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This paper shares results from a pedagogical experiment that assigns undergraduates to "cheat" on a final class essay by requiring their use of text-generating AI software. For this assignment, students harvested content from an installation of GPT-2, then wove that content into their final essay. At the end, students offered a "revealed" version of the essay as well as their own reflections on the experiment. In this assignment, students were specifically asked to confront the oncoming availability of AI as a writing tool. What are the ethics of using AI this way? What counts as plagiarism? What are the conditions, if any, we should place on AI assistance for student writing? And how might working with AI change the way we think about writing, authenticity, and creativity? While students (and sometimes GPT-2) offered thoughtful reflections on these initial questions, actually composing with GPT-2 opened their perspectives more broadly on the ethics and practice of writing with AI. In this paper, I share how students experienced those issues, connect their insights to broader conversations in the humanities about writing and communication, and explain their relevance for the ethical use and evaluation of language models. [ABSTRACT FROM AUTHOR]

Flexible thermoelectrics (TEs) involving simplified processeability are attractive, with limited choice of materials and low endurance, remaining as major challenges. Herein, an economic emery paper‐based thermoelectric generator (PTEG) from graphite as p‐type leg and bismuth as n‐type leg is introduced, prepared by simple bulk tracing. Tracing provides a feasible approach to modulate the TE properties, as supported by finite‐element analysis. From individual bismuth trace a power factor of 5.85 μW m−1 K−2 ≈100 °C (2.09 μWm−1 K−2 at room temperature (RT)) and for graphite a power factor 7.7 μW m−1 K−2 at ≈100 °C (≈5.1 μW m−1 K−2 at RT) is obtained. A six‐pair p–n module‐integrated PTEG drives an open‐circuit voltage of 35.8 mV producing an output power of 14.6 nW whereas from a 60‐pair PTEG, voltage ≈348 mV with ≈137.5 nW output power is produced for ΔT ≈ 70 K. The scalability of the fabricated PTEGs is understood from Seebeck voltage being directly proportional to the number of p–n modules and is further tested for harvesting waste heat from electronics. This facile approach may be extended to other organic and inorganic TE materials, contributing to the research in self‐powered heat sensors, wearables, and as thermal harvesters in IoT equipment. [ABSTRACT FROM AUTHOR]

Now days there has been an increase in the consumers demand for great food, produced in environmentally cost-effective and friendly processes, being increasingly necessary new cultivation techniques able to ensure the required standards for banana growers and consumers. Therefore, the research aimed to evaluate the effects of bunch cover materials on development and quality of 'BRS Platina' banana fruits. The experimental design was in randomized blocks, with six treatments, four replications and four plants per plot, being the treatments: WB - without bagging; BBTK - black polypropylene bag (TNT) + kraft paper; BBPK - black polyethilene bag + kraft paper; KP - kraft paper; WBT - non-woven polypropylene white bag (TNT) and WBP - white polyethylene plastic bag. The 'BRS Platina' banana bunches cover improved fruits quality and the treatment polyethylene white bag (WBP) can be an advantageous option for the banana growers because of its effectiveness on the reduction of damages caused by flowers thrips (Frankliniella spp), providing a good agronomic performance and also does not delay the harvesting. [ABSTRACT FROM AUTHOR]

Fruit bagging protects fruits from pest infestation and multiple biotic and abiotic stresses during development without reducing yield. It also can potentially improve the quality and market value of fruits. Hence, the present study aimed to determine the effect of pre-harvest fruit bagging on the yield and quality of tomato (Solanum lycopersicum) cv. BARI Tomato-2. The experiment was laid out in a Randomized Complete Block Design (RCBD) with three replications that constituted the various treatments-T0: Control (no bagging), T1: White paper bag (single layer), T2: Brown paper bag (double layer), T3: Non-woven fabric bag, and T4: Jute bag from October 2021 to March 2022. The results showed that the non-woven fabric bag had the highest fruit length (57.79 mm), diameter (57.81 mm), number of fruits plant-1 (55.9), yield (4.06 kg plant-1), vitamin C (24.63mg/ 100g-1), and lycopene content (0.12 mg g-1). The maximum insect incidence (18.41%) was recorded in control and the minimum was counted in T3 (2.17%). The highest total soluble solid (6.73%0Brix) and dry matter (8.63%) were observed in a white paper bag. The highest mean color score (6.08), firmness (26.81%), and shelf life (13 days) found in fabric bags outperformed non-bagged fruits. The fabric bag showed maximum net income (21.12 TK) and the highest benefit: cost ratio (2.36). Thus, the fabric bag was the best for improving the physical and chemical qualities of tomatoes (Solanum lycopersicum) among the various fruitcovering materials. [ABSTRACT FROM AUTHOR]

In this paper, we introduce multilateral strategies to improve the efficiency and brightness of quantum dot light‐emitting diodes. First, electrically and thermally endurable device architecture is adopted for highly bright light‐emission. Second, an exciton harvesting layer consisting of blue‐emitting thermally‐activated delayed fluorescent materials is introduced to improve device efficiency. Our approaches realize remarkable improvements in the device performance. [ABSTRACT FROM AUTHOR]

The hornification processes undergone by the fibers in the paper industry recycling processes lead to the loss of properties of the final products, which exhibit poor mechanical properties. Among the most promising solutions is the reinforcement of secondary fibers with cellulose nanofibers. The present work addresses two important issues: the efficient production of cellulose nanofibers from scarcely exploited agricultural wastes such as horticultural residues and vine shoots, and their application as a reinforcement agent in recycled linerboard recycling processes. The effect of the chemical composition and the pretreatment used on the nanofibrillation efficiency of the fibers was analyzed. Chemical pretreatment allowed a significantly higher nanofibrillated fraction (45–63%) than that produced by mechanical (18–38%), as well as higher specific surface areas (>430 m2/g). The application of the nanofibers as a reinforcing agent in the recycled linerboard considerably improved the mechanical properties (improvements of 15% for breaking length, 220–240% for Young's modulus and 27% for tear index), counteracting the loss of mechanical properties suffered during recycling when using chemically pretreated cellulose nanofibers from horticultural residues and vine shoots. It was concluded that this technology surpasses the mechanical reinforcement produced by conventional mechanical refining used in the industry and extends the number of recycling cycles of the products due to the non-physical modification of the fibers. [ABSTRACT FROM AUTHOR]

Background: The use of human amniotic membrane transplantation is rapidly increasing for the management of various ocular surface disorders. Despite its numerous advantages, amniotic membrane is not widely available due to the lack of awareness among ophthalmologists regarding its preparation and preservation techniques. Purpose: To provide an instructional video demonstrating the technique of harvesting, preparation, preservation of human amniotic membrane, and its uses in the management of ocular surface diseases. Synopsis: The amniotic membrane is the innermost layer of the human placenta. Properties of the amniotic membrane, like the presence of various growth factors, anti-inflammatory and anti-angiogenic factors, and its low immunogenicity, contribute to its ability to promote epithelial growth and differentiation with the reduction in fibrosis during healing. To harvest amniotic membrane, informed consent is obtained from a patient undergoing an elective caesarian section, and the donor is screened to exclude the risk of infections. Under sterile precautions, the amniotic membrane is separated from the chorion and washed free of blood clots. With the epithelial surface up, the amniotic membrane is spread uniformly without folds or tears on individually sterilized 0.22 µm nitrocellulose filter papers of the required sizes. The prepared filter paper with the adherent amniotic membrane is placed in freshly prepared Dulbecco's Modified Eagle's Medium and stored at -80°C. The amniotic membrane can be used for surgical procedures like symblepharon release, pterygium or dermoid excision, perforated ulcers, nonhealing epithelial defects, etc. Highlights: Instructional videos demonstrating the preparation and preservation of amniotic membrane are very sparse. This video clearly explains how any ophthalmologist can learn to prepare and preserve the human amniotic membrane and gives a glimpse of its properties, advantages, and scope of use. Video Link: https://youtu.be/8HfSjXUKoiY. [ABSTRACT FROM AUTHOR]

Blueberries have high nutritional and economic value and are easy to cultivate, so they are common fruit crops in China. There is a high demand for blueberry in domestic and foreign markets, and various technologies have been used to extend the supply cycle of blueberry to about 7 months. However, blueberry grows in clusters, and a cluster of fruits generally contains fruits of different degrees of maturity, which leads to low efficiency in manually picking mature fruits, and at the same time wastes a lot of manpower and material resources. Therefore, in order to improve picking efficiency, it is necessary to adopt an automated harvesting mode. However, an accurate maturity detection model can provide a prerequisite for automated harvesting technology. Therefore, this paper proposes a blueberry ripeness detection model based on enhanced detail feature and content-aware reassembly. First of all, this paper designs an EDFM (Enhanced Detail Feature Module) that improves the ability of detail feature extraction so that the model focuses on important features such as blueberry color and texture, which improves the model's ability to extract blueberry features. Second, by adding the RFB (Receptive Field Block) module to the model, the lack of the model in terms of receptive field can be improved, and the calculation amount of the model can be reduced at the same time. Then, by using the Space-to-depth operation to redesign the MP (MaxPool) module, a new MP-S (MaxPool–Space to depth) module is obtained, which can effectively learn more feature information. Finally, an efficient upsampling method, the CARAFE (Content-Aware Reassembly of Features) module, is used, which can aggregate contextual information within a larger receptive field to improve the detection performance of the model. In order to verify the effectiveness of the method proposed in this paper, experiments were carried out on the self-made dataset "Blueberry—Five Datasets" which consists of data on five different maturity levels of blueberry with a total of 10,000 images. Experimental results show that the mAP (mean average precision) of the proposed network reaches 80.7%, which is 3.2% higher than that of the original network, and has better performance than other existing target detection network models. The proposed model can meet the needs of automatic blueberry picking. [ABSTRACT FROM AUTHOR]

Macroalgae are beneficial for consumers and producers due to their high productivity, their chemical composition and their efficient cultivation without additional feed. Asia dominates global macroalgae production, while European production is still marginal and mainly based on wild harvesting in the North Atlantic. The European Commission has launched initiatives to promote the sustainable production, safe consumption and innovative use of macroalgae products in European regional seas, including the Mediterranean Sea. A variety of coastal and seabed types and a wide range of environmental conditions make the Mediterranean a hotspot of marine biodiversity while providing good conditions for the cultivation of macroalgae. The aim of this paper is to compare the global macroalgae production and macroalgae production in the Mediterranean Sea, focussing on the cultivation of edible macroalgae. The paper also discusses the limitations and possibilities of macroalgae production in the region. Macroalgae production is one of the most promising sectors of the blue economy in the Mediterranean. The production of edible macroalgae suitable for human consumption has great potential, considering future population growth and related food security and health issues, as well as the additional ecosystem benefits of macroalgae production. [ABSTRACT FROM AUTHOR]

The article "Harvesting Knowledge: Illuminating Advances in Brassica Crops Genomics and Breeding" discusses the importance of Brassica crops, which include vegetables, oil crops, ornamentals, and condiments, in global agriculture. The article highlights the unique morphological types and polyploidization events within Brassica species, making them ideal models for studying genetic complexities. The article also explores the progress in genomic sequencing technologies, functional genomics, and molecular marker-assisted breeding in Brassica crops. The research papers featured in the article provide valuable insights into genetic mechanisms, stress responses, biochemical characteristics, and genetic improvement of Brassica crops. The findings contribute to advancements in breeding research and hold promise for enhanced productivity, adaptability, and nutritional value of Brassica crops. [Extracted from the article]

This paper deals with the trade-off between conversion time and power in nW-power capacitance-to-digital converters (CDCs). The CDC used in this work operates at nW power and low voltage down to 0.3 V without the need for any additional circuitry, references, or voltage regulation. It is built on swappable oscillators and takes advantage of the delay-power flexibility of dual-mode logic. Its self-calibration corrects PVT changes and mismatches at any point in the chip lifecycle, doing away with the necessity for cutting during testing. A CDC's test chip in 180 nm demonstrates that its power consumption can be dynamically modified from 1.37 nW down to 418 pW at a conversion time down to hundreds of ms, making it suited for harvesting systems with a very tight power budget and changing power sources. [ABSTRACT FROM AUTHOR]

Surface charge density is a key factor that greatly enhances the performance of a natural-based triboelectric nanogenerator (TENG), which is essential for future sustainable sensing and harvesting devices. This work introduced a conductive interlayer between a main frictional layer and electrode. This approach can suppress the charge recombination rate and improve the amount of charges produced during the triboelectrification process. Bacterial cellulose (BC) film was selected as a main frictional layer for the TENG. A conductive nanomaterial, i.e. silver flake, was incorporated into the BC film as an intermediate layer for enhancing TENG performance. As firstly reported, the maximum electrical outputs for the multi-layer BC structure could be found when using silver flake/BC composite (ratio 1:5) as an intermediate layer, which has 122 V and 8.2 µA of output voltage and current, respectively. This is higher than the output voltage and current of a single layer BC TENG by approximately 3 and 8 times, respectively. The maximum output power of ∼440 µW is achieved by connecting with a load resistor of ∼10 MΩ. This demonstrates an efficient strategy for designing a high performance energy harvester by adding an intermediate layer for the target of practical purposes in sustainable systems. [ABSTRACT FROM AUTHOR]

The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP3Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP3Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters. [ABSTRACT FROM AUTHOR]

Pedoclimatic elements and smart technology act simultaneously on the biological processes of agricultural crops and on the level of harvest, which requires the study of their correlation to determine the continuous influence of these factors in obtaining the planned levels of production. In Romania, annually, over 2.1 million hectares of wheat are cultivated, with annual productions of over 10 million tons, placing wheat crops in the first place as economic importance. Knowing the management of natural resources in quantitative terms is especially valuable when we talk about their sustainable use. Our evaluation includes in this paper the relationship between the pedoclimatic resources of South-Eastern Romania and the zoning of wheat varieties in order to obtain superior quantitative and qualitative productions. The last 11 agricultural years are analyzed, both in terms of the influence of global warming and in terms of wheat zoning to obtain sustainable production to highlight the genetic potential of the varieties under the conditions of the Brailei Plain. A stability of the varieties Glosa, Miranda, Litera (varieties over 15 years old) was observed, but not least of the new ones created by the National Research and Development Institute from Fundulea such as: Pitar, Ursita, Adelina, Izvor. [ABSTRACT FROM AUTHOR]

The paper examines the fish population dynamics of a delay logistic model with restrictions on harvesting in a deterministic environment. This is the first time an autonomous model with a harvesting function has been created to regulate the harvesting of fish. The goal of the study is to help external regulatory bodies manage fishing harvests and replenish fish stocks. The dynamics scenario mirrors the facts of fishing systems and enhances the research that has already been done. Two restriction conditions are employed. In the event that the agency detects overfishing, all fish harvesting is prohibited, with the fishing control parameter raised in order to bring the fish back to full stock, while the catchability coefficient is set to zero. However, if the system is designed so that harvesting is carried out in accordance with the fish's sustainable growth level, then we have the partial restriction condition where no ban is imposed on fishing. This model's practical benefit is its adaptability; it may be redesigned to suit any physical system whose functioning is comparable to this phenomenon. As a result, it is advised that the regulatory bodies use the model to manage the fish stock. [ABSTRACT FROM AUTHOR]

From the perspective of ecological control, harvesting behavior plays a crucial role in the ecosystem natural cycle. This paper proposes a diffusive predator-prey system with predator harvesting to explore the impact of harvesting on predatory ecological relationships. First, the existence and boundedness of system solutions were investigated and the non-existence and existence of nonconstant steady states were obtained. Second, the conditions for Turing instability were given to further investigate the Turing patterns. Based on these conditions, the amplitude equations at the threshold of instability were established using weakly nonlinear analysis. Finally, the existence, direction, and stability of Hopf bifurcation were proven. Furthermore, numerical simulations were used to confirm the correctness of the theoretical analysis and show that harvesting has a strong influence on the dynamical behaviors of the predator-prey systems. In summary, the results of this study contribute to promoting the research and development of predatory ecosystems. [ABSTRACT FROM AUTHOR]

Rice lodging not only brings trouble to harvesting but also reduces yield. Therefore, the effective identification of rice lodging is of great significance. In this paper, we have designed a bilinear interpolation upsampling feature fusion module (BIFF) to decompose the quadruple upsampling of the connected part of encoder and decoder into two double upsampling processes and insert the intermediate feature layer in the backbone network for feature fusion in this process. The global attention mechanism(GAM) attention module is added to the feature extraction network, allowing the network to effectively focus on the lodging regions, thus effectively improving the segmentation effect. The average accuracy of the improved network is 93.55%, mrecall is 93.65%, and mIoU is 88.10%, and the feasibility of the improvement is demonstrated by ablation experiments and comparison with other algorithms. In addition, the angle calculation method is designed by combining the detection algorithm, adding a detection head branch to the output results for reading the distance information from the depth camera, and combining the distance information with mechanical analysis to determine the relationship between the angle of the stalk and the vertical direction when the rice is upright, tilted and fallen. A comparison of the calculated angle with the actual measured angle gives a final average error of approximately 5.364%, indicating that the harvest boundary extraction algorithm in this paper is highly accurate and has value for application in real-time harvesting scenarios. [ABSTRACT FROM AUTHOR]

The development of agricultural robots is an increasingly popular research field aiming at addressing the widespread labor shortages in the farming industry and the ever-increasing food production demands. In many cases, multiple cooperating robots can be deployed in order to reduce task duration, perform an operation not possible with a single robot, or perform an operation more effectively. Building on previous results, this application paper deals with a cooperation strategy that allows two heterogeneous robots to cooperatively carry out grape harvesting, and its implementation is demonstrated. More specifically, the cooperative grape harvesting task involves two heterogeneous robots, where one robot (i.e., the expert) is assigned the grape harvesting task, whereas the second robot (i.e., the helper) is tasked with supporting the harvesting task by carrying the harvested grapes. The proposed cooperative harvesting methodology ensures safe and effective interactions between the robots. Field experiments have been conducted in order firstly to validate the effectiveness of the coordinated navigation algorithm and secondly to demonstrate the proposed cooperative harvesting method. The paper reports on the conclusions drawn from the field experiments, and recommendations for future enhancements are made. The potential of sophisticated as well as explainable decision-making based on logic for enhancing the cooperation of autonomous robots in agricultural applications is discussed in the context of mathematical lattice theory. [ABSTRACT FROM AUTHOR]

Vietnam’s rice production accounts for a large proportion of rice production in region and the world. The income from rice production accounts for a large proportion of the Country’s total GDP (Gross Domestic Product). Therefore, the investment in machines for rice processing is being interested in this Country. Many types of rice processing machine need to be equipped such as rice harvesters, peelers, separators, etc. In which, the rice harvester is identified as the first type of machine that needs to be equipped for a rice processing line. However, with a large number of rice harvesters available on the market with many different parameters (specifications, selling price) will make it difficult for customers to choose the best rice harvester. In this paper, the selection of rice harvester is carried out using MCDM (Multi-Criteria Decision-Making) method. Five types of rice harvester were offered for ranking, including two drain-type rice harvesters and three bagged-type rice harvesters. Eighteen criteria were used to evaluate each machine type. Those eighteen criteria including: type of machine, length/width/height and weight of machine, length of the sprocket and width of the sprocket, maximum moving speed of the machine when working in slow-moving mode/average-moving mode and fast-moving mode, ground clearance, cutting jaw width, cutting width, lowest cutting height, the maximum cutting height, the width of rake reel, the container capacity, and cost. To determine the weights for the criteria, three different methods have been applied. FUCA (Faire Un Choix Adéquat) was selected as a multi-criteria decision-making method to rank alternatives. The results identified one best drain-type rice harvester and one best bagged-type rice harvester. [ABSTRACT FROM AUTHOR]

A crucial aspect that has to be considered in all fields and, especially, in smart farming, a rapidly developing industry, is maintenance. Due to the costs generated by both under-maintaining and over-maintaining the components of a system, a balance has to be achieved. The paper is focused on presenting an optimal maintenance policy used to ensure cost minimization by determining the optimal time to make a preventive replacement of the actuators of a harvesting robotic system. First, a brief presentation of the gripper with Festo fluidic muscles used in a novel way instead of fingers is given. Then, the nature-inspired optimization algorithm, as well as the maintenance policy are described. The paper also includes the steps and the obtained results of the developed optimal maintenance policy applied for the Festo fluidic muscles. The outcome of the optimization shows that a significant reduction in the costs is obtained if one performs a preventive replacement of the actuators a few days before the lifetime provided by the manufacturer and the lifetime estimated using a Weibull distribution. [ABSTRACT FROM AUTHOR]

This paper contributes to the literature and development of knowledge in the topic of energy harvesting by presenting the modelling and calculations of energy from vibration of railway tracks due to moving trains on floating-slab tracks with continuous slabs, considering both the quasi-static and dynamic effects. The floating-slab track is modelled as a double Euler–Bernoulli beam connected by continuous spring and damper elements. The dynamic excitation is accounted for by considering the un-sprung axles of a passing train with a number of coaches. The dynamic excitation is simulated using randomly generated unevenness from standard functions of power spectral density. The responses of rails' beam and slab are calculated for different unevenness realizations, and then used as inputs for a base-excited single-degree-of-freedom system that models the harvester. The change in the harvested energy is investigated due to the change of natural frequency of the harvester, the change of condition of track and change of train's velocity. The parameters used in this paper correspond to tracks and trains for Doha metro and unevenness information from the literature. The results show that more energy can be harvested by tuning the harvester's natural frequency to the frequency of axle-track resonance. It is found that a maximum mean-energy can be harvested from the rails of 0.35 J/kg for a train moving at 100 km/h for a track with poor condition and this is obtained at the axle-track resonance frequency. For the same track condition, a reduction of about 55% and 61% is observed for train's velocities of 70 km/h and 40 km/h, respectively. Using a track with medium and good conditions resulted in reduction of the mean harvested energy at the axle-track resonance by 73.5% and 99.9%, respectively. [ABSTRACT FROM AUTHOR]

Medicinal and aromatic plants (MAPs) are plants that have various uses for human health, nutrition, and well-being. They are sources of herbal medicines, dietary supplements, cosmetics, fragrances, spices, and other products. However, the production and utilization of MAPs also pose challenges and opportunities for environmental sustainability and economic viability. This work offers a wide-ranging review of the sustainable practices and circular economy approaches for the valorization of MAPs. The review discusses the importance and value of MAPs, as well as the principles and benefits of the circular economy, a sustainable model that aims to minimize waste and make the most of resources. The review identifies and analyzes various circular economy approaches for valorizing MAPs, like resource optimization through sustainable cultivation, waste minimization in harvesting and processing, closed-loop supply chains, product diversification and upcycling, circular design thinking for MAP-based products, recycling and reusing MAP-based waste, eco-certification and consumer awareness, and collaborative networks and knowledge sharing. The review paper provides examples and case studies of successful implementation of these approaches, as well as the challenges and limitations that need to be addressed. [ABSTRACT FROM AUTHOR]

Using the multi-product dual revenue function framework, this paper examines the nature and extent of technical and economic interactions among five demersal species of Al-Batinah demersal fishery, Oman during the 2010-2016 period. The parameters of the system of output supply functions are estimated using Zellner's seemingly unrelated regressions (SUR) technique. The likelihood ratio test in relation to the structure of the multi-output production technology rejects the null-hypothesis of input-output separability and non-jointness in inputs. These results suggest that the underlying production technology is non-separable between the outputs and the quasi-fixed input, and the harvesting level of one species is likely to have spill-over economic effects on the harvesting levels of others. The own-price elasticities of output supply are found to be positive, inelastic, and statistically significant in four out of five cases suggesting that, other things being equal, fishers' supply decisions are influenced by the prevailing market prices of outputs. The estimated values of the cross-price elasticity of supply are negative, inelastic and statistically significant at the 5% level for eight output pairs. The calculated values of the Morishima elasticity of substitution (MES) indicate the presence of substitutability between ten output pairs. The cross-price elasticity estimates are in the range of - 0.001 (between Emperor and Catfish) to - 0.275 (between Seabream and Grouper) which is lower (in absolute value) than that of the MES estimates. The magnitudes of the cross-price elasticity and the MES estimates indicate the extent of spill-over effects of one species on the other and signal the extent of species targeting by fishers. A statistically significant spatial, seasonal, and inter-annual variability is observed in all species cases. Finally, the implications of the findings are discussed from the perspective of developing effective management approaches, achieving sustainability of fisheries resources and improving fishers' socio-economic conditions stipulated in the Five-Year development plans for the sector. [ABSTRACT FROM AUTHOR]

Large industrial units generally consume considerable volumes of water for use by workers and sometimes in the manufacturing process, but on the other hand, they generally have large coverage areas that facilitate and enable the capture of large quantities of rainwater. Rainwater harvesting systems (RWHSs) are an alternative water supply with high potential for significant water and economic savings in buildings of this type, also with benefits for water resource sustainability. This paper presents a case study that refers to the design and economic viability determination of an RWHS to be installed in the industrial building of Continental Advanced Antenna Portugal, using an innovative tool called SAPRA—a rainwater harvesting and greywater reuse system in buildings. The main goal was to understand water consumption patterns in social areas (common to most of the industrial typologies) and determine whether RWHSs are feasible in such uses (discarding the production chain). The case study allowed for verification that the assumptions regarding the calculation period design flow significantly interfere with the design flow and the storage capacity. The analysis of the 10-year period yields the most realistic results, and can be framed, if necessary, within the range provided by the analysis of the driest and wettest years. The investment costs should between EUR 90 and 95 million, with annual savings of EUR 7 to 12 million, respectively. The expected payback period is between 7 and 11 years, which is quite feasible and very relevant. This may be an excellent example of how, even within the industries that do not need water for production, this may save significant volumes of water, contributing to the efficient use of this valuable resource. [ABSTRACT FROM AUTHOR]

In order to achieve fast and accurate detection of Gannan navel orange fruits with different ripeness levels in a natural environment under all-weather scenarios and then to realise automated harvesting of Gannan navel oranges, this paper proposes a YOLOv5-NMM (YOLOv5 with Navel orange Measure Model) object detection model based on the improvement in the original YOLOv5 model. Based on the changes in the phenotypic characteristics of navel oranges and the Chinese national standard GB/T 21488-2008, the maturity of Gannan navel oranges is tested. And it addresses and improves the problems of occlusion, dense distribution, small target size, rainy days, and light changes in the detection of navel orange fruits. Firstly, a new detection head of 160 × 160 feature maps is constructed in the detection layer to improve the multi-scale target detection layer of YOLOv5 and to increase the detection accuracy of the different maturity levels of Gannan navel oranges of small sizes. Secondly, a convolutional block attention module is incorporated in its backbone layer to capture the correlations between features in different dimensions to improve the perceptual ability of the model. Then, the weighted bidirectional feature pyramid network structure is integrated into the Neck layer to improve the fusion efficiency of the network on the feature maps and reduce the amount of computation. Lastly, in order to reduce the loss of the target of the Gannan Navel Orange due to occlusion and overlapping, the detection frame is used to remove redundancy using the Soft-NMS algorithm to remove redundant candidate frames. The results show that the accuracy rate, recall rate, and average accuracy of the improved YOLOv5-NMM model are 93.2%, 89.6%, and 94.2%, respectively, and the number of parameters is only 7.2 M. Compared with the mainstream network models, such as Faster R-CNN, YOLOv3, the original model of YOLOv5, and YOLOv7-tiny, it is superior in terms of the accuracy rate, recall rate, and average accuracy mean, and also performs well in terms of the detection rate and memory occupation. This study shows that the YOLOv5-NMM model can effectively identify and detect the ripeness of Gannan navel oranges in natural environments, which provides an effective exploration of the automated harvesting of Gannan navel orange fruits. [ABSTRACT FROM AUTHOR]

Identifying the in-field trajectories of harvests is important for the activity analysis of agricultural machinery. This paper presents a K-means-based trajectory identification method that can automatically detect the "turning", "working", and "abnormal working" trajectories for wheat harvester in-field operation scenarios. This method contains two stages: clustering and correction. The clustering stage performs by the two-step K-means iterative clustering method (D-K-means). In the correction stage, the first step (M1) is performed based on the three distance features between the trajectory segments and the cluster center of the trajectory segments. The second step (M2) is based on the direction change of the "turning" and "abnormal working" trajectories. The third correction step (M3) is based on the operating characteristics to specify the start and stop positions of the turning. The developed method was validated by 50 trajectories. The results for the three trajectories and the five time intervals from 1 s to 5 s both have f1-scores above 0.90, and the f1-score using only the clustering method and the method of this paper increased from 0.55 to 0.95. After removing the turning and abnormal operation trajectories, the error of calculating farmland area with distance algorithm is reduced by 17.04% compared with that before processing. [ABSTRACT FROM AUTHOR]

In recent years, 360° video cameras have become increasingly accessible and are now being used as valuable research tools across a range of disciplines. Their wide and flexible field of vision can provide immersive and/or alternative perspectives compared to standard video. This paper will present emerging findings from using a 360° video camera to capture natural dye craft practice from an auto-ethnographic perspective and as an observer of other dyers' practice during fieldwork visits. The 360° video data forms part of my doctoral study, in which I explore the embodied interactions between people, plants and materials that connect practitioners to their surroundings, linking them to other species and ecologies. The varied nature of the actions and processes that form the craft practice (e.g. foraging, tending, harvesting, mordanting, dyeing), and the different places and spaces in which these actions occur, presented a practical and observational challenge when trying to record the practice in a video format. Using a 360° camera proved to be a flexible, data-rich and engaging method for recording the craft. The ability to 'move' around and explore different perspectives from within the video after it was recorded was especially valuable, allowing a shift in the focus of the recording and presenting the opportunity to actively centre or decentre plants, people and materials. In this paper, I will reflect on my experiences recording and working with 360° video data and discuss some of the limitations and possible benefits of using this equipment in a craft research setting. [ABSTRACT FROM AUTHOR]

Addressing the challenge of the current harvester route detection method's reduced robustness within lodging-affected farmland environments and its limited perception of crop lodging, this paper proposes a harvesting operation image segmentation method based on SLIC superpixel segmentation and the AdaBoost ensemble learning algorithm. This segmentation enables two essential tasks. Firstly, the RANSAC algorithm is employed to extract the harvester's operational route through straight-line fitting from the segmented image. Secondly, the method utilizes a 3D point cloud generated by binocular vision, combined with IMU information for attitude correction, to estimate the height of the harvested crop in front of the harvester. Experimental results demonstrate the effectiveness of this method in successfully segmenting the harvested and unharvested areas of the farmland. The average angle error for the detected harvesting route is approximately 1.97°, and the average error for crop height detection in the unharvested area is around 0.054 m. Moreover, the algorithm exhibits a total running time of approximately 437 ms. The innovation of this paper lies in its simultaneous implementation of two distinct perception tasks, leveraging the same image segmentation results. This approach offers a robust and effective solution for addressing both route detection and crop height estimation challenges within lodging-affected farmland during harvesting operations. [ABSTRACT FROM AUTHOR]

Vibration energy harvesting using piezoelectric transduction has emerged as a promising approach for extracting energy from ambient mechanical oscillations. Nonetheless, linear energy harvesters exhibit a confined bandwidth and cannot proficiently capture energy from wide-spectrum or irregular vibrations. To address this limitation, nonlinear energy harvesters have been proposed. This review paper provides an overview of the mechanical design and vibration management aspects of both linear and nonlinear piezoelectric energy harvesters. It also discusses the advantages and shortcomings of both forms. A systematic methodology is applied in surveying the literature on piezoelectric vibration energy harvesting, selecting a set of relevant papers covering the period up until 2023. These papers are then classified dependent on the mechanical design and vibration management aspects of linear and nonlinear power harvesters. Key techniques are identified for harvesting energy from piezoelectric vibrations, including mechanical design methods such as springs, magnets, ropes, and other mechanisms, as well as vibration management techniques involving feedback control, tuned mass dampers, and other approaches. The paper concludes by offering a design viewpoint to enhance energy harvesting proficiency and presenting practical applications for both linear and nonlinear piezoelectric energy harvesting. [ABSTRACT FROM AUTHOR]

Smart agricultural harvesting robots' vision recognition, control decision, and mechanical hand modules all resemble the human eye, brain, and hand, respectively. To enable automatic and precise picking of target fruits and vegetables, the system makes use of cutting-edge sensor technology, machine vision algorithms, and intelligent control and decision methods. This paper provides a comprehensive review of international research advancements in the "eye–brain–hand" harvesting systems within the context of smart agriculture, encompassing aspects of mechanical hand devices, visual recognition systems, and intelligent decision systems. Then, the key technologies used in the current research are reviewed, including image processing, object detection and tracking, machine learning, deep learning, etc. In addition, this paper explores the application of the system to different crops and environmental conditions and analyzes its advantages and challenges. Finally, the challenges and prospects for the research on picking robots in the future are presented, including further optimization of the algorithm and improvement of flexibility and reliability of mechanical devices. To sum up, the "eye–brain–hand" picking system in intelligent agriculture has great potential to improve the efficiency and quality of crop picking and reduce labor pressure, and it is expected to be widely used in agricultural production. [ABSTRACT FROM AUTHOR]

Tactile sensing plays a pivotal role in achieving precise physical manipulation tasks and extracting vital physical features. This comprehensive review paper presents an in-depth overview of the growing research on tactile-sensing technologies, encompassing state-of-the-art techniques, future prospects, and current limitations. The paper focuses on tactile hardware, algorithmic complexities, and the distinct features offered by each sensor. This paper has a special emphasis on agri-food manipulation and relevant tactile-sensing technologies. It highlights key areas in agri-food manipulation, including robotic harvesting, food item manipulation, and feature evaluation, such as fruit ripeness assessment, along with the emerging field of kitchen robotics. Through this interdisciplinary exploration, we aim to inspire researchers, engineers, and practitioners to harness the power of tactile-sensing technology for transformative advancements in agri-food robotics. By providing a comprehensive understanding of the current landscape and future prospects, this review paper serves as a valuable resource for driving progress in the field of tactile sensing and its application in agri-food systems. [ABSTRACT FROM AUTHOR]

There will be great damage in the process of harvesting, transporting, and storing after grain matures. The injury rate is as high as 8% to 12%. After damage, the germination rate of the grain becomes lower, the quality decreases, and it is easily infected with pests and molds. This study of the grain-crushing characteristics is of great significance to ensure grain quality, and an accurate crushing model is a prerequisite for effectively simulating crushing characteristics. This paper studies the shattering characteristics of wheat grains. Two-dimensional slices of wheat grain were obtained using X-ray tomography technology. Then, an accurate three-dimensional outer contour model of the wheat particle was constructed using image filtering and segmentation algorithms. The particle filling process was conducted using EDEM 2018 software to establish a wheat particle simulation model based on the Hertz–Mindlin with a Bonding contact model. Using the DOE experimental design method, single-factor experiments, Plackett–Burman experiments, steepest-climb experiments, and Box–Behnken were designed to study the fragmentation characteristics of wheat particles combined with parameter calibration and physical experiments. The test results show that the normal stiffness per unit area is 7.392 × 1010 N/m3, critical normal stress is 5.293 × 106 Pa, critical tangential stress is 5.001 × 106 Pa, and the relative error about 3%, which verifies the reliability of the simulation parameters in the discrete-element crushing model of wheat grain. This study focuses on two essential aspects: 1. establishing an accurate wheat-grain contour model; and 2. calibrating the bonding parameters of the discrete-element simulation model of wheat grain. The wheat grain discrete-element crushing model and the calibration of its bonding parameters are constructed to provide a foundation for the study of wheat-grain crushing characteristics. It is of great significance to study the situation of wheat grains and where cracks are produced. In this paper, an accurate model of the wheat-grain contour is established, and the bonding parameters of the discrete-element simulation model of the wheat grain are calibrated. The calibration of the model of the discrete elements of wheat-grain fragmentation and its bonding parameters will provide a basis for studying the crushing characteristics of wheat grain. Understanding the condition of wheat grains and the causes of cracks carries significant academic significance. [ABSTRACT FROM AUTHOR]

This review paper provides an overview of various types of photobioreactors (PBRs) that could be used for the production of polyhydroxyalkanoates (PHAs) using anoxygenic photoheterotrophs, with a focus on the design and operation of these systems. The paper highlights the potential of different PBRs based on reactor geometry and growth mode, and also examines the advantages and disadvantages of each PBR type and summarizes their suitability for PNSB-PHA production. The optimization of reactor design and operation is crucial for maximizing PNSB growth and PHA productivity. The self-immobilization of bacteria in granular sludge is a promising technology for wastewater treatment and the production of PHAs, while grooved-surface PBRs and porous-substrate PBRs have limitations due to difficult biomass harvesting in the former and the presence of aerobic conditions incongruent with PNSB culturing in the latter. Limitations exist with all solutions for maximizing rapid growth and maintaining high biomass concentrations due to the requirements of phototrophic growth. [ABSTRACT FROM AUTHOR]

The present paper deals with mathematical modeling of a fishery resource system in an aquatic atmosphere consisting of two zones: a free fishing zone and a reserved zone, where fishing is strictly prohibited. The dynamics of the system is studied in the presence of bird predator. In this paper, the quadratic harvesting of the fish species in unreserved zone has been considered whereas bird predator species is subjected to linear harvesting. All the possible biological and bionomic equilibria of the system are studied extensively for their existence, local as well as global stability. We have found various ranges of harvesting parameter for maintaining the Sustainability in the proposed ecosystem. Optimal harvesting is discussed using Pontryagin's maximum principle. Numerical simulations are done to support the theoretical results obtained. [ABSTRACT FROM AUTHOR]

Soybean seeding monomers can realize the process of opening, seed throwing, covering, and compacting when they work. Due to the complexity of their working process, the relevant process cannot be analyzed by the discrete element method (DEM) alone. The DEM coupled with the multi-rigid body dynamics method (MBD) can solve the above problem, and the simulation analysis of the above process is realized by coupling the EDEM software with RecurDyn software. The changes in the position of soybean seed particles before and after covering and compacting are analyzed. The results show that when the working speed of the seeding monomer increases, the distance along the vertical direction of the soybean seed particles after covering gradually increases, and the distance along the horizontal direction gradually decreases. The effect of different working speeds of seeding monomer on the opening situation and the variation in seed particle positions is studied. The results show that the ditch angle gradually decreases as the working speed of the seeding monomer increases. The distribution of seed particle spacing is also analyzed. The above tests are simulated, and the results show a high agreement between the simulation and test results, proving the accuracy of the coupling method. This paper applies the coupling method for the first time to the simulation of the seeding monomer. This method can be applied not only to the analysis of the sowing process of soybean seeding monomers, but also be applied to the analysis of other machinery working processes, such as the tillage process, the sieving process, the planting and harvesting processes of crops, etc. It also deepens the application of the discrete element method in the field of agriculture. [ABSTRACT FROM AUTHOR]

The paper presents a two-species prey-predator scenario in which both exhibit swarm behaviour. As a result, due to this co-operative behaviour after harvesting an intra-specific competition occurs between predators. Our research demonstrates that the system's stability is significantly influenced by their inter-dependent competitiveness and joint harvesting effort. We start out by talking about the model's Positivity and Boundedness. The equillibria are analysed and displayed. A Hopf-Bifurcation criterion is discovered. The next step is to do numerical simulations to help explain some of the mathematical findings. We will better grasp the dynamics of predator-prey interaction in natural environments with the help of this model. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid marine renewable energy-harvesting system to increase energy production, reduce levelized costs of energy and promote renewable marine energy. Firstly, various marine renewable energy resources and state-of-art technologies for energy exploitation and storage were reviewed. The site selection criteria for each energy-harvesting approach were identified, and a scoring matrix for site selection was proposed to screen suitable locations for the hybrid system. The Triton Knoll wind farm was used to demonstrate the effectiveness of the scoring matrix. An integrated energy system was designed, and FE modeling was performed to assess the effects of additional energy devices on the structural stability of the main wind turbine structure. It has been proven that the additional energy structures have a negligible influence on foundation/structure deflection (<1%) and increased system natural frequency by 6%; thus, they have a minimum influence on the original wind system but increased energy yield. [ABSTRACT FROM AUTHOR]

Intermittent stop–move motion planning is essential for optimizing the efficiency of harvesting robots in greenhouse settings. Addressing issues like frequent stops, missed targets, and uneven task allocation, this study introduced a novel intermittent motion planning model using deep reinforcement learning for a dual-arm harvesting robot vehicle. Initially, the model gathered real-time coordinate data of target fruits on both sides of the robot, and projected these coordinates onto a two-dimensional map. Subsequently, the DDPG (Deep Deterministic Policy Gradient) algorithm was employed to generate parking node sequences for the robotic vehicle. A dynamic simulation environment, designed to mimic industrial greenhouse conditions, was developed to enhance the DDPG to generalize to real-world scenarios. Simulation results have indicated that the convergence performance of the DDPG model was improved by 19.82% and 33.66% compared to the SAC and TD3 models, respectively. In tomato greenhouse experiments, the model reduced vehicle parking frequency by 46.5% and 36.1% and decreased arm idleness by 42.9% and 33.9%, compared to grid-based and area division algorithms, without missing any targets. The average time required to generate planned paths was 6.9 ms. These findings demonstrate that the parking planning method proposed in this paper can effectively improve the overall harvesting efficiency and allocate tasks for a dual-arm harvesting robot in a more rational manner. [ABSTRACT FROM AUTHOR]

Purpose: Vertical Farming (VF) is a technique of growing food in vertically stacked layers or vertically inclined surfaces. Theoretically, it has the capability of growing any crops, fruits, or vegetables. The modern techniques of vertical farming through a controlled environment can support the growth and harvest of a crop irrespective of the season and climatic conditions. The newer modern techniques of VF have further reduced the dependency on sunlight, soil, and water. This paper analyses the appropriateness of vertical farming for developing countries like Oman. Design/Methodology/Approach: This paper is based on secondary data and establishes the arguments in favor and against vertical farming. Findings: The findings support the need for vertical farming and suggest that it is very appropriate for the urban setups, where it reduces the transportation cost and facilitates the availability of fresh and pesticide-less food products Research Limitations: The major limitation of this paper is that it only accounts the secondary information, the empirical investigation is missing. Managerial Implications: This paper implies government policies, allied agencies, and the agricultural sector. Originality/Value: This paper is an original paper based on secondary data, providing a conceptual model for Oman, on which very limited studies in the given context is available. [ABSTRACT FROM AUTHOR]

Extracting robust and discriminative local features from images plays a vital role for long term visual localization, whose challenges are mainly caused by the severe appearance differences between matching images due to the day-night illuminations, seasonal changes, and human activities. Existing solutions resort to jointly learning both keypoints and their descriptors in an end-to-end manner, leveraged on large number of annotations of point correspondence which are harvested from the structure from motion and depth estimation algorithms. While these methods show improved performance over non-deep methods or those two-stage deep methods, i.e., detection and then description, they are still struggled to conquer the problems encountered in long term visual localization. Since the intrinsic semantics are invariant to the local appearance changes, this paper proposes to learn semantic-aware local features in order to improve robustness of local feature matching for long term localization. Based on a state of the art CNN architecture for local feature learning, i.e., ASLFeat, this paper leverages on the semantic information from an off-the-shelf semantic segmentation network to learn semantic-aware feature maps. The learned correspondence-aware feature descriptors and semantic features are then merged to form the final feature descriptors, for which the improved feature matching ability has been observed in experiments. In addition, the learned semantics embedded in the features can be further used to filter out noisy keypoints, leading to additional accuracy improvement and faster matching speed. Experiments on two popular long term visual localization benchmarks (Aachen Day and Night v1.1, Robotcar Seasons) and one challenging indoor benchmark (InLoc) demonstrate encouraging improvements of the localization accuracy over its counterpart and other competitive methods. [ABSTRACT FROM AUTHOR]

Tea bud target detection is essential for mechanized selective harvesting. To address the challenges of low detection precision caused by the complex backgrounds of tea leaves, this paper introduces a novel model called Tea-YOLOv8s. First, multiple data augmentation techniques are employed to increase the amount of information in the images and improve their quality. Then, the Tea-YOLOv8s model combines deformable convolutions, attention mechanisms, and improved spatial pyramid pooling, thereby enhancing the model's ability to learn complex object invariance, reducing interference from irrelevant factors, and enabling multi-feature fusion, resulting in improved detection precision. Finally, the improved YOLOv8 model is compared with other models to validate the effectiveness of the proposed improvements. The research results demonstrate that the Tea-YOLOv8s model achieves a mean average precision of 88.27% and an inference time of 37.1 ms, with an increase in the parameters and calculation amount by 15.4 M and 17.5 G, respectively. In conclusion, although the proposed approach increases the model's parameters and calculation amount, it significantly improves various aspects compared to mainstream YOLO detection models and has the potential to be applied to tea buds picked by mechanization equipment. [ABSTRACT FROM AUTHOR]

The present paper presents a comparative study of the threshers that equip different types of self-propelled combines for cereal harvesting. Particular attention is paid to the problems related to the exploitation of the threshers, focusing on some more important aspects, including those regarding the technological adjustments, the organization and management of the harvesting chain, the execution of the harvesting works and their quality control. We measured the constructive and functional parameters of each threshing device and determined the hourly productivity of each combine and the consumption of diesel per ton of harvested wheat. This shows that worldwide harvesting combines tend to use double-flow threshers with tangential flow due to the following advantages: both threshing and separation of the grains from the straw are done, both length of the thresher and vibrations are reduced because there is no shaking for separation, the percentage of non-threshed ears is reduced by the fact that the material is trained in rotary motion inside the threshing device. [ABSTRACT FROM AUTHOR]

Accurately and effectively measuring the breaking quality of harvested corn kernels is a critical step in the intelligent development of corn harvesters. The detection of broken corn kernels is complicated during the harvesting process due to turbulent corn kernel movement, uneven lighting, and interference from numerous external factors. This paper develops a deep learning-based detection method in real time for broken corn kernels in response to these issues. The system uses an image acquisition device to continuously acquire high-quality corn kernel image data and cooperates with a deep learning model to realize the rapid and accurate recognition of broken corn kernels. First, we defined the range of broken corn kernels based on image characteristics captured by the acquisition device and prepared the corn kernel datasets. The corn kernels in the acquired image were densely distributed, and the highly similar features of broken and whole corn kernels brough challenges to the system for visual recognition. To address this problem, we propose an improved model called BCK-YOLOv7, which is based on YOLOv7. We fine-tuned the model's positive sample matching strategy and added a transformer encoder block module and coordinate attention mechanism, among other strategies. Ablation experiments demonstrate that our approach improves the BCK-YOLOv7 model's ability to learn effectively broken corn kernel features, even when high-density features are similar. The improved model achieved a precision rate of 96.9%, a recall rate of 97.5%, and a mAP of 99.1%, representing respective improvements of 3.7%, 4.3%, and 2.8% over the original YOLOv7 model. To optimize and deploy the BCK-YOLOv7 model to the edge device (NVIDIA Jetson Nano), TensorRT was utilized, resulting in an impressive inference speed of 33 FPS. Finally, the simulation system experiment for corn kernel broken rate detection was performed. The results demonstrate that the system's mean absolute deviation is merely 0.35 percent compared to that of manual statistical results. The main contribution of this work is the fact that this is the first time that a set of deep learning model improvement strategies and methods are proposed to deal with the problem of rapid and accurate corn kernel detection under the conditions of high density and similar features. [ABSTRACT FROM AUTHOR]

Introduction: Real-time fruit detection is a prerequisite for using the Xiaomila pepper harvesting robot in the harvesting process. Methods: To reduce the computational cost of the model and improve its accuracy in detecting dense distributions and occluded Xiaomila objects, this paper adopts YOLOv7-tiny as the transfer learning model for the field detection of Xiaomila, collects images of immature and mature Xiaomila fruits under different lighting conditions, and proposes an effective model called YOLOv7- PD. Firstly, the main feature extraction network is fused with deformable convolution by replacing the traditional convolution module in the YOLOv7- tiny main network and the ELAN module with deformable convolution, which reduces network parameters while improving the detection accuracy of multiscale Xiaomila targets. Secondly, the SE (Squeeze-and-Excitation) attention mechanism is introduced into the reconstructed main feature extraction network to improve its ability to extract key features of Xiaomila in complex environments, realizing multi-scale Xiaomila fruit detection. The effectiveness of the proposed method is verified through ablation experiments under different lighting conditions and model comparison experiments. Results: The experimental results indicate that YOLOv7-PD achieves higher detection performance than other single-stage detection models. Through these improvements, YOLOv7-PD achieves a mAP (mean Average Precision) of 90.3%, which is 2.2%, 3.6%, and 5.5% higher than that of the original YOLOv7-tiny, YOLOv5s, and Mobilenetv3 models, respectively, the model size is reduced from 12.7 MB to 12.1 MB, and the model’s unit time computation is reduced from 13.1 GFlops to 10.3 GFlops. Discussion: The results shows that compared to existing models, this model is more effective in detecting Xiaomila fruits in images, and the computational complexity of the model is smaller. [ABSTRACT FROM AUTHOR]

A circular economy (CE) calls for the value of materials and products to be maintained and recovered through narrowing, closing, and slowing loops. However, there remain challenges in capturing value through reuse of components in refurbished and repaired products. In this paper, we provide an overview of the research and practice of harvesting spare parts from used and waste electrical and electronic equipment (white goods and consumer electronic products). Through a literature review and case studies of Norway, Sweden, and California, we provide an overview of drivers and barriers for spare part harvesting. Applying a stakeholder value mapping framework, we identify the key stakeholders involved in spare part harvesting and map the values captured, missed, and destroyed to identify opportunities for increased value retention. Finally, we suggest further refinements for policy to upscale spare part harvesting in light of CE goals and objectives. [ABSTRACT FROM AUTHOR]

During the harvesting of cut-stem vegetables, the structural parameters of cutters have an important influence on the harvesting effect. Structural parameters of stalks directly affect the cutting effect of the cutter. The thickness of the stalk xylem has a strong influence on the cutting stability and cutting surface effect of the cutter. In this paper, the effect of the broccoli stalk xylem on conventional toothless cutters was investigated using broccoli stalks as the cutting object. It was found that a thicker xylem leads to shear force fluctuations, which in turn affects the smooth operation of the cutting device. Taking locust mouthparts as the research object, a bionic multi-tooth cutting method is proposed in this paper to obtain the contour curve based on the locust upper jaw cutting tooth lobe. By establishing the regression equation, the contour curve of the cutting teeth is fitted accurately. The cutter edge is designed with the locust's maxillary incisive lobe as the bionic object. ANSYS software was used to simulate the cutting of a double disc cutter and broccoli stalk. The effect of each factor was analyzed by response-surface regression to determine the optimal cutter speed, machine forward speed, cutting inclination, blade overlap, and optimal cutting position. The cutting test is verified via broccoli stalks and a cutting test bench to further determine the cutting device operating parameters. The optimal operating parameters of the cutting device were 0.239 m/s forward speed, 30.974-degree cutting edge angle, 10.066 mm blade overlap, and 467.511 rpm. [ABSTRACT FROM AUTHOR]