Vibrations which have been caused by non-seismic factors as high-speed rails, highway traffic, construction activities, machine foundations, blasting, underground explosions, etc. have not been considered safe for human life and structures around. Vibrations which have been generated by these sources have caused of occupant discomfort, settlement and shaking of building foundations, structural and non-structural damages to adjacent buildings, malfunctioning of ultra-sensitive medical equipment, etc. Vibration screeners in form of open or in-filled trench wave barriers have been used to reduce the effects of vibrations. This paper has described an experimental study for evaluating the performance of in-filled materials in trench wave barriers in an elastic, homogeneous and isotropic half space. The in-filled materials used are as rice husk, crumbed tyre rubber and sawdust. Key properties of the in-filled materials are have also been determined by geo-technical characterization to ascertain its suitability in terms of properties of in-filled materials. The important factors such as material damping and boundary conditions of soil have also been included. The effectiveness of geometric dimensions of the trench wave barrier, shear wave velocity and location of source from the barrier have also been investigated. In this study, a dimensionless approach have been used, where the geometrical parameters have been normalized by one of the characteristics i.e. wavelength of Rayleigh oscillatory wave in elastic half-space. The influence of parameters related to vibration isolation has been discussed in detail. Recommendations have been provided for the optimal selection of the geometry of trench wave barriers.
In high-rise buildings shear walls have performed very effectively as lateral load resisting system. Similar to columns, shear walls being generally subjected to flexure with axial forces should have been designed by P-M interaction charts obtained from the concept of capacity-based-approach. Following the design provisions of IS 456:2000,P-M interaction charts for RC slender rectangular shear walls have already been reported depicting failures within the domain of compression-bending. However, shear walls under seismic effects may fail in tension. In the present work P-M interaction charts have been proposed for slender rectangular shear walls for the entire domain of failure right from pure compression to pure tension following the design provisions of IS 456:2000. Quantification of ductility values to assess the seismic performance of shear walls designed as per Indian standards have been very scarcely reported. In the present work curvature ductility of rectangular shear walls have been quantified following the fundamental principles of Limit State Method and presented in the proposed P-M interaction charts. This can enable designers to assess the strength and ductility of shear walls simultaneously. The present study has indicated that ductility of shear wall failing in pure bending has been found to be maximum when designed with minimum percentage of vertical reinforcement (0.25). Damage assessment of shear wall sections as per Performance-based Criteria of ASCE 41-13 has indicated that the minimum vertical reinforcement percentage (0.25%) specified in IS13920:2016 requires modification to restrict the damage within the collapse prevention stage.
This research paper has presented the design, numerical analysis, and experimental validation of a bio-inspired morphing airfoil actuated through shape memory alloy (SMA) wire and spring elements. To facilitate controlled deflection, a corrugated morphing section has been embedded within an eppler airfoil, which has been selected through comparative aerodynamic analysis against a standard NACA 0012 profile. Finite element simulations have been conducted using ANSYS software to investigate tip deflection performance across varying morphing region lengths and actuator placements. A lightweight, three-dimensionally printed prototype incorporating shape memory alloy components has been developed and has been thoroughly evaluated using direct electrical heating methods. Experimental findings have shown a 4 mm downward deflection for a 1 mm actuator wire contraction, which has closely matched the numerical simulation predictions. To demonstrate scalability, a full-span wing prototype has been assembled utilizing three independent, actuated airfoil segments. The successful actuation of this system has validated the feasibility of shape memory alloy-driven morphing as a viable, lightweight alternative to traditional servo-based mechanisms in micro air vehicles and low-speed unmanned aerial vehicles. Furthermore, this research has established a practical foundation for future work aimed at enabling bi-directional actuation for enhanced aerodynamic control and maneuverability.
The ADC (Analog to Digital Converter) has become essential for interfacing analog signals with the digital domain, particularly in applications requiring high resolution and low voltage operations. Recent advancements in technology have led to reduced signal power and voltage through supply voltage scaling, which has posed significant challenges for ADC performance, including increased area, narrow input range, and process variation. To address these issues, this study has proposed a VCO (Voltage Controlled Oscillator)-based ADC integrated with an ACO-PSO (Ant Colony Optimization- Particle Swarm Optimization) algorithm, thereby enhancing stability, reliability, and effectiveness in chip design. The proposed system has utilized real-time input sources, specifically solar PV (Photo Voltaic) and wind energy, and has incorporated a SEPIC (Single-Ended Primary Inductance Converter) to deliver a controlled output voltage. The ACO-PSO algorithm has optimized the values obtained from the VCO-based ADC and has selected the best solutions to achieve improved performance. The efficiency of the framework has been assessed by measuring gain and delay through Verilog simulations, and the results have been obtained using MATLAB/Simulink. The circuit has occupied an area of 0.012 mu m2 and has achieved a Mean Square Error (MSE) of 0.2 and a Peak Signal-to-Noise Ratio (PSNR) of 52 dB, thereby demonstrating superior efficiency compared to conventional methods.
The Belanger equation has been found to be inadequate for predicting sequent depth in hydraulic jumps occurring on rough, sloping channel beds. To address this limitation, a roughness Froude number incorporating the effects of bed roughness and channel slope has been introduced in this study. Three sets of experimental runs have been conducted in a channel flume using three bed roughness values (0.0256 m, 0.0387 m, and 0.0438 m) and three adverse slopes (-0.025,-0.030, and-0.035). The resulting datasets have been used to tabulate sequent depth and jump length and to develop two regression-based semi-empirical relationships relating the roughness Froude number to the sequent depth ratio and the relative jump length, respectively, for hydraulic jumps on adverse slopes. The proposed equations have been validated against experimental observations and have shown agreement within deviations of 30% for sequent depth and 33% for jump length.
Single-electron transistors (SETs) have emerged as promising candidates for low-power nanoelectronic applications due to their ability to control electron flow at the single-charge level. However, conventional SETs have faced challenges such as limited charge-state tunability, cross-talk, and reduced electrostatic precision as device dimensions have scaled down. To address these limitations, this study has presented a theoretical investigation of a tri-gate single-electron transistor (TG-SET) that has incorporated a redox-active vanadium tris(dithiolene) complex, V(edt)3, as the molecular island. The TG-SET architecture has introduced three independently controlled gate electrodes positioned around the molecular channel to enable spatially resolved electrostatic modulation. Using density functional theory (DFT) and non-equilibrium Green's function (NEGF) calculations, the study has modeled the electronic structure and transport behaviour under varying gate voltages and charge states. The results have revealed stable Coulomb blockade plateaus, spin-resolved energy levels, and nonlinear current-voltage characteristics, demonstrating fine-tuned control over molecular charge and orbital alignment. A key outcome has included the extraction of gate-molecule coupling parameters through total energy fitting across multiple charge configurations. The TG-SET has exhibited enhanced gate sensitivity, reduced leakage potential, and improved charge selectivity compared to conventional SETs. This work has highlighted the potential of tri-gated architectures in advancing molecular-scale electronics and has provided a design framework for future experimental realization. The findings have opened new directions for the development of multifunctional, ultra-low-power devices in quantum computing, sensing, and molecular logic applications.
In a robotic cotton picker, the picking arm has required a suitable end-effector to pick cotton bolls from the plant. Additionally, a suction mechanism has been required to transport the picked bolls into the storage tank. In the present study, a suction-based system has been developed in which the end-effector has removed the cotton bolls from the plant and a vacuum unit has carried them through a hose into the storage tank. Field tests have been carried out on non-defoliated cotton plants using three types of end-effectors, namely roller-type, chain-type, and tubular anthracite, at suction pressures of 25, 50, and 75 mmHg. The results have shown that the roller-type end-effector has achieved the minimum time required to suck a cotton boll (1.65 s boll(-1)), the least cotton left in the boll (0.02 g boll(-1)), and the highest picking efficiency (99.36%). The developed suction mechanism has efficiently picked cotton bolls from the plant when equipped with the roller-type end-effector at a suction pressure of 75 mmHg.
Carbon fiber-reinforced polymers (CFRPs) have become critical materials in aerospace and automotive industries due to their outstanding strength-to-weight ratio. However, their abrasive characteristics have made machining particularly challenging. In this study, a novel hybrid Fuzzy AHP-GRA method has been introduced to optimize CFRP turning parameters-namely, cutting speed, depth of cut, and feed rate. Experiments have been conducted using an L9 orthogonal array with PVD-TiAlN-coated carbide inserts and a water-miscible coolant. The results have indicated that cutting speed (100 m/min) and depth of cut (0.1 mm) have been the most significant factors affecting tool wear and surface finish. While higher cutting speeds have enhanced material removal rates, they have also increased tool wear due to elevated temperatures. Similarly, greater depths of cut have intensified cutting forces and led to more frequent tool chipping. The optimal parameter setting determined by grey relational analysis (100 m/min, 0.1 mm depth of cut, 0.1 mm/rev feed rate) has reduced average flank wear by 2.77% and slightly improved average crater wear by 1.19%, while also yielding superior taper wall finish and cylindrical surface quality (improvements of 16.09% and 9.26%, respectively) compared to the controlled parameter setting (100 m/min, 0.1 mm, 0.3 mm/rev). This data-driven approach has effectively balanced productivity and tool longevity, thereby addressing key industrial challenges in CFRP machining
The metamaterial absorbers have been artificially designed structures that have controlled and have manipulated electromagnetic waves across different frequency regimes, which have been important in stealth technology, EMI shielding, sensing, and energy harvesting. In particular, the efficiency and reliability of such absorbers have had a close dependence on fabrication methods used to fabricate these absorbers. This review has given an orderly exploration of some recent developments in the fabrication techniques of metamaterial absorbers, grouped into four major classes: Top Down, printing-based, mold-assisted, and laser-based approaches. Each technique has been qualitatively compared based on resolution, manufacturing cost, scalability, substrate compatibility, structural complexity, duration of fabrication, frequency suitability, and absorption efficiency. A general comparison has summarized the advantages and limitations of each technique. The review also has outlined emerging trends that could shape the next generation of high-performance metamaterial absorbers.
Fossil fuel burning and chemical processes involved in the production of cement result in the continuous emission of harmful air pollutants like carbon monoxide, NOx, SOx, etc. Further, these pollutants have hazardous effects on the environment, like depletion of the ozone layer, the greenhouse effect, global warming, etc., exacerbating the situation. Stone is a fundamental component of cement, and its overexploitation has resulted in landscape deterioration, habitat destruction, landscape degradation, water contamination, etc. Geopolymer concrete (GPC) can be used as a substitute for cement concrete in construction applications. The production of GPC utilizing fly ash and micro silica fume has gotten minimal attention. In the present study, GPC has been synthesized at room temperature using Class C fly ash and micro silica fume as substitutes for cement, whereas aggregates have been prepared from waste rubber. The results of mechanical characterization have indicated that the strength parameters enhanced by 3-4% and the microstructural characteristics exhibited effective binding capabilities of GPC, including waste materials. This work serves as a baseline for future research on the incorporation of waste materials into sustainable construction materials.
This study has examined the combined influence of recycled rubber aggregates (RRA) and polypropylene (PP) fibers on the mechanical and durability properties of mortar composites. Nine mortar mixtures have been prepared with RRA contents of 0%, 10%, 20%, and 30% (by sand mass) and PP fiber volumes of 0%, 0.5%, and 1% (by binder mass). Results have shown that increasing RRA content has reduced compressive strength; however, the inclusion of PP fibers has significantly mitigated this loss. Durability parameters such as dynamic modulus, porosity, ultrasonic pulse velocity, and capillary absorption have also measured. A nonlinear empirical model has been developed to predict compressive strength based on RRA and PP content. The model has been validated using two-way ANOVA and Leave-One-Out cross-validation, achieving excellent agreement with experimental results (R-2 >0.98). Additionally, a simplified environmental analysis has indicated potential CO2 emission reductions through partial replacement of natural sand with RRA. Overall, the study has demonstrated that rubberized and fiber-reinforced mortars have offered a sustainable alternative in construction, combining improved durability and environmental benefits without excessively compromising mechanical performance.
Human foot has unique anatomical and functional structure to facilitate various movements. It is difficult to classify the foot typology by visual assessment and by using static analysing tools. The aim of the study is to predict foot typology and classify the foot type using instrumental gait analysis and machine learning techniques, focusing on women aged 20-29 and 40-49 years to identify key features contributing to conditions like flat feet and high arch feet, and to facilitate early detection and correction of abnormal foot typologies. Data collection has involved 25 participants in Group 1 (aged 20-29 years) and 15 participants in Group 2 (aged 40-49 years), each undergoing 3 trials in instrumented treadmill gait analysis (ITGA), alongside questionnaires, consent forms, and body composition analysis. Participants have been selected based on specific inclusion and exclusion criteria to ensure valid results. The data has been used machine learning models to classify foot typology into Normal Arch, High Arch, and Flatfoot. The dataset has been separated as 70% training and 30% testing for multi-class classification. The foot typology has been classified using machine learning models based on 49 features of gait analysis. The features identify the patterns and differences among the different foot typologies for accurate classification. The application of synthetic minority over-sampling technique (SMOTE) to balance the dataset also improved the accuracy across all models. Among different machine learning models employed, bagging algorithm has achieved the highest accuracy of 95.6% and 94.3% for Group 1 and Group 2 respectively. The study has indicated effectiveness of machine learning techniques in classification of foot typology using gait analysis proving valuable insights into foot biomechanics and improved precision of diagnosis for personalized intervention strategies.
The increasing demand for sustainable construction materials has encouraged the utilization of industrial and agricultural by-products as alternatives to conventional cementitious materials. These wastes have been recognized as cost-effective, readily available, and capable of significantly reducing environmental pollution when reused in construction applications. This study has examined the feasibility of using wheat husk ash (WHA), an agricultural residue rich in amorphous silica, as a partial replacement for cement in concrete. Concrete mixtures have been prepared by replacing cement with WHA at levels ranging from 10% to 25% by mass, with water-to-cement ratios between 0.30 and 0.40. An extensive experimental program has been conducted to evaluate the influence of WHA as a supplementary cementitious material on the fresh, physical, and hardened properties of concrete. Fresh properties have been evaluated through workability measurements, while physical properties have included density and water absorption. Hardened properties have been assessed in terms of compressive strength and overall mechanical performance. The results have indicated that incorporating WHA up to an optimum replacement level has improved concrete performance due to enhanced pozzolanic activity and micro structural densification. Beyond the optimum content, reductions in strength and workability have been observed. Overall, the findings have demonstrated that wheat husk ash can be effectively utilized as a sustainable cement replacement material, contributing to reduced cement consumption and improved environmental sustainability in concrete construction.
Traditional electric vehicle (EV) chargers typically employ a dual-stage configuration to achieve a significant step-down in voltage, which results in discontinuous input current and compromised efficiency. This paper has proposed a single-stage, single-phase high step-down bifold converter featuring synchronous rectification and ripple mitigation to address these challenges. By regulating the intermediate bus voltage (IBV) to provide only half of the output voltage, the converter ensures continuous input current with a near-unity power factor. A highly efficient 1.55KW prototype, converting from 230V AC and PV panel to 48V DC, has been developed, simulated and analyzed using MATLAB. The charger's performance has been rigorously analyzed and harmonic compensator has been introduced to eliminate ripple components causing improvements in efficiency and reduction in THD along with operational stability.
A scientific approach is essential for evaluating pavement surface conditions at the network level. The prime objective airport pavement in terms of functional condition analysis is to focus on the current and future pavement conditions. The Pavement Condition Index (PCI) is a well-known method widely used to assess the surface conditions of airport pavements. The aircraft movement from the Runway while take-off and landing or taxiing at the taxiway and parking at aprons induces a high magnitude of repetitive loads that create excessive stresses due to which pavement layers are affected and distress appears on the surface of the pavement. This study has been computed the analysis of the distress and traffic measurement evaluate pavement condition index (PCI), Structural condition index (SCI), and FOD index. In addition, the aircraft classification number and pavement classification number (ACN-PCN) methods have been applied to all sections of airport branches to estimate the structural bearing capacity of the airport pavement network. By adopting the traditional method the PCI, the relationship between the pavement condition index and pavement classification number has been studied. Finally, based on the combined rating index, a treatment methodology has been proposed for the improvement of existing critical pavement section by using a decision tree (DT).
In this paper, a valuable evaluation of four common substrate materials Rogers RT5870, PTFE, PEC, and FR4 will be presented in combination with the microstrip patch antennas at the frequency range of 2-3 GHz. As environmental sustainability in electronic design becomes more and more popular, the paper discusses the RF performance of every substrate in terms of return loss (S11) and voltage standing wave ratio (VSWR) as well as its environmental sustainability in terms of measures like recyclability, material toxicity, and lifecycle footprint. The analyses in simulation by using CST Studio Suite 2023 will bring the fairness of all materials. The PTFE and Rogers RT5870 also perform better in RF, but FR4 also has a reasonable RF performance at a lower cost hence can be used in low-cost designs. PEC is a theoretical standard, which is not practical to implement in reality. The outcomes underscore the necessity to have a balanced performance and sustainability that will help in the development of green electronics and environmentally friendly antenna system.
Groundwater contamination has been posing a significant threat to sustainable water resource management, particularly in industrialized and urbanized regions. This research has introduced a novel, data-driven framework that integrates machine learning, statistical data analysis, and feature optimization to evaluate and forecast groundwater quality. Analytical results of 488 groundwater samples had been tested, and four feature reduction scenarios had been implemented using Pearson correlation to evaluate predictive performance with minimal input variables. Statistical analysis has highlighted elevated levels of parameters such as Electrical Conductivity, Chloride, Magnesium, and Total Hardness, exceeding permissible limits, and have been causing most samples to be unsuitable for consumption without treatment. To enhance groundwater monitoring and reduce laboratory testing costs, six machine learning algorithms, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, XGBoost, and Artificial Neural Network, have been used to predict the Weighted Arithmetic Water Quality Index. Model accuracy had been tested using statistical metrics such as R2, RMSE, MAE, MAPE, and CRMSE, with effectiveness assessed using Taylor diagrams. ANN exhibited the highest accuracy even when using a single input (K), while SVM maintained consistent reliability with only two inputs (Mg and K), providing a cost-effective monitoring solution. Validation with 70 independent datasets has confirmed the robustness and applicability of the suggested methodology. The study has presented an innovative modeling strategy that has substantially decreased laboratory testing needs while preserving predictive reliability. Additionally, it has offered practical implications for scalable, cost-effective deployment in areas with water scarcity or insufficient dataset.
Surface vibrations caused by human activities such as industrial activities, high-speed traffic (e.g. on highways and railways), Piling and blasting during construction and demolition works, usually reach limits where they have become problematic for people and buildings. Very often, it even reaches dangerous limits where they are no longer safe for human life. So many solutions have devised to mitigate effects of vibration and one of them is vibration screening by construction of trench wave barriers. This paper has focused on evaluating screening efficiency of in-filled materials and impact of different parameters of trench wave barriers on efficiency. This paper has presented a numerical study and subsequent validation, 3D finite element study of vibration screening in PLAXIS-3D against stationary surface vibrations using a trench wave barrier filled with an elastic, isotropic, homogeneous half-space. Key parameters have determined studied thereof. The results of numerical and experimental study have found in close agreement.
The rapid electrification of goods vehicles in India, particularly in urban centers like Delhi, has transformed last-mile freight dynamics. This study has analyzed the impact of electrification on trip rates and vehicle choice within Delhi's urban freight sector. Using vehicle registration data, trip rates from the trip generation study, and vehicle count surveys, we have examined the shift from traditional light commercial vehicles (LCVs) toward electric goods three-wheelers (3WTs). The findings have revealed a significant rise in 3WT registrations and trip frequencies, alongside a relative decline in LCV usage. The projected sales of the 3WT segment have shown a steady rise from around 12,500 in 2025 to over 20,000 units by 2030, while the sales of LGVs have declined to 11,000 by 2030. Key drivers of this modal shift included operational advantages, fewer restriction on EV's entry timings, and supportive government policies promoting electric vehicle adoption. These insights highlight the changing nature of freight mobility in Indian cities and the need for strategies to further support sustainable urban logistics.
This study investigates the effects of crumb rubber (CR) modification on the rheological and performance characteristics of asphalt binders, with a focus on rutting and fatigue resistance. Modified binders were prepared with CR contents 10%, 15%, 20% and 24% by weight of viscosity grade-30 (VG-30) binder. These binders are evaluated for their rheological properties, including Multiple Stress Creep Recovery (MSCR), Zero Shear Viscosity (ZSV), Shenoy parameter, and Linear Amplitude Sweep (LAS) testusing the Dynamic Shear Rheometer (DSR). The addition of CRhassignificantly increased the complex shear modulus (G*) and storage modulus (G ') by up to 3.1 and 30.3 times respectively, while reduced the phase angle (delta) by up to 34.6 degrees, indicating improved stiffness and elasticity. Enhanced values of G*/sin delta and Shenoy parameter haveincreased by 5.34 and 10.4 times respectively for CR24 binder demonstrating improved rutting resistance. MSCR results have shown thatpercent recovery increased from -0.2% to 75.1%, and non-recoverable creep compliance (J(nr)) decreased from 2.81 to 0.07 kPa(-1). The Rutting Resistance Index Ratio (RRIR) has been effective in evaluating crumb rubber modified bitumen (CRMB) performance with J(nr) showing the highest sensitivity to CR content, establishing it as robust indicator of rutting resistance. Fatigue analysis has revealed that the binder with 20% CR has offered the best balance between fatigue resistance and strain tolerance, identifying it as the optimal dosage. A strong inverse correlation with r-square value 0.91 has been found between elastic recovery (ER-DSR) and Jnr, and a moderate positive correlation with r-square value 0.68 has been found between ER-DSR and fatigue life (N-f), highlighting the interconnected nature of elasticity, rutting resistance, and fatigue performance.
