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This study integrates a hydrodynamic model with a structural analysis model to investigate the behavior of a reinforced concrete (RC) frame building under the influence of various hydrodynamic variables, such as debris flow materials, flow intensity, and flow direction. The impact force generated during the first phase of fluid-structure interaction is significantly larger around (20 ÷ 30) % than that in the steady phase, resulting in a substantially bigger maximum value of bending moment (Mmax) and displacement (δ) in every RC column. An increase in flow intensity and solid concentration leads to a larger impact load, resulting in higher values of Mmax and δ. Furthermore, the flow that passes through the porous exterior of the house poses a greater risk than the flow that affects the non-porous side of the house. By comparison with two Vietnamese standards of bending moment and displacement, all columns of the target building are susceptible to collapse in all scenarios when taking into account the bending moment. Nevertheless, in relation to the key threshold of displacement, the building is likely to remain secure.

This paper introduces an approach to the automated measurement and analysis of dairy cows using 3D point cloud technology. The integration of advanced sensing techniques enables the collection of non-intrusive, precise data, facilitating comprehensive monitoring of key parameters related to the health, well-being, and productivity of dairy cows. The proposed system employs 3D imaging sensors to capture detailed information about various parts of dairy cows, generating accurate, high-resolution point clouds. A robust automated algorithm has been developed to process these point clouds and extract relevant metrics such as dairy cow stature height, rump width, rump angle, and front teat length. Based on the measured data combined with expert assessments of dairy cows, the quality indices of dairy cows are automatically evaluated and extracted. By leveraging this technology, dairy farmers can gain real-time insights into the health status of individual cows and the overall herd. Additionally, the automated analysis facilitates efficient management practices and optimizes feeding strategies and resource allocation. The results of field trials and validation studies demonstrate the effectiveness and reliability of the automated 3D point cloud approach in dairy farm environments. The errors between manually measured values of dairy cow height, rump angle, and front teat length, and those calculated by the auto-measurement algorithm were within 0.7 cm, with no observed exceedance of errors in comparison to manual measurements. This research contributes to the burgeoning field of precision livestock farming, offering a technological solution that not only enhances productivity but also aligns with contemporary standards for sustainable and ethical animal husbandry practices.

This study proposes a low-cost and reliable smart fire alarm system that utilizes ultraviolet (UV) detection technology with an aspherical lens to detect fires emitting photons in the 185–260 nm range. The system integrates the aspherical lens with an accelerator and a digital compass to determine the fire source’s direction, allowing for safe evacuation and effective firefighting. Artificial intelligence is employed to reduce false alarms and achieve a low false alarm rate. The system’s wide detection range and direction verification make it an effective fire detection solution. Upon detecting a fire, the system sends a warning signal via Wi-Fi or smartphone to the user. The proposed system’s advantages include early warning, a low false alarm rate, and detection of a wide range of fires. Experimental results validate the system’s design and demonstrate high accuracy, reliability, and practicality, making it a valuable addition to fire management and prevention. The proposed system utilizes a parabolic mirror to collect UV radiation into the detector and a simple classification model that uses Fourier transform algorithm to reduce false alarms. The results showed accuracies of approximately 95.45% and 93.65% for the flame and UVB lamp, respectively. The system demonstrated its effectiveness in detecting flames in the range of up to 50 m, making it suitable for various applications, including small and medium-sized buildings, homes, and vehicles.

The study evaluates the surface water quality of the Bach Dang river basin. During the rainy season, the temperature, pH, EC, BOD _5 , COD, coliforms, ammonium and phosphate values were 27 °C–37 °C, 6.6–8.9, 300–2200 μ S cm ^−1 , 5–25 mg l ^−1 , 5–26 mg l ^−1 , 0–3000 MPN/100 mL, below 0.01 mg l ^−1 , and 0.02 mg l ^−1 , respective. During the rainy and dry seasons, Zn, Pb, Cr, As, Fe and Mn levels were below drinking water limits. In the rainy season, the water quality index varied from 76 to 91, signifying medium to excellent water quality. The most outstanding water quality sites were the QN12, QN18, and QN22. During the dry season, most areas had moderate to excellent (WQI values between 61 and 93), except for QN01, QN03, QN04, QN05, QN06, QN07, QN11, QN16, QN17, QN19, and QN23, where it dropped to moderate (61–73). The temperatures were greater and higher EC values in the southeast. The DO concentrations were higher in the northwest and southeast in both seasons. The southern and southeastern regions exhibited more excellent COD and BOD _5 values than the western, southwest, north, and northeast regions. The ammonium concentrations were higher in the southeast. During the dry season, the phosphate levels exceeded the permissible limit in the west, southeast, east, and northeast. Coliforms were below acceptable in both seasons, with greater levels in the northwest and northeast during the dry season. In addition, the Zn and Pb are favorably associated with the As, Mn, ammonium and phosphate in the rainy season. The COD positively correlated with the coliforms and BOD _5 . In the dry season, the pH inversely correlated with the Pb and Fe positively. The ammonium correlated negatively with the DO and positively with the phosphates. The pH negatively connected with the alkalinity, while the As positively correlated with the BOD _5 and COD.

Three-dimensional point cloud generation systems from scanning data of a moving camera provide extra information about an object in addition to color. They give access to various prospective study fields for researchers. With applications in animal husbandry, we can analyze the characteristics of the body parts of a dairy cow to improve its fertility and milk production efficiency. However, in the depth image generation from stereo data, previous solutions using traditional stereo matching algorithms have several drawbacks, such as poor-quality depth images and missing information in overexposed regions. Additionally, the use of one camera to reconstruct a comprehensive 3D point cloud of the dairy cow has several challenges. One of these issues is point cloud misalignment when combining two adjacent point clouds with the small overlapping area between them. In addition, another drawback is the difficulty of point cloud generation from objects which have little motion. Therefore, we proposed an integrated system using two cameras to overcome the above disadvantages. Specifically, our framework includes two main parts: data recording part applies state-of-the-art convolutional neural networks to improve the depth image quality, and dairy cow 3D reconstruction part utilizes the simultaneous localization and calibration framework in order to reduce drift and provide a better-quality reconstruction. The experimental results showed that our approach improved the quality of the generated point cloud to some extent. This work provides the input data for dairy cow characteristics analysis with a deep learning approach.

Silica nanoparticles (SiO2 NPs) synthesized by the Stober method were used as drug delivery vehicles. Doxorubicin hydrochloride (DOX·HCl) is a chemo-drug absorbed onto the SiO2 NPs surfaces. The DOX·HCl loading onto and release from the SiO2 NPs was monitored via UV-VIS and fluorescence spectra. Alternatively, the zeta potential was also used to monitor and evaluate the DOX·HCl loading process. The results showed that nearly 98% of DOX·HCl was effectively loaded onto the SiO2 NPs’ surfaces by electrostatic interaction. The pH-dependence of the process wherein DOX·HCl release out of DOX·HCl-SiO2 NPs was investigated as well. For comparison, both the free DOX·HCl molecules and DOX·HCl-SiO2 NPs were used as the labels for cultured cancer cells. Confocal laser scanning microscopy images showed that the DOX·HCl-SiO2 NPs were better delivered to cancer cells which are more acidic than healthy cells. We propose that engineered DOX·HCl-SiO2 systems are good candidates for drug delivery and clinical applications.

Optical spectrometers and sensors have gained enormous importance in metrology and information technology, frequently involving the question of size, resolution, sensitivity, spectral range, efficiency, reliability, and cost. Nanomaterials and nanotechnological fabrication technologies have huge potential to enable an optimization between these demands, which in some cases are counteracting each other. This paper focuses on the visible and near infrared spectral range and on five types of optical sensors (optical spectrometers): classical grating-based miniaturized spectrometers, arrayed waveguide grating devices, static Fabry–Pérot (FP) filter arrays on sensor arrays, tunable microelectromechanical systems (MEMS) FP filter arrays, and MEMS tunable photonic crystal filters. The comparison between this selection of concepts concentrates on (i) linewidth and resolution, (ii) required space for a selected spectral range, (iii) efficiency in using available light, and (iv) potential of nanoimprint for cost reduction and yield increase. The main part of this review deals with our own results in the field of static FP filter arrays and MEMS tunable FP filter arrays. In addition, technology for efficiency boosting to get more of the available light is demonstrated.

The spring-back phenomenon is one of the key problems in sheet metal forming process, especially with a complex shape. In this paper, the experiments of spring back have been first carried out to study the effect of punch strokes (different bending angles) on the spring-back angle for 6 (mm) thickness of SS400 sheet steel by V-bending process. The experimental results are then validated by finite element analysis (FEA) of ABAQUS software. To simulate spring-back of V-bending, deformed results of V-shape from a dynamic forming simulation in Abaqus/Explicit would be imported into Abaqus/Standard, and then a static analysis will calculate the spring-back of V-bending. Simulation studies have shown that there are different variations in the initial bending angle and the spring-back angle after the removal of the bending force based on isotropic and kinematic hardening models. Therefore, the combined hardening model has been proposed to overcome the mismatch between prediction results and the corresponding experiments. Based on the accuracy of FEM simulation, this study also verifies the effect of parameters such as a thickness of the sheet, punch radius and punch stroke on the spring-back angle in order to select the optimum input parameters by using Taguchi method.

This research studies the robustness of permanence and the continuous dependence of the stationary distribution on the parameters for a stochastic predator–prey model with Beddington–DeAngelis functional response. We show that if the model is extinct (resp. permanent) for a parameter, it is still extinct (resp. permanent) in a neighbourhood of this parameter. In the case of extinction, the Lyapunov exponent of predator quantity is negative and the prey quantity converges almost to the saturated situation, where the predator is absent at an exponential rate. Under the condition of permanence, the unique stationary distribution converges weakly to the degenerate measure concentrated at the unique limit cycle or at the globally asymptotic equilibrium when the diffusion term tends to 0. [ABSTRACT FROM AUTHOR]

The pursuit of precision machining in engineering materials has ignited an imperative to advance electrical discharge machining (EDM) techniques. This study is a deep dive into the domain of micro-electrical discharge machining (μ EDM) with a specific focus on the utilization of carbon-coated tool electrodes. The primary objective is to elucidate the influence of carbon-coated electrodes on vital machining parameters, including machining depth (Z coordinate), tool wear rate (TWR) and overcut (OC) in μ EDM operations. Moreover, the investigation extends to scrutinizing surface characteristics generated by μ EDM with carbon-coated tool electrodes. To enhance performance, carbon-coated tungsten carbide tool electrodes are employed for machining titanium alloy (Ti–6Al–4V) workpieces. The optimization process seeks to identify the optimal combination of process parameters using a Taguchi-DEAR-based multi-response approach. Experimental findings reveal that integrating carbon-coated electrodes significantly improves the μ EDM process, leading to enhanced surface performance metrics. Notably, this study identifies specific parameter settings that effectively optimize machining quality indicators. By achieving a voltage of 160 V, a capacitance of 10,000 pF and a tool rotation of 600 RPM, the research contributes valuable insights into the intricate realm of μ EDM, highlighting the potential for enhanced surface performance through strategic parameter manipulation. [ABSTRACT FROM AUTHOR]

Deep drawing processes play a pivotal role in the manufacturing of sheet and shell products, making it a widely adopted method. This research employs numerical simulations to investigate the impact of various process parameters on the fracture height of cylindrical cups made from SECC (Steel Electrogalvanized Commercial Cold rolled) material. Specifically, it examines parameters such as blank holder force (BHF), punch corner radius (Rp), die corner radius (Rd), and punch-die clearance (Wc). The study extends to optimizing fracture height, offering a solution to this challenge. Subsequently, the selected parameters are validated through experimental deep drawing of cylindrical cups, resulting in a minimal deviation of 1.55% between simulation and experiment outcomes. A precise mathematical equation is developed to estimate fracture height under diverse machining conditions, with a maximum deviation of 4.52% observed between the mathematical model and simulation. These findings represent a substantial advancement in deep drawing processes technology, particularly in reducing error rates during the production of cylindrical cups. [ABSTRACT FROM AUTHOR]

This study aims to investigate the interplay between cutting speed and Thermal-Assisted Machining (TAM) concerning surface roughness during the high-speed machining of SKD11 steel. The integration of pre-cutting workpiece heating introduces a temperature factor that intricately affects surface roughness. The primary objective is to ascertain optimal speed and temperature ranges that synergistically enhance machining efficiency, curtail costs, and elevate surface quality. The experimental protocol initiates with room temperature milling of SKD11 steel, progressively elevating the temperature gradient to systematically appraise temperature's impact on surface roughness under both conventional and elevated cutting speeds. Subsequent experimentation, conducted within specific temperature thresholds, entails stepwise augmentation of cutting speed to elucidate the influence of highspeed conditions on surface roughness. The ensuing analysis meticulously examines the ramifications of distinct cutting speed intervals on surface roughness. Ultimately, the study furnishes pragmatic recommendations for judiciously selecting cutting speeds and heating temperature parameters across diverse machining scenarios. [ABSTRACT FROM AUTHOR]