The wear resistance of gears plays a crucial role in ensuring the longevity and efficiency of machinery, particularly in industries like textile manufacturing, where gears are subjected to continuous operation under high loads and long-term motion. R6M5 steel, commonly used for high-performance gears, possesses excellent hardness and durability but still faces wear challenges under extreme conditions. Laser processing has emerged as an effective technique to enhance the wear resistance of such materials through precise surface modification.
This study investigates laser processing parameters for a miniature-sized (10 mm diameter) R6M5 steel gear to achieve the desired melting depth and improved surface properties. The objective is to enhance wear resistance while maintaining the structural integrity of the gear. Experiments were conducted using laser powers ranging from 1.5 to 2.4 kW and scanning speeds from 15 to 25 mm/s. The results were analyzed to identify the best-performing combination of parameters within this range.
The study found that using a 2.4 kW power and 25 mm/s speed achieved a melting depth of 0.30 mm, enhancing the surface hardness without causing excessive thermal distortion. This combination significantly improved the gear's wear resistance, making it more suitable for high-demand applications in textile manufacturing. However, as these were the maximum tested values, further studies are needed to determine whether increasing power or speed beyond this range would lead to additional improvements or adverse effects.
In conclusion, laser processing with these parameters offers a reliable method for improving the durability and wear resistance of mini R6M5 steel gears, providing a foundation for further optimization and application in high-demand industries.

Smart agriculture systems have several applications and features that aim to provide an automated agriculture process with zero human intervention. Monitoring is one of the most demanding applications of the smart agriculture system. Image and video processing are very important features in the application of smart agriculture monitoring. However, the transmission of video and images requires a large bandwidth, stable connectivity, and noiseless transmission. Notably, high-quality images usually require more bandwidth. On the other hand, the smart agriculture system usually adopts wireless communication among its elements. However, the wireless communication channel generally has some noise which inversely affects the transmission system bandwidth. There are several research efforts found in the literature to address this issue. Some of the distinguished research efforts found address that by either compressing the image or correcting the image errors. However, the smart agriculture system elements are limited in the hardware capabilities. The limitation of the system’s hardware configuration is a permanent constraint for this type of solution. This paper proposes an optimization technique to mitigate the issues encountered within the wireless channel while considering the limitation of the hardware resources. The paper jointly optimizes the resources by compressing the image and encoding it using the reed Solomon encoding technique. The results provided a 98% efficiency against the traditional unlimited resources system, along with better BER.

Background : Advanced IoT Agriculture presents a transparent review of emerging technologies like IoT-based smart Agriculture. Today’s Agriculture industry is data-centered, advanced, and smarter than ever. Smart Agriculture moved the industry from a statistical to a quantitative approach.

Objective: The objective of this paper is to monitor the plant growth using machine learning technique, as well as predicting the plant growth patterns; to integrate and analyze machine learning models for assessing data obtained from IoT devices in order to predict plant health and growth; and to assess the performance of several IoT communication protocol LoRa in terms of data transmission, dependability, and energy efficiency in agricultural settings.

Material/Methods : In this paper , we have collected the real-time data through the different IoT sensors, namely soil moisture, temperature, and humidity, that are crucial for plant health. The collected data are transmitted to a cloud-based platform, where they undergo preprocessing and analysis. Advanced IoT devices generally automate environmental responses, requiring control systems. The key feature of this system is the deployment of the smart devices and sensors for the collection of data like average wet growth, plant height rate, average leaf area of the plant, average root length, and decisions based on the monitoring of trees. In this paper, our objective is to estimate the effectiveness of various machine-learning approaches for predicting plant growth outcomes based on the collected data.

Result We used several machine learning classifiers including Decision Trees, Naïve Bayes, and K-Nearest Neighbors. It has been observed that out of all the classifiers, the Support Vector Machine (SVM) performs well as comparison to other classifiers, i.e., by 99.96%. Other models also performed well, with Naïve Bayes and Decision Trees, both achieving 99.91% accuracy, and K-Nearest Neighbors achieving 98.99%. The result reveals the efficacy of integrating IoT solutions with advanced machine-learning techniques to enhance plant growth monitoring.

In today’s world, conventional vehicles are being replaced by electric vehicles due to their eco-friendly operation and reduced maintenance. Though the EVs are better than the conventional vehicles, the charging stations for EVs are very few, and there are many power quality issues that have been arising in these charging stations. This is due to voltage, current, or frequencies are abnormal which leads to sudden voltage drops, voltage swells, long interruptions, and short interruptions that occur in the charging stations. Conventional FACTS devices are attached closer to the load end to overcome problems caused by client-side anomalies. One such dependable custom power gadget for dealing with voltage sag is the developed in this article; it is called an enhanced dynamic voltage restorer (DVR). The proposed device continuously monitors the load voltage waveform and injects (or absorbs) the balance (or surplus) voltage into (or away from) the load voltage whenever a sag occurs. A reference voltage waveform is developed to achieve the aforementioned capabilities. In this paper, the methods of compensation for these problems in charging stations are discussed. Further the power quality problems are compensated by the proposed system using a SVPWM controller. Simulation and real-time implementation is carried out, and the results discussed are here.

The aging of infrastructure has become a significant social issue, drawing attention to distributed optical fiber sensors for monitoring structural defects. Among these, Brillouin optical correlation domain reflectometry (BOCDR), which uses continuous light correlation control for spatial resolution, has been advanced to improve performance. BOCDR measures the distribution of Brillouin gain spectra in the fiber under test, allowing the extraction of Brillouin frequency shift distributions, which can be related to strain and temperature. However, traditional slope-assisted (SA-) BOCDR techniques have limited accuracy in real-time operation due to the slow frequency sweep of the electrical spectrum analyzer. To address this, double-slope-assisted (DSA-) BOCDR has been proposed, utilizing power ratios of two different frequencies for enhanced loss point detection accuracy. In this study, we experimentally demonstrated improved localization accuracy of loss points using DSA-BOCDR. We applied two distinct strains and a bending loss at different positions on a silica single-mode fiber and measured power variations at two frequencies (10.815 and 10.865 GHz). The results showed that the DSA-BOCDR could accurately detect strain and loss locations, with the power ratio method yielding sharper indications of loss compared to single-frequency measurements. The differential analysis further quantified the loss range, showing higher positional accuracy for loss detection using the power ratio. These findings confirm that using distinct power ratios of two frequencies in DSA-BOCDR significantly enhances the precision of loss point detection, offering a promising advancement for real-time structural health monitoring in aging infrastructures.

The mechanical properties of polymers change over time when they are exposed to UV light and moisture. This work presents the results of continuously exposing a nylon-based composite used in additive manufacturing (AM) to UV light and humidity for 24-, 48-, 96-, 168-, 336- and 504-hour periods. Sample coupons were printed in a Markforged Two ® composite printer using Onyx ®, which is a nylon matrix composite reinforced with short carbon fibers. For UV exposure, the samples were exposed to commercial 253 nm UV lamps, whereas for humidity, an ACE UV-260 humidity chamber was used at 50% relative humidity and 22°C with bi-distilled water. The effects of said variables were measured using the Charpy impact energy (per ASTM D6110), water absorption, and Shore hardness D (per ‎ASTM D2240). It was found that nylon indeed presents 1.03% ±0.28 water absorption over as little as 24 hr of exposure and about 5.6% ±0.48 water absorption for 504 hrs. Regarding the Charpy impact energy, the absorbed energy decreases from 450 kJ/m^2 ±15.96 at 24 hr to 254 kJ/m^2 ±33.9 at 504 hr of humidity exposure. The Shore hardness D varies from 59.1±0.82 for zero exposure to 59.7 ±1.5 at 24 hr and 66.8±2.5 after 504 hr of UV exposure. We can conclude that water absorption makes nylon a more fragile material, whereas UV exposure hardens the material. Future results could include using tensile axial tests and infrared spectroscopy to assess water absorption.

A recent survey shows that 30% of women in developed countries fear going out because of violence happening to them. To ensure their safety in public places, a smart handbag is designed which provides enhanced security to them. The handbag is developed in such a way that it operates in wireless mode so that it can be controlled remotely without any need for human presence. Also, the smart bag includes GSM and GPS technology to track the person in need. This technology provides a sense of security to women in public spaces, ensuring their safety and well-being. But tools that use only these technologies for their operation are insecure and inefficient. The proposed handbag has an alternate approach to wireless control of a device by incorporating a fingerprint identification module, which increases the authenticity of the device and enables multiple users to control the device with the integration of hardware that creates a system that continuously communicates their location using GPS to their loved ones nearby and the police station via a GSM network. This system is incorporated with a camera module that captures images; further, the images are classified to check whether the threat is due to humans, animals, vehicles, etc., using a learning algorithm. For enhanced security, the smart bag is equipped with an electric shock generator and a siren that give instant defensive response against potential attackers and provide the space for women to escape. All of these modules are integrated into the Arduino UNO controller, which triggers their entire functionalities; therefore, with all of these advancements, the smart handbag provides safety to women through instant alert, location sharing, etc.

Vortex tubes are an intriguing application of energy separation, featuring one inlet and two outlets on either side. A high-pressure fluid is introduced into the tube via a vortex generator, where energy separation occurs, resulting in hot fluid being released from one outlet and cold fluid from the other. Despite the interest in vortex tubes over the past few decades, the exact phenomena occurring within them remain largely unknown. This research aims to analyze the effect of varying geometries on the performance of a typical vortex tube using Ansys Fluent. Specifically, the study focuses on the hot and cold outlets to determine the most efficient design. The fluid’s temperature, pressure, and mass flow rate are analyzed using Computational Fluid Dynamics (CFD). Three different geometries were designed and analyzed by varying the radius of the hot and cold exits. The results indicate that the temperature difference between the hot and cold exits is maximized in the pointed cone geometry model compared to the truncated cone geometry. These findings demonstrate a clear correlation between geometry and vortex tube performance, suggesting that shape modifications can be used to control or vary the temperature for different applications, such as refrigeration and air conditioning systems.

Thanks to their optical and electronic properties, lead halide perovskites have become prominent materials in optoelectronics. CsPbBr₃ nanocrystals (NCs) stand out as excellent candidates for implementing lasers due to their excellent photoluminescence quantum yields and their facile and low-cost production. Elucidating their stimulated emission mechanisms is fundamental in addressing their possible limitations and to achieve more efficient perovskite lasers, specially continuous wave or electrically excited ones. Two questions remain open: why the Amplified Spontaneous Emission (ASE) band is shifted from the fluorescence one, and why the former seems to coexist with the latter. These characteristics have led to a debate about which is the mechanism behind the ASE band shift. Some reports claim that the fluorescence comes from single excitons, while the ASE has a biexcitonic origin. Others defend that both fluorescence and ASE are generated by localized single excitons, and the shift owes to reabsorption.

In this communication, we address these questions through experimental ASE measurements, combined with numerical simulations, and the use of a novel analytical expression to retrieve the optical gain from these experiments [1]. We show that the ASE behaviour in CsPbBr3 NCs thin films stems from four processes: reabsorption due to a large overlap between the absorption and fluorescence spectra, a strong contribution of excited state absorption at the fluorescence window, the excitation of differently polarized waveguide modes, and the coexistence of short- and long-lived localized single excitons. The results in this work establish guidelines with which to analyse the optical gain in perovskite samples, which can help in rationalizing the ASE signatures of both CsPbBr₃ NCs and perovskites in general, and which provide insightful information on research avenues to increase the efficiency of perovskite light-emitting devices.

Reference:

[1] S. Milanese, M. L. De Giorgi, M. Anni, M. Bodnarchuk, and L. Cerdán, Adv. Opt. Mater., 2401078 (2024)

Double perovskite oxides A₂BBO₆ have attracted significant attention because of its eccentric multiferroic properties. Among such materials, double perovskites containing rare earth metals are widely studied due to their interesting physical, optical and chemical properties. Presently, the lead-free double perovskite La₂FeMnO₆ was synthesized using citrate combustion method. The orthorhombic phase formation of La₂FeMnO₆ with Pbnm space group was confirmed using powder X-ray diffraction (XRD) technique. The grain size was calculated to be 47.5 nm computed using Debye-Scherrer formula. The characteristics of double perovskites was investigated using Fourier Transform Infrared Spectroscopy (FT-IR) technique. The band gap energy properties were studied using Ultra-violet Visible Diffusive Reflectance Spectroscopy (UV-DRS). The calculated band gap of La₂FeMnO₆ was about 1.53 eV. The calculated experimental band gap value of La₂FeMnO₆ suggests that it could be a better candidate for light harvesting applications and other energy storage devices. The dielectric properties of La₂FeMnO was studied using Dielectric Analyser in the range of 100 Hz – 1 MHz at room temperature. From the observed results, impedance, dielectric and modulus studies support the existence of a non-Debye type relaxation peak. The material’s polarization mechanism was explained using Koop's theory and the Maxwell-Wagner interfacial polarization model highlighting the presence of relaxation behaviour in La₂FeMnO₆. These findings render that La₂FeMnO₆ a fascinating material for scientific research as well as for practical uses.