Collection Costs Research Articles

Tackling Low-Value Plastics: Environmental and Health Concerns

Plastic pollution, spanning macroplastics to nanoplastics, poses multifaceted challenges. Low-value plastics (LVP), like multi-layered plastics (MLP) and soft plastics, are often discarded, exacerbating environmental issues. LVP's classification, with high collection costs and complex recycling, demands immediate attention. This study explores LVP management solutions, including converting to plastic lumber and RDF products. Anticipated advancements in multilayer plastic recycling offer hope, with downcycling as an option. LVP's environmental and health impacts, including microplastic contamination and toxin release during incineration, underscore the need for sustainable waste practices and plastic alternatives. Addressing LVP is crucial for environmental and human health, ensuring sustainability. Keywords: Low-value plastics, Multi-Layered Plastic (MLP), Health implications, Recycling challenges

Efficacy of prophylactic antibiotics for the prevention of neutropenic fever in patients with multiple myeloma receiving high-dose cyclophosphamide for stem cell mobilization

High-dose cyclophosphamide (HD-Cy) (3 g/m2) plus granulocyte colony-stimulating factor (G-CSF) is a very effective regimen for peripheral blood stem cell (PBSC) mobilization. Unfortunately, it is associated with an increased risk of neutropenic fever (NF). We analyzed the effect of NF on PBSC apheresis results and the efficacy of prophylactic antibiotics for the prevention of NF associated with HD-Cy plus G-CSF for PBSC mobilization in patients with newly diagnosed multiple myeloma (MM). First, patients were divided into NF ( +) and NF ( −) groups according to whether they suffered from NF during mobilization. Second, we divided patients into an antibiotic prophylaxis group and a nonantibiotic prophylaxis group according to whether antibiotic prophylaxis was used during the mobilization period. Our study showed that NF( +) patients (n = 44) had lower CD34 + cell dose collection (median 2.60 versus 5.34 × 106/kg, P < 0.001) and slower neutrophil engraftment and platelet engraftment (median 11 versus 10 days, P = 0.002, and median 13 versus 11 days, P = 0.043, respectively) than NF( −) patients (n = 234). Of note, the nonantibiotic prophylaxis group patients (n = 30) had a 26.7% incidence of NF. In the patients receiving antibiotic prophylaxis (n = 227), the incidence was reduced to 9.3% (P = 0.01). The antibiotic prophylaxis patients had higher CD34 + cell collection (median 5.41 versus 2.27 × 106/kg, P < 0.001) and lower hospitalization cost of mobilization ($ median 3108.02 versus 3702.39, p = 0.012). Thus, our results demonstrate that NF is associated with lower CD34 + cell collection and that antibiotic prophylaxis can reduce the incidence of NF and improve stem cell mobilization and collection outcomes, which reduces the hospitalization cost of mobilization.

Optimising customer retention: An AI-driven personalised pricing approach

Customer churn has become one of the most important challenges that telecom companies have to deal with. Churn cases not only cause revenue losses but also impose extra costs of finding new customers. To overcome this issue, they develop various strategies to retain their customers. In this regard, this study presents an integrated artificial intelligence-based model that can meet the expectations of these companies not only to profile the customer churn, but also to predict a service fee that is likely to be accepted by the customers. The model first identifies the customers who are likely to churn and then offers the customers a personalised service fee that is likely to be acceptable. In this study, the K-nearest neighbours, Decision Tree, Random Forest, and Support Vector Machine methods are adapted as classifiers for churn prediction, and regression models of the same methods are utilised to predict the most optimum “personalised” service fee for potential customer churns. Additionally, to reduce the cost of data collection for companies, the feature selection method is used to determine the most optimal feature subset in churn analysis and service fee prediction. The results show that the Random Forest method is superior to other methods in both churn and price predictions and has resulted in as much as a predictive accuracy of 94% and AUC of 98%. The outcome of this comprehensive analysis using four artificial intelligence methods over three diverse telecom datasets, suggests that the proposed “personalised” pricing model in the telecom sector could prevent the churn and increase the profitability by up to 36%. In addition, the model based on SVM suggests that it is possible to reduce the number of required data to be collected by as much as 20%. As the robustness and generalisation ability of the models has been demonstrated over three diverse data sets, it can be further adapted in different sectors.

WA-Zone: Wear-Aware Zone Management Optimization for LSM-Tree on ZNS SSDs

ZNS SSDs divide the storage space into sequential-write zones, reducing costs of DRAM utilization, garbage collection, and over-provisioning. The sequential-write feature of zones is well-suited for LSM-based databases, where random writes are organized into sequential writes to improve performance. However, the current compaction mechanism of LSM-tree results in widely varying access frequencies (i.e., hotness) of data and thus incurs an extreme imbalance in the distribution of erasure counts across zones. The imbalance significantly limits the lifetime of SSDs. Moreover, the current zone-reset method involves a large number of unnecessary erase operations on unused blocks, further shortening the SSD lifetime. Considering the access pattern of LSM-tree, this article proposes a wear-aware zone-management technique, termed WA-Zone , to effectively balance inter- and intra-zone wear in ZNS SSDs. In WA-Zone, a wear-aware zone allocator is first proposed to dynamically allocate data with different hotness to zones with corresponding lifetimes, enabling an even distribution of the erasure counts across zones. Then, a partial-erase-based zone-reset method is presented to avoid unnecessary erase operations. Furthermore, because the novel zone-reset method might lead to an unbalanced distribution of erasure counts across blocks in a zone, a wear-aware block allocator is proposed. Experimental results based on the FEMU emulator demonstrate the proposed WA-Zone enhances the ZNS-SSD lifetime by 5.23×, compared with the baseline scheme.

Tree ring segmentation using UNEt TRansformer neural network on stained microsections for quantitative wood anatomy.

Forests are critical in the terrestrial carbon cycle, and the knowledge of their response to ongoing climate change will be crucial for determining future carbon fluxes and climate trajectories. In areas with contrasting seasons, trees form discrete annual rings that can be assigned to calendar years, allowing to extract valuable information about how trees respond to the environment. The anatomical structure of wood provides highly-resolved information about the reaction and adaptation of trees to climate. Quantitative wood anatomy helps to retrieve this information by measuring wood at the cellular level using high-resolution images of wood micro-sections. However, whereas large advances have been made in identifying cellular structures, obtaining meaningful cellular information is still hampered by the correct annual tree ring delimitation on the images. This is a time-consuming task that requires experienced operators to manually delimit ring boundaries. Classic methods of automatic segmentation based on pixel values are being replaced by new approaches using neural networks which are capable of distinguishing structures, even when demarcations require a high level of expertise. Although neural networks have been used for tree ring segmentation on macroscopic images of wood, the complexity of cell patterns in stained microsections of broadleaved species requires adaptive models to accurately accomplish this task. We present an automatic tree ring boundary delineation using neural networks on stained cross-sectional microsection images from beech cores. We trained a UNETR, a combined neural network of UNET and the attention mechanisms of Visual Transformers, to automatically segment annual ring boundaries. Its accuracy was evaluated considering discrepancies with manual segmentation and the consequences of disparity for the goals of quantitative wood anatomy analyses. In most cases (91.8%), automatic segmentation matched or improved manual segmentation, and the rate of vessels assignment to annual rings was similar between the two categories, even when manual segmentation was considered better. The application of convolutional neural networks-based models outperforms human operator segmentations when confronting ring boundary delimitation using specific parameters for quantitative wood anatomy analysis. Current advances on segmentation models may reduce the cost of massive and accurate data collection for quantitative wood anatomy.

Single-shot Fourier ptychographic microscopy with isotropic lateral resolution via polarization-multiplexed LED illumination.

Fourier ptychographic microscopy (FPM) has emerged as a new wide-field and high-resolution computational imaging technique in recent years. To ensure data redundancy for a stable convergence solution, conventional FPM requires dozens or hundreds of raw images, increasing the time cost for both data collection and computation. Here, we propose a single-shot Fourier ptychographic microscopy with isotropic lateral resolution via polarization-multiplexed LED illumination, termed SIFPM. Three LED elements covered with 0°/45°/135° polarization films, respectively, are used to provide numerical aperture-matched illumination for the sample simultaneously. Meanwhile, a polarization camera is utilized to record the light field distribution transmitted through the sample. Based on weak object transfer functions, we first obtain the amplitude and phase estimations of the sample by deconvolution, and then we use them as the initial guesses of the FPM algorithm to refine the accuracy of reconstruction. We validate the complex sample imaging performance of the proposed method on quantitative phase target, unstained and stained bio-samples. These results show that SIFPM can realize quantitative imaging for general samples with the resolution of the incoherent diffraction limit, permitting high-speed quantitative characterization for cells and tissues.

Shortage of plasma-derived medicinal products: what is next? narrative literature review on its causes and counteracting policies in Italy.

Introduction: This paper describes the peculiarities of the plasma-derived medicinal product (PDMP) market and illustrates the results of a review of the literature on policies aimed at counteracting the shortage of PDMPs. Characteristics of PDMPs: Plasma is primarily used for the industrial production of blood products (80%). The demand for PDMPs, particularly immunoglobulins (IGs), is increasing. However, the production of PDMPs is complex, long (7-12months), and expensive, accounting, according to US estimates, for 57% of the total costs of PDMPs compared to 14% for small molecules. PDMP market: Unexpected increases in clinical need cannot be addressed in the short term. Once the demand for some diseases is satisfied, the collection and fractionation of plasma will only be used to supply some specific patients. Hence, the full weight of the marginal costs, which remain constant, are borne by a few products. According to last liter economics, the industry stops producing when the marginal revenue equals the marginal cost, thereby reducing the convenience of producing the most commonly used PDMPs (albumin and IG). The imbalance between the demand and supply of PDMPs was exacerbated by the COVID-19 pandemic, which further increased the cost of plasma collection. Shortage issue and possible solutions: Policies to counteract this imbalance have also been discussed. If the demand is inappropriate, it should be reduced. If the demand is appropriate and supply cannot be increased, the demand should be prioritized for patients for whom PDMPs are the only available treatment. If the shortage depends on insufficient supply and technical and allocative efficiency, both production and supply should be improved, together with incentives for all stakeholders involved in the PDMP market to increase the sustainability of production/supply. The paper is focused on this second issue, that is supply-driven unbalance.

GRAPE LEAF DISEASE RECOGNITION BASED ON A MULTISCALE MIXED ATTENTION RESIDUAL NETWORK

A multiscale efficient channel attention spatial-residual network (MECAS-ResNet) is proposed in this paper. On the basis of ResNet50, the training speed and model volume are reduced by group convolution. Through multiscale convolution, the spatial attention and effective channel attention are fused into the model to improve the network's attention toward various regions of lesions. A Wasserstein generative adversarial network is used for data enrichment, and the resulting images are diverse and variable. The enriched dataset is fed into the MECAS-ResNet model for identification and comparison to other models. After the improvement, the optimal recognition accuracy reached 95.31&amp;#37;, which is 10.02&amp;#37; higher than that on the original dataset; the model size is only 30.88 MB; and the average F1-score reached 95.30&amp;#37;. The improved model has higher recognition performance for similar diseases in different degrees, which is better than other models. The data generated by the experiment can be used for grape disease recognition, which greatly reduces the cost of data collection and provides technical support for grape disease recognition and precise drug treatment.

MSW Management in Two Italian Mountainous Areas

Abstract Two case studies are reported in the present work. They regard municipal solid waste (MSW) management trends in the last decades in two Italian provinces selected to analyse some issues of waste management in mountainous areas. The first case study refers to a selective collection (SC) rate expected to reach 80 % soon. This scenario assumes SC mainly in kerbside mode. The scenario is based on specific local conditions: the absence of a thermo-chemical plant in the territory (but with a part of the residual MSW burnt in a neighbour province), the presence of local plant of anaerobic, and a sanitary landfill for pre-treated residual MSW. The adopted tariff helped increasing the SC rate: indeed, since 2013, a tariff calculated at user level, depending on the behaviour of each user, has been adopted. This is called punctual tariff. The second case study concerns an area where SC reached about 75 %. The punctual tariff has been recently introduced. In this scenario, an incineration plant and a Solid Recovered Fuel (SRF) plant allow implementing an industrial symbiosis solution in conjunction with a cement factory. The work demonstrates that SC can be the core of a correct MSW management and that kerbside collection is fully compatible with mountainous area. However, other issues remain to be optimized yet: the low density of mountain areas makes difficult to implement enhanced solutions of SC (e.g. for diapers) and increases costs for light packaging collection; moreover, the presence of tourist fluxes can significantly affect SC efficiency.

Reverse Engineering of an Automatic Household Composter

In Latin America and the Caribbean, it is reported that 52% of household waste is food waste. Redirecting this waste away from the landfills and into composting systems represent significant wins for the environment, through the reduction in greenhouse gases; the state, by reducing the cost of garbage collection; and homeowners, through the conversion of waste into an organic planting medium. Based on a survey of homeowners in Trinidad and Tobago, design specifications were developed for a composter, which was then fabricated and tested at a workshop at The University of West Indies. The results showed a 98% reduction in the volume of waste to compost and that the composter took 94 hours to produce stable compost. The chemical properties of the resulting compost were a bit higher than expected, but this was due to the small size of the composting chamber and the composition of the waste which was composted. This paper concludes that upscaling the composter to a commercial scale and testing it in a controlled community is necessary to determine both the financial and technological feasibility of the composter.

Optimizing Waste Management through IoT and Analytics: A Case Study Using the Waste Management Optimization Test

This research examines how Internet of Things (IoT) technology and advanced analytics may be integrated into trash management. The results show a notable improvement in waste collection efficiency, cost savings, and environmental sustainability. Significant operational cost reductions were achieved by reducing the number of overfilled trash cans by 20% and the frequency of collections by 15% as a consequence of real-time data capture using IoT sensors. Additionally, a 25% reduction in trip distance was made possible by data-driven route optimization, which also resulted in a 10% drop in fuel use and a decrease in carbon emissions. The data-driven strategy also found areas for recycling, which increased the amount of recyclables collected by 15%. These findings highlight the promise that data-driven trash management has for improving both environmental and economic sustainability while tackling the problems associated with urban garbage.

Optimizing water quality classification using random forest and machine learning

Water is the most precious and essential resource among all natural resources. With the increase in industrialization and human activities over recent decades, the state of water resources has been significantly impacted. Effective water quality monitoring has become a priority for cities worldwide. Modern technologies such as cloud computing, artificial intelligence, remote sensing, and the Internet of Things provide new opportunities to enhance water resource monitoring systems. This paper explores the application of the random forest model for water quality classification based on chemical attributes. The study includes three experiments: using the full set of features, excluding the pH feature, and using only the top three significant features. The random forest model trained on the full dataset achieved 100% accuracy. When the pH feature was excluded, the model maintained an accuracy of 76%, highlighting the importance of this feature but also showing the potential for compensation by other parameters. Using only the top three significant features (pH, conductivity, and nitrate), the model again achieved 100% accuracy. The results demonstrate that feature optimization without significant loss of model accuracy is a promising approach to improve water quality monitoring and assessment processes. This approach allows for reduced data collection time and costs while maintaining high predictive accuracy. The findings confirm that machine learning, particularly random forest models, can be effectively used for water quality classification, ultimately supporting better management and conservation of water resources.

Space-time mapping of soil organic carbon stock and its local drivers: Potential for use in carbon accounting

The measurement and tracking of changes in soil organic carbon (SOC) stocks and its underpinning drivers are important in greenhouse gas accounting and carbon trading schemes. While repeated direct measurement of a location over time is the ‘gold standard’ for SOC stock change estimation, this is currently unfeasible at scale due to sample collection and analysis costs. Here, we used a data-driven, space–time digital soil mapping modelling approach (ST-DSM) coupled with an interpretive machine learning (IML) technique to both predict and quantify the relative contributions of covariates to annual SOC stocks and their uncertainty at 0–30 cm depth and at a 90 m spatial resolution for Australia between 1990 and 2018. A quantile regression forest model was calibrated using data from a datacube comprising of SOC stock values collected over the prediction years and a suite of dynamic and static environmental covariates. The evaluation of the ST-DSM model on a test set was satisfactory. Long-term annual mean (Mg C/ha), and total (Pg C) topsoil SOC stock were 37.6 (12.3 – 101.1, 90 % PI) and 28.3 (9.25 – 83.1, 90 % PI) respectively. The mean annual rate of SOC stock change over the Australian continent was 0.11 (±0.22) Mg C/ha yr−1 and the total net change was 23.5 Tg C yr−1, indicating Australia’s soils were a net sink of SOC over the 29-year period. However, SOC stocks declined in the montane ecoregion, a great concern given its fragility and high SOC stock. The long-term mean fraction of photosynthetic vegetation was the most important individual local contributor to SOC stock prediction in many parts of Australia. The aggregated effect of covariates based on the soil forming factors indicates vegetation as the principal local driver of SOC stock across most of the Australian continent, followed by climate. The evaluation of our results against independent, temporally paired field data and estimates from a nationally accepted SOC process-based model yielded promising results. Our approach demonstrates the use of readily available and inexpensive covariates to estimate SOC stock changes, as well as the underlying drivers over a larger area but at a spatial resolution suitable for decision making. This approach presents a new monitoring and verification possibility for carbon projects and national carbon accounting especially with the growing number of existing profile observations in soil databases.

The importance of social oocyte cryopreservation in supporting local municipalities: A prospective study.

With the trend toward late marriages and late childbearing, cryopreservation of oocytes for fertility preservation is attracting attention as a method to counteract the declining birthrate. To examine the impact of social oocyte cryopreservation on local communities by assessing the significance of government assistance for cryofreezing and capturing the participants' subsequent feelings regarding this assistance. Descriptive study. A prospective study was conducted on city-dwelling women <35 years old attending monthly seminars on oocyte retrieval/cryopreservation to whom the study concept was explained. Egg collection and storage management costs were free for 3 years after the project completed, and subsequent actual storage costs were borne by the individuals. After oocyte retrieval, we conducted a questionnaire on oocyte cryopreservation and administrative assistance. Of the 62 seminar participants, 2 became pregnant naturally without oocyte retrieval. Oocytes were retrieved in 34 women (average age: 32.8 years, number of oocytes obtained: 8.3), among whom 4 subsequently became pregnant and gave birth through natural pregnancy or artificial insemination, and 1 became pregnant and gave birth using frozen oocytes. In a follow-up questionnaire given to these 34 subjects, all responded that they were glad to have oocyte cryopreservation, but 23 subjects (67.6%) answered that they could not perform cryopreservation without financial assistance. Twenty-five participants (73.5%) wanted to try to conceive without using frozen oocytes as a post-cryopreservation plan. As a countermeasure against the declining birthrate, oocyte cryopreservation and associated workshops that can provide the information and education needed to conduct this task in a "planned" manner may be useful in providing women with additional reproductive options. Financial assistance will also be required to offer this service to the women who need it.

Cost-Aware Generalized α-Investing for Multiple Hypothesis Testing

We consider the problem of sequential multiple hypothesis testing with nontrivial data collection costs. This problem appears, for example, when conducting biological experiments to identify differentially expressed genes of a disease process. This work builds on the generalized α-investing framework which enables control of the marginal false discovery rate in a sequential testing setting. We make a theoretical analysis of the long term asymptotic behavior of α-wealth which motivates a consideration of sample size in the α-investing decision rule. Posing the testing process as a game with nature, we construct a decision rule that optimizes the expected α-wealth reward (ERO) and provides an optimal sample size for each test. Empirical results show that a cost-aware ERO decision rule correctly rejects more false null hypotheses than other methods for $n=1$ where n is the sample size. When the sample size is not fixed cost-aware ERO uses a prior on the null hypothesis to adaptively allocate of the sample budget to each test. We extend cost-aware ERO investing to finite-horizon testing which enables the decision rule to allocate samples in a non-myopic manner. Finally, empirical tests on real data sets from biological experiments show that cost-aware ERO balances the allocation of samples to an individual test against the allocation of samples across multiple tests.

Reliability assessment of off-policy deep reinforcement learning: A benchmark for aerodynamics

Abstract Deep reinforcement learning (DRL) is promising for solving control problems in fluid mechanics, but it is a new field with many open questions. Possibilities are numerous and guidelines are rare concerning the choice of algorithms or best formulations for a given problem. Besides, DRL algorithms learn a control policy by collecting samples from an environment, which may be very costly when used with Computational Fluid Dynamics (CFD) solvers. Algorithms must therefore minimize the number of samples required for learning (sample efficiency) and generate a usable policy from each training (reliability). This paper aims to (a) evaluate three existing algorithms (DDPG, TD3, and SAC) on a fluid mechanics problem with respect to reliability and sample efficiency across a range of training configurations, (b) establish a fluid mechanics benchmark of increasing data collection cost, and (c) provide practical guidelines and insights for the fluid dynamics practitioner. The benchmark consists in controlling an airfoil to reach a target. The problem is solved with either a low-cost low-order model or with a high-fidelity CFD approach. The study found that DDPG and TD3 have learning stability issues highly dependent on DRL hyperparameters and reward formulation, requiring therefore significant tuning. In contrast, SAC is shown to be both reliable and sample efficient across a wide range of parameter setups, making it well suited to solve fluid mechanics problems and set up new cases without tremendous effort. In particular, SAC is resistant to small replay buffers, which could be critical if full-flow fields were to be stored.

Features gradient-based signals selection algorithm of linear complexity for convolutional neural networks

&lt;abstract&gt;&lt;p&gt;Recently, convolutional neural networks (CNNs) for classification by time domain data of multi-signals have been developed. Although some signals are important for correct classification, others are not. The calculation, memory, and data collection costs increase when data that include unimportant signals for classification are taken as the CNN input layer. Therefore, identifying and eliminating non-important signals from the input layer are important. In this study, we proposed a features gradient-based signals selection algorithm (FG-SSA), which can be used for finding and removing non-important signals for classification by utilizing features gradient obtained by the process of gradient-weighted class activation mapping (grad-CAM). When we defined $ n_ \mathrm{s} $ as the number of signals, the computational complexity of FG-SSA is the linear time $ \mathcal{O}(n_ \mathrm{s}) $ (i.e., it has a low calculation cost). We verified the effectiveness of the algorithm using the OPPORTUNITY dataset, which is an open dataset comprising of acceleration signals of human activities. In addition, we checked the average of 6.55 signals from a total of 15 signals (five triaxial sensors) that were removed by FG-SSA while maintaining high generalization scores of classification. Therefore, FG-SSA can find and remove signals that are not important for CNN-based classification. In the process of FG-SSA, the degree of influence of each signal on each class estimation is quantified. Therefore, it is possible to visually determine which signal is effective and which is not for class estimation. FG-SSA is a white-box signal selection algorithm because it can understand why the signal was selected. The existing method, Bayesian optimization, was also able to find superior signal sets, but the computational cost was approximately three times greater than that of FG-SSA. We consider FG-SSA to be a low-computational-cost algorithm.&lt;/p&gt;&lt;/abstract&gt;

Constraint-Aware Learning for Fractional Flow Reserve Pullback Curve Estimation from Invasive Coronary Imaging.

Estimation of the fractional flow reserve (FFR) pullback curve from invasive coronary imaging is important for the intraoperative guidance of coronary intervention. Machine/deep learning has been proven effective in FFR pullback curve estimation. However, the existing methods suffer from inadequate incorporation of intrinsic geometry associations and physics knowledge. In this paper, we propose a constraint-aware learning framework to improve the estimation of the FFR pullback curve from invasive coronary imaging. It incorporates both geometrical and physical constraints to approximate the relationships between the geometric structure and FFR values along the coronary artery centerline. Our method also leverages the power of synthetic data in model training to reduce the collection costs of clinical data. Moreover, to bridge the domain gap between synthetic and real data distributions when testing on real-world imaging data, we also employ a diffusion-driven test-time data adaptation method that preserves the knowledge learned in synthetic data. Specifically, this method learns a diffusion model of the synthetic data distribution and then projects real data to the synthetic data distribution at test time. Extensive experimental studies on a synthetic dataset and a real-world dataset of 382 patients covering three imaging modalities have shown the better performance of our method for FFR estimation of stenotic coronary arteries, compared with other machine/deep learning-based FFR estimation models and computational fluid dynamics-based model. The results also provide high agreement and correlation between the FFR predictions of our method and the invasively measured FFR values. The plausibility of FFR predictions along the coronary artery centerline is also validated.

Optimal route selection using network analysis in terms of time, cost and fuel savings: The case of İskenderun, Türkiye

Solid waste generation has increased significantly as a result of increasing population and living standards. In this context, variables such as waste classification, amount produced, collection and transportation status, disposal method, socioeconomic status of the region include important stages in system planning. It is important to optimize these variables, waste collection route, time, cost, environmental impacts and examine the current situation. The cost of collection and processing accounts for roughly 65-80% of the total cost of the solid waste management system managed by local governments. Therefore, by optimization studies, the trash collection-transport route can significantly reduce this rate. Depreciation and fuel expenditures can be significantly reduced as a result of improvements in operational costs. Denizciler neighborhood was selected as an example of all district characteristics within the study's scope when the socio-economic, demographic, and topographic conditions of the Iskenderun district of Hatay province were evaluated. The ESRI ARCGIS software's tools have been used to test the optimization path. A 421-meter shorter route was discovered using network analysis. Considering the current route, which is 14340 meters long, a 3% improvement has been achieved with the analysis made. Garbage collection was done using the newly developed route in the study's final section. As a result, the viability of the network analysis enhancement has been evaluated in practice, and it has been discovered that time and fuel savings are realized in terms of cost.

Vortexing specimens to disaggregate platelet clumps in EDTA specimens.

To compare platelet count results of specimens that yield platelet clump flags to platelet count results on these specimens after vortexing. Specimens that generated platelet count flags on Sysmex XN 3000 instruments were vortexed and rerun. Only data from specimens demonstrating elimination of platelet clump flags were used in this study. Pearson r analysis was performed on data. Comparison of complete blood count results (white blood cell count, red blood cell count, hemoglobin, hematocrit, and platelet count) all yielded Pearson r scores >0.9. Additional patient comfort and safety concerns, as well as concerns over additional specimen collection and processing costs, may be avoided by vortexing and rerunning specimens flagged for platelet clumps when the platelet count is normal.