Efficient Modulation Research Articles

Sacral neuromodulation update in 2024: Insights from the ICS workshop 2023

Purpose:This review is based on the International Continence Society (ICS) Workshop on Hands-On Sacral Neuromodulation (SNM)- Ideal Lead Placement presented at the Annual Congress in 2023. This workshop briefly reviewed appropriate patient selection, focused most of the time on hands on skills to teach appropriate needle and lead placement techniques. Six very experienced experts worked with attendees on lifelike models to teach them how to achieve lead placement efficiently and appropriately. Tips and tricks for getting the best responses were reviewed. Materials and methods: The review follows the structure adopted by workshop, where SNM-Ideal Lead Placement is discussed in four sections: main/extended indications and ideal patient profile, sacral neuromodulation techniques included basic evaluation (PNE), advanced evaluation, ideal lead placement and novel technology. Results:The learning objectives were achieved at the end of the workshop. These were: understanding the role of sacral neuromodulation, recognize the latest technological developments, their clinical implications and getting the best outcome with SNM, stress the importance of patient selection process and current SNM indications, reviewing standard surgical technique for optimal lead placement and gain understanding of choices for recharge versus fixed battery (as well as use in neurological disease). Conclusions:Hands-on training workshops on sacral neuromodulation is efficient training modules to ensure learning objectives for participants. Accurate needle/temporary lead placement, lead placement with permanent lead, smart programming with programmers are the main parts of hands-on training session with using realistic phantom and 3D printed models in workshop.

Solution‐processing induced H‐aggregate of Perylene‐Diimide Zwitterion Exhibiting Compact Molecular Stacking toward Efficient Cathode Modification in Organic Solar Cells

Solution‐processing induced H‐aggregate of Perylene‐Diimide Zwitterion Exhibiting Compact Molecular Stacking toward Efficient Cathode Modification in Organic Solar Cells

Solution‐processing induced H‐aggregate of Perylene‐Diimide Zwitterion Exhibiting Compact Molecular Stacking toward Efficient Cathode Modification in Organic Solar Cells

High‐performance organic cathode interlayers (CILs) play a crucial role in the advance of organic solar cells (OSCs). However, organic CILs have exhibited inferior performances to their inorganic counterparts over a long time, due to the inherent shortcoming of poor charge transporting capability. Here, we designed and synthesized a perylene‐diimide (PDI) zwitterion PDI‐B as high‐performance organic CIL for OSCs. We revealed that an obvious H‐aggregate of PDI‐B was formed during the solution processing, thereby significantly enhancing the charge transporting capability of the CIL. Compared to the classic PDINN, the π‐π stacking distance of PDI‐B was reduced from 4.2 Å to 3.9 Å, which further facilitated the charge transport. Consequently, PDI‐B showed a high conductivity of 1.81×10−3S/m; this is comparable to that of inorganic CILs. The binary OSC showed an elevated PCE of 19.23%, which is among the highest PCE values for binary OSCs. Benefitting from improved solvent resistance and good compatibility with large‐area processing method of PDI‐B, the photovoltaic performances of inverted and 1‐cm2 OSC were significantly improved. The results from this work provide a new approach of optimizing the condensed structure of PDI film to boost the charge conductivity, opening an avenue to develop high‐performance PDI‐based CILs.

Long-term performance analysis of a large-scale photoVoltaic plant in extreme desert conditions

This study comprehensively evaluates the performance and operational challenges of a 9 MW grid-connected photovoltaic (PV) system in Timmimoun, southern Algeria, after eight years of operation. We identified a significant linear correlation (R2 = 0.73) between increased ambient temperatures and decreased PV module efficiency, underscoring the need for effective thermal management strategies. The investigation also examines potential solutions, such as cooling mechanisms, optimization of panel orientation and tilt angles, and development of temperature-resistant materials. Adhering to the International Energy Agency IEC 61724 standard, our analysis reveals a yield factor of 5.04 h/day, a yield ratio of 7.04 h/day, a performance ratio of 73 %, and a capacity factor of 21 % in 2023. Over its operational lifespan, the system has generated 133.43 GW h of energy, mitigating 1.01 105 tons of CO2 emissions. This long-term study provides critical insights into the performance and reliability of PV systems in hot desert climates, offering valuable guidance for future large-scale solar installations and contributing to the transition towards a sustainable energy future.

Dual Redox-active Covalent Organic Framework-based Memristors forHighly-efficient Neuromorphic Computing.

Organic memristors based on covalent organic frameworks (COFs) exhibit significant potential for future neuromorphic computing applications. The preparation of high-quality COF nanosheets through appropriate structural design and building block selection is critical for the enhancement of memristor performance. In this study, a novel room-temperature single-phase method was used to synthesize Ta-Cu3 COF, which contains two redox-active units: trinuclear copper and triphenylamine. The resultant COF nanosheets were dispersed through acid-assisted exfoliation and subsequently spin-coated to fabricate a high-quality COF film on an indium tin oxide (ITO) substrate. The synergistic effect of the dual redox-active centers in the COF film, combined with its distinct crystallinity, significantly reduces the redox energy barrier, enabling the efficient modulation of 128 non-volatile conductive states in the Al/Ta-Cu3 COF/ITO memristor. Utilizing a convolutional neural network (CNN) based on these 128 conductance states, image recognition for ten representative campus landmarks was successfully executed, achieving a high recognition accuracy of 95.13% after 25 training epochs. Compared to devices based on binary conductance states, the memristor with 128 conductance states exhibits a 45.56% improvement in recognition accuracy and significantly enhances the efficiency of neuromorphic computing.

Anti-icing transparent coatings modified with bi- and tri-functional octaspherosilicates for photovoltaic panels

In recent years, to progress towards carbon emissions reduction, photovoltaic technology has become one of the most significant methods of generating electricity using renewable sources. However, the accumulation of ice and snow during the winter season affects the decrease in the power generation efficiency of photovoltaic modules. As a promising solution, coatings that exhibit anti-icing properties can be used. To date, no efficient ice-phobic coating has been developed for use on photovoltaic panels. In this paper development of transparent silicone-epoxy coatings modified with bi- and tri-functionalized octaspherosilicates was presented. The used organosilicone-based modifiers reveal both types of functional groups: one increases the surface properties and the second compatibility with the polymer matrix. The icephobic properties were discussed by determining an ice adhesion (IA) and a freezing delay time of water droplets (FDT). The coating exhibiting the highest anti-icing performance achieved the IA of 102 kPa and the FDT of 210 min, a 43 % reduction and a 70 times increase in value compared to the unmodified coating, respectively. The performed study included also the characterization of hydrophobic properties: water contact angle (WCA) at room temperature and below 0°C, contact angle hysteresis(CAH), and roll-off angle (RoA) as well as surface roughness and coatings thickness. In addition, the correlations between them and ice adhesion/freezing delay time were determined. It was found that roughness as well as dynamic parameters of the wettability of CAH and RoA have a significant effect on the obtained IA values. Moreover, the developed coatings can be potentially applied to photovoltaic panels, since the conducted modification did not affect the optical properties of the investigated coatings.

Phase modulation analysis on a cavity-structured single-unit thermoacoustic refrigerator

The establishment of traveling-wave-dominated acoustic fields within the regenerator is crucial for developing highly efficient thermoacoustic refrigeration systems. The present work introduces a novel looped, single-unit, direct-coupling thermoacoustic refrigerator, incorporating an optimally dimensioned cavity at a strategic location to maximize system performance. Central to this research is the systematic exploration of the phase modulation mechanism. This exploration involves adjusting the dimensions of the cavity structure, tracking its evolution from initial absence to full integration. The research method includes comprehensive evaluations of steady-state performance, onset characteristics, exergy loss, and axial parameter distribution analysis. The findings are visually depicted through simulations, highlighting the cavity's role in acoustic field modulation and its consequent impact on system efficiency. With the integration of an optimally sized and positioned cavity, the system's cooling efficiency improves by more than 2.5 times compared to the efficiency of a structure without cavities. A prototype replicating the proposed design was then constructed and experimentally tested. The results reveal a close correlation between the simulated and experimental data, affirming the analysis's accuracy and dependability. These studies effectively elucidate the significance of the cavity in looped single-unit thermoacoustic refrigerators and provide valuable insights for the development of more efficient phase modulation structures.

Energy-saving path planning navigation for solar-powered vehicles considering shadows

Globally, the adverse climate effects caused by greenhouse gas emissions are becoming increasingly apparent, and solutions to increase the use of eco-friendly transportation methods are urgently needed. Introducing solar-powered vehicles (SPVs), which are cars integrated with solar panels capable of generating power, presents a promising solution to reduce urban carbon footprints. However, the low adoption rate of SPVs implies that the benefits—such as environmental friendliness and ability to charge while driving—need to be more palpably experienced by consumers. To address this aspect, in this study, we aimed to develop a navigation system algorithm that guides users along routes that optimize energy consumption and solar energy production from the starting point to the destination. This was done with the objective of providing more tangible benefits from using SPVs. The study focused on the high-traffic urban center of Seoul, where determining solar power availability for a moving SPV is challenging, given the presence of shadows cast by roadside features such as buildings and trees. To achieve this, panoramic images from Google Street View were collected at 10 m intervals from all roads within the research area. From these images, sky and non-sky elements were separated. Subsequently, a hemispherical map was constructed and superimposed with the sun's path. The presence of shadows was determined by assessing whether the sun's path was obstructed by non-sky elements; if the path was unimpeded in the sky, no shadow was recorded. The shadow data obtained at each spot were efficiently stored in a database for quick retrieval and application based on specific locations and departure times. Using this shadow information, the navigation algorithm calculates power generation along a given route and considers the energy consumption of the SPV. Analysis led to the identification of an energy-saving route, which enabled the achievement of energy conservation and CO2 reduction benefits. Furthermore, a comprehensive sensitivity analysis was conducted to examine the impact of four critical parameters—module efficiency, solar panel area, vehicle speed, and departure time—on route selection and net energy consumption. The energy-saving path planning algorithm enhances the economic feasibility of solar charging for SPVs during travel; thus, this study can contribute significantly to the widespread adoption of SPVs, which play a definitive role in reducing transportation's carbon footprint.

Double rectangular-grooves metasurface for highly efficient electric modulation

With the rapid development of optical communication, how to achieve efficient modulation (fast response speed and high modulation depth) of optical signals has attracted more and more attention from researchers. Among all electro-optical modulator (EOM) designs, the electro-optical metasurface is undoubtedly a competitive solution for optical signal modulation in free space. Although current research on electro-optical metasurfaces has realized improving response speed owing to the Pockels effect, there are still difficulties in achieving high modulation depth under CMOS-compatible voltage and developing rational designs of metasurfaces to achieve voltage application that trigger electro-optical effects. In this work, an ultrahigh-Q factor BaTiO3 (BTO) electro-optical metasurface, which consists of a periodic array of rectangular grooves, was designed to provide a feasible solution to address these shortcomings. Based on bound states in the continuum (BIC) theory, ultrahigh-Q factor (2.87 × 105) quasi-BIC (Q-BIC) was obtained around 1550 nm by breaking the in-plane symmetry of the two rectangular grooves in a unit cell, which could significantly deepen the modulation depth. The concave and continuous structure of rectangular grooves made the application of voltage more efficient. The simulation results show that an optical signal modulation in free space with a modulation depth of 100% could be achieved. Multipole decomposition indicated that toroidal dipole (TD) was dominant in this Q-BIC. Our work may further promote the development of electro-optical modulation towards faster and deeper modulation.

MEDANet: More Efficient Dual Attention Network for Scene Segmentation

The dual attention module is a potent semantic segmentation technique renowned for its capabilities, yet it often faces significant computational demands and GPU memory usage. To tackle these challenges, we introduce an advanced dual perception network comprising two modules: A streamlined Multi-scale Efficient Position Attention Module (MEPAM) and an optimized Efficient Channel Attention Module (MECAM). MEPAM incorporates multi-scale global average pooling into the Position Attention Module (PAM), substantially cutting computational overhead and memory consumption without compromising performance. Meanwhile, MECAM integrates compressed convolutions into the Channel Attention Module (CAM), improving segmentation accuracy and inference speed compared to conventional methods like DANet. Our approach underwent comprehensive evaluation on a semantic segmentation benchmark dataset, showcasing superior performance. For instance, on the Cityscapes dataset, our method achieves an IoU of 82.2%. In terms of efficiency gains, MEPAM operates nearly 1.97 times faster than the standard PAM module on GPU, while requiring 7.55 times less memory with a [Formula: see text] input. Similarly, MECAM achieves approximately 2.2 times faster processing than CAM, while cutting GPU memory usage by 7.53 times. This innovative dual perception network not only enhances segmentation accuracy and speed but also addresses the computational challenges associated with traditional dual attention modules.

Efficiency enhancement of photovoltaic-thermoelectric generator hybrid module by heat dissipating technique

Exploring substantial solar irradiation and recuperating excess heat generated during photovoltaic energy conversion is a critical issue. The efficiency of photovoltaic systems (PV) is significantly depend on the increased operating temperatures encountered by solar radiation. One conceivable option for improving the conversion of solar energy is to integrate a photovoltaic (PV) panel with a thermal-electric generator (TEG) material module to create a hybrid system. This study proposed a parallel PV-TEG hybrid module that effectively harvests the maximum solar energy spectrum while maximizing the use of heat generated by the thermoelectric material to improve the overall system efficiency. The proposed module consists of a photovoltaic unit, thermoelectric material module and passive cooling of fluid channels. The aim of this work was to develop a PV-TEG hybrid system and create an energy simulation model in a MATLAB environment to analyze the model's performance under various operational conditions by applying both theoretical and experimental approaches. Findings showed considerable concurrence. At 13:00, when the PV surface temperature was 54 °C, the PV efficiency reached to its lowest value of 12.0 %. Nevertheless, the highest TEG efficiency recorded was 4.7 % at 12:00 h. The efficiency of the TEG module was significantly affected by weather conditions, inlet cooling water temperature, and fluid flow rate in comparison to both the PV efficiency and the thermal efficiency.

Effect of Cell-to-Cell Internal Resistance Variations on the Thermal Performance of Lithium-Ion Batteries for Urban Air Mobility

This study examines the thermal behavior of lithium-ion battery modules intended for Urban Air Mobility (UAM), a forthcoming urban transport system designed to facilitate efficient and secure passenger and cargo transport within city centers. UAM applications necessitate batteries with high energy densities capable of sustaining elevated discharge rates during critical phases such as takeoff and landing. The battery module evaluated in this study comprises four cells arranged in series and configured as a submodule for UAM applications. A three-dimensional thermal model was utilized to analyze the impact of external temperature fluctuations and high discharge rates on the performance of the battery module. The numerical findings indicated considerable variations in temperature and internal resistance among the cells, especially under high discharge rates at low temperatures, with a maximum temperature deviation of 32.952 °C observed at an 8 C discharge rate. These thermal non-uniformities were attributed to variations in cell capacity and internal resistance, which were amplified by manufacturing inconsistencies and operational conditions. The study underscores the necessity of robust thermal management strategies to mitigate the risk of thermal runaway and ensure the operational safety and reliability of UAM systems. The results emphasize the critical role of advanced Battery Management Systems (BMS) in monitoring and controlling cell voltage and temperature to achieve consistent performance across the battery module. This research contributes valuable insights into the design of more efficient and reliable battery modules for UAM, highlighting the importance of addressing cell-to-cell performance discrepancies to enhance overall module efficacy and durability.

CBAM-RIUnet: Breast Tumor Segmentation With Enhanced Breast Ultrasound and Test-Time Augmentation.

This study addresses the challenge of precise breast tumor segmentation in ultrasound images, crucial for effective Computer-Aided Diagnosis (CAD) in breast cancer. We introduce CBAM-RIUnet, a deep learning (DL) model for automated breast tumor segmentation in breast ultrasound (BUS) images. The model, featuring an efficient convolutional block attention module residual inception Unet, outperforms existing models, particularly excelling in Dice and IoU scores. CBAM-RIUnet follows the Unet structure with a residual inception depth-wise separable convolution, and incorporates a convolutional block attention module (CBAM) to eliminate irrelevant features and focus on the region of interest. Evaluation under three scenarios, including enhanced breast ultrasound (EBUS) and test-time augmentation (TTA), demonstrates impressive results. CBAM-RIUnet achieves Dice and IoU scores of 89.38% and 88.71%, respectively, showcasing significant improvements compared to state-of-the-art DL techniques. In conclusion, CBAM-RIUnet presents a highly effective and simplified DL model for breast tumor segmentation in BUS imaging.

Research on the Wild Mushroom Recognition Method Based on Transformer and the Multi-Scale Feature Fusion Compact Bilinear Neural Network

Wild mushrooms are popular for their taste and nutritional value; however, non-experts often struggle to distinguish between toxic and non-toxic species when foraging in the wild, potentially leading to poisoning incidents. To address this issue, this study proposes a compact bilinear neural network method based on Transformer and multi-scale feature fusion. The method utilizes a dual-stream structure that integrates multiple feature extractors, enhancing the comprehensiveness of image information capture. Additionally, bottleneck attention and efficient multi-scale attention modules are embedded to effectively capture multi-scale features while maintaining low computational costs. By employing a compact bilinear pooling module, the model achieves high-order feature interactions, reducing the number of parameters without compromising performance. Experimental results demonstrate that the proposed method achieves an accuracy of 98.03%, outperforming existing comparative methods. This proves the superior recognition performance of the model, making it more reliable in distinguishing wild mushrooms while capturing key information from multiple dimensions, enabling it to better handle complex scenarios. Furthermore, the development of public-facing identification tools based on this method could help reduce the risk of poisoning incidents. Building on these findings, the study suggests strengthening the research and development of digital agricultural technologies, promoting the application of intelligent recognition technologies in agriculture, and providing technical support for agricultural production and resource management through digital platforms. This would provide a theoretical foundation for the innovation of digital agriculture and promote its sustainable development.

SAR-NTV-YOLOv8: A Neural Network Aircraft Detection Method in SAR Images Based on Despeckling Preprocessing

Monitoring aircraft using synthetic aperture radar (SAR) images is a very important task. Given its coherent imaging characteristics, there is a large amount of speckle interference in the image. This phenomenon leads to the scattering information of aircraft targets being masked in SAR images, which is easily confused with background scattering points. Therefore, automatic detection of aircraft targets in SAR images remains a challenging task. For this task, this paper proposes a framework for speckle reduction preprocessing of SAR images, followed by the use of an improved deep learning method to detect aircraft in SAR images. Firstly, to improve the problem of introducing artifacts or excessive smoothing in speckle reduction using total variation (TV) methods, this paper proposes a new nonconvex total variation (NTV) method. This method aims to ensure the effectiveness of speckle reduction while preserving the original scattering information as much as possible. Next, we present a framework for aircraft detection based on You Only Look Once v8 (YOLOv8) for SAR images. Therefore, the complete framework is called SAR-NTV-YOLOv8. Meanwhile, a high-resolution small target feature head is proposed to mitigate the impact of scale changes and loss of depth feature details on detection accuracy. Then, an efficient multi-scale attention module was proposed, aimed at effectively establishing short-term and long-term dependencies between feature grouping and multi-scale structures. In addition, the progressive feature pyramid network was chosen to avoid information loss or degradation in multi-level transmission during the bottom-up feature extraction process in Backbone. Sufficient comparative experiments, speckle reduction experiments, and ablation experiments are conducted on the SAR-Aircraft-1.0 and SADD datasets. The results have demonstrated the effectiveness of SAR-NTV-YOLOv8, which has the most advanced performance compared to other mainstream algorithms.

Unipolar quantum optoelectronics for high speed direct modulation and transmission in 8–14 µm atmospheric window

The large mid-infrared (MIR) spectral region, ranging from 2.5 µm to 25 µm, has remained under-exploited in the electromagnetic spectrum, primarily due to the absence of viable transceiver technologies. Notably, the 8–14 µm long-wave infrared (LWIR) atmospheric transmission window is particularly suitable for free-space optical (FSO) communication, owing to its combination of low atmospheric propagation loss and relatively high resilience to turbulence and other atmospheric disturbances. Here, we demonstrate a direct modulation and direct detection LWIR FSO communication system at 9.1 µm wavelength based on unipolar quantum optoelectronic devices with a unprecedented net bitrate exceeding 55 Gbit s−1. A directly modulated distributed feedback quantum cascade laser (DFB-QCL) with high modulation efficiency and improved RF-design was used as a transmitter while two high speed detectors utilizing meta-materials to enhance their responsivity are employed as receivers; a quantum cascade detector (QCD) and a quantum-well infrared photodetector (QWIP). We investigate system tradeoffs and constraints, and indicate pathways forward for this technology beyond 100 Gbit s−1 communication.

Cooling performance enhanced by integrating multicomponent phase change material and compression spring into air-cooled battery module

In order to improve the cooling efficiency and temperature consistency of the air-cooled prismatic battery module, a novel multicomponent phase change material-spring plate (MPCM-SP) was developed in this work. The MPCM was formed by combining lauric acid (LA), polyethylene glycol (PEG), palmitic acid (PA), and carboxylated multi-walled carbon nanotubes (MWCNT-COOH). The melting temperature of MPCM can be adjusted within the range 37 °C to 65 °C, and the associated latent heat of phase change varied from 159 J∙g−1 to 209 J∙g−1. The optimized proportions of LA, PEG, PA, and MWCNT-COOH achieved through multiple-objective optimization were 65.55 %, 7.07 %, 25.38 %, and 2.00 %, respectively. The cooling performance of the MPCM-SP was examined by experimental verification and numerical evaluation. The inclusion of MPCM-SP in the air-cooling system resulted in a reduction of 3.41 °C, 3.74 °C, and 3.32 °C in the maximum temperatures of the U-shaped, Z-shaped, and I-shaped battery modules, respectively. Moreover, the temperature difference was decreased by 3.60 °C, 3.16 °C, and 2.42 °C, respectively. Additionally, in comparison to a single battery, a battery equipped with MPCM-SP had a 32.8 % reduction in thermal deformation in the thickness direction under a thermal expansion coefficient of 4.06 × 10-6 K−1.

A solar-powered multi-functional portable charging device (SPMFPCD) with internet-of-things (IoT)-based real-time monitoring—An innovative scheme towards energy access and management

In the absence of portable charging devices, sectors such as transportation, communication, and emergency services deal with various challenges towards electric power needs while compromising on (1) operational efficiency, (2) insufficient portable charging solutions, and (3) limited versatility. This highlights the critical need for reliable and multi-functional power solutions. To provide a portable charging solution across diverse sectors, this paper proposes an innovative development of a solar-powered multi-functional portable charging device (SPMFPCD) with internet- of-thing (IoT)-based monitoring capabilities. The proposed scheme introduces a comprehensive model integrating advanced technologies which include a highly efficient solar panel, charge controller, sensors, and IoT module. The proposed system facilitates versatile charging solutions for a wide range of power requirements with real-time monitoring and data analysis through the IoT platform. Moreover, the proposed work explores the applications of the SPMFPCD in (1) emergency medical scenarios, (2) outdoor adventures, (3) disaster management, and 4) public spaces. Performance evaluation was made by proposing case studies to validate the (1) economic viability, (2) power management, and (3) environmental impact of widespread deployment of SPMPFCD in public spaces. Furthermore, detailed analysis of battery energy storage system (BESS) and photovoltaic (PV) integration for load management, seasonal dynamics, and renewable energy integration (REI) contribute to a comprehensive understanding of the proposed solution.

Multiple mechanisms of action of an extremely painful venom.

Evolutionary arms races between predator and prey can lead to extremely specific and effective defense mechanisms. Such defenses include venoms that deter predators by targeting nociceptive (pain-sensing) pathways. Through co-evolution, venom toxins can become extremely efficient modulators of their molecular targets. The venom of velvet ants (Hymenoptera: Mutillidae) is notoriously painful. The intensity of a velvet ant sting has been described as "Explosive and long lasting, you sound insane as you scream. Hot oil from the deep fryer spilling over your entire hand." [1] The effectiveness of the velvet ant sting as a deterrent against potential predators has been shown across vertebrate orders, including mammals, amphibians, reptiles, and birds [2-4]. The venom's low toxicity suggests it has a targeted effect on nociceptive sensory mechanisms [5]. This leads to the hypothesis that velvet ant venom targets a conserved nociception mechanism, which we sought to uncover using Drosophila melanogaster as a model system. Drosophila larvae have peripheral sensory neurons that sense potentially damaging (noxious) stimuli such as high temperature, harsh mechanical touch, and noxious chemicals [6-9]. These polymodal nociceptors are called class IV multidendritic dendritic arborizing (cIV da) neurons, and they share many features with vertebrate nociceptors, including conserved sensory receptor channels [10,11]. We found that velvet ant venom strongly activated Drosophila nociceptors through heteromeric Pickpocket/Balboa (Ppk/Bba) ion channels. Furthermore, we found a single venom peptide (Do6a) that activated larval nociceptors at nanomolar concentrations through Ppk/Bba. Drosophila Ppk/Bba is homologous to mammalian Acid Sensing Ion Channels (ASICs) [12]. However, the Do6a peptide did not produce behavioral signs of nociception in mice, which was instead triggered by other non-specific, less potent, peptides within the venom. This suggests that Do6a is an insect-specific venom component that potently activates insect nociceptors. Consistent with this, we showed that the velvet ant's defensive sting produced aversive behavior in a predatory praying mantis. Together, our results indicate that velvet ant venom evolved to target nociceptive systems of both vertebrates and invertebrates, but through different molecular mechanisms.

Comparative Study of Photovoltaic Thermal Performance with Water and Aloe Vera Heat Extracting Fluids

Crystalline solar panels are widely used in households and for public road lighting. Mono-crystalline panels are well-known for their higher efficiency and long service life. However, their efficiency decreases as the module temperature increases under consistent solar radiation conditions. To enhance module power generation and efficiency, effective temperature reduction techniques are necessary. This study investigates the use of water and aloe vera fluid as cooling agents for a mono-crystalline photovoltaic thermal (PVT) system. The system was designed with a circulating mass flow rate of 0.016 kg/s or 1 LPM (liter per minute) and tested under the climate conditions of Phnom Penh city, Cambodia. The specific heat of aloe vera fluid was determined and found to be 4236 j/kg·K. The optical efficiency of PVT systems using water and aloe vera fluid was compared in this paper. The experiment results indicate that cooling with aloe vera fluid led to a 0.21% higher electrical power generation compared to water cooling, due to more effective temperature reduction and thermal heat absorption rate of aloe vera fluid is higher than water 32.41%.