raw depth did not improve unseen-test segmentation and reduced threshold robustness during robot deployment.
Copyright (c) 2026 Ebubechukwu Amasiatu, Salem Ameen https://creativecommons.org/licenses/by/4.0
Development of an IOT-Based Smart Waste Management Web Application with a Spatial Monitoring Dashboard
Increasing urban waste volumes create logistical hurdles for the Regional Technical Implementation Unit (UPTD) of Waste Services in Sleman Regency. Currently, truck fleets operate on static schedules without real-time capacity data, causing severe fuel inefficiencies. To resolve this, the study designed an Internet of Things (IoT) smart waste management system integrated with a spatial dashboard using the Waterfall model. An ESP32 microcontroller and HC-SR04 ultrasonic sensor were wired to track available container space. This telemetry is transmitted to a cloud server and displayed instantly on a digital map. Testing involved 10 participants ranging from truck drivers to managers at UPTD Sleman. Evaluation results showed the sensor achieved 97.2% accuracy (2.8% error) with a rapid data transmission time of 7.46 seconds. User Acceptance Testing (UAT) and black-box assessments recorded a functional success rate exceeding 95%. During simulation, the smart routing feature successfully reduced the fleet’s daily travel distance from 25 kilometers to 12 kilometers. This logistical optimization directly lowered monthly diesel fuel consumption by 52 percent, dropping from 150 liters to 72 liters.
Copyright (c) 2026 Kurniawan D. Irianto, Muhammad Ammar Fariz Baihaqi https://creativecommons.org/licenses/by/4.0
Distributed Autonomous Snow-Clearing Systems for Public Roads
Winter road conditions kill more than 1,300 people and injure over 116,800 annually in the United States on snowy, slushy, or icy pavement, while national winter-maintenance operations consume approximately $2.3 billion per year and roughly 20% of state Department of Transportation budgets. The prevailing operational model human-operated plow trucks working overnight storm shifts is capacity-constrained, fatigue-limited, and budget-fragile; documented state responses to cost pressure include deliberately leaving low-traffic highways unplowed overnight. This paper proposes a distributed autonomous snow-clearing system: compact autonomous plow units operating in coordinated convoys during low-traffic overnight windows, protected by an escort/signal vehicle following the beacon-signaling precedent established in active U.S. DOT truck-platooning pilots. The paper presents a quantified problem analysis from federal safety and cost data; a systems architecture with orthographic general-arrangement drawings; first-order sizing calculations covering blade forces, power, traction, stopping distance, battery capacity, and fleet sizing; an economic and nationwide-scaling analysis benchmarked against current DOT expenditure; and a phased deployment pathway grounded in three operating precedents commercial autonomous haulage in mining, winter-condition truck platooning on public highways, and a decade of academic autonomous-snowplow robotics. First-order results indicate per-unit blade power of order 14 kW, unit mass of order 1,200 kg, and a 12–15 unit fleet sufficient for a 500 priority-lane-mile city within a four-hour window physically plausible scales using present-day components.
autonomous vehicles · autonomous snowplow · winter road maintenance · snow and ice control · robotics · physical AI · vehicle-to-everything (V2X) · fleet automation · road safety · transportation infrastructure · mechatronics · winter operations economics
Copyright (c) 2026 Samuel Ayo Oyedemi https://creativecommons.org/licenses/by/4.0
Discussion on "COMPARISON OF THE HYPERBOLIC AND ASAOKA OBSERVATIONAL METHODS OF MONITORING CONSOLIDATION WITH VERTICAL DRAINS"
This paper presents a discussion on the hyperbolic and Asaoka observational methods for monitoring soil consolidation with vertical drains, specifically addressing their limitations in accounting for missing time problem & secondary consolidation. To overcome these constraints, the discussion introduces two alternative settlement-velocity methods: the settlement-velocity (δ-v) and settlement-log(velocity) (δ-log(v)) techniques. These methods allow for the evaluation of the true coefficient of consolidation (Cv) and ultimate primary settlement (δ_ult) by isolating the effects of secondary compression. Unlike traditional time-centric approaches, these velocity-based methods do not require instantaneous loading & initial conditions, known initial boundary timelines, or the clock time of the load increment. By mapping the settlement state directly against its rate of change, the proposed framework offers a more robust and mathematically simplified approach to extracting fundamental consolidation parameters from standard field data beyond the 60% consolidation stage.
Department of Structural, Geotechnical and Building Engineering
Copyright (c) 2026 Sudhir Kumar Tewatia https://creativecommons.org/licenses/by/4.0
General principles for modeling of thermal prebiotic synthesis and protocell self-organization in planetary environment simulators
This paper considers the problem of achieving plausible and effective (leading to the formation of structures similar to biological ones) conditions for modeling thermally induced prebiotic processes from the perspective of thermodynamics, thermal physics, and thermal engineering. The article’s presentation is based on the principle of transition from thermal physics and thermodynamics to thermal engineering for constructing planetary environment simulators with controlled environmental conditions. Based on the physical premises from the first parts of this comprehensive review, a conclusion is made regarding the need to consider an additional set of controlled physical parameters or additional factors influencing the models of prebiotic synthesis and formation of precellular/protocell structures implemented in planetary environment simulators and hydrothermal synthesis reactors. Non-thermal heating sources are also considered, such as radiofrequency and microwave irradiation for dielectric heating, as well as radioisotope heating in the early Earth/protoplanet conditions. The coexistence of physical and chemical pathways for heating the prebiotic environment and the feasibility of modeling this separation in planetary environment simulators and controllable climate chamber facilities are highlighted. Exothermic reactions during hydrothermal serpentinization are considered as an example of chemical heating in prebiotic environments. This can lead to the abiotic synthesis of organic matter (which correlates well with the existing hypotheses of the origin of life in alkaline hydrothermal vents, with the mineral genesis of protocells and membrane potential generation on inorganic catalytic protomembranes). Thermal proteinoids and thermal proteinoid microspheres that form on their basis are considered an example of purely organic prebiotic structures obtained via hydrothermal heating. It is demonstrated that the application of thermophysical and thermal engineering approaches to modeling prebiotic systems enables the simulation of many key cellular functions by varying the precursor medium composition and the conditions in thermal experimental setups. Protobiocatalysis and protometabolism, surface (or membrane) potential oscillations, excitability, and physical heredity (thermal fission) are observed in such structures. After addition of the genetic components, the machinery for transcription and translation of the expressed genetic code can also operate within them. The possibility of implementing such models as “hypercycle in protocells” and other proto-metabolic and protogenetic networks capable of oscillation is indicated in prebiotic system models obtained in thermally cycling experimental setups. Kompanichenko’s thermodynamic theory, which also applies to hydrothermal and volcanic conditions that can be simulated in planetary environment simulators, is considered a theoretical justification for the possibility of assembling prebiological systems under such oscillatory conditions. Thus, it becomes possible to move from formal thermodynamics for describing the origin of life to engineering thermodynamics and thermal physics, which allows for the development of experimental setups and simulation chambers for validating various models of prebiological synthesis and the formation of protobiological structures, based on thermodynamic and thermochemical criteria.
Copyright (c) 2026 Oleg Gradov, Margaret Gradova https://creativecommons.org/licenses/by/4.0
Drowsiness Detection Based on Eye Aspect Ratio and Head Pose Estimation with IoT Integration
This study proposes a real-time driver drowsiness detection system integrating computer vision with Internet of Things (IoT) technology on an affordable embedded platform. The system uses a Camera Module 3 Wide NoIR connected to a Raspberry Pi 3 Model B+ to capture driver facial images. Two visual indicators are computed in parallel from 68 facial landmarks extracted using dlib: Eye Aspect Ratio (EAR) for detecting prolonged eye closure, and Head Pose Estimation using solvePnP for detecting head nodding. An OR-logic decision mechanism triggers an audio alarm when EAR falls below 0.25 for five consecutive frames or Pitch angle exceeds 15 degrees for ten consecutive frames. Events are classified as KANTUK_MATA, KANTUK_KEPALA, or KEDUANYA and sent to firebase Realtime Database for remote monitoring. Black Box testing with 11 scenarios confirms all core functions operate correctly. Average response times of 2.00 seconds via EAR and 2.70 seconds via Head Pose are within acceptable ranges for early drowsiness warning. The multi-indicator approach demonstrates that head nodding is detected earlier than eye closure in gradual drowsiness scenarios, providing earlier warning than single-indicator systems.
Copyright (c) 2026 Kurniawan D. Irianto, Rama Amirul Ramadhan https://creativecommons.org/licenses/by/4.0
Coolant Intrusion in Mercedes EQ High-Voltage Battery Systems: Causes, Diagnostic Indicators, and the Risk of Unnecessary Battery Replacement
The rapid expansion of the global electric vehicle market, which surpassed 17 million units sold in 2024 according to the International Energy Agency, has made the reliable diagnosis of high-voltage battery faults a critical competency for automotive dealerships worldwide. In Mercedes EQ vehicles, coolant intrusion into the battery enclosure represents a frequently underdiagnosed failure mode that shares its primary symptom with genuine cell-level degradation: a drop in isolation resistance triggering Battery Management System fault codes. The present study examines the technical causes of coolant ingress in EQ platform battery systems (EQA, EQB, EQC, EQS, and derivatives), characterizes the diagnostic indicators that distinguish this event from primary battery failure, and evaluates the downstream risk of unnecessary high-voltage battery replacement. Through systematic review of manufacturer recall documentation, technical service bulletins, peer-reviewed literature on battery thermal management and fault diagnosis, and documented dealer case studies, this paper shows that coolant intrusion can depress isolation resistance to levels that trigger emergency shutdowns without causing irreversible cell damage. A structured six-step diagnostic protocol and a replacement decision matrix are proposed to guide technicians. The findings are of direct relevance to Mercedes-Benz authorized dealers, independent EV specialists, and automotive service engineers seeking cost-effective, evidence-based diagnostic workflows.
Copyright (c) 2026 Alexey Samoilov https://creativecommons.org/licenses/by/4.0
Stability of Structures: Kinematics, Equivalent Single-Layer Theories, and Energy-based Semi-Analytical Methods
This part of the course details the fundamental principles of structural stability, focusing on kinematic relations and equivalent single-layer (ESL) theories for composite plates and shells. It derives three-dimensional strain-displacement equations for planar, cylindrical, conical, and spherical geometries. The formulations are systematically reduced to two-dimensional ESL models, specifically the Classical Laminated Plate Theory (CLPT), First-order Shear Deformation Theory (FSDT), and Third-order Shear Deformation Theory (TSDT). The limitations of ESL theories regarding interlaminar stress continuity are analysed, introducing Zig-Zag and Layerwise theories as advanced alternatives. Furthermore, the material covers energy-based formulation frameworks, deriving the principle of minimum potential energy, the semi-analytical Ritz method utilizing Legendre polynomials, and the neutral equilibrium criterion for extracting geometric stiffness matrices and linear buckling equations.
Copyright (c) 2026 Saullo G. P. Castro https://creativecommons.org/licenses/by/4.0
Cross-consistency of system and characteristic-field models in a coupled liquid-rocket simulation (Merlin 1D–class case study)
Coupled liquid-rocket system models and multidimensional nozzle solvers are usually developed separately; when they are made to share a chamber state, the mass-flow consistency between them is easy to leave unmeasured. We describe a transient LOX/RP-1 open-cycle gas-generator engine architecture - capacitance–inertance liquid feed networks, a compressible gas-flow network, shaft dynamics, multi-layer regenerative wall response, offline equilibrium thermochemistry tables, and orifice injector hydraulics- coupled at analysis stop to an axisymmetric method-of-characteristics (MOC) nozzle field evaluated at the shared chamber stagnation state. A headless extraction harness regenerates all tabulated results from frozen inputs and grades two mass residuals: conservation through the marched nozzle field, and agreement between field and system mass flow. The architecture is demonstrated on a Merlin 1D–class case assembled from public data. On the frozen case, field mass conservation closes to +0.92%, the field–system mass-flow residual is -1.78%, sea-level field thrust is ~831 kN, and engine specific impulse on total propellant flow (~305 kg/s) is ~277.7 s, consistent with the public Merlin-class bands that guided case authoring. The result is a documented, reproducible open-cycle composition in which system–field consistency is measured rather than assumed; the case study is a representative reconstruction, and all artefacts are archived on Zenodo.
Copyright (c) 2026 Daniel Sim https://creativecommons.org/licenses/by/4.0
Five-Year Trends in PFAS Contamination (2021-2025) in Maine Drinking Water Wells Around Sludge and Septage Sites Using Statewide Data from the Maine Department of Environmental Protection
In 2016, the State of Maine became aware of PFAS contamination in a drinking water well in West Kennebunk1, Maine. Since then, significant PFAS contamination of drinking water wells in Maine has been discovered through the “PFAS Soil and Groundwater Investigation at Sludge and Septage Land Application Sites”3, completed by the Maine Department of Environmental Protection (MEDEP). The MEDEP has been taking PFAS samples in drinking water wells near sludge and septage sites since January of 20176. In the fall of 2025, I discussed with the MEDEP the possibility of evaluating statewide PFAS sample data to date, to look for PFAS contamination trends in PFAS contaminated drinking water wells. With thorough supportive assistance from the MEDEP, in late 2025 and early 2026 I received spreadsheet data of PFAS sample test results for all samples taken by the MEDEP or their contractors around sludge and septage land application sites (~220,000 PFAS parameter test results)4. All of the samples were taken from drinking water wells throughout Maine4. This report describes the methodology used to identify trends in PFAS concentration in drinking water wells throughout the State of Maine over a five-year period: 2021-2025. Results include: Trend graphs for PFAS 6 and PFOS independently, for 99 and 126 wells respectfully, that had sample results in each of those five years. The percentage of those wells with increasing trends of PFAS6 or PFOS concentration over the five-year period 2021-2025. Graphs showing PFAS trends for a subset of those wells with at least one sample over 1000 ppt (parts per trillion) PFAS 6 or PFOS, and for the subset of wells with at least one sample over 10,000 ppt PFAS 6 or PFOS. Conclusions and Recommendations are included.
Maine Department of Environmental Protection
PFAS contaminated wastewater treatment sludge
PFAS contamination of drinking water wells
Copyright (c) 2026 Nathan Saunders https://creativecommons.org/licenses/by/4.0
Sustainable and Explainable Machine Learning for Risk-Based Maintenance Prioritization of Airport Stormwater Pipe Networks
Airport stormwater pipe networks operate under demanding conditions where infiltration, structural defects, debris accumulation, hydraulic inefficiencies, and water-quality concerns can gradually reduce system performance. Conventional inspection-based maintenance can identify these deficiencies, but reacting to large inspection datasets and determining which pipe segments require the earliest intervention remains challenging. This study proposes an explainable machine-learning framework for risk-based maintenance prioritization of airport stormwater infrastructure. An anonymized dataset containing pipe characteristics, inspection observations, hydraulic conditions, infiltration indicators, and water-quality measurements is used to develop and evaluate a proof-of-concept machine learning framework for identifying higher-priority maintenance locations. Random Forest and gradient-boosting algorithms are explored within the framework, while explainable-AI techniques are used to identify the factors influencing maintenance priority. The framework is intended to support faster engineering response, more targeted inspections and rehabilitation, reduced unnecessary intervention, and improved asset life-cycle management. The proposed approach provides a data-driven pathway toward more resilient and sustainable maintenance of underground drainage infrastructure in complex airport environments.
Copyright (c) 2026 Emanuel Muniez, Mateo Fernando García, Faustus Ricardo Sulla https://creativecommons.org/licenses/by/4.0
Integrated Performance Assessment of the Gezira Irrigation Scheme
The Gezira Scheme (GS) is considered one of the oldest irrigation systems in Africa and the Arab region. Moreover, it is the largest scheme (0.9 million ha) in the world under a single management. The irrigation network is 11,000 km long with 29,000 field channels. This paper evaluates the performance of the GS irrigation system after a century of operation, particularly given the serious deterioration over the past two decades, during which the system has failed to meet crop water requirements. The assessment draws on MOIWR sediment monitoring and desilting records for 1987-2005, HR Wallingford field measurements, and published technical efficiency and hydro-economic studies. This issue may be primarily attributed to the significant amounts of sediment entering the scheme each year and the mismanagement of the irrigation system. Inflowing sediment concentration rose from about 700 ppm in the 1930s to about 8,000 ppm in the 1990s; desilting now consumes more than 60% of O&M expenditure; and the scheme's debt grew from USD 34 million in 1999 to over USD 150 million by 2005. The consequence of this situation is water shortage in many areas of the scheme, while others experience flooding, leading to inequitable water distribution. This disparity leads to a decline in crop production and returns from the scheme. In light of these issues, a critical review of the irrigation system is warranted. The concept and procedure for designing and remodelling the silted canals to restore the original section are highlighted, based on the rich experience and performance of this irrigation system. It is concluded that aquatic weeds increase sedimentation, and the deposited sediment enhances aquatic weeds' growth, thereby reducing canal capacity. The impact of this on the socio-economic development of the scheme inhabitants is explained. The paper finally examines the possibility of maintaining stable regime conditions in the canals. It also reports on the future of irrigation system management and the possibility of achieving an efficient and effective system to improve crop production.
Copyright (c) 2026 Abdalla A. Ahmed https://creativecommons.org/licenses/by/4.0
Numerical Investigation into Laminar-Flow-Sensitive Aerodynamic Behavior of the NACA 0012 Airfoil at Subsonic Mach Numbers
Laminar flow behavior over airfoil surfaces plays a critical role in determining the aerodynamic efficiency in subsonic flight, particularly through its influence on viscous drag and boundary-layer development. The thickness of an airfoil affects the pressure-gradient distribution along the surface and can therefore influence laminar flow stability and aerodynamic performance. Quantitative investigations on isolating the thickness as an independent geometric parameter under consistent numerical conditions remain limited. This study presents a numerical investigation into the influence of airfoil thickness on laminar flow characteristics at subsonic Mach numbers. A symmetric NACA 0012 airfoil was adopted as the baseline geometry, and systematic thickness variations were introduced while maintaining a constant chord length and camber characteristics. The aerodynamic performance was analyzed using XFLR5 predictions combined with computational fluid dynamics (CFD) simulations performed using a Reynolds-averaged Navier–Stokes framework with transition-sensitive modeling. The analysis was conducted under subsonic operating conditions representative of low-speed aerodynamic applications. The results showed that increasing the airfoil thickness strengthened the adverse pressure gradients on the suction surface, leading to increased boundary-layer sensitivity and drag penalties. Thinner configurations exhibited delayed pressure recovery and improved aerodynamic efficiencies within the investigated operating range. CFD flow-field visualization further illustrated the relationship between pressure gradient development, wake growth, and aerodynamic performance trends. The findings provide insights into the sensitivity of laminar flow behavior to airfoil thickness and highlight the efficacy of the XFLR5 and CFD approaches in early-stage airfoil design and aerodynamic evaluations.
Copyright (c) 2026 Ayush Kamboj https://creativecommons.org/licenses/by/4.0
Quantum Dots Additized Lubricating Oils: A Review
In this review, the studies pertaining to the quantum dots dispersed lubricating oils is systematically reviewed. Various aspects such as synthesis of the QDs additive, their dispersion method and stability, dispersion stability enhancement methods, and their tribological performance is presented. It is found that, such additized oils result in friction and wear reduction for steel tribopair by about 37.07 ± 17.25 % and 58.91 ± 23.77 % respectively.
Copyright (c) 2026 Prasad https://creativecommons.org/licenses/by/4.0
Online-Causal Forecasting of Physics-Derived Lower-Limb Torque Proxies from Low-Cost Dual-View Motion Capture: A Subject-Specific Feasibility Study
Low-cost motion capture may support anticipatory biomechanical surrogates, but causal deployability and target validity must be distinguished. We evaluated subject-specific 100-ms forecasting of physics-derived lower-limb torque proxies from dual-view marker video, with four-channel surface electromyography (sEMG) as an optional modality, using one 60-kg adult recording. Approximate 3D trajectories generated proxy targets through an engineering Lagrangian model; no force-plate or validated inverse-dynamics reference was available. Video-sEMG alignment was estimated on training data only and frozen at 559 frames (9.317 s). A six-channel causal gait-context receptive-field-matched temporal convolutional network (RF-TCN) achieved strict-test MAE 0.1326 Nm/kg, RMSE 0.2069, R² 0.4427, and r 0.6529 over 1564 windows; five-seed MAE was 0.1338 ± 0.0011 Nm/kg. Relative to the earlier causal-context reference, MAE decreased by 2.47%, while other metrics showed no supported improvement. A controlled K7-versus-K9 ablation showed only a small average MAE advantage for K9 and no statistically resolved overall effect. Sparse sEMG added no benefit beyond gait context. In stateful streaming replay, GPU complete-update p95/p99 latency was 21.78/25.03 ms with 0/6069 steady-state 50-ms deadline misses. These results support leakage-controlled, training-stable surrogate forecasting and GPU computational feasibility, not validated biological kinetics or hard-real-time exoskeleton control.
Copyright (c) 2026 Ange Tong https://creativecommons.org/licenses/by/4.0
Certification-Informed Analytical Preliminary Structural Design of a Box-Beam Engine Pylon for a Representative Wide-Body Civil Aircraft
Objectives: This study develops a certification-informed analytical framework for the preliminary structural design of an under-wing box-beam engine pylon representative of a twin-engine wide-body civil aircraft. The objective is to link airworthiness-derived loading requirements, engine–pylon reactions, structural architecture and geometry-based mass assessment within a traceable early-design workflow. Methods: Three aircraft mass states, five characteristic speeds and nine altitude conditions define the baseline design space. Gust, gyroscopic, engine failure, engine seizure, and emergency loading are evaluated to establish six-component reaction envelopes. These loads are transferred through an idealised closed box-beam representation to derive internal shear-force, bending-moment and torsional distributions and to support preliminary structural sizing of skins, ribs, stiffeners, spar caps and attachment lugs. Results: The final configuration contains 12 ribs and transitions from integral blade stiffeners in the central box to four L-section spar caps in geometrically constrained tapered regions. The CAD-based structural mass is approximately 596 kg per pylon, with the Ti-6Al-4V primary structure contributing approximately 85%. Conclusions: The methodology provides a transparent analytical route from certification-informed loading to preliminary structural architecture and mass assessment. No finite-element stress, displacement or buckling results are used as validation evidence; higher-fidelity numerical and experimental verification remains necessary.
The RopeComb: A Configurable Rope-and-Pulley Transmission for Impedance-Matched Mechanical Launch
Transferring kinetic energy from a slow, high-force source, such as a descending mass, a ram or a spring, to a fast, light payload requires a transmission whose mechanical advantage rises continuously through the stroke. We introduce the RopeComb: a passive, configurable rope-and-pulley transmission in which a translating carriage progressively deflects a tension member into successive fixed spans. Two independent geometric freedoms per member (span width and engagement offset) make essentially any monotonically rising profile reachable, and because the resulting displacement ratio has a closed form, matching a target is a smooth least-squares fit rather than a search. By specifying a source deceleration rather than a payload force, and solving for the force that produces it, the geometry can be optimised for uniform payload force. Across mass ratios of 100:1, 1,000:1 and 10,000:1, arrays of eight to thirteen members track that target to within 1.5% and reach its exit velocity in arrays 1.9-2.8 m wide. Measured against the work needed to raise the source again, through the braking distance as well as the height that sets its entry speed, the transmission delivers 86.3% of the input to the payload; that figure is fixed by the entry and final speeds and the braking distance alone, and does not depend on mass ratio. The three simulated designs realise it to within 0.4%. It precedes a correction for the rotational inertia of sheaves in the fast path, which is first-order, scale-invariant, and costs a further 9 to 12% of exit velocity, leaving the payload 66 to 72% of cycle input rather than 86.3%. The stroke is terminated by a traction-loss instability, and the specified deceleration is the hardest that leaves the tension member continuously loaded throughout, with release occurring before the terminal transient. Pairing the RopeComb with a compliant, pre-tensioned output member absorbs the transients of discrete engagement, holding peak-to-mean payload force to 1.09-1.19x, where the rigid mechanism gives 2.0-2.1x. That places its force uniformity at or beyond the nominal 1.25x of a steam catapult and approaching the 1.05x of an electromagnetic launcher, achieved with passive mechanics and no electrical storage, power conditioning or steam plant. The RopeComb figures reported here are simulated; the steam and electromagnetic figures they are compared against are measured on fielded hardware.
Copyright (c) 2026 Rami Mahdi https://creativecommons.org/licenses/by/4.0
Intelligent Smart Water Quality Monitoring and Autonomous Protection System Using Differential Evolution and EPANET Digital Twin
Modern urban water distribution systems require intelligent monitoring and autonomous protection mechanisms to prevent contamination and ensure public safety. This paper presents an intelligent decision-support framework integrating Differential Evolution (DE) optimization, EPANET hydraulic simulation, rule-based contamination risk assessment, and a real-time digital twin dashboard. The proposed system continuously monitors water quality parameters including chlorine concentration, turbidity, pressure, and temperature while performing rule-based contamination risk estimation and emergency response control. The developed framework can automatically isolate contaminated network zones using smart valve control and provide intelligent operational recommendations. Beyond the original single-function demonstration, this extended study validates the DE implementation against ten standard benchmark functions, benchmarks it against a particle swarm optimizer under a matched evaluation budget, quantifies the contamination-detection logic against twenty synthetic test scenarios spanning safe, boundary, and multi-fault conditions, and couples a second DE search directly to the EPANET hydraulic model to test pump-speed and valve-setpoint operating envelopes against real simulated pressure constraints rather than an abstract proxy objective. Experimental results demonstrate that the proposed system successfully improves contamination detection, visualization, and autonomous response capabilities for smart city water infrastructures; the EPANET-coupled search further shows that, within the physically reasonable pump-speed range tested, the network's surplus hydraulic capacity leaves no interior energy-pressure trade-off for the optimizer to find, a genuine, if modest, finding about the network's spare capacity. The study also surfaces implementation considerations, including a still-simplified default optimization objective and several software integration issues, that mark the clearest next steps toward a deployable system.
Copyright (c) 2026 Mathew Luv Christe, Junior Charlie https://creativecommons.org/licenses/by/4.0
An Edge-Assisted IoT Framework for Intelligent Water Quality Anomaly Detection and Early Warning in Bangladesh Aquaculture
In sustainable aquaculture, maintaining optimal water quality is essential, as sudden environmental changes can lead to disease outbreaks, reduced productivity, and significant fish mortality. This paper presents an edge-assisted Internet of Things (IoT)-Cyber Physical System (CPS) framework for intelligent water quality monitoring and real-time anomaly detection. The proposed framework integrates IoT-enabled water quality sensors, an ESP32 edge device, and a Raspberry Pi 4 Model B to perform real-time data acquisition, preprocessing, and anomaly detection using the Isolation Forest algorithm. While the ESP32 is responsible for collecting and transmitting sensor data, the Raspberry Pi executes the computationally intensive anomaly detection process and supports intelligent early-warning generation with low communication latency. Four critical water quality parameters, namely temperature, dissolved oxygen (DO), pH, and turbidity, were selected from the publicly available IoTMLCQ dataset. After data preprocessing and feature normalization, the proposed framework was evaluated using confusion matrix analysis, receiver operating characteristic (ROC) analysis, and standard classification metrics. Experimental results demonstrate that the proposed model achieved an accuracy of 97.25%, a precision of 66.67%, a recall of 66.45%, an F1-score of 66.56%, and an ROC-AUC of 98.44%, outperforming One-Class Support Vector Machine and Local Outlier Factor under identical experimental conditions. The proposed low-cost edge-assisted architecture provides timely anomaly detection and intelligent early warning, offering a practical solution to reduce fish losses, improve aquaculture farm management, and support sustainable smart aquaculture.
Copyright (c) 2026 Sunnymoon Rafi, Md. Mahibullah, Kazi Wakilur Rahman Rumman, Shah Muhammad Azmat Ullah, Gazi Muhammad Abdullah Mahfuz, Toufik Ahmed https://creativecommons.org/licenses/by/4.0
WLVocGenArg: Development of a Wireless Voice-Generating Arduino Gloves For The Deaf People Used For Virtual Communication
Sign Language is the means for deaf people to communicate - they are more visually oriented people since most are deprived of the ability to hear and consequently, to speak in return. However, most non-deaf or ordinary people are incapable of communicating and understanding sign language. This study aims to develop wireless voice-generating gloves using Arduino that aid the deaf in communicating with normal people by translating signs and gestures into synthetic speech. In being able to do feedback, a normal person’s voice is transcribed into text as he/she presses the microphone button on the MIT App Inventor-developed mobile application named “FSL” before he/she speaks and then the text will display on the glove’s attached 20x4 LCD Screen for the deaf user to be able to receive the former’s response - two-way communication. The study envisions bridging the gaps in similar previous innovations for the beneficiary to benefit from the project fully. Through the quantitative method in the lens of the Technology Acceptance Model by Fred Davis (1989), a survey on Perceived Usefulness (PU) and Perceived Ease of Use (PEU) is administered to 20 respondents - Grade 7 and 8 deaf students of Parañaque National High School - Special Education Department (SPED), as well as Grades 5 and 6 deaf students of Las Piñas Elementary School Central - SPED. In the findings, to be able to perform in class better (e.g., assignments and projects) is the highlighted PU with the level of “Quite Likely,” while the PEU level is also “Quite Likely” due to the newness of the technology to the students, which underlines that there is still some perceived difficulty in using it. The core purpose of this intervention is to aid young deaf individuals in seeing the bigger horizons they ought to see and to make them feel that they are never isolated or treated as outcasts in today’s world equipped with technology.
Copyright (c) 2026 John Syte Tiopes, Jericho Gerald Baluyot, Ericka Mae Rodillo https://creativecommons.org/licenses/by/4.0
SPECTRA-CQ: Incipient Anomaly Detection in Bridge Health Monitoring via Capacity-Constrained Quantum and Classical Autoencoders
Detecting incipient structural damage in civil infrastructure requires isolating transient acoustic emissions that are heavily obscured by macroscopic environmental noise. This paper introduces SPECTRA-CQ (Spectrogram Pattern Evaluation via Classical and Quantum Architectures), a hybrid anomaly detection framework evaluated on continuous vibration telemetry from the operational openLAB reference bridge in Bautzen, Germany. Raw 1-dimensional accelerometer signals are mapped into 2-dimensional 64 × 1000 energy-density spectrograms via Continuous Wavelet Transform (CWT) to preserve critical time-frequency localization. A synthetic acoustic emission (AE) proxy is then injected into a target node to establish strict physical detection limits. This AE is bounded at an amplitude of 0.35 to mathematically simulate early-stage micro-cracking rather than obvious macroscopic failure. Initial unconstrained baseline ablations establish that both architectures are mathematically sound. Both Classical Convolutional Autoencoders (CAE) and Hybrid Quantum Autoencoders (HQAE) easily capture the structural variance at a latent space of 8 qubits on a single sensor and yield highly accurate reconstructions with a mean squared error of approximately 0.008. A fundamental bottleneck emerges when enforcing Noisy Intermediate-Scale Quantum (NISQ) capacity constraints. Aggressively compressing the latent space to just 2 qubits forces isolated single-sensor models to fail. Neither the classical nor the quantum architecture can effectively separate low-amplitude anomalies from standard reconstruction noise without a broader spatial context. This limitation, called “Single-Sensor Spatial Blindness” prevents reliable incipient damage detection. This barrier is overcome by expanding the architecture into a 6-sensor Siamese convolutional graph. It fuses spatial data from all six sensors while strictly maintaining the two-qubit bottleneck for each specific sensor. This setup allows the model to anchor itself against the geometric state of adjacent healthy nodes. This multi-sensor fusion successfully recovers localized detection for the classical CAE architecture and scales to an AUC-ROC of 0.888 for incipient damage evaluated at higher spectral mask widths. The quantum equivalent, however, hits a hard scaling wall. Ablation studies on circuit topology investigate the exact cause of this collapse. Standard parameterized quantum circuits, ranging from completely uncoupled rotations to fully entangled arrays, are evaluated against two custom entanglement topologies. These custom designs include a rigidly structured global mapping and a Physics-Informed Neural Network (PINN) configuration designed to mathematically embed continuous wave attenuation. Despite these targeted configurations, forcing global entanglement across 12 qubits still reliably triggers gradient vanishing in the current NISQ-era hardware. Heavily entangled spatial quantum networks succumb to barren plateaus and degrade incipient detection back to baseline levels. Ultimately, the data highlights a clear divergence. Classical architectures readily exploit spatial coherence to bypass extreme latent compression. Forced quantum entanglement currently acts as a strict mathematical barrier for continuous multi-sensor structural monitoring.
Copyright (c) 2026 Yesha Pandya, S N Pandya, Dr. N K Solanki https://creativecommons.org/licenses/by/4.0
Edge AI-Driven Targeted Fertilizer Dispensing System for Mid-Sized Oil Palm Plantations Using Sensor Fusion
Nutrient management in commercial oil palm plantations presents a significant logistical challenge. Manual fertilizer application is labor-intensive and inefficient, while conventional tractor-mounted broadcast spreaders lead to substantial material wastage. These standard machines dispense fertilizer in a 360-degree pattern, causing much of the nutrient load to fall in inter-row spaces where it encourages weed growth rather than crop development. To address these inefficiencies for mid-sized operations, this paper proposes a low-cost, automated fertilizer dispensing system utilizing Edge AI and sensor fusion [4]. The architecture incorporates a Raspberry Pi 4B integrated with an HC-SR04 ultrasonic sensor for proximity measurement and a Pi Camera executing a lightweight YOLOv8 object detection model for real-time oil palm trunk identification [1]. Upon detection of a tree within a 2.5-meter threshold, the system activates a 12V linear actuator to open a targeted chute, delivering a precise dose of granular fertilizer directly to the weeded root basin. The design further includes a mechanical agitator within the hopper to prevent clumping of hygroscopic fertilizers. Initial benchtop evaluations indicate that affordable edge computing devices can achieve the requisite low-latency performance for precision agricultural applications. This system offers a scalable bridge between manual labor and high-cost commercial spreaders, significantly reducing fertilizer waste and operational overhead.
Copyright (c) 2026 Akhil Kommineni https://creativecommons.org/licenses/by/4.0