Quantity Limits Research Articles

The Double-Layer Clustering Based on K-Line Pattern Recognition Based on Similarity Matching

Candlestick charts provide a visual representation of price trends and market sentiment, enabling investors to identify key trends, support, and resistance levels, thus improving the success rate of stock trading. The research presented in this paper aims to overcome the limitations of traditional candlestick pattern analysis, which is constrained by fixed pattern definitions, quantity limitations, and subjectivity in pattern recognition, thus improving its effectiveness in dynamic market environments. To address this, a two-layer clustering method based on a candlestick sequence simlarity matching model is proposed for identifying valid candlestick patterns and constructing a pattern library. First, the candlestick sequence similarity matching model is used to address the pattern matching issue; then, a two-layer clustering method based on the K-means algorithm is designed to identify valid candlestick patterns. Finally, a valid candlestick pattern library is built, and the predictive ability and profitability of some patterns in the library are evaluated. In this study, ten stocks from different industries and of various sizes listed on the Shanghai Stock Exchange were selected, using nearly 1000 days of their data as the test set. The predictive ability of some patterns in the library was evaluated using out-of-sample data from the same period. This selection method ensures the diversity of the dataset. The experimental results show that the proposed method can effectively distinguish between bullish and bearish patterns, breaking through the limitations of traditional candlestick pattern classification methods that rely on predefined patterns. By clearly distinguishing these two patterns, it provides clear buy and sell signals for investors, significantly improving the reliability and profitability of trading strategies.

Ultra-Broadband Perfect Absorbers Based on Biomimetic Metamaterials with Dual Coupling Gradient Resonators.

Ultra-broadband metamaterial absorbers can achieve near-perfect absorption of omnidirectional electromagnetic waves, crucial for light utilization and manipulation. Traditional ultra-broadband metamaterials rely on the superposition of different resonator units either in the plane or in perpendicular directions to broaden absorption peaks. However, this approach is subject to quantity restrictions and complicates the fabrication process. This study introduces a novel concept for broadband absorption metamaterial design-Metal-Insulator-Metal metamaterials with gradient resonators (GR-MIMs) to surpass limitations in quantity and fabrication. The GR-MIMs absorber features gradient resonant cavities in both nanoscale and microscale dimensions, each with continuous resonance points. By converting "resonance points" into "resonance bands" and perfectly coupling the two gradient resonators, the GR-MIMs absorber with a thickness of only 200nm demonstrates 93% ultra-broadband high absorption across the UV, visible, near-infrared, and mid-infrared spectra (0.2-5µm). Moreover, the solar spectrum absorption rate of the GR-MIMs absorber can reach 94.5%, offering broad prospects for applications in solar energy utilization. The design of gradient resonators provides a new approach for the development of ultra-broadband metamaterials and photothermal conversion metamaterials.

SNP Genotype Imputation in Forensics-A Performance Study.

Emerging forensic genetic applications, such as forensic investigative genetic genealogy (FIGG), advanced DNA phenotyping, and distant kinship inference, increasingly require dense SNP genotype datasets. However, forensic-grade DNA often contains missing genotypes due to its quality and quantity limitations, potentially hindering these applications. Genotype imputation, a method that predicts missing genotypes, is widely used in population and medical genetics, but its utility in forensic genetics has not been thoroughly explored. This study aims to assess the performance of genotype imputation in forensic contexts and determine the conditions under which it can be effectively applied. We employed a simulation-based approach to generate realistic forensic SNP genotype datasets with varying numbers, densities, and qualities of observed genotypes. Genotype imputation was performed using Beagle software, and the performance was evaluated based on the call rate and imputation accuracy across different datasets and imputation settings. The results demonstrate that genotype imputation can significantly increase the number of SNP genotypes. However, imputation accuracy was dependent on factors such as the quality of the original genotype data and the characteristics of the reference population. Higher SNP density and fewer genotype errors generally resulted in improved imputation accuracy. This study highlights the potential of genotype imputation to enhance forensic SNP datasets but underscores the importance of optimizing imputation parameters and understanding the limitations of the original data. These findings will inform the future application of imputation in forensic genetics, supporting its integration into forensic workflows.

Medicaid managed care restrictions on medications for the treatment of opioid use disorder.

To examine whether Medicaid managed care plan (MCP) utilization management policies for buprenorphine-naloxone and injectable naltrexone are related to key state Medicaid program policy decisions. We abstracted data on state Medicaid regulatory and policy information from publicly available sources and publicly available insurance benefit documentation from all 241 Medicaid MCPs operating in 2021. In this cross-sectional study, we used bivariate and multivariate analyses to examine whether Medicaid MCP prior authorization and quantity limits on receipt of buprenorphine and injectable naltrexone were associated with key state Medicaid choices to leverage federal funds to expand coverage and eligibility (Medicaid expansion, 1115 waivers) and to regulate Medicaid MCPs (uniform preferred drug lists, medical loss ratio remittance). Models were adjusted for MCP characteristics, including profit status, behavioral health contracting arrangement, National Committee for Quality Assurance accreditation, size, market share, and state opioid overdose death rates. Average marginal effects (AME) were reported. Utilization management was common among MCPs, and restrictions were more commonly applied to buprenorphine than injectable naltrexone, despite its higher cost. States that required MCPs to comply with utilization management policies stipulated in a uniform preferred drug list were more likely to require prior authorization for buprenorphine (AME: 0.29, 95% CI: 0.15-0.42) and injectable naltrexone (AME: 0.25, 95% CI: 0.12-0.38). MCPs in states that required plans to pay back earnings above a certain threshold were less likely to require prior authorization for buprenorphine (AME: -0.30, 95% CI: -0.43 to -0.18). Restrictions on medications for opioid use disorder are widespread among MCPs and vary by medication. State Medicaid regulatory and policy characteristics were strongly linked to MCPs' utilization management approaches. State Medicaid policy and contracting approaches may be levers to eliminate utilization management restrictions on medications for opioid use disorder.

The Influence of Hemp Fibers (Cannabis sativa L.) on the Mechanical Properties of Fiber-Gypsum Boards Reinforcing the Gypsum Matrix.

The modern construction industry is looking for new ecological materials (available, cheap, recyclable) that can successfully replace materials that are not environmentally friendly. Fibers of natural origin are materials that can improve the properties of gypsum composites. This is an important issue because synthetic fibers (hardly biodegradable-glass or polypropylene fibers) are commonly used to reinforce gypsum boards. Increasing the state of knowledge regarding the possibility of replacing synthetic fibers with natural fibers is another step towards creating more environmentally friendly building materials and determining their characteristics. This paper investigates the possibility of manufacturing fiber-gypsum composites based on natural gypsum (building gypsum) and hemp (Cannabis sativa L.) fibers grown in Poland. The effect of introducing hemp fibers of different lengths and with varying proportions of mass (mass of gypsum to mass of fibers) into the gypsum matrix was investigated. The experimental data obtained indicate that adding hemp fibers to the gypsum matrix increases the static bending strength of the composites manufactured. The highest mechanical strength, at 4.19 N/mm2, was observed in fiber-gypsum composites with 4% hemp fiber content at 50 mm in length. A similar trend of increased strength was observed in longitudinal tension. Again, the composite variant with 4% fiber content within the gypsum matrix had the highest mechanical strength. Manufacturing fibers-gypsum composites with more than 4% hemp fiber content negatively affected the composites' strength. Mixing long (50 mm) hemp fibers with the gypsum matrix is technologically problematic, but tests have shown a positive effect on the mechanical properties of the refined composites. The article indicates the length and quantity limitations of hemp fibers on the basis of which fiber-gypsum composites were produced.

Research on Lithology Identification based on Machine Learning

Machine learning has great potential in lithology identification. Through supervised learning, unsupervised learning, semi-supervised learning, deep learning and other methods, features can be automatically extracted from complex seismic data and logging data to achieve efficient and accurate lithology classification. These methods not only improve the accuracy and efficiency of lithology identification, but also reduce the workload of geologists, allowing them to focus on higher-level analysis and decision making. Despite significant progress, machine learning still faces many challenges in lithology identification. First, data quality and quantity limitations remain a major problem, especially in certain areas where obtaining high-quality seismic and logging data is difficult. Second, the training and interpretability of complex models also need to be addressed, especially because the "black box" nature of deep learning models makes it difficult for geologists to understand their internal mechanisms and predictions. In addition, the generalization ability and overfitting of models, as well as the need for real-time data processing in practical applications, are also urgent challenges to be solved. To address these challenges, this paper proposes several solutions and future directions. Data enhancement and synthesis techniques can extend existing data sets and improve the robustness and accuracy of models. The development of interpretative models and visualization tools helps geologists understand and trust the decision-making process of models. Multi-source data fusion technology can effectively use multi-source heterogeneous data such as seismic data, well logging data and geological map to improve model performance. Online learning and transfer learning technologies can update models in real time, improve the adaptability and generalization ability of models, and develop more accurate and interpretable models.

Phenotypically plastic responses to environmental variation are more complex than life history theory predicts.

For insects that exhibit wing polyphenic development, abiotic and biotic signals dictate the adult wing morphology of the insect in an adaptive manner such that in stressful environments the formation of a flight-capable morph is favored and in low-stress environments, a flightless morph is favored. While there is a relatively large amount known about the environmental cues that dictate morph formation in wing polyphenic hemipterans like planthoppers and aphids, whether those cues dictate the same morphs in non-hemipteran (i.e., cricket) wing polyphenic species has not been explicitly investigated. To experimentally test the generality of environmental cue determination of wing polyphenism across taxa with diverse life histories, in this study, we tested the importance of food quantity, parasitic infection, and tactile cues on wing morph determination in the wing polyphenic sand field cricket, Gryllus firmus. Our results also show that certain stress cues, such as severe diet quantity limitation and parasitic infection, actually led to an increase in the production of flightless morph. Based on these findings, our results suggest that physiological and genetic constraints are important to an organism's ability to respond to environmental variation in an adaptive manner beyond simple life history trade-offs.

A multi-ship power sharing strategy: Using two-stage robust optimization and Shapley value approach

In recent years, the use of cold ironing (CI) has effectively reduced the carbon emissions of ships in port areas. However, due to the limitation of port CI quantity and power supply capacity, the power demand of some ships cannot be met. To solve this problem, this paper proposes a multi-ship power sharing strategy based on two-stage robust optimization and improved Shapley value method. Firstly, considering the uncertainty of ship photovoltaic power generation, a multi-ship power sharing model based on two-stage robust optimization is established. This model is a three-level optimization model with mixed integer variables, which is a NP-hard problem. In this paper, the nested column-and-constraint generation algorithm combined with duality theory and big-M method is used to obtain the optimal scheduling strategy of ship shared power, CI power and load demand response power. Secondly, an improved Shapley value method considering berth and remaining berthing time is proposed to realize the cost allocation in the process of ship power sharing. Finally, the effectiveness of the multi-ship power sharing strategy in reducing berthing costs and carbon emissions is verified by simulation. This method is expected to provide a new idea for the development of green ports.

Optical signals measurement of discharge plasma in air switch cabinet using a fluorescent optical fiber as the sensor

Air switch cabinets are widely employed in the power industry, there is, however, no reliable methods for detecting partial discharges (PDs). Because fiber optic-based sensors are easy to install and have anti-electromagnetic interference capabilities, they are well suited for PDs detection in switch cabinets with their many components and metal shell structure. In this work, corona discharge in an air switch cabinet was detected using fluorescent fiber, and the characteristics of PDs were analyzed. Furthermore, experimental investigations were conducted to explore the impact of gas pressure and fiber length on the PDs detection. The results exhibit a correlation between the aforementioned factors and the PDs’ total amplitude, maximum amplitude, number of pulses and detection limit of PD quantity. The results can provide references for the application of fluorescent optical fiber on the PD detection in air switch cabinet.

Photocatalysis-enhanced synthesis and stabilization of silver nanoparticles by methanol-based phytochemicals extract of Trigonella foenum-graecum seeds

Trigonella foenum-graecum is an economically important plant that has significant nutraceutical properties. Various parts of the plant have previously been reported to synthesize metal nanoparticles. However, the seeds of the plant have limited potential to synthesize metal nanoparticles. Green synthesis of silver nanoparticles requires phytochemicals as reducing and metal chelating agents, in addition to the stabilizing agents that play critical role in nanoparticles stabilization. The quantitative analysis of the methanol extract of the seeds suggest that the extract has significant antioxidant activity and reducing potential which is comparable to that of ascorbic acid. Likewise, GCMS data of the extract identified several phytochemical components that have nanoparticles stabilizing potential. Evidently, the extract indeed synthesized silver nanoparticles in dark, albeit in very low quantity. This limitation of low quantity of nanoparticles synthesis was overcome by photocatalysis. The rate of nanoparticles synthesis increased significantly with increase in the intensity of the white light-emitting diode (LED) light. Furthermore, the photocatalytic effect of the white light also has significant impact on the physicochemical characterisation of the nanoparticles. Particle size, nanoparticles yield and elemental analysis demonstrated that the 2000 lumens white LED light is optimum for photocatalysis as compared to the 250 lumens and 825 lumens light. However, the stability of nanoparticles is not influenced by photoirradiation, and is rather controlled by the phytochemical composition of the extract. Methanol extract of the seeds significantly enhanced the stability of the silver nanoparticles irrespective of the light intensities used for photocatalysis.

Access barriers and cost of varenicline for smoking cessation: A national comparative analysis of Medicare standalone part D and Medicare Advantage plan.

11080 Background: Smoking cessation is a critical part of cancer prevention and control. Oral varenicline is the most cost-effective medication for smoking cessation, yet seniors enrolled in Medicare Part D Prescription Drug Plans (PDPs) can potentially face costs and access barriers to varenicline in the forms of prior authorization and quantity limits. Although over half of Medicare beneficiaries are now insured and receiving drug benefits through Medicare Advantage, it remains unclear how costs and access barriers compare between PDPs and MA drug plans (MADPs). This study compared plan-level costs and prevalence of prior authorization and quantity limit policies between PDPs and MADPs for oral varenicline. Methods: We used 2023 Q3 MA and PDP data from the Center for Medicare and Medicaid Services, excluding employer-sponsored and Supplemental Need Plans (including dual-eligible plans) and plans with <10 enrollees. We focused on two generic varenicline forms: 0.5mg and 1mg tablets. To compare medication quantities, we calculated the plan-level median (IQR) cost for 30-day supply, based on the average unit cost. Price benchmarks were defined based on average wholesale acquisition cost (WAC) from Micromedex Red Book. T-test and Chi-squared tests were used to compare costs and prevalence of prior authorization and quantity limits. Results: Across 3512 MADPs and 813 PDPs, median costs for a 30-day supply of varenicline were similar (0.5mg tab: $375.32 vs. $376.87, p=0.64; 1mg: $371.88 vs. $376.87, p=0.49, respectively). Median costs for both PDPs and MAPDs were higher than benchmarks; p<0.01 for all). For both varenicline dosages, MAPDs had higher rates of prior authorization (27.1% vs 16.2%, p<0.01) and quantity limits (48.0% vs 30.4%, p<0.01) than PDPs. Conclusions: Varenicline prices in both PDPs and MAPDs were much higher than benchmarks. Compared to PDPs, MAPDs used more prior authorization and quantity limit to control cost. This can create access barriers to this cost-effective medication, burdening patients, physicians, health systems, and pharmacies. [Table: see text]

Costs and access barriers to ondansetron: A national analysis of Medicare part D and Medicare Advantage plans.

e23126 Background: Seniors living with cancer and receiving prescription drug benefits through Medicare Part D Prescription Drug Plans (PDPs) can face cost and access barriers in the forms of prior authorization and quantity limit policies. While over half of Medicare beneficiaries are now insured with drug benefits through Medicare Advantage, it remains unclear how costs and access barriers compare between PDPs and MA drug plans (MADPs). This study compared plan-level costs and prevalence of prior authorization or quantity limit policies between PDPs and MADPs for oral ondansetron, an essential and widely used antiemetic for patients with cancer. Methods: We used 2023 Q3 MA and PDP data from the Center for Medicare and Medicaid Services, excluding employer-sponsored and Supplemental Need Plans (including dual-eligible plans) and plans with < 10 enrollees. We focused on the four most common generic ondansetron forms: 4mg and 8mg tablets, and 4mg and 8mg orally disintegrating tablets (ODT). To compare medication quantities, we calculated the median (IQR) cost of 30-day supply (90 dosage units), defining plan-level price as the average unit cost for each formulation. Price benchmarks were defined using Mark Cuban Cost-plus Drug Company cash prices, which reflect fixed markup to negotiated wholesale prices. T-test and Chi-square tests were used for comparison. Results: Across 813 PDPs and 3512 MADPs, MAPDs had lower median costs for a 30-day supply of ondansetron than PDP plans (4mg tab: $16.1 vs $29.2; 4mg ODT: $38.0 vs $56.4; 8mg tab: $24.7 vs $35.6; 8mg ODT: $37.2 vs $54.4, all p < 0.01). Median costs for both PDPs and MAPDs were higher than benchmarks (66-155% across formulations; all p < 0.01). PDPs and MAPDs had similarly high prior authorization rates (83.6% vs 83.4%, p = 0.89), but MAPDs used quantity limits more frequently (22.5% vs 16.5%, p < 0.01). There was little variation in prior authorization and quantity limit policies across formulations. Conclusions: Ondansetron costs in both PDPs and MAPDs were higher than benchmarks. Compared to PDPs, MAPDs offered ondansetron at lower costs but used policies creating access barriers for patients either at similar (prior authorization) or higher (quantity limit) frequency, burdening patients, physicians, health systems, and pharmacies. [Table: see text]

Multi-batch OBMA technique for large structures under rotational excitation

Operation Modal Analysis (OMA) is a viable option for structures where Experimental Modal Analysis (EMA) is challenging to apply. However, large structures may sometimes be measured in batches due to limitations in sensor quantity, which may result in incorrect scaling of mode shapes. This paper introduces a multi-batch Order Based Modal Analysis (OBMA) technique for large structures under rotational excitation, this technique uses common reference points, and the classic auto- and cross-powers will be replaced by the auto- and cross-power spectrum based on the angle domain. For comparison and validation, this technique was applied to a compressor rotor test rig, the first six modes of its casing obtained through OBMA, EMA and FEA are presented. It is concluded that multi-batch OBMA is a promising technique when confronting with large structures under rotational excitation, it can identify modal parameters under real boundary conditions with limited sensor quantity.

Understanding stoichiometric constraints on growth using resource use efficiency imbalances

Growth is a function of the net accrual of resources by an organism. Energy and elemental contents of organisms are dynamically linked through their uptake and allocation to biomass production, yet we lack a full understanding of how these dynamics regulate growth rate. Here, we develop a multivariate imbalance framework, the growth efficiency hypothesis, linking organismal resource contents to growth and metabolic use efficiencies, and demonstrate its effectiveness in predicting consumer growth rates under elemental and food quantity limitation. The relative proportions of carbon (%C), nitrogen (%N), phosphorus (%P), and adenosine triphosphate (%ATP) in consumers differed markedly across resource limitation treatments. Differences in their resource composition were linked to systematic changes in stoichiometric use efficiencies, which served to maintain relatively consistent relationships between elemental and ATP content in consumer tissues and optimize biomass production. Overall, these adjustments were quantitatively linked to growth, enabling highly accurate predictions of consumer growth rates.

Abstract 5781: Development of RNA-Seq library preparation method for highly degraded FFPE RNA and ultra-low RNA input

Abstract RNA sequencing (RNA-Seq) has been widely used for investigating genetic variations, splicing, and gene expression in cancer research. However, RNA-Seq library preparation from archived formalin fixed paraffin embedded (FFPE) samples tends to fail due to both limited quantity and high degradation. We aimed to overcome the sample quality and quantity limitations by leveraging both in-house cDNA synthesis modules and the unique, single-stranded ligation strategy of the IDT xGen cfDNA & FFPE DNA Library Prep chemistry and workflow. This approach delivers high library conversion of input molecules and maximizes high quality data from sequencing. To represent the types of challenging cancer samples that may be lost during traditional RNA-Seq analyses, we collected thirty-seven low quality RNA FFPE samples (RIN <3) from lung, breast, colorectal, thyroid tumors and more. In addition, twenty-one frozen tissue RNA samples and Nine RNA reference materials were used as controls. Regardless of sample origin, 25 ng of total RNA were taken for RNA-Seq library preparation. In-house cDNA synthesis modules were used for the 1st and 2nd strand cDNA synthesis followed by Illumina library generation using IDT’s xGen™ cfDNA & FFPE DNA Library Prep Kit. All sample types generated sufficient high-quality libraries (>1 ug) for hybridization capture enrichment. The RNA-seq libraries were captured using IDT’s custom designed panel with 56 known fusion targets. We detected all expected fusion targets from the tested samples. No correlation was observed between sample quality and the number of genes detected between the different types of samples including the low quality FFPE. To test the lower limits of the workflow, a degraded FFPE sample and a Universal Human Reference RNA (UHR) were used to construct libraries with inputs from 1ng to 10pg. High quality libraries were successfully constructed with as low as 10pg of UHR and 50pg of the FFPE RNA sample. We demonstrated the flexibility of our current cfDNA & FFPE DNA Library Prep whose high conversion rate could be used for constructing libraries from low quality and degraded FFPE RNA in conjunction with our in-house cDNA synthesis modules. Libraries were successfully constructed from ultralow RNA input which could be leveraged for cfRNA applications. The high RT efficiency enables processing FFPE samples with very low-quality scores and provides users with an effective solution for their degraded RNA samples challenges. Citation Format: Ekaterina Star, Noreen Karim, Berlin DeGroen, Michael Salgado, David Wang, Jing Li, Katica Ilic, Sarah Beaudoin, Christopher Vakulskas, Bosun Min, Peilin Chen. Development of RNA-Seq library preparation method for highly degraded FFPE RNA and ultra-low RNA input [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5781.

Casimir effect in axion electrodynamics with lattice regularizations

The Casimir effect is induced by the interplay between photon fields and boundary conditions, and in particular, photon fields modified in axion electrodynamics may lead to the sign-flipping of the Casimir energy. We propose a theoretical approach to derive the Casimir effect in axion electrodynamics. This approach is based on a lattice regularization and enables us to discuss the dependence on the lattice spacing for the Casimir energy. With this approach, the sign-flipping behavior of the Casimir energy is correctly reproduced. By taking the continuum limit of physical quantity calculated on the lattice, we can obtain the results consistent with the continuum theory. This approach can also be applied to the Casimir effect at nonzero temperature. Published by the American Physical Society 2024

Experience in developing human resources for market surveillance and lessons learned for the South Central Coast, Vietnam

Human resources are always regarded as a crucial key with determining characteristics for the success of an organization. Therefore, the development of human resources is an important task that needs attention in any organization, particularly in the current context of the Vietnamese civil service. The human resources responsible for market surveillance in the South Central Coast are a vital part of the public service workforce in Vietnam. The significant role of this workforce in minimizing fraudulent trade practices and contributing to market stability cannot be denied, especially in the aftermath of the Covid-19 pandemic. However, during their operations, the human resources for market surveillance in the South Central Coast reveal many limitations in both quantity and quality. Based on the research and experience in developing human resources market surveillance in similar regions in Vietnam, this study draws lessons and insights for the future development of human resources market surveillance in the South Central Coast.

Automatic evaluation method for vehicle audio warning system using MFCC-polynomial hybrid feature

In the evaluation of vehicle audio warning system, there is no automatic method. Besides, due to the noise interference of in-vehicle environmental, the quantity limitation and only positive training samples, the accuracy of traditional template matching or identification methods for audio is low. To solve the above problems, an efficient, accurate, and automatic evaluation method is proposed for vehicle audio warning system. Firstly, logmmse-spectrum subtraction method is used to filter the dynamic noise and static noise of the evaluation audio acquired in the in-vehicle environment. Secondly, the end point detection based on short-time energy is used to obtain the effective audio segment after noise reduction, and the start time of the audio warning segment can be accurately obtained. Then, the Mel Frequency Cepstrum Coefficient (MFCC) feature and the polynomial fitting feature of each audio segment are extracted. The hybrid features are treated as the input of the Hidden Markov Model-Gaussian Mixture Model (GMM-HMM) based audio matching model. Finally, according to frame shift set by endpoint detection and the audio sampling frequency, the emitted time of matched audio warning can be calculated to support the evaluation of vehicle audio warning system. The experimental result shows that, with dynamic-static noise reduction and MFCC-polynomial hybrid feature, the average matching accuracy of the proposed method reaches 99.6% in the case of only five training samples.

A study on vegetable stocking-pricing decision-making in supermarkets based on regression analysis and planning models

Vegetable products in the superstore require high freshness and must meet market demand while maximizing profit. This study proposes a pricing strategy based on the proportion of sales for each product category. Using data from the previous four weeks, a relationship model is developed. Additionally, a BP neural network regression is used to analyze the relationship between vegetable sales and cost-plus pricing. A planning model is then established to determine the replenishment quantity and daily maximum profit, with the objective of maximizing profitability. Adjustments to the replenishment program and pricing strategy are made to accommodate changes in sales conditions. By extracting sellable goods data and using prediction results, the planning model is further refined by setting constraints such as ordering limits, historical pricing records, and quantity limits. This model enables the determination of replenishment quantities and pricing strategies for each individual product, ultimately maximizing profit for the superstore.

Separation and Spectroscopic Determination of Ge(IV) as an Ion Pair Association Complex Using DB18C6 Ligand

A solvent extraction was applied using liquid ion exchange as a sensitive method to extract Germanium (IV) as anion complex K[GeO2(OH)2]- from basic media of KOH by used DB18C6 as a ligand dissolved in chloroform to form the ion pair association complex K-DB18C6+; K[GeO2(OH)2]-, the maximum wavelength of the extracted complex was λmax=290nm, and molar absorptive 1.269and#215;103 L/mol.cm. the Beerand#39;s –lambert law was adhered to within the specified range (1-20)ppm. The limits of detection and quantity were 0.034 and 0.102 ppm, respectively. The research studied optimal conditions of extraction efficiency, such as KOH concentration, shaking time, methanol effect, and electrolyte effect. In addition, a new spectrophotometric method was utilized to determine the remain of Ge(IV) in aqueous solutions and to calculate its distribution ratio (D) using Janus green B. This method was more sensitive when comparing the results with previous studies. DB18C6 as a ligand was used to determine Germanium (IV) in some canned foods widely sold in Iraq.