Search Results (47)

This paper presents an innovative solution to address agricultural irrigation needs through a hybrid renewable energy system (HRES) that was specifically designed for a farm located in the Skikda region of Algeria. This system is tailored to irrigate 830 fruit trees spread across 3 hectares with a total perimeter of 770 m. The proposed approach integrates two main renewable energy sources (while eliminating the use of traditional batteries for electrical energy storage): solar and wind. Instead, a large water reservoir is employed as an energy storage medium in the form of potential energy. Utilizing gravity, this reservoir directly powers the irrigation system for the fruit trees, thereby reducing the costs and environmental impacts associated with conventional batteries. This innovative design not only enhances sustainability, but also improves the system’s energy efficiency. To ensure precise and customized sizing of the system for the irrigation area, a detailed mathematical modeling of the key system components (solar panels, wind turbines, and reservoir) was conducted. This modeling identifies the critical design variables required to meet technical specifications and irrigation needs. A multi-objective optimization approach was then developed to determine the optimal configuration of the HRES, and this was achieved by considering both technical and economic constraints. The optimization algorithm used was tailored to the formulated problem, ensuring reliable and applicable results. The robustness of the optimization approach was shown by the precise match between energy production (24 kWh at 16,119.40 $) and the minimum demand. This alignment prevents over- or under-designing the system, which increases costs and reduces energy use. The findings highlight the relevance and effectiveness of the proposed methodology, demonstrating its practical utility and significant potential for generalization and adaptation to different agricultural zones with varying conditions. This work paves the way for sustainable and innovative solutions for agricultural irrigation, particularly in remote areas or regions lacking traditional energy infrastructure. Full article

Background: Within the solar ultraviolet (UV) spectrum, ultraviolet A rays (UVA, 320–400 nm), although less energetic than ultraviolet B rays (UVB, 280–320 nm), constitute at least 95% of solar UV radiation that penetrates deep into the skin The UV rays are associated with both epidermal and dermal damage resulting from the generation of reactive oxygen species (ROS). Among them, the longest UVA wavelengths (UVA1, 340–400 nm) can represent up to 75% of the total UV energy. Therefore, UVA radiation is linked to various acute and chronic conditions, including increased skin pigmentation and photoaging. Despite many advances in the skin photoprotection category, there is still a growing demand for natural daily photoprotection active ingredients that offer broad protection against skin damage caused by UVA exposure. In our quest to discover new, disruptive, next generation of photoprotective ingredients, we were drawn to pomegranate, based on its diverse polyphenolic profile. We investigated the pericarp of the fruit, so far considered as byproducts of the pomegranate supply chain, to design a novel patented extract “POMAOX” with a desired spectrum of phenolic components comprising of αβ-punicalagins, αβ-punicalins and ellagic acid. Methods: Antioxidant properties of POMAOX were measured using in-tubo standard tests capable of revealing a battery of radical oxygen species (ROS): peroxyl radical (ORAC), singlet oxygen (SOAC), superoxide anion (SORAC), peroxynitrite (NORAC), and hydroxyl radical (HORAC). In vitro, confirmation of antioxidant properties was first performed by evaluating protection against UVA-induced lipid peroxidation in human dermal fibroblasts (HDF), via the release of 8 iso-prostanes. The protection offered by POMAOX was further validated in a 3D in vitro reconstructed T-Skin^TM model, by analyzing tissue viability/morphology and measuring the release of Matrix Metallopeptidase 1 (MMP-1) & pro-inflammatory mediators (IL-1α, IL-1ra, IL-6, IL-8, GM-CSF, and TNF-α) after UVA1 exposure. Results: POMAOX displayed strong antioxidant activity against peroxynitrite (NORAC) at 1.0–3.0 ppm, comparable to the reference vitaminC, as well as singlet oxygen (SOAC) at 220 ppm, and superoxide radicals with a SORAC value of 500 ppm. Additionally, POMAOX demonstrated strong photoprotection benefit at 0.001% concentration, offering up to 74% protection against UVA-induced lipid peroxidation on HDF, in a similar range as the positive reference, Vitamin E at 0.002% (50 µM), and with higher efficacy than ellagic acid alone at 5 µM. Moreover, our pomegranate-derived extract delivered photoprotection at 0.001%, mitigating dermal damages induced by UVA1, through inhibition of MMP-1 and significant inhibition of pro-inflammatory mediators release (including IL-1α, IL-1ra, IL-6, IL-8, GM-CSF, and TNFα) on an in vitro reconstructed full-thickness human skin model with a similar level of protection to that of Vitamin C tested at 0.035% (200 µM). Conclusions: Overall, the novel pomegranate-derived extract “POMAOX” significantly reduced the impact of UVA on human skin, due to its broad-spectrum antioxidant profile. These findings suggest that POMAOX could offer enhanced protection against the detrimental effects of UV exposure, addressing the growing consumer demand for strong photoprotection with skincare benefits. Full article

The paper presents a double-radio wireless multimedia sensor node (WMSN) with a camera on board, designed for plant proximal monitoring. Camera sensor nodes represent an effective solution to monitor the crop at the leaf or fruit scale, with details that cannot be retrieved with the same precision through satellites or unnamed aerial vehicles (UAVs). From the technological point of view, WMSNs are characterized by very different requirements, compared to standard wireless sensor nodes; in particular, the network data rate results in higher energy consumption and incompatibility with the usage of battery-powered devices. Avoiding energy harvesters allows for device miniaturization and, consequently, application flexibility, even for small plants. To do this, the proposed node has been implemented with two radios, with different roles. A GPRS modem has been exclusively implemented for image transmission, while all other tasks, including node monitoring and camera control, are performed by a LoRaWAN class A end-node that connects every 10 min. Via the LoRaWAN downlink, it is possible to efficiently control the camera settings; the shooting times and periodicity, according to weather conditions; the eventual farming operations; the crop growth stages and the season. The node energy consumption has been verified in the laboratory and in the field, showing that it is possible to acquire one picture per day for more than eight months without any energy harvester, opening up further possible implementations for disease detection and production optimization. Full article

The main purpose of this study was to create a prototype of an unmanned aerial system equipped with intelligent hardware and software technologies necessary for surveillance and monitoring the health and growth of crops from orchards with vines and fruit trees. Using low-cost sensors that accurately measure ultraviolet solar radiation was an important objective. The device, which needed to be attached to the commercial DJI Mini 4 Pro drone, had to be small, portable, and have low energy consumption. For this purpose, the widely used Vishay VEML6075 digital optical sensor was selected and implemented in a prototype, alongside a Raspberry Pi Zero 2 W minicomputer. To collect data from these sensors, a program written in Python was used, containing specific blocks for data acquisition from each sensor, to facilitate the monitoring of ultraviolet (UV) radiation, or battery current. By analyzing the data obtained from the sensors, several important conclusions were drawn that may provide valuable pathways for the further development of mobile or modular equipment. Furthermore, the plantation state analysis results with proposed models in the geographic information system (GIS) environment are also presented. The visualization of maps indicating variations in vegetation conditions led to identifying problems such as hydric stress. Full article

As the world’s second-largest palm oil producer, Malaysia heavily depends on its extensive oil palm cultivation, which accounts for nearly 90% of the country’s lignocellulosic biomass waste. Approximately 20–22 tonnes of empty fruit bunches (EFBs) can be derived from an initial yield of 100 tonnes of fresh fruit bunches (FFBs) from oil palm trees. The average annual amount of EFBs produced in Johor is 3233 tonnes per day. Recognising that urban areas contribute significantly to anthropogenic greenhouse gas emissions, and to support Malaysia’s transition from fossil fuel-based energy to a low-carbon energy system, this research employed HOMER Pro software 3.18.3 to develop an optimal hybrid renewable energy system integrating solar and biomass (EFB) energy sources in Johor, Malaysia. The most cost-effective system (solar–biomass) consists of 4075 kW solar photovoltaics, a 2100 kW biomass gasifier, 9363 battery units and 1939 kW converters. This configuration results in a total net present cost (NPC) of USD 44,596,990 and a levelised cost of energy (LCOE) of USD 0.2364/kWh. This system satisfies the residential load demand via 6,020,427 kWh (64.7%) of solar-based and 3,286,257 kWh (35.3%) of biomass-based electricity production, with an annual surplus of 2,613,329 kWh (28.1%). The minimal percentages of unmet electric load and capacity shortage, both <0.1%, indicate that all systems can meet the power demand. In conclusion, this research provides valuable insights into the economic viability and technical feasibility of powering the Kulai district with a solar–biomass system. Full article

The disassembly of spent lithium batteries is a prerequisite for efficient product recycling, the first link in remanufacturing, and its operational form has gradually changed from traditional manual disassembly to robot-assisted human–robot cooperative disassembly. Robots exhibit robust load-bearing capacity and perform stable repetitive tasks, while humans possess subjective experiences and tacit knowledge. It makes the disassembly activity more adaptable and ergonomic. However, existing human–robot collaborative disassembly studies have neglected to account for time-varying human conditions, such as safety, cognitive behavior, workload, and human pose shifts. Firstly, in order to overcome the limitations of existing research, we propose a model for balancing human–robot collaborative disassembly lines that take into consideration the load factor related to human involvement. This entails the development of a multi-objective mathematical model aimed at minimizing both the cycle time of the disassembly line and its associated costs while also aiming to reduce the integrated smoothing exponent. Secondly, we propose a modified multi-objective fruit fly optimization algorithm. The proposed algorithm combines chaos theory and the global cooperation mechanism to improve the performance of the algorithm. We add Gaussian mutation and crowding distance to efficiently solve the discrete optimization problem. Finally, we demonstrate the effectiveness and sensitivity of the improved multi-objective fruit fly optimization algorithm by solving and analyzing an example of Mercedes battery pack disassembly. Full article

Unmanned Aerial Vehicles (UAVs) are a novel up-and-coming technology with wide applicability and great potential to be used in agricultural systems for spraying applications. However, the cost-effectiveness of this application is still rather uncertain. The present study utilized actual data from field applications to analyze the critical components and parameters in the potential case of using UAV sprayers for the control of olive fruit flies in order to assess the operational costs. The results are compared with the costs of two traditional spraying methods: manual spaying by workers using backpack sprayers and manual spraying assisted by a tractor. The case of the olive fruit fly was selected because it involves costly, time consuming, and laborious manual spraying. Furthermore, the bait character of spraying in these applications does not require full canopy coverage, making it ideal for UAV applications. A parameterized computational model was developed to assess the costs of labor, capital spending, repair and maintenance, energy, licensees, fees and taxes, and storage for each of the three methods. In addition, the cost for surveillance was also accounted for with the UAV method. Consequently, a sensitivity analysis was performed to examine the impact of the most crucial parameters. The results showed that the cost of spraying with a UAV was 1.45 to 2 times higher than the traditional methods, mainly due to the high capital spending resulting from a low economic life. There are opportunities, however, of improving the economic performance, making it compatible to the traditional methods, by using a smaller UAV with longer lasting batteries and by expanding its annual use beyond the needs of olive fruit fly control. Full article

Background: Parkinson’s disease (PD) is a neurodegenerative disorder that features motor and non-motor deficits. The use of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced dopamine neuron degeneration has been widely practiced to produce reliable animal models of PD. However, most previous preclinical studies focused on motor dysfunction, and few non-motor symptoms were evaluated. Thus far, there is a lack of comprehensive investigations of the non-motor symptoms in animal models. Objectives: In this study, we aim to use a battery of behavioral methods to evaluate non-motor symptoms in MPTP-induced non-human primate PD models. Methods: Cognitive function, sleep, and psychiatric behaviors were evaluated in MPTP-treated cynomolgus monkeys. The tests consisted of a delayed matching-to-sample (DMTS) task, the use of a physical activity monitor (PAM), an apathy feeding task (AFT), the human intruder test (HIT), novel fruit test (NFT), and predator confrontation test (PCT). In addition, we tested whether the dopamine receptor agonist pramipexole (PPX) can improve these non-motor symptoms. Results: The present results show that the MPTP-treated monkeys exhibited cognitive deficits, abnormal sleep, and anxiety-like behaviors when compared to the control monkeys. These symptoms were relieved partially by PPX. Conclusions: These results suggest that MPTP-induced PD monkeys displayed non-motor symptoms that were similar to those found in PD patients. PPX treatment showed moderate therapeutic effects on these non-motor symptoms. This battery of behavioral tests may provide a valuable model for future preclinical research. Full article

The battery electric passenger vehicle (BEPV) has the potential to conserve electric energy and reduce carbon emissions, making it an effective tool for achieving low-carbon development in the road transport industry by replacing the internal combustion engine vehicle (ICEV). Several factors, such as comprehensive electricity power generation efficiency, proportion of thermal power, vehicle technical performance, regional mileage credibility and low temperature, affect the BEPV’s electricity energy consumption and carbon emissions. In this study, an electricity conservation index model and a carbon emission reduction index model for multilevel BEPVs are established to evaluate their capabilities of electricity energy conservation and carbon emissions reduction, considering the electricity supply chain, including the generation and transmission of electricity. The research shows that the electricity energy conservation ability of BEPVs is not outstanding, but their carbon emissions reduction ability is strong. When the composition of energy for electricity generation is transformed from 2025 to 2035, with a 10% increase in comprehensive electricity generation efficiency, all levels of BEPVs show fruitful electricity energy conservation ability. When the proportion of thermal power decreases to 10%, the carbon emissions reduction is exponentially reduced to 1/25 to 1/30 of ICEV’s total carbon emissions. However, the regional mileage credibility weakens the BEPVs’ ability to save energy and reduce emissions in most Chinese provinces except for the southwest and the south regional provinces, where the regional mileage credibility parameter can increase the energy conservation and carbon emission reduction performance of A00+A0 level BEPV. Low temperatures make BEPV models lose their electricity energy conservation advantage, but most models still have the characteristic of carbon emissions reduction. On this basis, the electricity energy consumption and carbon emissions of all BEPV models are higher than those of ICEVs when the low temperature endurance mileage accuracy is added. Full article

Chili recognition is one of the critical technologies for robots to pick chilies. The robots need locate the fruit. Furthermore, chilies are always planted intensively and their fruits are always clustered. It is a challenge to recognize and locate the chilies that are blocked by branches and leaves, or other chilies. However, little is known about the recognition algorithms considering this situation. Failure to solve this problem will mean that the robot cannot accurately locate and collect chilies, which may even damage the picking robot’s mechanical arm and end effector. Additionally, most of the existing ground target recognition algorithms are relatively complex, and there are many problems, such as numerous parameters and calculations. Many of the existing models have high requirements for hardware and poor portability. It is very difficult to perform these algorithms if the picking robots have limited computing and battery power. In view of these practical issues, we propose a target recognition-location scheme GNPD-YOLOv5s based on improved YOLOv5s in order to automatically identify the occluded and non-occluded chilies. Firstly, the lightweight optimization for Ghost module is introduced into our scheme. Secondly, pruning and distilling the model is designed to further reduce the number of parameters. Finally, the experimental data show that compared with the YOLOv5s model, the floating point operation number of the GNPD-YOLOv5s scheme is reduced by 40.9%, the model size is reduced by 46.6%, and the reasoning speed is accelerated from 29 ms/frame to 14 ms/frame. At the same time, the Mean Accuracy Precision (MAP) is reduced by 1.3%. Our model implements a lightweight network model and target recognition in the dense environment at a small cost. In our locating experiments, the maximum depth locating chili error is 1.84 mm, which meets the needs of a chili picking robot for chili recognition. Full article

Biofuel cells have been in the spotlight for the past century because of their potential and promise as a unique platform for sustainable energy harvesting from the human body and the environment. Because biofuel cells are typically developed in a small platform serving as a primary battery with limited fuel or as a rechargeable battery with repeated refueling, they have been interchangeably named biobatteries. Despite continuous advancements and creative proof-of-concept, however, the technique has been mired in its infancy for the past 100 years, which has provoked increasing doubts about its commercial viability. Low performance, instability, difficulties in operation, and unreliable and inconsistent power generation question the sustainable development of biofuel cells. However, the advancement in bioelectrocatalysis revolutionizes the electricity-producing capability of biofuel cells, promising an attractive, practical technique for specific applications. This perspective article will identify the misconceptions about biofuel cells that have led us in the wrong development direction and revisit their potential applications that can be realizable soon. Then, it will discuss the critical challenges that need to be immediately addressed for the commercialization of the selected applications. Finally, potential solutions will be provided. The article is intended to inspire the community so that fruitful commercial products can be developed soon. Full article

Disease detection in plants is essential for food security and economic stability. Unmanned aerial vehicle (UAV) imagery and artificial intelligence (AI) are valuable tools for it. The purpose of this review is to gather several methods used by our peers recently, hoping to provide some knowledge and assistance for researchers and farmers so that they can employ these technologies more advantageously. The studies reviewed in this paper focused on Scab detection in Rosaceae family fruits. Feature extraction, segmentation, and classification methods for processing the UAV-obtained images and detecting the diseases are discussed briefly. The advantages and limitations of diverse kinds of UAVs and imaging sensors are also explained. The widely applied methods for image analysis are machine learning (ML)-based models, and the extensively used UAV platforms are rotary-wing UAVs. Recent technologies that cope with challenges related to disease detection using UAV imagery are also detailed in this paper. Some challenging issues such as higher costs, limited batteries and flying time, huge and complex data, low resolution, and noisy images, etc., still require future consideration. The prime significance of this paper is to promote automation and user-friendly technologies in Scab detection. Full article

Electrically conducting and semiconducting polymers represent a special and still very attractive class of functional chromophores, especially due to their unique optical and electronic properties and their broad device application potential. They are potentially suitable as materials for several applications of high future relevance, for example flexible photovoltaic modules, components of displays/screens and batteries, electrochromic windows, or photocatalysts. Therefore, their synthesis and structure elucidation are still intensely investigated. This article will demonstrate the very fruitful interplay of current electropolymerization research and its exploitation for science education issues. Experiments involving the synthesis of conducting polymers and their assembly into functional devices can be used to teach basic chemical and physical principles as well as to motivate students for an innovative and interdisciplinary field of chemistry. Full article

Hazelnuts (Corylus avellana L.) are among the most consumed dry fruits all over the world. Their commercial quality is defined, above all, by origin and dimension, as well as by lipid content. Evaluation of this parameter is currently performed with chemical methods, which are expensive, time consuming, and complex. In the present work, the near-infrared (NIR) spectroscopy, using both a benchtop research spectrometer and a retail handheld instrument, was evaluated in comparison with the traditional chemical approach. The lipid content of hazelnuts from different growing regions of origin (Italy, Chile, Turkey, Georgia, and Azerbaijan) was determined with two NIR instruments: a benchtop FT-NIR spectrometer (Multi Purpose Analyser—MPA, by Bruker), equipped with an integrating sphere and an optic fibre probe, and the pocket-sized, battery-powered SCiO molecular sensor (by Consumer Physics). The Randall/Soxtec method was used as the reference measurement of total lipid content. The collected NIR spectra were inspected through multivariate data analysis. First, a Principal Component Analysis (PCA) model was built to explore the information contained in the spectral datasets. Then, a Partial Least Square (PLS) regression model was developed to predict the percentage of lipid content. PCA showed samples distributions that could be linked to their total crude fat content determined with the Randall/Soxtec method, confirming that a trend related to the lipid content could be detected in the spectral data, based on their chemical profiles. PLS models performed better with the MPA instrument than SCiO, with the highest R² of prediction (R²_PRED = 0.897) achieved by MPA probe, while this parameter for SCiO was much lower (R²_PRED = 0.550). Further analyses are necessary to evaluate if more acquisitions may lead to better performances when using the SCiO portable spectrometer. Full article

Battery electric tractors (BETs) demonstrate considerable advantages over diesel-fueled tractors, including higher conversion efficiency, higher torque, less maintenance, and no tailpipe emissions. Converting to BETs also requires tradeoffs in the form of the batteries’ high cost, increased weight, limited energy capacity, finite charging cycles, and lengthy charging time. The extent to which small-scale organic vegetable, fruit and cut-flower growers are aware of these tradeoffs is unknown. Little research exists examining these growers’ perceptions, concerns, and willingness to pay for or adopt BETs. Here, we address that gap by conducting qualitative semi-structured interviews with 14 organic growers in the US Midwest, most operating in Michigan. We focus our questions on growers’ motivations, existing tractor-use patterns, and the evaluation of different configurations of a belly-mount open-station cultivating BET. Our results suggest interest in and potential for growers to transition to BETs, including an estimated willingness to pay 14 percent more for a BET compared to a diesel-fueled alternative. This premium is driven by most growers’ preferences for reduced noise, fumes, fuel, and greenhouse gases, as well as beliefs about BETs ultimately being a more sustainable long-term option than diesel-fueled tractors. Growers also identify significant concerns and uncertainty about the long-term performance, maintenance, storage, cost, safety, and weight of the tractors’ battery systems. While growers linked some environmental values and motivations to their interest in BETs, altruistic value signaling was absent, and growers focused considerably more on financial and instrumental concerns and motivations for BET adoption. Full article