Optimization Stage Research Articles

Digitalization of processes of transport enterprises as the basis of its strategic planning

Methods. To achieve the outlined goal, a set of general scientific and specific methods of cognition were used, namely: analysis and comparison, synthesis and theoretical generalization – to determine the essence and role of digitization, namely the introduction of electronic document management in the activities of a transport enterprise; abstract-logical – when determining the factors that prevent the enterprise from switching to electronic document management and the reasons for the need for the switch; grapho-analytical – for a visual presentation of the obtained research results. Results. The authors substantiated in the article that digitization processes are taking place in Ukraine, namely the introduction of electronic document management in the activities of a transport enterprise, which will make it possible to reduce the number of divisions of the enterprise that deal with documentation, save time and financial resources, as well as achieve success and further strategic development by improving the quality of management, which helps to create a complete electronic system. There are determined factors that prevent the enterprise from switching to electronic document management (EDM), namely: employees of the enterprise and counterparties do not want to introduce innovations related to EDM, there is a possibility of hacking of the EDM system by competitors or criminals, and they believe that an electronic document has no legal value. The reasons for the company's transition to electronic document management are substantiated, which will make it possible: to perform work for employees remotely from the office or to be out of office; optimize imperfect communication and establish interaction with those clients whose documents were lost or damaged, comply with regulatory requirements for document management, optimize enterprise costs and prevent cyber threats. The stages of optimization of the EDO implementation process at the transport enterprise are analyzed and examples of the introduction of EDO into the accounting system of the transport, namely logistics enterprise are given. There is summarized the experience of international companies and the results of the transition to EDO, the use of which will lead to the digitalization of all processes of the transport enterprise. Novelty. The authors justified the processes of digitalization, namely the introduction of electronic document management in the activities of a transport enterprise to improve its work and improve the quality of management, which will help create a complete electronic system. Practical value. The obtained results can be used in the further implementation of electronic document flow in the activities of enterprises of various directions, which will help to more effectively use the resources of the enterprise and engage in its strategic development.

Effect of growth stage on Italian ryegrass (Lolium multiflorum Lam.) silage fermentation from microbiological perspective

BackgroundItalian ryegrass is a temperate climate crop, which is widely cultivated in the winter fallow paddy fields of subtropical China. The utilization efficiency of Italian ryegrass depends greatly on its growth stage at harvest. Previous studies have reported the optimum stage for harvesting various forage to balance their quality and quantity. However, when considering the practice condition, such as rainy or unavailability of harvest equipment, the harvest stage of forage cannot always be implemented according to the production schedules. Thus, to characterize the effect of growth stage on the silage fermentation profile, bacterial community construction and metabolisms of carbohydrates and amino acids, Italian ryegrass were naturally ensiled at the filling stage (FSN) and the dough stage (DSN), respectively. After ensiling for 1, 3, 7, 15, 30, and 60 days, triplicate silos were opened for sampling.ResultsThe growth of Italian ryegrass increased the pH, dry matter, neutral, and acid detergent fiber contents, but decreased buffering capacity, crude protein and water-soluble carbohydrates contents, and the epiphytic microbiota of Italian ryegrass harvested at the filling stage was simpler than that harvested at the dough stage. During ensiling, FSN had lower pH and higher organic acid content than DSN. The bacterial succession rate in FSN was also faster than DSN, which showed that Lactobacillus becomes the dominant genus in the early stage of ensiling. The predicted metabolisms revealed that carbohydrate and amino acid metabolisms were the two main metabolisms in silage fermentation. When compared with epiphytic microbiota, ensiling enhanced carbohydrate metabolism and diminished amino acid metabolism. The difference of these two metabolisms between FSN and DSN was obvious at the early stage of ensiling.ConclusionsGrowth stage affected the chemical and microbial composition of Italian ryegrass, so as to the fermentation profile, bacterial community and its metabolisms intensity. Italian ryegrass harvested at the filling stage was prone to ferment. The complexity of epiphytic microbiota made Italian ryegrass harvested at the dough stage more difficult to ferment.Graphical

Abstract P5-04-10: Consensus guidelines for breast cancer staging during pregnancy. A Delphi Study

Abstract Background: Breast cancer is the second most common cancer occurring during pregnancy with limited evidence for appropriate staging (1,2). A Delphi study was performed to develop consensus guidelines. Methods: Guideline recommendations were constructed based on available evidence and included statements targeting highlighted areas of uncertainty from a clinician-based survey. Statements were divided into two domains: one focused on indications for staging and the second addressed imaging selection. A two round Delphi study was performed. Medical, radiation and surgical oncologists from Australia and New Zealand were invited to participate. Participants who had worked in their field for >5 years were considered experts. Participants voted using a 9-point Likert scale selecting from 1 (strongly disagree) to 9 (strongly agree). Consensus was achieved when >75% participants selected < 3, or >7 for a statement. Statements that did not reach consensus in the first round were refined and re-presented for subsequent voting. Results: 15 Australian and New Zealand experts agreed to participate: 8 medical oncologists, 3 radiation oncologists and 4 breast surgeons. 87% (13/15) of participants completed round one. Of the 18 recommendations, six did not meet consensus. These were revised, with seven recommendations re-presented in round two. 11/13 (85%) participants completed round two, with one further recommendation achieving consensus. Consensus was achieved on indications for staging including women with locally advanced or inflammatory breast cancer and clinical suspicion of metastatic disease. Staging should be delayed until after pregnancy if it will not immediately change management decisions. Staging should not routinely be used in stage I and II breast cancers. Where staging is indicated, it was agreed that visceral disease should be screened for; however, consensus was not achieved for whether screening for bone metastases should be performed. There was consensus for liver ultrasound as the imaging modality of choice for liver metastases screening. Participants did not agree on whether chest x-ray or CT chest was best practice for pulmonary metastases nor optimal imaging for bone metastases. There was also discord as to whether there is a role for PET scan. Conclusion: Consensus guidelines have been developed to standardise breast cancer staging during pregnancy. Consensus was achieved for indications for staging and use of liver ultrasound to screen for liver metastases. Optimal staging practices for bone and pulmonary metastases warrants further investigation. Acknowledgements: We thank the experts who participated in this Delphi study.

Derived Functions for MATLAB Non-Linear Solvers: An optimal rotor slot and bar design stopgap

The most striking difference between the Squirrel cage induction motor (SCIM) and other electric motors lies invariably in the rotor. The major running performance characteristics, together with the entire acceleration performance range, are all influenced, in varying degrees, by the rotor slot and bar design. Therefore, proper dimensioning of the slots and bars is therefore critical to attaining a high performing machine. The preliminary stage of the SCIM design, if well done, facilitates the refinement and optimization stages; serving as a good take-off point for ultimately realizing that final state of the art design. This study therefore attempts to derive relevant design functions that could be fed into the algorithms of various non-linear solvers, one of which is the Genetic Algorithm (GA), to output the rotor slot initial main dimensions for the SCIM; from where proper design refinement/optimization could cheaply evolve. The SCIM design obtained from implementing the derived equations, when compared with the appropriate reference datasheets, showed only minor deviations from the expected performance indices – an outcome deemed satisfactory for a preliminary design attempt. The so obtained initial dimensions may in practice be subjected to the appropriate refinement, depending on the target performance requirement of the motor to be designed.

The Classification of Peaches at Different Ripening Stages Using Machine Learning Models Based on Texture Parameters of Flesh Images

The ripening stage can affect consumer preference and the market value of peaches. This study was aimed at developing an objective, effective, and fast procedure for distinguishing the different stages of ripeness of peaches using image texture parameters and models built using traditional machine learning algorithms. The ripeness classes (distinguished using DA-Meter-based nondestructive VIS/NIR method) 0.1, 0.4, and 0.9 for ‘Redhaven’ peaches and 0.1, 0.4, and 1.0 for ‘Royal Glory’ peaches were considered. Fruit weight, ethylene production, total soluble solids content (SSC), titratable acidity (TA), and fruit firmness (FF) were measured. The slice images for each class were acquired. Selected texture parameters from images in color channels R, G, B, L, a, b, X, Y, and Z were used to develop classification models for distinguishing peach ripening stages in pairs. Models were built for combined textures selected from images in all color channels, individual color spaces, and individual color channels using various machine learning algorithms. The ethylene production and SSC was higher in peaches with a ripeness class of 0.1 than in less ripe fruit. The least ripe fruit of ‘Redhaven’ and ‘Royal Glory’ peaches were characterized by the highest fruit firmness. Furthermore, statistically significant differences in SSC between classes 0.1 and 0.9 of ‘Redhaven’ were observed. For ‘Royal Glory’, statistically significant differences in TA were determined between all classes. These differences may be related to classification performance metrics. In the case of ‘Redhaven’ peaches, two extreme ripeness classes 0.1 (the greatest ripeness) and 0.9 (the least ripeness) were correctly classified with the highest accuracy reaching 100% for models built based on textures selected from all color channels (random forest and Bayes net algorithms) and color space lab (random forest). For individual color channels, the accuracy reached 99% for color channel G (random forest) and color channel a (logistic). The accuracy of classifying ripening stages 0.1 and 0.4 reached 98% for the model built using textures from all color channels and color space lab (Bayes net). The ripening stages 0.4 and 0.9 were distinguished with an accuracy of up to 96% (all color channels, random forest). The classification of ripening stages of ‘Royal Glory’ peaches reached 100% for all pairs, 0.1 vs. 1.0 (all color channels, color spaces RGB, color space lab, color channel G, color channel a), 0.1 vs. 0.4 (all color channels, color space RGB, color space lab), and 0.4 vs. 1.0 (all color channels). The developed procedure can be useful in practice. Distinguishing peaches at different stages of ripeness and the selection of fruit at the optimal stage can be important for consumption and processing.

Incidence, risk factors, and prognostic value of nodal involvement in sinonasal cancers.

Incidence, risk factors, and prognostic value of nodal involvement in sinonasal cancers.

Derived Functions for MATLAB Non-linear Solvers – an optimal Stator Slot Design Stopgap

In the three-phase squirrel cage induction motor (SCIM), like other rotating electromechanical machines, the active length of the stator windings lies in the slots. The proper dimensioning of the slots is therefore critical to the design of both the windings and the stator core, so as to attain a high performing machine. The preliminary stage of the SCIM design, if well done, facilitates the refinement and optimization stages; serving as a good take-off point for that final state of the art design. The aim of this article isn’t to come up with a novel optimization method for the SCIM, but to provide another way of obtaining an initial stator slot design template, from where proper design refinement/optimization techniques could obtain very good starting values and be facilitated. Therefore, an attempt is being made here to derive relevant design functions that could be fed into the algorithms of various non-linear solvers such as the Genetic Algorithm (GA), to output the main stator slot dimensions for the SCIM. The final designed SCIM obtained from implementing the derived equations, when compared with the appropriate reference datasheets, showed only minor deviations from the expected performance indices – an outcome deemed satisfactory for a preliminary design attempt. These initial dimensions may in practice be subjected to the appropriate refinement. Depending on the target performance requirement of the motor.

Real-time management of distributed multi-energy resources in multi-energy networks

The replacement of fossil fuel power plants by variable renewable energy sources is reducing the flexibility of the energy system, which puts at risk its security. Exploiting the flexibility of distributed multi-energy resources through aggregators presents a solution for this problem. In this context, this paper presents a new hierarchical model predictive control framework to assist multi-energy aggregators in the network-secure delivery of multi-energy services traded in electricity, natural gas, green hydrogen, and carbon markets. This work builds upon and complements a previous work from the same authors related to bidding strategies for day-ahead markets — it closes the cycle of aggregators’ participation in multi-energy markets, i.e., day-ahead bidding and real-time activation of flexibility services. This new model predictive control framework uses the alternating direction method of multipliers on a rolling horizon to negotiate the network-secure delivery of multi-energy services between aggregators and distribution system operators of electricity, gas, and heat networks. We used the new model predictive control framework to conduct two studies. In the first study, we found that considering multi-energy network constraints at both day-ahead and real-time optimization stages produces the most cost-effective and reliable solution to aggregators, outperforming state-of-the-art approaches in terms of cost and network security. In the second study, we found that the adoption of a green hydrogen policy by multi-energy aggregators can reduce their consumption of natural gas and respective CO2 emissions significantly if carbon and green hydrogen prices are competitive.

Maximizing Bio-Hydrogen Production from an Innovative Microbial Electrolysis Cell Using Artificial Intelligence

In this research work, the best operating conditions of microbial electrolysis cells (MECs) were identified using artificial intelligence and modern optimization. MECs are innovative materials that can be used for simultaneous wastewater treatment and bio-hydrogen production. The main objective is the maximization of bio-hydrogen production during the wastewater treatment process by MECs. The suggested strategy contains two main stages: modelling and optimal parameter identification. Firstly, using adaptive neuro-Fuzzy inference system (ANFIS) modelling, an accurate model of the MES was created. Secondly, the optimal parameters of the operating conditions were determined using the jellyfish optimizer (JO). Three operating variables were studied: incubation temperature (°C), initial potential of hydrogen (pH), and influent chemical oxygen demand (COD) concentration (%). Using some measured data points, the ANFIS model was built for simulating the output of MFC considering the operating parameters. Afterward, a jellyfish optimizer was applied to determine the optimal temperature, initial pH, and influent COD concentration values. To demonstrate the accuracy of the proposed strategy, a comparison with previous approaches was conducted. For the modelling stage, compared with the response surface methodology (RSM), the coefficient of determination increased from 0.8953 using RSM to 0.963 using ANFIS, by around 7.56%. In addition, the RMSE decreased from 0.1924 (using RSM) to 0.0302 using ANFIS, whereas for the optimal parameter identification stage, the optimal values were 30.2 °C, 6.53, and 59.98 (%), respectively, for the incubation temperature, the initial potential of hydrogen (pH), and the influent COD concentration. Under this condition, the maximum rate of the hydrogen production is 1.252 m3H2/m3d. Therefore, the proposed strategy successfully increased the hydrogen production from 1.1747 m3H2/m3d to 1.253 m3H2/m3d by around 6.7% compared to RSM.

Realization of Highly Efficient InP Quantum Dot Light‐Emitting Diodes through In‐Depth Investigation of Exciton‐Harvesting Layers

AbstractQuantum dot light‐emitting diodes (QLEDs) are considered promising candidates for several optoelectronic applications; however, they are plagued by the over‐injection of electrons compared to holes, which limits device efficiency. Studies have attempted to reuse the leaked electrons and transfer recombination energies via inserting an exciton‐harvesting layer (EHL) between the emissive layer (EML) and hole transport layer (HTL). This study conducts a detailed analysis of the energy transfer mechanisms to obtain better insights into improving the device performance. First, by analyzing the electroluminescence (EL) spectra and exciton dynamics, the effect of EHLs comprising phosphorescence (PH) or thermally activated delayed fluorescence (TADF) blue dopant is compared. Through parallel incorporation of those EHLs on QLEDs and organic LEDs, the minimal contribution of the PH‐EHL to energy transfer in QLEDs is confirmed, whereas the TADF‐EHL has a significant contribution. Second, highly efficient top‐emission green QLEDs with the TADF‐EHL are achieved. They exhibit a maximum luminance (L) and current efficiency (CE) of 40700 cd m−2 and 68.0 cd A−1, respectively, which are the highest among the reported values for green‐emitting InP QLEDs. The proposed approaches are expected to provide aid in the realization of highly efficient QLEDs from the analysis to the device optimization stage.

Exploring Texture Diversity of Beech-Spruce-Fir Stands through Development Phase Analysis in the Frakto Virgin Forest of Greece

The structural diversity of old-growth forests is a fundamental element as regards ecosystem stability and functionality. The current study aims at exploring the texture diversity in the unique virgin Frakto forest of Greece through the determination of the forest development phases and their related stages. Eight sample plots of 0.25 hectares each were randomly distributed to serve the field research needs. During the single phases, a significant number of variables at the stand level, such as the total dead and living timber volume and the density expressed as the number of stems per hectare and basal area, were calculated, and their values were merged into three main stages. The Frakto virgin forest was found to be dominated by the optimal stage (61.7%), followed by the decay stage (22.7%), and the initial stage (15.6%), in alignment with relevant distributions reported for other European virgin forests. Statistically significant differences in terms of stem density and woody volume between the stages demonstrated increased structural diversity and heterogeneity, a typical characteristic of primeval forests. The results offer an insight into forest growth dynamics under natural processes, thus providing a knowledge base for the promotion of sustainable forest management.

Effect of Stress Interaction on Multi-Stress Accelerated Life Test Plan: Assessment Based on Particle Swarm Optimization

Sustainability of products that seek to maintain ecosystem balance, such as electric vehicles or solar system inverters, often require extensive testing during their developmental stages in a manner that minimizes wastage and drives creativity. Multi-stress accelerated test planning is often used for these products, their subsystems and components if their in-service failures are driven by multiple stress factors. Multi-stress accelerated life testing (ALT) often expedites time to failure for highly reliable products. Many studies assume model parameters that may not be appropriate for the considered stress factors. Most importantly, the effect stress interaction has on the ALT plan is often ignored, especially for cases where historical data are lacking. To address this gap, in this work, a technique based on a combination of rapid experimental data collection and heuristic-based optimization is proposed for ALT planning. In addition, the effect of stress interaction on the ALT plan was also evaluated. Specifically, the Arrhenius model was used to develop a maximum likelihood mathematical expression for multi-stress factor scenarios with and without interaction. Subsequently, two optimization stages based on particle swarm optimization (PSO) were carried out using time varying inertia weight constants to drive early and late global and local searches, respectively. In the first stage, model parameters were estimated, while, in the second stage, an ALT optimal plan was generated based on a D-optimality criterion. Verification of stress factor interactions was carried out using graphical response analysis. An experiment, designed to investigate electromigration in solder joints under three stress factors (temperature, current density and mechanical load), was used to validate the study. The variation in the choice of Latin hypercube design (LHD) results in disparity in the levels of stress within each stress combination as well as sample allocation. Our results clearly show the need to investigate stress interactions prior to multi-stress acceleration planning.

МЕТОДОЛОГИЧЕСКИЕ ОСНОВЫ И ПРАКТИЧЕСКИЕ АСПЕКТЫ РЕШЕНИЯ ЗАДАЧ ОПТИМИЗАЦИИ НЕСУЩИХ СИСТЕМ БЕСПЛАТФОРМЕННЫХ ИНЕРЦИАЛЬНЫХ НАВИГАЦИОННЫХ СИСТЕМ

This article describes approaches to solving problems of optimization of bearing structureof strapdown inertial navigation systems (SINS). A typical optimization problem in this case ismultiobjective parametric optimization of the bearing structure of the SINS accelerometer triad inorder to minimize the mass of the bearing structure and minimize deviation angles of the accelerometeraxes under the action of external loads. The ANSYS Mechanical and ANSYSDesignXplorer modules are used as a tool for numerical modeling and optimization, respectively.Practical issues related to parameterization of SINS bearing structure 3D-models, calculation ofaccelerometer axes deviation angles, possible variants of numerical experiment plans, estimationof response sensitivity to input parameters, generation and refinement of the response surface, andmultiobjective optimization are considered. For the rational parametrization of geometry, theSINS device assembly was decomposed, as a result of which the parts and structural elements that have the greatest influence on the considered objective functions were identified. To calculate thedeviation angles of the sensitive elements axes, special two-node finite elements and relations forthe Bryant angles were used, which describe the relative position in space of two coordinate systems.When planning a numerical experiment, at the first stage of optimization, a central compositionplan was used, and at subsequent stages, the parameter space was filled using the Latin hypercubemethod with the option of relations between parameters, which made it possible to avoiddegenerate design options. The response surface was built using the genetic aggregation methodand subsequently refined based on a set of optimal solutions. Optimization for conflicting goals ofmass minimization and stiffness maximization was carried out using a multiobjective genetic algorithm.The described set of approaches to solving optimization problems as a result of an exemplaryseries of calculations made it possible to reduce the mass of a serial SINS bearing structurepart by 23% with fixed stiffness.

Treatment of extranodal NK/T-cell lymphoma: From past to future.

Extranodal NK/T-cell lymphoma (ENKTCL) is the most common subtype of T/NK-cell lymphoma in Asia and Latin America, but very rare in North American and Europe. Patient survival has improved significantly over the past two decades. However, standard treatment has not yet been established, although dozens of prospective trials have been conducted. To help understand how the treatment of ENKTCL has evolved in the past and what trends lie ahead, we have comprehensively reviewed the treatment of this aggressive malignancy, with a particular focus on neglected or unanswered issues, such as the optimal staging method, the best partner of asparaginase (Asp), the individualized administration of Asp, the preferred sequence of CT and RT and so on. Overall, the 5-year overall survival (OS) of patients with Ann Arbor stage I/II disease increased from < 50% in the early 20th century to > 80% in recent years, and the median OS of patients with Ann Arbor stage III/IV disease increased from < 1 year to more than 3 years. The improvement in patient survival is largely attributable to advances in radiation technology and the introduction of Asp and anti-PD-1/PD-L1 immunotherapy into practice. Radiotherapy is essential for patients with early-stage disease, while Asp-based chemotherapy (CT) and PD-1/PD-L1 inhibitors significantly improved the prognosis of patients with advanced-stage disease. ENKTCL management is trending toward simpler regimens, less toxicity, and higher efficacy. Novel drugs, such as manufactured T cells, monoclonal antibodies, and small molecule inhibitors, are being intensively investigated. Based on the fact that ENKTCL is highly resistant to cytotoxic drugs except Asp, and aggressive CT leads to higher toxicity rather than better outcomes, we recommend it is unnecessary to expend additional resources to compare different combinations of Asp with cytotoxic agents. Instead, more efforts should be made to optimize the use of Asp and immunotherapy to maximize efficacy and minimize toxicity, explore ways to overcome resistance to Asp and immunotherapy, identify novel treatment targets, and define subpopulations who may benefit more from specific treatments.

The effects of platelet-rich plasma injections in different stages of knee osteoarthritis

PurposePlatelet-rich plasma (PRP) injections are challenging the current treatment options for knee osteoarthritis (KOA). Targeting the treatment to optimal stage of the symptomatic KOA may be crucial in the success and failure of treatment. The aim of this study is to compare the outcomes of PRP injections at different stages of KOA in order to determine the optimal stage of the KOA for PRP injection treatments.MethodsA total of 89 consecutive patients with symptomatic KOA Kellgren–Lawrence grade 1 to 3 (Group A grade 1, group B grade 2 and group C grade 3) were given three intra-articular injections of PRP with 2 weeks interval between injections. Visual Analogue Scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Range of Motion were measured before injection, at 15 days, 6 months, 12 months and, at last follow-up. Intergroup outcomes were compared.ResultsThe comparison of groups A and C showed that WOMAC scores were significantly higher in group C at 15 days [p = 0.047] and at last follow-up [p = 0.008] than in group A, as well as VAS scores at 6 months [p = 0.031] and at last follow-up [p = 0.008]. The overall WOMAC and VAS scores showed decrease in all the groups in minimum follow-up of 14 months. The other comparisons did not show significant differences in outcomes.ConclusionAll the groups showed decrease in WOMAC and VAS, but patients with mild KOA benefit significantly more from the treatments than patients with more severe KOA.

CPA-Planner: Motion Planner With Complete Perception Awareness for Sensing- Limited Quadrotors

Motion planning in unknown environments suffers from the limited sensing range of cameras and occlusions. Therefore, perception awareness (PA) should be fully considered to ensure flight safety. Specifically, the unknown part of the planned trajectory should be sensed in advance (visibility constraint) at a safe distance (safety constraints). However, most of the optimization-based methods considered PA merely in the optimization stage in terms of the occlusions and ignored the limitation of the sensing model. As a result, the PA constraints remain violated due to poor initialization. Meanwhile, existing sampling methods can only ensure the visibility constraint rather than the safety constraint. In this paper, an optimization-based planning framework is proposed to solve the raised problems in real-time. Specifically, in the path-finding stage, a fast PA checking algorithm based on incremental convex sensing hulls is proposed and combined with the motion primitive propagation to find the initial path that satisfies the PA constraints. Subsequently, in the trajectory optimization stage, perception awareness with respect to occlusion and sensing model are jointly considered and formulated into the nonlinear optimization problem based on a grid map. Benchmarks with the state-of-the-art and physical experiments show that the proposed planner can generate safe trajectories with the highest success rate in different occluded environments and achieve comparable efficiency and aggressiveness simultaneously.

An improved method to efficiently acquire rice black-streaked dwarf virus viruliferous small brown planthoppers.

Accurate phenotypic identification is the basis of research for resistance genetics and rice breeding for resistance to RBSDV disease. Obtaining rice black-streaked dwarf virus (RBSDV) viruliferous small brown planthoppers (SBPHs) with high transmission efficiency is an essential part of accurate phenotypic identification. Here, through quantifying number of RBSDV copies in infected rice plants, optimizing times of SBPHs fed on RBSDV-infected rice plants and leaf stage of rice seedlings, a method to acquire an RBSDV-carrying SBPH population more efficiently was improved. The results showed that rate of viruliferous SBPHs was significantly higher when fed on RBSDV-infected rice plants that had the copy numbers of RBSDV S10 of 3.0*104 and 1.1*104 than 8.3*102. Therefore, it is more efficient for SBPHs to acquire the virus when fed on RBSDV-infected rice plants that have copy numbers of RBSDV S10 above 1.1*104. The rate of viruliferous SBPHs were 50% and 54%, respectively, after the insects fed on RBSDV-infected rice plants for 7 and 9days and being transferred to healthy rice seedlings for 5 and 3days, which was significantly higher than those at other feeding times. The optimal inoculation leaf stage of rice seedlings was the 2-3-leaf stage (3 effective SBPHs per seedling for 72h), but a high rate of viruliferous SBPHs may be suggested for inoculation of older rice seedlings.

Possibilities of the Hybrid Method (Three-Phase Scintigraphy-SPECT/CTAngiography) in the Diagnosis and Staging of Acute Lower Limb Ischemia

ABSTRACT Acute lower limb ischemia (ALLI) is a limb threatening condition with high risk of adverse outcomes. The timely and proper diagnosis of the severity of limb ischemia is crucial in selecting the optimal treatment method.AIM OF STUDY To evaluate the advantages of hybrid imaging in diagnosis of ALLI, patient selection and its severity in order to choose an optimal revascularization method.MATERIAL AND METHODS we examined 163 patients with grade 1–3A ALLI based on I.I. Zatevakhin et al. classification (2002) on a Discovery 670 NM/CT hybrid device, using X-ray (computed tomographic angiography) and radionuclide method (three-phase scintigraphy/single photon emission computed tomography with osteotropic or perfused radiopharmaceutical).RESULTS CT angiography accurately showed level of acute occlusion as well as distal arterial blood flow in all ALLI cases, thus allowing optimal choice of possible revascularization. CT angiography findings alone did not allow optimal staging of ALLI, which is essential for choosing an optimal revascularization method and were considered as supplementary. Three-phase scintigraphy allowed to differentiate the degrees of ALLI based on the visual picture and calculated data, while studies with osteotropic radiopharmaceutical 99mТс-PYP provided information on viability of affected tissues showing the areas of muscle necrosis, compared to perfused radiopharmaceutical.CONCLUSION 1. CT angiography makes it possible to assess in detail the level and severity of occlusive lesions of the arterial system and study the vascular anatomy of the lower limb to resolve the issue of the possibility of revascularization, and also indirectly characterizes the presence of ischemic changes in soft tissues. 2. The radionuclide method (three-phase scintigraphy with an osteotropic radiopharmaceutical/single photon emission computed tomography), performed in addition to computed tomographic angiography, allows not only to detect the presence of ischemic changes in soft tissues, but also to differentiate the degree of acute ischemia of the lower. 3. The hybrid method (three-phase scintigraphy in combination with computed tomographic angiography) contributes to an objective assessment of the state of blood flow in the lower extremities in acute ischemia, both at the main and at the microcirculatory

Establishment yield and nutrient composition of four legumes as influenced by age of growth in a cool tropical climate at Jos, Plateau State, Nigeria

An experiment was conducted with 2 temperate (Trifolium pratense and T. repens) and 2 tropical (Stylosanthes guianensis and Centrosema molle syn. C. pubescens) forage legumes in an elevated tropical environment of Jos, Nigeria to determine the influence of age of growth on forage yield and nutrient concentrations in the establishment year. The experiment was a 4 harvest times (9, 13, 17 and 21 weeks after sowing; WAS) × 4 legume species (2 temperate and 2 tropical) factorial treatment arrangement in a randomized complete block design with 4 replications, conducted in the growing seasons of 2015 and again 2016. In 2015, S. guianensis produced highest (P&lt;0.05) dry matter yield (8.2 t DM/ha), while T. pratense produced the highest yield (3.6 t DM/ha) in 2016. In both years leaf:stem ratio decreased significantly with age. In 2016 crude protein (CP) concentration declined in all species as age at harvest increased (P&lt;0.05), while at any given age highest CP concentration occurred in T. repens and lowest in S. guianensis (P&lt;0.05). At any age, concentration of calcium followed the pattern T. pratense&gt;T. repens&gt;C. molle&gt;S. guianensis (P&lt;0.05), while phosphorus concentration in forage declined with age at harvest with significant (P&lt;0.05) differences only for tropical legumes. The detergent fiber concentrations (NDF and ADF) were higher in S. guianensis (P&lt;0.05) at any harvest stage. Non-linear regression analysis suggested that these forage legumes, when planted in early June in this environment, could be harvested at the optimum stages of 15, 16, 18 and 21 WAS for T. pratense, T. repens, S. guianensis and C. molle, respectively. However, more studies, especially with earlier planting dates, need to be conducted on the temperate legumes to determine their full yield potential in this environment, especially over a wider range of seasonal conditions.

Fuzzy Modelling and Optimization of Yeast-MFC for Simultaneous Wastewater Treatment and Electrical Energy Production

Microbial fuel cells convert the chemical energy conserved in organic matter in wastewater directly to electrical energy through living microorganisms. These devices are environmentally friendly thanks to their ability to simultaneously produce electrical energy and wastewater treatment. The output power of the yeast microbial fuel cell (YMFC) depends mainly on glucose concentration and glucose/yeast ratio. Thus, the paper aims to boost the power of YMFC by identifying the best values of glucose concentration and glucose/yeast ratio. The suggested approach comprises fuzzy modelling and optimization. Fuzzy is used to build the model based on the measured data. In the optimization stage, the marine predators’ algorithm (MPA) is applied to identify the best glucose concentration values and glucose/yeast ratio corresponding to the maximum output power of YMFC. The results revealed the superiority of the combination of fuzzy and MPA compared with the response surface methodology (RSM) approach. Regarding the modelling accuracy, the coefficient of determination increased by 13.32% and 8.37%, respectively, for without methylene blue and with methylene blue compared with RSM. The integration between fuzzy and MPA succeeded in maximizing the output power from YMFC. Without MB, the power density increased by 25% and 29.3%, respectively, compared with measured data and RSM. In addition, with MB, the power density increased by 22.4% and 26%, compared with measured data and RSM.