Variable Quantities Research Articles

Class-specific representation based distance metric learning for image set classification

Image set classification, which compares the similarity between image sets with variable quantity, quality and unordered heterogeneous images, has drawn increased research attention in recent years. Although many effective image set classification algorithms have been developed, they struggle to overcome issues such as intra-set diversity, inter-set similarity, and input data that are not linearly separable. In this paper, we propose a class-specific representation based distance metric learning (CSRbDML) framework to improve the classification performance. Specifically, CSRbDML aims to learn an inter-set distance metric on a kernel space such that the distance between truly matching sets is smaller than that between incorrectly matching sets. Furthermore, we also propose a novel and powerful image set classifier based on the learned distance metric. Extensive experiments on several well-known benchmark datasets demonstrate the effectiveness of the proposed methods compared with the existing image set classification algorithms.

CAR-NK Cells: A Chimeric Hope or a Promising Therapy?

Simple SummaryIn recent years, innovative immunotherapy-based treatments have paved the way for a new approach to hematological malignancies. Instead of conventional chemotherapy, T cells have been genetically engineered to detect—and engage their cytotoxicity against—tumor cells, and their success story is astonishing. However, many setbacks—including insufficient efficacy, deficient autologous source, heavy side effects, and a hefty price—limit their use. A promising alternative could be chimeric antigen receptor NK cells, which possess interesting cytotoxicity and minimal graft-versus-host disease risk. In this article, we review the possible sources, the development techniques, the potential advantages, and the challenges faced in the field of chimeric antigen receptor NK cells.Immunotherapy with chimeric antigen receptor-engineered T cells (CAR-T) has revolutionized the treatment landscape of relapsed/refractory B-cell malignancies. Nonetheless, the use of autologous T cells has certain limitations, including the variable quality and quantity of collected effector T cells, extended time of cell processing, limited number of available CAR cells, toxicities, and a high cost. Thanks to their powerful cytotoxic capabilities, with proven antitumor effects in both haploidentical hematopoietic stem cell transplantation and adoptive cell therapy against solid tumors and hematological malignancies, Natural Killer cells could be a promising alternative. Different sources of NK cells can be used, including cellular lines, cord blood, peripheral blood, and induced pluripotent stem cells. Their biggest advantage is the possibility of using them in an allogeneic context without major toxic side effects. However, the majority of the reports on CAR-NK cells concern preclinical or early clinical trials. Indeed, NK cells might be more difficult to engineer, and the optimization and standardization of expansion and transfection protocols need to be defined. Furthermore, their short persistence after infusion is also a major setback. However, with recent advances in manufacturing engineered CAR-NK cells exploiting their cytolytic capacities, antibody-dependent cellular cytotoxicity (ADCC), and cytokine production, “off-the-shelf” allogeneic CAR-NK cells can provide a great potential in cancer treatments.

Microwave Irradiation Technique, a Green Chemistry Approach for Dissolution Enhancement of Lopinavir

Aim: This research was focused on improvement of aqueous solubility of Lopinavir (LPO), Antiretroviral (ART) drug by preparing solid dispersion (SD) through microwave irradiation (MWI) technique as a green chemistry approach.
 Place and Duration of Study: Department of Pharmaceutics, M. C. E. Society’s Allana College of Pharmacy, CIF-SPPU, Pune, between June 2018 and July 2021
 Methodology: In MWI different batches of SD were formulated by 32 factorial approach with time of exposure (X1) and power of radiation (X2) as variable quantity and dissolution rate as response (Y1). β-CD was used as hydrophilic carriers. Drug carrier magnitude relation of 1:1 resolve by phase solubility analysis and SD were assessed for drug content, percentage dissolution rate studies, FTIR, XRD, DSC and SEM analysis.
 Results: The FTIR, XRD, DSC and SEM studies exhibited no interaction between LPO and excipient. In Physical mixture (PM) it shows less intensity and disappearance of sharp peaks while in SD indicates the conversion of crystalline state of LPO to amorphous state that discovered the dissolution enhancing, so the SD prepared by MWI proved to be a promising approach to increasing the dissolving rate of BCS class II drug LPO.
 Conclusion: Hence, from the all analysis studies, it absolutely was evident that factorial batch F1 was the higher. F1 coded batch (LPO: β-CD within the magnitude relation of 1:1 with time of exposure (4 min) and power of radiation (300 Watt), shows 07 folds increase i.e. 84% compared with drug discharged inside 60 min to plane LPO and SD i.e. 12 % only and 84% respectively.

Application of Mathematical Modeling in Cost Control of Medical Equipment Procurement in Public Hospitals.

The purpose of this study is to strengthen the procurement management of public hospitals and ensure the quality of medical equipment. In this paper, combined with the procurement process of medical equipment in public hospitals, on the basis of RFID technology, a JRP model of random demand for medical equipment procurement in public hospitals is established. At the same time, according to the type and quantity of decision variables, the DE solution algorithm is redesigned, and based on the support of the algorithm, a mathematical model for cost control of medical equipment procurement in public hospitals is established. It can be seen from the analysis results that the cost control system for medical equipment procurement in public hospitals based on the mathematical model proposed in this paper can play an important role in public hospital equipment procurement and promote the standardized operation of public hospital procurement management.

Comparative Palynological, Physicochemical, Antioxidant and Antibacterial Properties of Romanian Honey Varieties for Biomedical Applications.

The aim of this study was to investigate the melissopalynology, physicochemical characteristics, antioxidant and antibacterial activity of seven honey samples harvested from different geographical regions and climates of Romania. The melissopalynological analysis revealed that monofloral and multifloral samples contained a wide diversity of minor pollen types from Romanian flora. The moisture, pH and free acidity values were within international limit. HPLC analysis indicated high content of fructose and glucose and low content of sucrose. Bioactive compounds including proteins, phenolics, flavonoids and ascorbic acid were present in variable quantities, according to the botanical origin and geographical area. The highest phenolics and ascorbic acid content was in multifloral honeys from Crisana mountain and meadow and the extrafloral honeydew honey. The same honey samples have exerted free radical scavenging and antibacterial activity against Staphylococcus aureus and Pseudomonas aeruginosa. The free radical scavenging activity was strongly correlated to phenolics and ascorbic acid content, while the antimicrobial activity was medium correlated only to phenolics content. In conclusion, the selected Romanian honey samples with best antioxidant and antimicrobial activity could be further tested for the development of novel biomedical products.

Activity of Artemisia pallens leaf extracts against Culex quinquefasciatus larva and in silico docking of selected compounds against Acetylcholinesterase

Developed mosquitocidal resistance and associated health problems caused by synthetic insecticides, pave the way to control the mosquito population by using botanicals. Artemisia pallens / Davana, is renowned for its fragrance properties and thus well exploited in perfumery industries. The present study aimed to assess the larvicidal activity of Artemisia pallens leaf extracts against Culex quinquefasciatus. The preliminary qualitative analysis of the phytoconstituents and larvicidal activity against the fourth instar larvae of Culex quinquefasciatus was performed to analyse the efficacy of the chloroform, methanolic, and aqueous extracts of Artemisia pallens leaves. The chemical profiling of two extracts (Chloroform, Methanol) with better larvicidal activity was done by GC-MS. 3 compounds with insecticidal activity was docked against Acetylcholinesterase enzyme. All the extracts contain variable quantities of important secondary metabolites. The chloroform and methanolic extracts of Artemisia pallens leaves exhibit better larvicidal effect on Culex quinquefasciatus with a low LC50 value. The GC-MS analysis revealed the presence of 21 and 36 phytocomponents in chloroform and methanolic extracts of Artemisia pallens leaves respectively. Amongst the chosen metabolites valproic acid, pseudo solasodine diacetate, and alpha-terpineol, pseudo solasodine diacetate binds with lowest energy (-9. 86 Kcal/Mol) against Acetylcholinesterase. Thus Artemisia pallens leaves could serve as a natural weapon against Culex quinquefasciatus in the larval stage and could also control the adult form by blocking their nervous activity. As plant-based mosquitocides, are safe to humans and environment, Artemisia pallens leaves could be used to control mosquito borne diseases.

On Some Optimization Problems on Permutations

Numerous studies consider combinatorial optimization problems and their solution methods, since a large number of practical problems are described by means of combinatorial optimization models. Among these problems, the most prominent ones are function optimization problems on combinatorial configurations. Many of the studies mentioned above propose approaches and describe methods to solve combinatorial optimization problems for linear and fractionally linear functions on combinatorial sets such as permutations and arrangements. This work describes new approaches and methods to solve some maximization problems for linear, fractionally linear and quadratic functions on permutation set. Algorithms for solving these problems are given. For linear function, we provide a considerably easy method to find the permutation on which the function attains its maximum value. We describe the general algorithm to find fractionally linear function maximum. We consider the cases in which variables of numerator and denominator do not intersect, and when the numerator function and the denominator function have one common variable. We also describe the case in which the numerator function and the denominator function in total have incomplete variable list, i.e. when the total quantity of variables is less than the quantity of numbers in the permutation set. As for solving the problem of finding quadratic function maximum on permutation set, it turned out that there is no universal algorithm to solve this problem for any quadratic function. In this paper, we describe a method of finding maximum on permutation set for quadratic function consisting of two items. We provide examples of solving the considered optimization problems. Keywords: function, permutation, set, transposition, coefficients.

O-029 The Vaginal Microbial Predictors of the Endometrial Microbiota

Abstract Study question Can we find specific biomarkers in vaginal microbiota predicting the state of endometrial microbiota? Summary answer The higher levels of Eubacterium spp. in the vagina could serve as a predictor of the non-Lactobacillus-dominated microbial community in the uterus. What is known already The absence of microorganisms or the Lactobacillus-dominated community in the uterus cavity is considered more favorable than the non-Lactobacillus-dominated community. The latter is associated with poor pregnancy outcomes in women undergoing in vitro fertilization. However, endometrial microbiota analysis is challenging due to the possibility of contaminating the samples with vaginal microbiota during transvaginal sampling. Also, there is always a risk of upper genital tract inflammation after the sampling. For these reasons, the idea of finding markers predicting the state of endometrial microbiota is promising. Study design, size, duration It is a cross-sectional study of the vaginal and endometrial microbiota in 61 reproductive-age women with the normal histological pattern of endometrial samples. Endometrial and vaginal samples were collected simultaneously on days 7–10 of the menstrual cycle. Depending on the type of endometrial microbiota, the women were divided into two groups: Group 1 – the Lactobacillus-dominated microbiota or no microbiota (n = 43), Group 2 – the non-Lactobacillus-dominated microbiota (n = 18). Participants/materials, setting, methods All women came to the “Garmonia” Medical Center (Yekaterinburg, Russia) seeking infertility treatment from 2019 to 2021. Vaginal and endometrial microbiota was analyzed using Femoflor and Androflor real-time PCR kits (DNA-Technology, Russia). For the vaginal samples, the quantities and proportions of 22 microorganism groups were compared in Group 1 and Group 2 using Mann-Whitney test. For the variables with significant differences, ROC curves were constructed to assess the diagnostic value of the tests. Main results and the role of chance When comparing vaginal microbiota in Groups 1 and 2, we registered the statistically significant differences for 6 out of 44 variables (quantities and proportions of 22 microorganism groups). These variables were the quantities of Eubacterium spp. (10° vs. 104.7, p = 0.002) and Gardnerella vaginalis (10° vs. 104.0, p = 0.033), the proportions of obligate anaerobes (0% vs. 6.0%, p = 0.010), Peptostreptococcus spp./Parvimonas spp. (0% vs. 0%, p = 0.011), G. vaginalis (0% vs. 0.2%, p = 0.012), Eubacterium spp. (0% vs. 0.6%, p = 0.033). “Eubacterium spp.’ quantity” variable demonstrated the highest discriminatory power (the area under the ROC curve value — 0.737 [CI 0.593-0.882], p = 0.004). The AUC value was considered as acceptable to divide patients into Group 1 and Group 2. The cutoff value of Eubacterium spp. 103.15 genome-equivalent/ml (GE/ml)) allowed to predict the non-Lactobacillus-dominated microbiota in the uterus cavity with 77.8% sensitivity and 62.8% specificity. Limitations, reasons for caution The results were obtained on a small population group of women with the normal histological pattern of endometrial samples. It may be different if the larger pool of samples is investigated. It is also possible, that the results will not apply to the patients with endometrial abnormalities. Wider implications of the findings Further research should determine the diagnostic value of Eubacterium spp. identified in vaginal samples as the predictor for the endometrial microbiota composition in women with endometrial abnormalities. The next step is research dedicated to the link between different signatures in vaginal microbiota and endometrial abnormalities. Trial registration number Protocol № 7 dated September 20, 2019

The Effect of Quality, Quantity and Work Motivation on Employee Performance of the Cirebon Regency Investment and One Stop Service (DPMPTSP)

Purpose of the study — a study to find out about the quality, quantity and work motivation that affect the performance of employees at the Office of Investment and One Stop Integrated Services (DPMPTSP)Research method—This research is a quantitative research with regression. This study uses a quantitative approach to see the causal relationship of several factors that influence employee performance. The population in this study were the employees of the Regency Investment Office and PT. XYZ totaling 65 employees. Data were collected by questionnaire and document study, after which the data were tested for validity and reliability. Result—The results showed that Quality, Quantity and Work Motivation had an effect on employee performance simultaneously. The Multiple Determination Coefficient in this study uses the adjusted R square value because the independent variables in this study are more than 2 in estimating the influence of the two independent variables studied. From the adjusted R Square value data of 0.578, This means that together the influence of variables X1, X2, and X3 is 57.8% on variable Y or in other words the influence of independent variables (Quality, Quantity and Work Discipline) on the dependent variable (Performance) is 57.8% while the rest namely 42.2% caused by other variables that are not included in the research framework.

Abstract 831: Discovery of a novel soluble Neuropilin-2 isoform with antiangiogenic and antitumorigenic activity

Abstract Upregulation of the non-tyrosine kinase receptor neuropilin-2 (NRP2) is described for several tumor entities and correlates with disease progression. Two membrane-anchored isoforms are transcribed from the NRP2 gene that only differ in their terminal exons to encode transmembrane stretches and short cytoplasmic tails, respectively. While NRP2a was shown to support tumor cell proliferation, NRP2b facilitates tumor cell migration, resulting in enhanced metastatic spreading. A third, soluble NRP2 isoform encoding only aminoterminal protein domains exists as dimeric protein and functions in sequestration of the VEGF-C ligand. By using a next-generation sequencing approach to enrich for NRP2 terminal exons, we identified NRP2-Mo83, a novel NRP2 isoform, from a high expressing tumor cell line, and verified secreted NRP2-Mo83 protein by mass spectrometry in cell supernatants. Preliminary mass spectrometric data give an indication of the presence of soluble NRP2-Mo83 protein in human serum samples.NRP2-Mo83 mRNA is expressed at variable quantities in vitro in a panel of bladder carcinoma cell lines, but is downregulated in a small cohort of macrodissected urothelial carcinoma samples. Transcript abundances of a NRP2 transcript classified as non-coding with high sequence similarity to the NRP2-Mo83 3’ terminus confirm the observed downregulation in the TCGA bladder carcinoma cohort.Preliminary data indicate that recombinant NRP2-Mo83 protein exerts a dose-dependent inhibitory effect on tumor cell growth in vitro in all tested bladder carcinoma cell lines in the presence of serum. However, a fraction of cell lines does not respond to NRP2-Mo83 under serum-starved conditions. Proliferation of human endothelial cells (HUVEC) is inhibited in a dose-dependent manner by recombinant NRP2-Mo83 protein in vitro. In vivo, NRP2-Mo83 reveals an anti-angiogenic effect on the chicken chorioallantoic membrane.Recombinant NRP2-Mo83 is secreted by HEK293 cells. Enrichment of endogenous and purification of recombinant NRP2-Mo83 protein is achieved by exploiting its capacity to bind heparin. In contrast to the described s9NRP2 isoform, NRP2-Mo83 is largely monomeric in solution, as verified by size exclusion chromatography. We identified N and O glycosylation as well as the capacity for polysialation as posttranslational modifications of recombinant NRP2-Mo83 protein, similar to membrane anchored NRP2 isoforms. NanoDSF experiments indicate that calcium ions stabilize NRP2-Mo83. We present NRP2-Mo83 as a novel NRP2 splice isoform, validated its expression at the mRNA and protein level. NRP2-Mo83 appears down-regulated during bladder tumorigenesis, congruent with its function in bladder tumor growth inhibition and its anti-angiogenic properties in vitro and in vivo. The identification of NRP2-Mo83 in human serum may suggest a suppressive function even under non-pathological conditions. Citation Format: Thomas Mayr, Paul L. Dix, Sayed-Mohammad Hasheminasab, Chuanpit Hampel, Marc Sylvester, Niels Schneberger, Sarah Foerster, Gregor Hagelueken, Samikshan Dutta, Glen Kristiansen, Regine Schneider-Stock, Kaustubh Datta, Michael H. Muders. Discovery of a novel soluble Neuropilin-2 isoform with antiangiogenic and antitumorigenic activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 831.

Determining of the Fatigue Crack Growth Rate of HSLA Steel at Room Temperature

Welded joint is a critical region of a welded structure and fracture mechanics analysis is inevitable in the structural integrity assessment of all welded structures. This paper shows the determining of parameters of the fatigue crack for constituents of welded joints produced of high strength low alloyed steel. The applied methodology refers to the Paris relation where the link was established between the variable load quantity or the corresponding stress intensity factor range and crack growth per cycle. Results have shown that the position of the notch and crack initiation affect the values of the stress intensity range of fatigue threshold ΔKth and parameters in the Paris’ equation. This is mostly expressed when determining growth parameters of the fatigue crack in heat affected zone of HSLA steel, where different changes of growth speed of the fatigue crack clearly express differences in structure of the crack pass.

An EPQ Model for Delayed Deteriorating Items with Two-Phase Production Period, Exponential Demand Rate and Linear Holding Cost.

The economic production quantity (EPQ) model for delayed deteriorating items considering two-phase production periods, exponential demand rate and linearly increasing function of time holding cost is proposed to solve a production problem similar to the one caused by the Covid-19 pandemic. Without shortages, the necessary and sufficient conditions for optimality of this model are characterized through a theorem and lemmas while a solution methodology based on differential calculus is adopted. This paper determines the best replenishment cycle length corresponding to the optimal total variable cost and production quantity of imperfect production industry. To illustrate this model, a numerical experiment is conducted. The results demonstrate that a higher carrying charge decreases the production quantity and a longer demanding period decreases the total variable cost of an industry with a distracted production period. Finally, managerial insights are discussed using sensitivity analysis and future research directions are exposed.

Automated Movement Analysis to Predict Cerebral Palsy in Very Preterm Infants: An Ambispective Cohort Study.

The General Movements Assessment requires extensive training. As an alternative, a novel automated movement analysis was developed and validated in preterm infants. Infants < 31 weeks’ gestational age or birthweight ≤ 1500 g evaluated at 3–5 months using the general movements assessment were included in this ambispective cohort study. The C-statistic, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for a predictive model. A total of 252 participants were included. The median gestational age and birthweight were 274/7 weeks (range 256/7–292/7 weeks) and 960 g (range 769–1215 g), respectively. There were 29 cases of cerebral palsy (11.5%) at 18–24 months, the majority of which (n = 22) were from the retrospective cohort. Mean velocity in the vertical direction, median, standard deviation, and minimum quantity of motion constituted the multivariable model used to predict cerebral palsy. Sensitivity, specificity, positive, and negative predictive values were 55%, 80%, 26%, and 93%, respectively. C-statistic indicated good fit (C = 0.74). A cluster of four variables describing quantity of motion and variability of motion was able to predict cerebral palsy with high specificity and negative predictive value. This technology may be useful for screening purposes in very preterm infants; although, the technology likely requires further validation in preterm and high-risk term populations.

Optimizing automotive inbound logistics: A mixed-integer linear programming approach

The optimization of inbound logistics from first-tier suppliers at the aggregation level of a factory is a challenging coordination problem. In practice, different transport modes and multiple constraints exist and need to be taken into account: the available number of storage places, capacity of goods-entry, delivery profile and transportation pattern constraints, truck utilization, demand satisfaction, and the reverse flow of empty load carriers. A scalable solution method that is tailored to an automotive factory is currently not available. Optimization is usually carried out manually, e.g. experience-based or by using decision trees that follow an iterative optimization approach. Most of the mathematical models in the literature only deal with parts of the requirements and do not validate results in the required complexity. We present a mixed-integer linear programming approach to solve this complex problem using Gurobi. The solution capability and quality are enhanced by the introduction of a cyclic inventory constraint and valid inequalities. The evaluation of the approach is based on a case study of a German automotive factory with 570 suppliers and 3927 stock keeping units and reduces the as-is total cost by 33.86%. Additionally, CO2 emissions are reduced by 74.66%. We further show that variable order quantities, along with a courier and express service as an additional transport mode, are of high economic relevance. As part of a sensitivity analysis, we show how the total costs change, when (a) goods-entry handling constraints, (b) truck utilization, (c) storage constraints, or (d) transportation costs vary, and (e) the effect of a cyclic inventory constraint.

Improving Parameter Estimation of Fuel Cell Using Honey Badger Optimization Algorithm

In this study, we proposed an alternative method to determine the parameter of the proton exchange membrane fuel cell (PEMFC) since there are multiple variable quantities with diverse nonlinear characteristics included in the PEMFC design, which is specified correctly to ensure effective modeling. The distinctive model of FCs is critical in determining the effectiveness of the cells’ inquiry. The design of FC has a significant influence on the simulation research of such methods, which have been used in a variety of applications. The developed method depends on using the honey badger algorithm (HBA) as a new identification approach for identifying the parameters of the PEMFC. In the presented method, the minimal value of the sum square error (SSE) is applied to determine the optimal fitness function. A set of experimental series has been conducted utilizing three datasets entitled 250-W stack, BCS 500-W, and NedStack PS6 to justify the usage of the HBA to determine the PEMFC’s parameters. The results of the competitive algorithms are assessed using SSE and standard deviation metrics after numerous independent runs. The findings revealed that the presented approach produced promising results and outperformed the other comparison approaches.

Stagnation point heat flux characterization under numerical error and boundary conditions uncertainty

The numerical simulation of hypersonic atmospheric entry flows is a challenging problem. Prediction of quantities of interest, such as surface heat flux and pressure, is strongly influenced by the mesh quality using conventional second-order spatial accuracy schemes while depending on boundary conditions, which may generally suffer from uncertainty. This paper illustrates one of the first systematic quantification of the numerical error and the uncertainty-induced variability for the simulation of hypersonic flows. Specifically, a mesh-convergence study using grid adaptation tools is coupled with surrogate-based approaches to Uncertainty Quantification. The illustrative example is the simulation of the EXPERT vehicle of the European Space Agency employing the US3D solver. First, we show the benefits in using mesh adaptation to simulate hypersonic flows under uncertainty. On the one hand, this practice reduces the numerical uncertainty associated with each prediction and, on the other hand, allows us to obtain a more reliable surrogate model for Uncertainty Quantification by preventing non-physical heat flux values.Secondly, we perform a sensitivity analysis to compare the numerical uncertainty associated with a given mesh with the UQ-induced variability for a specific quantity of interest. In the case considered, the impact of the numerical uncertainty turned out to be at least one order of magnitude less than the quantity of interest variability. This result indicates the possibility of using coarse and adapted meshes for future UQ studies.

The Influence of Municipal Wastewater Treatment Technologies on the Biological Stabilization of Sewage Sludge: A Systematic Review

Various wastewater treatment technologies are available today and biological processes are predominantly used in these technologies. Increasing wastewater treatment systems produces large amounts of sewage sludge with variable quantities and qualities, which must be properly managed. Anaerobic and aerobic digestion and composting are major strategies to treat this sludge. The main indicators of biological stabilization are volatile fatty acids (VFAs), volatile solids (VS), the carbon/nitrogen (C/N) ratio, humic substances (HS), the total organic carbon (TOC), the carbon dioxide (CO2) evolution rate, the specific oxygen uptake rate (SOUR), and the Dewar test; however, different criteria exist for the same indicators. Although there is no consensus for defining the stability of sewage sludge (biosolids) in the research and regulations reviewed, controlling the biological degradation, vector attraction, and odor determines the biological stabilization of sewage sludge. Because pollutants and pathogens are not completely removed in biological stabilization processes, further treatments to improve the quality of biosolids and to ensure their safe use should be explored.

Tuning the Thermoelectric Properties of Boron‐Doped Silicon Nanowires Integrated into a Micro‐Harvester

AbstractSemiconductor nanowires demonstrate outstanding properties within a wide range of application fields, including energy harvesting. Consequently, increasing attention has been focused on silicon nanowires for thermoelectric power generation after their successful implementation in miniaturized devices. In this work, the effect of tuning the p‐type doping level in silicon nanowires is studied in thermoelectric micro‐generators. In situ heavily boron‐doped silicon nanowires are grown by the vapor‐liquid‐solid mechanism in a chemical vapor deposition reactor. Variable quantities of dopant gas—diborane or B2H6—are used to control the doping level and study the direct influence on the thermoelectric properties of the nanowires. The relationship between dopant partial pressure and resulting carrier concentration is evaluated, and all thermoelectric properties are studied as a function of each doping level. A maximum power factor of 2.0 ± 0.2 mW mK−2 is found for a carrier concentration of 2.5 × 1019 cm−3, corresponding to a figure of merit of zT = 0.04 at near room temperature. Optimized nanowires are included in a micro‐machined thermoelectric generator, yielding an open circuit voltage of 4.5 mV and maximum power output of 1.08 μW cm−2 upon hot surfaces at 250 °C.

Improving Liquid Film Thickness Uniformity of Semiconductor Etching Equipment Using Flow Field Visualization and CFD Simulation

With the rapid development of semiconductor-based products, semiconductor and integrated circuit (IC) design industries have become indicators of global technological advance in this field. As IC structure becomes smaller and smaller, improved process technology and equipment are necessary to maintain and improve wafer yield rates and quality. Among them, the etching technology in the process has a close relationship with the size of IC structure. According to extensive research and analyses put forward by predecessors, the etching process and methods that affect the yield rates and quality of wafers have been well characterized. This study investigates the spray etching method of the single nozzle and double nozzle. Using computational fluid dynamics software developed by Ansys Fluent, the fluid is injected from the center as the starting point at the same flow rate and speed, and according to 2, 4, and 8 seconds of a different cycle and nozzle forms, moving back and forth to supply liquid on the surface of the 300 mm disc. It is assumed that fluid properties do not change with temperature. Additionally, the chemical reaction and thermal reaction of the etching process are not considered. Changes in the surface flow field resulting from different nozzles and cycle conditions are observed. This study involved setting up a set of flow field visualization systems, replacing the etching liquid with colored water, using a digital camera to capture the relationship between the gray index and the thickness of the water film, and calculate the film thickness. The average film thickness deviation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${t}_{avg}$ </tex-math></inline-formula> ) and variable quantity ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${dt}$ </tex-math></inline-formula> ) were characterized and used as a means to compare performance. The results show that the average film thickness deviation of the double nozzles is higher than the single nozzle, and variable quantity ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${dt}$ </tex-math></inline-formula> ) is lower than that of the single nozzles. In general, the double nozzles increase the thickness and stability of the liquid film thickness on the surface of the disc.

A Literature Review on Billing System using Machine Learning

Abstract: The process of billing a product in a retail outlet is done using the system of pre-made barcodes or RFIDs. This is done to keep a track of the items and identify them in the billing process, but it is time-consuming and labour-intensive as all items need to be affixed with barcodes before being used. Most of the small to medium scale retail stores also have a large part of their sales with items of a variable quantity, which cannot be affixed with barcodes as their quantity is determined dynamically and according to the customer’s needs. In this paper, we have explored various technologies including but not limited to computer vision, object detection and image recognition, especially due to its latest boom with machine learning and deep learning. We have briefed our findings on technologies that can be used to build efficient and novel systems to solve the issues that arise with using systems that are labour intensive, redundant, time-consuming and explore ways to incorporate items that can be packed with variable quantities. Keywords: Machine Learning, Deep Learning, Object Detection, Billing System, Computer vision, Image recognition