Conventional Tree Research Articles

Reliability analysis of parted rope injuries during ship mooring operation under neutrosophic environments

At the national and international levels, many efforts have been made over recent decades to ensure the safety of ships at sea. In marine environments, crews face serious concerns during parted rope injuries during ship mooring operation. This problem can be tackled with the help of failure probability analysis which plays a vital role in detecting and eliminating the aforesaid threats in maritime transportation. Therefore, determining the fundamental causes of parted rope injuries during ship mooring operation is essential for maritime safety researchers. When the failure probability values of basic events are exact, conventional fault tree analysis has previously been successfully applied to determine the cause of failure of the top events. However, sometimes the failure information provided by different analysts for mooring operation may be uncertain and inconsistent. To properly address this important issue, the proposed research reflects a novel approach by extending crisp sets, fuzzy sets, and intuitionistic fuzzy sets into neutrosophic sets. The aforesaid approach is used to compute failure probabilities and reliability intervals for truth-membership, indeterminacy-membership and falsity-membership functions, along with a measure of how much each basic failure event contributes to the failure of the top event. The results obtained by incorporating the proposed approach are compared with those obtained using earlier approaches.

Effect of Prior Tree Marking on Cutting Productivity and Harvesting Quality

Prior tree marking is used to guide loggers or forest machine operators on which trees to cut to achieve the desirable silvicultural quality of a thinning treatment. In the future, this beneficial but expensive human work could be automated with advanced driver assistance systems. This study aimed to investigate the effect of conventional prior tree marking on cutting productivity and harvesting quality of the first and later thinnings. A comparative time study was conducted with four experienced harvester operators. The operators thinned 4825 stems with the cut-to-length (CTL) harvesting method in eight thinning stands. The time consumption of the different time elements of cutting work was measured to model the cutting productivity with average values or regress these values against the stem volume or density of removal. Prior tree marking increased the cutting productivity by an average of 2.8% in the first thinnings and 2.7% in later thinnings by reducing the time consumption of boom-out (positioning the harvester head for cut) and moving. The operator effect was notable, even though only experienced operators participated in the study. For some operators, prior tree marking did not make cutting work more efficient, and sometimes hampered it. Prior tree marking improved the quality of the remaining stands in thinnings by producing a more accurate density of remaining trees after the harvesting operation in relation to thinning guidelines. When the stands were not marked, the operators chose trees of poor quality with almost the same accuracy as the forester. These findings lay the foundation for the next-generation operators' guidance and decision support systems, which could detect trees around the harvester and guide the operator in tree selection and managing better thinning intensity in cutting work. Although prior tree marking increased productivity only marginally, the improvement in the quality of harvesting operations must be acknowledged.

Enhancing Electric Vehicle Reliability and Integration with Renewable Energy: A Multi-Faceted Review

Electric vehicles (EVs) are gaining attention due to their zero carbon emissions, but concerns about their reliability, especially critical components, persist. Previous research has primarily focused on EV drive motor reliability, neglecting the motor controller. To address this gap, this study assesses the reliability of the entire motor system in electric vans, including both drive motor and motor controller components. It predicts failure rates for these components, highlighting vulnerabilities and shortcomings in existing research, which can inform future design and maintenance. In addition, the integration of EVs and renewable energy resources has garnered attention, but concerns about component reliability have arisen. A novel approach called " Innovative Incentive-Driven Fuzzy Fault Tree Analysis " (IIFFTA) is introduced for power systems incorporating EVs and renewable energy, addressing vague and imprecise events and data deficiencies in conventional fault tree analysis. IIFFTA considers different component failure rates and probability values of fault occurrences, offering a more effective risk assessment method. Lastly, plug-in electric vehicles (PEVs) impact distribution systems, and this paper explores distribution feeder reconfiguration (DFR) as a reliability-enhancing strategy for coordinating vehicle-to-grid (V2G) services from PEV fleets within a stochastic framework. The study accounts for uncertainties related to network demand, energy prices, wind power generation, and PEV fleet behavior, employing a self-adaptive evolutionary algorithm (SOS) to address the stochastic optimization problem.

High-Density Espalier Trained Mangoes Make Better Use of Light

Mango productivity and fruit quality in Australia can be improved through transforming low-density plantings to high-density plantings and intensive training systems. Several planting density and training systems were established in Australia to investigate optimizing light interception and distribution, yield and fruit quality, and to reduce tree vigor and biennial bearing through the manipulation of canopy architecture. In this research, we studied light relations and yield in conventional low-density commercial orchards of different ages and investigated light relations and yield of ‘National Mango Breeding Program NMBP1243’, ‘Keitt’ and ‘Calypso’ grafted to ‘Kensington Pride (KP)’ rootstock in a replicated experiment. Trees were trained to five planting systems: high-density espalier and conventional, medium-density single leader and conventional, and low-density conventional. Our study in commercial orchards showed that maximum yield was ~16,000 kg/ha when light interception reached ~49% and declined at higher levels of light interception. In the high-density intensive training systems, we found that light interception increased with canopy volume, with high-density espalier training systems intercepting more available light compared to medium-density training systems and low-density conventional trees. Yield/ha increased to ~50,000 kg/ha in espalier training systems when light interception was ~40%. Light interception, canopy volume and yield/ha varied between varieties.

Enhanced Quasi-Static Energy Recovery and Modified Clock Gating for the Wallace Tree Multiplier with Reversible Adders

High-speed, low-power digital circuits are essential in many digital applications. The objective of the paper is to design high-speed low-power multipliers with reversible gates. The realization of a multiplier with an efficient clocking system is also presented to reduce leakage power consumption. The existing reversible configurations do not possess an effective clocking system for reducing power in an idle state. In this work, a modified clock gating (MCG) and Enhanced Quasi-Static Energy Recovery Logic (EQSERL) are designed to generate a clock pulse. The EQSERL reduces the dynamic state in the circuit and it is designed with reduced complexity. This process effectively reduces leakage power consumption in the circuit. The conventional clock gating circuit is modified for connecting it with the EQSERL. This MCG circuit reduces unwanted power consumption. The conventional Wallace tree multiplier (WTM) involves full adder and half adder circuits. In this proposed work, a square root carry select adder (SQRT CSLA) with a reversible binary to excess-1 converter (BEC) and a ripple carry adder is presented. The reversible gates such as the Feynman gate, the Toffoli gate, and the dual key gate are employed. Finally, the proposed architecture is tested with different clocking circuits using Synopsys tools and the performance metrics like delay and power are observed.

Exploring membrane vesicles in citrus fruits: a comparative analysis of conventional and organic farming approaches.

Recently, vesicles derived from plant cell membranes have received attention for their potential use as active biomolecules and nanocarriers, and obtaining them from organic crops may be an interesting option because different farming systems can affect production, plant secondary metabolism and biochemistry of cell membranes. The present study aimed to determine how organic and conventional farming affects the mineral nutrition, gas exchange, CO2 fixation and biochemical composition of lemon fruits, which could have an impact on the different fractions of cell membranes in pulp and juice. Organic trees had higher intrinsic water use efficiency (WUEi) but conventional trees had higher stomatal conductance (gs) and nitrogen use efficiency (NUtE). Also, organic lemons had significantly higher levels of some micronutrients (Ca, Cu, Fe and Zn). Second, the main differences in the membrane vesicles showed that organic pulp vesicles had a higher antioxidant activity and more oleic acid, whereas both types of vesicles from conventional lemons had more linoleic acid. In conclusion, organic farming did not alter carbon fixation parameters but impacted nitrogen fixation and water uptake, and resulted in higher micronutrient levels in lemons. These mineral nutritional changes could be related to the higher production of membranes that showed suitable morphological traits and a high antioxidant activity, positively correlated with a high amount of oleic acid, which could have stronger cell protection characteristics. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Lac - a Good Source of Livelihood in Jharkhand

Jharkhand state is blessed with the presence of tiny insects kerria lacca belonging to the family Tachardiidae. Lac is one of the most valuable gifts of nature to the Tribal of Jharkhand who dwell in and around the forest areas which have the conventional host trees, Kusum, Palash and Ber. The present study examines the role of Lac in providing sustainable livelihood in Khunti, Simdega and Gumla districts of Jharkhand which are the largest producer of lac in the state of Jharkhand. Through interviewing the villagers, conducting FGDs and visiting the local markets in twelve villages of three districts this research study has tried to bring out the volcanic potentiality of lac production in these three districts of Jharkhand. The Government intervention and role of JASCOLAMP have to be increased to give technical and marketing support to villagers for producing more LAC so that the income of the tribal could increase.

Forest Tree Species Diversity Mapping Using ICESat-2/ATLAS with GF-1/PMS Imagery

Forest ecosystems depend on species of tree variety. Remote sensing for obtaining large-scale spatial distribution information of tree species diversity is a geoscience research hotspot to overcome the limitations of conventional tree species diversity survey approaches. Airborne LiDAR or synergy with airborne optical imagery has been used to model and estimate tree species diversity for specific forest communities, with many revealing results. However, the data collection for such research is costly, the breadth of monitoring findings is limited, and obtaining information on the geographical pattern is challenging. To this end, we propose a method for mapping forest tree species diversity by synergy satellite optical remote sensing and satellite-based LiDAR based on the spectral heterogeneity hypothesis and structural variation hypothesis to improve the accuracy of the remote sensing monitoring of forest tree species diversity while considering data cost. The method integrates horizontal spectral variation from GF-1/PMS image data with vertical structural variation from ICESat-2 spot data to estimate the species diversity of trees. The findings reveal that synergistic horizontal spectral variation and vertical structural variation overall increase tree species diversity prediction accuracy compared to a single remote sensing variation model. The synergistic approach improved Shannon and Simpson indices prediction accuracy by 0.06 and 0.04, respectively, compared to the single horizontal spectral variation model. The synergistic model, single vertical structural variation model, and single horizontal spectral variation model were the best prediction models for Shannon, Simpson, and richness indices, with R2 of 0.58, 0.62, and 0.64, respectively. This research indicates the potential of synergistic satellite-based LiDAR and optical remote sensing in large-scale forest tree species diversity mapping.

A neural builder for spatial subdivision hierarchies

Spatial data structures, such as k-d trees and bounding volume hierarchies, are extensively used in computer graphics for the acceleration of spatial queries in ray tracing, nearest neighbour searches and other tasks. Typically, the splitting strategy employed during the construction of such structures is based on the greedy evaluation of a predefined objective function, resulting in a less than optimal subdivision scheme. In this work, for the first time, we propose the use of unsupervised deep learning to infer the structure of a fixed-depth k-d tree from a constant, subsampled set of the input primitives, based on the recursive evaluation of the cost function at hand. This results in high-quality upper spatial hierarchy, inferred in constant time and without paying the intractable price of a fully recursive tree optimisation. The resulting fixed-depth tree can then be further expanded, in parallel, into either a full k-d tree or transformed into a bounding volume hierarchy, with any known conventional tree builder. The approach is generic enough to accommodate different cost functions, such as the popular surface area and volume heuristics. We experimentally validate that the resulting hierarchies have competitive traversal performance with respect to established tree builders, while maintaining minimal overhead in construction times.

Hybrid DFM – Petri net approach for dynamic reliability analysis of smart transmitters

AbstractSmart transmitters are increasingly being deployed in safety critical systems. Although the processing and diagnostic features provided by digital systems are advantageous, it introduces additional complexity in reliability modelling of systems. The conventional Fault Tree Analysis (FTA) method for system reliability analysis has its shortcomings in modelling digital systems, to incorporate its dynamic system interaction and failure modes in detail. This is because some of the failure modes may arise due to the dynamic interactions between the modules of the system. Dynamic Flowgraph Methodology (DFM) was identified as one of the optimistic approaches to handle these interactions. DFM approach combines multi‐valued logic modelling and analysis capabilities to handle systems consisting of components that have multiple degraded states and exhibit dynamic behaviour. However, only few DFM tools have been developed and some of them are not available in public domain. The DFM approach relies heavily on development and implementation of its decision tables. The logic information present within the decision tables can be meticulously implemented using Petri Nets. Also, there are plenty of free Petri Nets tools available online. Thus, the idea of DFM was used in this work to capture the dynamic interactions by developing decision tables. The decision tables were transformed into the Petri Net models to perform the analysis. The paper demonstrates the potential of the hybrid DFM – Petri Net approach for dynamic reliability analysis of smart transmitter.

SCF-FDPS: A Fast N-body Code for Simulating Disk–Halo Systems

A fast N-body code has been developed for simulating a stellar disk embedded in a live dark matter halo. In generating its Poisson solver, a self-consistent field (SCF) code that inherently possesses perfect scalability is incorporated into a tree code that is parallelized using a library termed Framework for Developing Particle Simulators (FDPS). Thus, the code developed here is called SCF-FDPS. This code has realized the speedup of a conventional tree code by applying an SCF method not only to the calculation of the self-gravity of the halo but also to that of the gravitational interactions between the disk and halo particles. Consequently, in the SCF-FDPS code, a tree algorithm is applied only to calculate the self-gravity of the disk. On a many-core parallel computer, the SCF-FDPS code has performed at least 3 times (in one case, nearly an order of magnitude) faster than an extremely tuned tree code on it, if the numbers of disk and halo particles are, respectively, fixed for both codes. In addition, the SCF-FDPS code shows that the central processing unit cost scales almost linearly with the total number of particles and almost inversely with the number of cores. We find that the time evolution of a disk–halo system simulated with the SCF-FDPS code is, in large measure, similar to that obtained using the tree code. We suggest how the present code can be extended to cope with a wide variety of disk-galaxy simulations.

High Density Planting and Meadow Orcharding in Guava for Higher Return

Due to the extensive tree canopy, the traditional method of cultivation has had difficulty achieving the intended levels of productivity. As a result, it became necessary to enhance the current manufacturing system in addition to impressing with its productivity. There is a current tendency for fruit trees to be planted more densely or in meadow orchards to control tree size, keep desired architecture, and make operations like pruning, pest control, and harvesting easier. The meadow orchard system is a novel idea for guava planting, providing densities of 5000 trees per hectare at a spacing of 1 m by 2 m. In contrast to conventional well-branched trees, this system is controlled to create a simpler and smaller structured framework starting in the first year. Guava trees are topped and hedged to encourage the development of fruit buds in the first year. Regular topping and pruning helped to reduce the size of the tree canopy. In the first year after sowing, trees produced flowers and fruit.

Tailoring tree ideotypes for multiple purposes in agroforestry

Domestication of indigenous tree species in agroforestry can play a seminal role to mitigate and diversify existing farm ecosystems to adapt against the vagaries of climate change by imitating “Mother Nature” in its pristine state. In this direction, developing elite genotypes in agroforestry matching with the need of farmers and associated industry can fetch higher prices in the market, while ensuring economic and livelihood security of subsistence farmers. To apprehend and enhance the flow of benefits from tree domestication, direct selection of superior genotypes or the conventional breeding approach for desired gene ingression followed by clonal propagation needs to be prioritized for developing ideotypes for specific end use. Further, exploring and tapping the potential of molecular breeding by collaborative efforts of conventional tree breeders, molecular geneticists, and tree physiologists in developing desired ideotypes in agroforestry is highly desirable.

Stakeholder perceptions on Community‐Based Ecological Mangrove Restoration (CBEMR): a case study in Thailand

Mangroves are a unique ecosystem that provides various benefits to people. In Thailand, Mangrove Action Project (MAP) has introduced Community‐Based Ecological Mangrove Restoration (CBEMR), a novel technique to restore degraded mangroves. Unlike conventional tree planting, CBEMR does not require a planting phase. It emphasizes preproject assessments of mangrove ecology and hydrology to facilitate natural regeneration. Therefore, this study aims to (1) investigate the perception of stakeholders on the value of mangroves; (2) examine the understanding of stakeholders on conventional mangrove planting and CBEMR as mangrove restoration techniques; (3) understand the challenges and motivations of the CBEMR efforts in the Andaman Coast of Southern Thailand. We use Q Methodology, a semi‐quantitative method that requires participants to rank a set of statements to analyze their perceptions according to the research questions. As a result, we can identify three clusters of perceptions; D1 demands more CBEMR and less conventional planting. D2 believes mangrove planting is good, given suitable conditions. D3 thinks restoring mangroves brings benefits, but most conventional planting often fails. This study highlighted that all CBEMR stakeholders believe inadequate technical knowledge is the root cause of mismatch restoration practices. Mangrove planting should be done with suitable species, conditions, and areas. Conventional planting of mangroves without considering ecological factors and local engagement should be avoided as it fails to achieve sustainable outcomes. Enhancing knowledge of mangrove ecology and restoration techniques is therefore a sustainable route to the long‐term success of mangrove restoration in Thailand.

Practice of the new supervised machine learning predictive analytics for glioma patient survival after tumor resection: Experiences in a high-volume Chinese center.

This study aims to assess the effectiveness of the Gradient Boosting (GB) algorithm on glioma prognosis prediction and to explore new predictive models for glioma patient survival after tumor resection. A cohort of 776 glioma cases (WHO grades II-IV) between 2010 and 2017 was obtained. Clinical characteristics and biomarker information were reviewed. Subsequently, we constructed the conventional Cox survival model and three different supervised machine learning models, including support vector machine (SVM), random survival forest (RSF), Tree GB, and Component GB. Then, the model performance was compared with each other. At last, we also assessed the feature importance of models. The concordance indexes of the conventional survival model, SVM, RSF, Tree GB, and Component GB were 0.755, 0.787, 0.830, 0.837, and 0.840, respectively. All areas under the cumulative receiver operating characteristic curve of both GB models were above 0.800 at different survival times. Their calibration curves showed good calibration of survival prediction. Meanwhile, the analysis of feature importance revealed Karnofsky performance status, age, tumor subtype, extent of resection, and so on as crucial predictive factors. Gradient Boosting models performed better in predicting glioma patient survival after tumor resection than other models.

Dynamic Signature Verification Technique for the Online and Offline Representation of Electronic Signatures in Biometric Systems

Biometric systems input physical or personal human characteristics for identification, authentication, and security purposes. With the advancement in communication and intelligent security systems, biometrics are programmed to validate electronic signatures (E-signatures) for online and offline authentication. This article introduces a dynamic signature verification technique (DSVT) using mutual compliance (MC) between the security system and the biometric device. The security system is responsible for online and offline signature approval using personal inputs from humans. This personal verification is related to the stored online/offline signatures using certificates provided for authentication. The certificate-based authentication is valid within a session for online representation. Contrarily, this authentication is valid for persons under offline conditions. In this mode of segregation, application-level authentication verification is performed. A conventional tree classifier for dynamic signature verification is used for differentiating online and offline signatures. Moreover, the security metrics—such as signing bit, key, and size—are verified for both modes using classifier learning. For the segregated mode, the validation of the above is required to be unanimous to accelerate the dynamicity. The proposed technique’s performance is analyzed using the authentication success rate, verification failing ratio, verification time, and complexity.

Secondary somatic embryogenesis in Cork oak: influence of plant growth regulators

Cork oak (Quercus suber L.) is one of the most important Mediterranean forest tree species, and the Maâmora forest-Morocco is considered to be the world largest cork oak stand. Clonal propagation of Quercus suber via somatic embryogenesis is an alternative to conventional tree propagation methods. However, complete maturation of somatic embryos is considered the major bottleneck for mass propagation of Quercus suber during somatic embryogenesis. This study focuses on influences of cytokinins on secondary somatic embryogenesis. Cytokinins showed a negative effect on the induction of secondary somatic embryogenesis. For the various concentrations tested, the number of secondary somatic embryos was significantly reduced except in the case with low dose of 2iP, which showed a slight increase. Abscisic acid (ABA) may be beneficial at low concentrations between 0.3 and 0.5 mg/l. The best number of secondary embryos was given at 0.5 mg/l ABA with an average number of 5 embryos from each primary embryo, which represents a slight increase below 10% compared to control medium. Also, we found that the number of responsive embryos presenting secondary somatic embryos based on their cotyledons decreased once adding ABA. Phenylurea derivatives TDZ and DPU do not promoted the process of secondary somatic embryogenesis. DPU showed no significant effect with a similar response to that of the control medium and TDZ showed an inhibitory effect. Gibberellic acid (GA3) partially blocked the process of somatic embryos multiplication, but promoted remarkably the germination of cotyledonary embryos without requesting a particular protocol. Our results provide a new insight into embryo development, establishing the basis for further research toward improvement of secondary somatic embryogenesis in Moroccan cork oak.

Differential seed removal, germination and seedling growth as determinants of species suitability for forest restoration by direct seeding – A case study from northern Thailand

BackgroundDirect seeding is potentially a more cost-effective alternative to conventional tree planting for restoring tropical forest ecosystems. However, seed loss, due to removal and damage by animals, can substantially reduce seedling establishment. Therefore, this study examined the impact of seed predation on seedling establishment of five tree species, native to upland evergreen forests of northern Thailand: Hovenia dulcis, Alangium kurzii, Prunus cerasoides, Choerospondias axillaris and Horsfieldia amygdalina. We tested the hypothesis that excluding animals would significantly reduce seed removal, and increase both germination and seedling survival. The objective was to calculate a composite index of the relative suitability of the species studied for direct seeding. MethodsSeeds were placed on the ground in a deforested site and subjected to five predator-exclusion treatments: wire cage, insecticide, cage ​+ ​insecticide, open cage and no exclusion (control). ResultsSeed loss was highest for H. amygdalina (the largest seed tested). Across species, wire cages significantly reduced seed loss by 12.4% compared with controls (P ​< ​0.001) suggesting that vertebrates were the major seed predators. Seed germination ranged from 0 to 77% among the species tested. Based on relative species-performance scores (combining measures of survival and seedling growth), P. cerasoides was the most suitable species for direct seeding, followed by A. kurzii and C. axillaris, whilst H. dulcis and H. amygdalina were unsuitable. H. dulcis had small seeds with low seed germination, whereas H. amygdalina was subjected to high seed removal. ConclusionExclusion of seed predators and the selection of suitable species may substantially increase the success of direct seeding, as a technique for restoring upland evergreen forest ecosystems. Testing more species for their suitability is needed, to provide more diverse options for forest restoration.

Greenhouse Gas Emissions and Carbon Sequestration from Conventional and Organic Olive Tree Nurseries in Tuscany, Italy

In this study, conventional and organic olive tree nurseries were compared through a Life Cycle Assessment (LCA) analysis to identify processes that have a greater environmental impact and which of the two systems leads to lower greenhouse gas (GHG) emissions. Carbon sequestration in the woody biomass of the plants grown with both management systems was also considered. The research was carried out on six olive tree nurseries, four conventional and two managed also with an organic system, located in the nursery district of Pescia (Tuscany, Italy). The functional unit considered was two-year-old pot-grown plants (pot 15 cm Ø) and the results were expressed in terms of kg of CO2 equivalent (CO2eq). In all the nurseries analyzed, LCA showed that pots were the highest CO2eq emission source (45–63%), followed by potting mix (22.6–32.1%). This was due to the use of plastic in pots and peat for the growing media. Organic management was found to have a definite positive influence on the decrease of GHG, reducing the emissions up to 13% compared with conventional nurseries. Considering carbon stocked in the woody tissues of seedlings, the reduction of emissions attained 15.7% though a slightly lower (−6.7%) amount of CO2 incorporated into biomass was detected in the olive plants grown in organic nurseries. In light of our results, conversion of the nursery industry from conventional to organic management has the potential to reduce its carbon footprint.

Learn decision trees with deep visual primitives

In this paper, we strive to propose a self-interpretable framework, termed PrimitiveTree, that incorporates deep visual primitives condensed from deep features with a conventional decision tree, bridging the gap between deep features extracted from deep neural networks (DNNs) and trees’ transparent decision-making processes. Specifically, we utilize a codebook, which embeds the continuous deep features into a finite discrete space (deep visual primitives) to distill the most common semantic information. The decision tree adopts the spatial location information and the mapped primitives to present the decision-making process of the deep features in a tree hierarchy. Moreover, the trained interpretable PrimitiveTree can inversely explain the constituents of the deep features, highlighting the most critical and semantic-rich image patches attributing to the final predictions of the given DNN. Extensive experiments and visualization results validate the effectiveness and interpretability of our method.