Optimal Harvesting Research Articles

Ab Initio Predictions of Adsorption in Flexible Metal-Organic Frameworks for Water Harvesting Applications.

Metal-organic frameworks such as MOF-303 and MOF-LA2-1 have demonstrated exceptional performance for water harvesting applications. To enable a reticular design of such materials, an accurate prediction of the adsorption properties with chemical accuracy and fully accounting for the flexibility is crucial. The computational prediction of water adsorption properties in MOFs has become standard practice, but current methods lack the predictive power needed to design new materials. Limitations stem from the way the interatomic potential is described and the inadequate consideration of the framework flexibility. Herein, we showcase a methodology to obtain chemically accurate adsorption isotherms that fully account for framework flexibility. The method relies on very accurate and efficiently trained machine learning potentials and transition matrix Monte Carlo simulations to account for framework flexibility. For MOF-303, quantitatively accurate adsorption isotherms are obtained, provided an accurately benchmarked electronic structure method is used to train the machine learning potential, and local and global framework flexibility is accounted for. The broader applicability is shown through the study of MOF-333 and MOF-LA2-1. Analysis of the water density profiles in the MOFs gives insight into the factors governing the shape and origin of the isotherm. An optimal water harvester should have initial seeding sites with intermediate adsorption strength to prevent detrimental low-pressure water uptake. To increase the working capacity, linker extension strategies can be used while maintaining the initial seeding sites, as was done in MOF-LA2-1. The methodology can be applied to other guest molecules and MOFs, enabling the future design of MOFs with specific adsorption properties.

Investigation of networked SSHI configurations for plate-based piezoelectric energy harvesters

Piezo-patch energy harvesters attached to plate-like structures can be used to extract multimodal vibrational energy. For each piezo-patch, SSHI circuits can boost the harvested power compared to standard rectifier circuits. However, the effect of using different configurations of the SSHI network for multiple piezo-patches on plate structures has not yet been understood. This study aims to assess the performance of the networked SSHI configurations compared to networked rectifier configurations and investigate the optimum configurations for energy harvesting purposes. For this, three piezo-patches are bonded to an aluminum plate and connected to single and/or multiple circuits. The equivalent circuit model (ECM) of the electromechanical system is developed from the experimentally validated analytical model and utilized in simulation software LTspice. The performances of considered configurations are evaluated regarding peak power outputs, and the optimal load resistances are obtained. Based on the experimental findings, the best configuration is determined and verified by simulations. The results show that by using respective SSHIs, the power output can be improved by preventing charge cancellation despite the cost of increased diode loss. This study contributes to our understanding of optimal energy harvesting techniques by investigating the effectiveness of networked SSHI configurations for multiple piezo-patches on plate structures.

The Impact of Culture Time on Genistin and Genistein Accumulation in Flemingia macrophylla (Willd.) Prain Seedlings and the Differentially Expressed Genes

Background Genistein and genistin are the main active constituents of Flemingia macrophylla (Willd.) Prain and the amount of active constituents in the roots varies depending on the cultivation time. The harvesting period has a great influence on its quality, and there are fewer studies on the optimal harvesting time of F. macrophylla, which is urgently needed to explore the optimal harvesting period to improve its use value. Purpose F. macrophylla (Willd.) Prain is an important medicinal plant distributed in China. This study aimed to enhance the accumulation of active ingredients genistin and genistein in F. macrophylla, and explore the mechanism of biological synthesis of these active compounds at molecular levels. Methods Transcriptome sequencing and high-performance liquid chromatography (HPLC) technology were applied to F. macrophylla tissue culture seedlings for 30 days after culturing (DAC), 45 DAC, 60 DAC, and 75 DAC. Then, gene expression, representative active substances, and important phenotypic indexes were analyzed. Results The highest fresh weight and root thickness were attained at 45 DAC, the highest plant height at 60 DAC, and the maximum rooting rate at 75 DAC. The genistin content reached the highest level at 45 DAC, and the content of genistein reached the highest level at 30 DAC. The total content of genistin and genistein achieved the maximum levels at 45 DAC. A total of 7,735 differently expressed genes (DEGs) in the three comparable groups, including T45d vs T30d, T60d vs T30d, and T60d vs T45d, were identified. Through the Kyoto Encyclopedia Genes and Genomes (KEGG) enrichment analysis of DEGs, isoflavonoid biosynthesis and phenylpropanoid biosynthesis pathways were significantly enriched in two groups, including T45d vs T30d and T60d vs T30d. A total of 41 DEGs involved in the production of isoflavones were discovered in the three groups. Conclusion The optimism culture time for the accumulation of genistein and genistin in F. macrophylla seedlings ranged from 30 to 45 days. Forty-one DEGs in the genistein and genistin synthesis pathways were found through transcriptomics analysis. This study sheds light on the biosynthesis of genistin and genistein in F. macrophylla.

A novel linear Fresnel solar CPV/T hybrid system with advanced film beam splitter for optimal energy harvesting: Optical and thermodynamic analysis

A novel linear Fresnel solar CPV/T hybrid system with advanced film beam splitter for optimal energy harvesting: Optical and thermodynamic analysis

Aplikasi Pendeteksi Kematangan Tanaman Menggunakan Metode Transformasi Ruang Warna HSI (Hue, Saturation, Intensity) dan K-NN (K- Nearest Neighbor)

Tomatoes and chili peppers are essential commodities in the agricultural and food industry, playing a crucial role in nutritional diversity and flavor in human diets. Identifying the ripeness of these fruits is a critical step in the food supply chain, yet it is often done manually by directly observing the ripeness of chilies and tomatoes, which is time-consuming and susceptible to observer subjectivity. Therefore, a system that can identify the ripeness of tomatoes and chili peppers is needed. This system implements the HSI color space extraction method and the K-NN method. K-NN can classify plants based on colors extracted using the HSI color space, which includes three dimensions: Hue (H), Saturation (S), and Intensity (I). The research results in a model from the tomato and chili pepper dataset with an accuracy of 92% and a data split ratio of 80%:20%. This model is implemented in web and mobile formats, expected to efficiently and accurately identify the ripeness of tomatoes and chili peppers. This can help farmers determine the optimal harvest time, improve agricultural production and quality, and provide more reliable information in the food supply chain

MOF-Derived Hollow C, N-Doped Co3O4 Dodecahedral Nanostructure Enwrapped with MgIn2S4 Nanosheets for Enhanced Photocatalytic N2 Reduction.

Design of hierarchical hollow nanoheterostructure materials through interfacial and defect engineering is an innovative approach for achieving optimal charge separation dynamics and photon harvesting efficiency. Herein, we have described a facile technique to fabricate hollow MOF-derived C, N-doped-Co3O4 (C, N-Co3O4) dodecahedral particles enwrapped with MgIn2S4 nanosheets for enhanced N2 reduction performance. ZIF-67 was initially used as a sacrificial template to prepare hollow C, N-Co3O4 using a carbonization route followed by low-temperature calcination treatment. The controlled synthetic protocol not only led to nonmetal doping but also produced an interwoven carbon matrix that improved the photoelectron mobility. Density functional theory calculations further substantiated the creation of atomic defects through substitution of C at tetrahedral Co2+ sites and N at lattice O2- sites of the Co3O4 structure. C, N-Co3O4 was subsequently coupled with MgIn2S4 nanosheets to prepare the C, N-Co3O4/MgIn2S4 [C, N-CM (X)] p-n heterojunctions. The photocatalytic study revealed that the NH4+ ion production activity of the optimal C, N-CM (1:1) material (334 μmol g-1 h-1) was significantly higher (4-10 times) than that of pure components. The enhanced activity of the composite was ascribed to its distinct topological features, superior charge carrier dynamics, and creation of atomic defects that afforded a large number of surface-active sites.

OPTIMAL HARVEST CONTROL OF PREDATOR–PREY SYSTEMS IN FISHERIES WITH STAGE STRUCTURE AND ADDITIONAL FOOD SUPPLY

In this paper, we consider a fishery predator–prey system with stage structure and additional food incorporating fluctuations in resource prices influenced by supply and demand. The fishery system can be regarded as a slow–fast system under some suitable assumptions. Through variable aggregation methods, we derive a simplified four-dimensional model governing the density of prey fish, the juvenile and adult predator densities, and fishing effort in the fishery. We perform a stability analysis of the simplified system and give the optimal harvesting policy by using Pontryagin’s maximum principle. In our analysis, we find that the catastrophe equilibrium point corresponding to overfishing leading to fish extinction is unstable, which indicates that the predator stage structure has an impact on the stability of the system. Moreover, we find that due to the additional food item the system appears to have prey-free equilibrium points, which indicates that the system does not collapse due to the extinction of one prey item. We also give thresholds for the amount of additional food needed to maintain the balance of the system under different circumstances.

Optimal Harvest Maturity Changes Depending on the Season Throughout the Year in Papaya Grown in Mediterranean Climate-Improved Greenhouses

Papaya is a tropical fruit that is highly appreciated worldwide for its organoleptic properties and its nutritional and medicinal value. The optimal fruit quality for market requirements is determined by genotype, environmental conditions, and crop management, as well as fruit handling at harvest and adequate postharvest conservation. The aim of this work was to determine the optimal maturity stage, based on skin color, at which to harvest papayas grown in Mediterranean greenhouses with active climate control in south-east Spain, depending on the harvest season. We confirm that it is possible to produce high-quality papayas in our conditions, but the optimal harvest maturity stage in each season, along with the postharvest storage capacity, vary throughout the year, even with active climate control. Thus, 10% yellow skin color (S0) is the optimal for harvesting in autumn, mainly due to its better conservation capacity and firmness, having a high TSS content (>11 °Brix). In summer and spring, the lower TSS content discourages harvesting at S0, with S1 (30–40% yellow skin color) or S2 (50–60% yellow skin color) being the optimal stages for harvest to ensure the minimum TSS content required for papaya commercialization (10 °Brix). In any case, papayas produced under our conditions are generally well rated by consumers.

Examination of Red Clover Optimum Harvesting Status in Seed Production With Unmanned Aerial Systems (UAS)

Red clover is the second most valuable and intensively produced leguminous fodder crop in Hungary. Optimum harvesting should start when 75-80 per cent of seed-heads are brown and 90 per cent or more of the seeds are past the hard-dough stage of maturity. Harvesting must be done before an appreciable amount of seed-head deterioration begins. On fall plantings the first-year crop normally matures during the months of August and September. Seed from second-year stands, if not cut back, will mature somewhat earlier. The harvesting of red clover is divided into two distinct operations: (1) the curing or preparation and (2) the hulling and separation of the seed. The curing is by windrowing or contact desiccant spraying. It is the timing of each step that can make all the difference between a good yield and a mediocre one. The agronomists decide those actions with personal on-site inspections of the heads. Their decisions rely on random point sampling. A trend has developed with the recent interest in the harvesting time estimation of red clover in seed production to meet the demands of sustainable and precise agriculture. Unmanned aerial systems (UAS) are one of the current state-of-the-art tools when it comes to supporting farm-scale phenotyping trials. In this examination, we explored the red clover seed heads classification using deep learning on a UAS RGB aerial image. We assessed different ripening status of the flower heads with a single-shot detector (SSD) deep learning model. It is proposed that drone data collection was suggested to be optimum in this study, closer to the harvest date, but not later than the ageing stage. With SSD model we can calculate the number of heads on the vegetation surface but to differentiate them (e.g., purple-, light-brown-, dark-brown flower heads) with high confidence, we need more examinations, spectral bands or fine adjustments of the model with additional data.

Evaluation of the combine thresher efficiency by grain losses under operating conditions

BACKGROUND: The throughput capacity of the thresher of the combine is determined with permissible grain losses of 1.5% of its total amount collected in the combine bunker. The range of feeding of bread mass, for example, from a minimum of 0.5-1.0 kg/s to – is called the working characteristic of the threshing machine (WCT) of the combine harvester. The actual grain loss in the considered feed range is less than 1.5%, but their overall digital estimate is unknown. Nevertheless, this is the most important indicator of the miner's efficiency, since these losses are summed up (for example, at the level of 1-1.2%) and can reach a significant value, therefore, the assessment of the miner only in terms of throughput is insufficient to assess its efficiency, since the miner rarely works at the throughput level, and in WCT mode, grain losses are summed up every second/ AIMS operation consists in development of criterion for evaluation of combine thresher operation efficiency by grain losses at operating modes. METHODS: The article proposes a new methodology for assessing the efficiency of the combine. Statistical dependences of grain losses change depending on change of bread mass supply into combine are used. The throughput capacity of the combine thresher in kg/s is determined with grain losses behind the thresher at the level of 1.5%. RESULTS: Numerous laboratory-field tests have revealed a close correlation between the four threshing parameters (engine power and separation area of ​ ​ the hump, straw and cleaning, and the dynamics of grain loss growth depending on the feed of bread mass with great reliability in the range of WCT feeds is approximated by a logistic dependence. For practical calculations, computer software has been developed. This makes it possible to more quickly compare harvesters of different designs with each other. CONCLUSION: For the first time, to assess the efficiency of the combine thresher, a new criterion is introduced with the name "average integral operational losses of grain behind the thresher in the working range of bread mass supply"; An increase in grain losses behind the thresher was detected with an increase in the throughput of the combine; The optimal harvesting area is determined, during harvesting of which the growth of grain losses is compensated during the operating mode of combines of high productivity per unit of bread mass supply. The optimum harvesting area depends to a greater extent on the throughput of the harvester and harvesting conditions according to the state of the agrofon and the zoning coefficient, that is, the degree of adaptation of the harvesters to the zonal features of the agrofon and the applied machine use methods; When calculating the economic efficiency of combines, it is advisable to take into account the average integral operational losses of grain for alternative versions of combines and, on this basis, give them a more comprehensive assessment. The use of the logistic function of grain losses from the supply of bread mass to the combine and, on its basis, the introduction of the criterion of average integral operational losses of grain behind the thresher of the combine represents scientific and practical novelty.

Intelligent Agriculture System using KNN Algorithm

The agricultural sector faces the challenge of maximizing crop yields while maintaining sustainability and resource efficiency. Nostalgic or Romantic Views, oftenreliant on manual monitoring and subjective decision-making, can result in inefficiencies and lower productivity. To address these issues, an intelligent agriculture system utilizing the K-Nearest Neighbors (KNN) algorithm has been proposed. This paper presents an intelligent agriculture system that leverages the K- Nearest Neighbors (KNN) algorithm to enhance crop management and yield prediction. By analyzing various environmental factors such as soil moisture, temperature, and humidity, the system accurately classifies the optimal planting and harvesting times for different crops. Additionally, it provides real-time recommendations for irrigation and fertilization based on predictive analysis. The integration of KNN allows for adaptive learning from historical data, thereby improving decision-making processes for farmers. This approach aims to optimize resource utilization, increase productivity, and promote sustainable agricultural practices. The proposed intelligent agriculture system leverages the K- Nearest Neighbors (KNN) algorithm to enhance crop management and sustainability. By analyzing real-time environmental data and historical agricultural records, the system provides precise recommendations for optimal planting and harvesting times, efficient irrigation schedules, and tailored fertilization strategies. This data-driven approach aims to increase crop yields, optimize resource utilization, and reduce the environmental impact of farming practices. The system's continuous learning capability allows it to adapt to changing conditions, ensuring that farmers receive up-to-date and accurate guidance. Overall, the integration of KNN in agriculture represents a significant advancement towards sustainable and efficient farming.

Late Harvest: Freezing Temperatures Reduce the Root Yield and Sugar Content of Beta vulgaris L.

ABSTRACTSugar beet (Beta vulgaris L.) is the main source of white sugar in northern China, and an optimal harvesting time is key for maximising its yield and sugar content. The goal of this study was to investigate the effect of gradually extending harvest times on the growth, physiological characteristics, yield and sucrose accumulation in sugar beet and identify the optimal harvesting time. We conducted a 3‐year experiment across different harvesting times from 23 Sep to 28 Oct (harvest every 7 days) to examine the effects of different magnitudes of temperature reduction. The yield and sugar content were the highest in time III (the daily mean/minimum temperature: 9.5°C/5°C) in 2020, time III (the daily mean/minimum temperature: 8.2°C/2°C) and IV (the daily mean/minimum temperature: 10.1°C/2°C) in 2021 and time IV (the daily mean/minimum temperature: 10.5°C/5°C) and V (the daily mean/minimum temperature: 7°C/−1°C) in 2022. The yield and sugar content were low at an early harvest, as the biomass and sucrose accumulation process was not complete. However, the decrease in temperature (daily minimum temperature below 0°C) during late harvest leads to a decrease in the yield and sugar content because of the decreased sucrose accumulation of source leaves, increased sucrose decomposition and the poor transport capacity of phloem in the stem. Therefore, the optimal harvesting time for sugar beet in northern China depends on temperature conditions. It is optimal when (1) the daily minimum temperature gradually decreases to 0°C but not lower than 0°C and when (2) the daily mean temperature is approximately 10°C. This work will help sugar beet producers harvest high‐quality crops and reduce unnecessary losses in northern China.

Updates on Breast Reconstruction: Surgical Techniques, Challenges, and Future Directions.

The increasing global incidence of breast cancer underscores the significance of breast reconstruction in enhancing patients' quality of life. Breast reconstruction primarily falls into two categories: implant-based techniques and autologous tissue transfers. In this study, we present a comprehensive review of various aspects of implant-based reconstruction, including different types of implants, surgical techniques, and their respective advantages and disadvantages. For autologous breast reconstruction, we classified flaps and optimal harvest sites and provided detailed insights into the characteristics, benefits, and potential complications associated with each flap type. In addition, this review explores the emerging role of fat grafting, which has received increasing attention in recent years. Despite advancements, there remains substantial scope for further improvements in breast reconstruction, emphasizing not only aesthetic outcomes, but also a reduction in complications and postoperative recovery. By offering a comprehensive overview of the historical evolution, current landscape, and future prospects of breast reconstruction, this review aims to provide readers with a comprehensive understanding of breast cancer management strategies.

Seed Germination and Composition of Double and Triple Endosperm Mutants of Tropical Sweet Corn under Different Harvest and Storage Conditions

The development of inbred sweet corn lines that combine multiple endosperm mutants is an important aspect of breeding for eating quality. This study aimed to investigate the impact of different endosperm mutants, harvest times, and storage durations on traits related to seed quality and kernel carbohydrates. Sweet corn genotypes with specific gene combinations were carefully selected using single-nucleotide polymorphism (SNP) marker-assisted selection. Sixteen corn genotypes underwent germination assays through two tests: the standard between-paper test (BP test) and sand test (S test). The genotypes in each group were further refined based on germination and then evaluated for kernel carbohydrate dynamics during milk stages and postharvest storage. Each corn genotype exhibited distinct kernel carbohydrate dynamics during postharvest at each storage period. Out of 16 genotypes, four genotypes (K99, K30, 101L, and C13) were tolerant to degradation with slow rates [<0.27 mg·g−1 fresh weight (FW) per hour] for sucrose and total sugar. Three corn genotypes K118 (bt2bt2sese), K116 (bt2bt2sh2sh2sese), and K127 (bt2bt2sh2sh2sese) exhibited the highest germination percentage and germination speed in both tests. The optimal harvest date to achieve maximum sucrose, total sugar, and phytoglycogen was 22 days after pollination in the dry season of tropical savanna. When corn kernels have an increased shelf life, the amount of sucrose decreases, but the accumulation of fructose, glucose, and phytoglycogen increases. Two genotypes, K108 (bt2bt2sese) and K30 (bt2bt2sh2sh2sese), were identified as interesting alternatives for testers in sweet corn breeding programs. Genotype K108 showed moderate seed quality, tenderness, and high creaminess, and genotype K30 had low seed quality and tenderness, but high sweetness. This information is important for enhancing the efficiency of producing synergistic and augmented sweet corn F1 hybrids.

A Review of The Effects Of Light-Emitting Diodes (LEDs) on The Growth of Sunflower Microgreens and Their Nutritional Potential

Sunflower (Helianthus annuus) microgreens have become known as a potent source of essential nutrients and bioactive compounds with numerous health benefits. The microgreens industry has traditionally favored popular microgreens from the Brassicaceae family such as kale, rocket, and broccoli. Sunflower microgreens are characterized by their richness in vitamins, minerals, antioxidants, and phytochemicals that contribute significantly to a nutritious diet. However, their nutrient content can be influenced by various factors, including growing conditions and lighting. Light-emitting diodes (LEDs) offer precise control of light spectrum, light intensity, and lighting duration, enabling customized lighting systems optimized for growing sunflower microgreens. Pre-treatment and optimal harvest timing affect the quality and yield of microgreens, and sunflower microgreens are no exception. Accordingly, sunflower microgreens are typically harvested within 7 days of cultivation, making them ideal for mass production. The use of LED technology in the cultivation of microgreens offers the opportunity to further enhance their nutritional value and therapeutic potential. This review provides an overview of the benefits of sunflowers, sunflower microgreens, pre-treatments, and the ideal harvest period. The potential improvements from LED lighting are discussed and its impact on human health is explained.

Development of a visuo-tactile sensor for non-destructive peach firmness and contact force measurement suitable for robotic arm applications

Precise measurement of firmness was crucial for determining optimal harvesting times, implementing rational storage strategies and minimizing avoidable waste. Current technologies for assessing peach firmness struggled to balance high precision and non-destructive methods, while demonstrating high sensitivity to environmental disturbances, thereby limiting their application to production line. Future various scenarios in agriculture would increasingly rely on robotic arms, yet existing firmness assessment technologies were not compatible with these automated systems. Additionally, monitoring contact force was essential for flexible operation of the robotic arms. This work introduced a visuo-tactile sensor equipped with markers to capable of measuring peach firmness and monitoring contact force simultaneously during a single contact process, making it suitable for robotic arm applications. The contact was operated by the texture analyzer to simulate the fruit grasping process by a robotic arm. Utilizing deep neural networks and machine learning-based techniques to process high-precision geometric images collected by an internal camera, the visuo-tactile sensor achieved non-destructive measurements of peach firmness and contact force. For firmness measurement in the test set, the sensor achieved coefficient of determination (R2) of 0.878 and a root mean square error (RMSE) of 0.732. For contact force detection, the R2 was 0.942, and RMSE was 1.115 in the test set. The results showed visuo-tactile sensor was feasible for non-destructive detection of peach firmness and contact force, and has a broad application prospect in the field of agricultural robotics.

Co-cultivation of high-value microalgae species with filamentous microalgae for dairy wastewater treatment

The study examined the feasibility of co-culturing high-value microalgae sp. (Chlorella vulgaris (C.), and Scenedesmus (S.)) with filamentous microalgae sp. (Tribonema (T.) and Lyngbya (L.)) to remediate dairy wastewater (DW) and enhance biomass production and harvesting. The results showed that biomass productivity increased by 12‒174% compared to monocultures, and the best consortium was S:T. This consortium achieved the highest biomass productivity of 84.25 mg L−1 d−1 while removing 86.7% of chemical oxygen demand (COD), >88.7% of NO3−-N and >98.5% of PO43–-P. The study also tested the effect of harvesting time on the accumulation of biochemical components and found the optimal harvesting times of day 9 and day 11 to achieve maximum carbohydrate and lipid productivity, respectively. Additionally, the microalgae consortium S:T achieved a high biomass recovery of 78.5%, compared to 32.4% obtained for S. alone, highlighting its potential for efficient DW remediation and resource recovery.

Looking for the optimal harvest time of red grapes with an enzymatic electrochemical multisensory system

The timing of the grape harvest is a critical decision for winemakers, as it greatly impacts the quality and organoleptic characteristics of the resulting red wines. One key indicator for determining the optimal harvest time is the phenolic content of the grapes. As the berries ripen, phenolic compounds in the grape skin cells migrate from the seeds to the pulp and finally to the skin. However, monitoring these phenolic changes, particularly in the seeds, presents a challenge. This research addresses this problem by developing a novel technique to track the phenolic composition of seeds during ripening. The objective is to provide a reliable method for winemakers to monitor the phenolic evolution and improve harvest decision-making.In this study, a multisensory system consisting of four electrochemical enzymatic carbon paste sensors, modified with tyrosinase and electrocatalytic materials (AuNPs, lutetium phthalocyanine, and nickel oxide nanoparticles), was employed to analyze the phenolic content in grape seed extracts. The system monitored weekly changes in the phenolic composition of the seeds from three red grape varieties—Cabernet Sauvignon, Tempranillo, and Prieto Picudo—during their ripening from veraison to being overripe. Using voltammetric techniques, the electrochemical responses were characterized by shifts in peak positions and intensity changes, reflecting the oxidation/reduction of phenols. Principal Component Analysis (PCA) demonstrated the ability of the array of sensors to discriminate phenolic changes across ripening stages, while Partial Least Squares (PLS) regression provided robust correlation models between the electrochemical responses and seed phenolic content, with correlation coefficients ranging from 0.93 to 0.99. The developed methodology successfully tracked phenolic changes, offering a promising tool for monitoring grape seed maturation and assisting in determining the optimal harvest time.

Oxygen-Doped 2D In2Se3 Nanosheets with Extended In-Plane Lattice Strain for Highly Efficient Piezoelectric Energy Harvesting.

With the emergence of electromechanical devices, considerable efforts have been devoted to improving the piezoelectricity of 2D materials. Herein, an anion-doping approach is proposed as an effective way to enhance the piezoelectricity of α-In2Se3 nanosheets, which has a rare asymmetric structure in both the in-plane and out-of-plane directions. As the O2 plasma treatment gradually substitutes selenium with oxygen, it changes the crystal structure, creating a larger lattice distortion and, thus, an extended dipole moment. Prior to the O2 treatment, the lattice extension is deliberately maximized in the lateral direction by imposing in situ tensile strain during the exfoliation process for preparing the nanosheets. Combining doping and strain engineering substantially enhances the piezoelectric coefficient and electromechanical energy conversion. As a result, the optimal harvester with a 0.9% in situ strain and 10 min plasma exposure achieves the highest piezoelectric energy harvesting values of ≈13.5 nA and ≈420 µW cm-2 under bending operation, outperforming all previously reported 2D materials. Theoretical estimation of the structural changes and polarization with gradual oxygen substitution supports the observed dependence of the electromechanical performance.

Penggunaan Machine Learning untuk Pelayanan Monitoring Kegiatan Pemanenan Kelapa Sawit pada Perusahaan PT BKI SSB

Palm oil is one of the main commodities in Indonesia which has an important role in the agribusiness sector. An efficient palm oil harvesting process greatly determines the productivity and quality of the harvest. PT BKI SSB, as a company operating in the palm oil sector, continues to strive to improve operational efficiency and ensure optimal harvest quality. One way to achieve this is by implementing machine learning technology in the harvesting activity monitoring system. This article discusses the application of machine learning to monitor and analyze oil palm harvesting activities at PT BKI SSB. Through the use of this technology, it is hoped that it can increase accuracy, efficiency, and provide better data-based solutions in managing company resources.