Constant Demand Research Articles

Leveraging Walnut Somatic Embryos as a Biomanufacturing Platform for Recombinant Proteins and Metabolites.

Biomanufacturing enables novel sources of compounds with constant demand, such as food coloring and preservatives, as well as new compounds with peak demand, such as diagnostics and vaccines. The COVID-19 pandemic has highlighted the need for alternative sources of research materials, thrusting research on diversification of biomanufacturing platforms. Here, we show initial results exploring the walnut somatic embryogenic system expressing the recombinant receptor binding domain (RBD) and ectodomain of the spike protein (Spike) from the SARS-CoV-2 virus. Stably transformed walnut embryo lines were selected and propagated in vitro. Both recombinant proteins were detected at 3-14 µg/g dry weight of tissue culture material. Although higher yields of recombinant protein have been obtained using more conventional biomanufacturing platforms, we also report on the production of the red pigment betanin in somatic embryos, reaching yields of 650 mg/g, even higher than red beet Beta vulgaris. This first iteration shows the potential of biomanufacturing using somatic walnut embryos that can now be further optimized for different applications sourcing specialized proteins and metabolites.

ECONOMIC ORDER QUANTITY (EOQ) MODEL: WEIBULL AMELIORATING ITEMS WITH CONSTANT DEMAND RATE

In this study, we investigate the behavior of inventory items having the potential of incurring significant increase in their utilities or values while in stock in accordance to the Weibull distribution law. The amelioration of an inventory occurs when it remains in stock but depletes as a result of constant demand rate. The Weibull amelioration function plays the role of depicting the amelioration state of an inventory whereas the constant demand function describes the different possible stages of market demand. We describe the application of simple, yet analytical, approach for successful derivation and analyses of the underlying inventory system. We furthermore, analyze the model while providing, for the model, the necessary but sufficient and optimal replenishment policy. We use numerical results and examples to better illustrate the solution and successfully carryout the sensitivity analysis and the evaluation of the model.

A stochastic analysis of the dynamic space utilization in warehouses

Improving the volume space utilization of modern warehouses is a critical objective in warehouse layout design, making the accurate estimation of space utilization across different layouts essential. However, the dynamic demand and replenishment processes of heterogeneous stock keeping units (SKUs) in modern warehouses present significant challenges in accurately assessing space utilization. Traditional models have relied on deterministic frameworks that assume constant demand and production processes, which do not fully capture the heterogeneity and stochasticity in demand arrivals, sizes, and replenishment times encountered in practical scenarios. This study aims to develop a framework for computing volume space utilization in modern block stacking warehouses, incorporating both heterogeneity and stochasticity. We investigate two models in warehouse scenarios: the lost sale model, where excess demand is lost, and the backorder model, where unmet demand is backlogged. For each model, we derive the closed-form expressions of three key types of space wastes that characterize the space utilization of warehouses: honeycombing, aisle, and top-of-lane space wastes, using a continuous-time Markov chain analytical framework. Utilizing these expressions, we analyze the tradeoffs among these wastes and identify the optimal lane depth that maximizes space utilization. Our case studies show that, in a stochastic environment, the proposed computational framework allows for a more accurate estimation of space utilization, and the application of the obtained optimal lane depth can achieve significantly higher space utilization than deterministic methods found in the literature. This underscores the importance of incorporating stochasticity and heterogeneity of demand and replenishment in layout design. Additionally, by investigating the sensitivity of space utilization to varying demand processes and replenishment strategies, we provide managerial insights for adapting warehouse layout designs in response to changes in SKUs' demand and replenishment patterns.

Identification of morphological and variety diagnostic molecular markers for commercial varieties of nutmeg (Myristica fragrans Houtt.)

Abstract Nutmeg (Myristica fragrans Houtt; family: Myristicaceae) is an important tree spice of great export value due to the presence of secondary metabolites with scope in health, wellness and cosmetic industries. Its dioecious in nature due to which it is propagated by budding and grafting which requires specialised skill and as a result authentic planting material production is very expensive. Based on the constant demand from farmers and stakeholders for a suitable, fast and easy technique for determining the authenticity of nutmeg varieties, an attempt was made to develop morphological and molecular markers for the identification of three commercial varieties that are in high demand in India; viz. IISR Vishwashree, IISR Keralashree, Sindhushree and a monoecious nutmeg (MN) accession, along with their biochemical profiles. Among the various morphological characters as per distinctiveness, uniformity and stability guidelines, the varieties showed distinctness mainly in leaf shape, shape of female flowers, fruit shape and seed shape. Out of 35 ISSR primers screened, six primers viz., IS 02, ISSR 12, ISSR 05, ISSR 14, ISSR 01 and UBC 834 generated clear, unique reproducible polymorphic bands capable of distinguishing the varieties. Among the varieties analysed, MN was found to be superior in terms of seed butter and essential oil in nut, mace and leaves. IISR Vishwashree was on par with MN in nut essential oil content. IISR Keralashree was superior in terms of oleoresin in nuts and mace. The morphological and molecular markers identified may be used for effective checking of authenticity of planting materials of commercially grown nutmeg varieties.

Classification of Faults in Power System Transmission Lines Using Deep Learning Methods with Real, Synthetic, and Public Datasets

Every part of society relies on energy systems due to the growing population and the constant demand for energy. Because of the high energy demands of transportation, industry, and daily life, energy systems are crucial to every part of society. Electrical transmission lines are a crucial component of the electrical power system. Therefore, in order to determine the power system’s protection plan and increase its reliability, it is critical to foresee and classify fault types. With this motivation, the main goal of this paper is to design a deep network model to classify faults in transmission lines based on real, generated, and publicly available datasets. A deep learning architecture that was based on a one-dimensional convolutional neural network (CNN) was utilized in this study. Accuracy, specificity, recall, precision, F1 score, ROC curves, and AUC were employed as performance criteria for the suggested model. Not only synthetic but also real data were used in this study. It has been seen that the created model can be used successfully for both real data and synthetic data. In order to measure the robustness of the network, it was tested with three different datasets consisting of real, generated, and publicly available datasets. In the paper, 1D CNN, one of the machine learning methods, was used on three different power systems, and it was observed that the machine learning method was successful in all three power systems.

How higher education teachers see their professional identity

An important dimension to understand the student learning process is the professional identity of teachers, which is conceptualized as a continuous process of construction and reconstruction. In this process, teachers define and assume their roles in a specific educational context that is characterized by tensions, adjustments, and transformations. This study focuses on understanding the professional identity of higher education teachers, a field characterized by constant evolution and demands. The personal characteristics of this identity are explored, including experiences and biographical representations, as well as its interrelationship with the institutional environment. Using a qualitative interpretive approach, six teachers recognized for their pedagogical innovation and educational commitment were interviewed. The results reveal outstanding motivation and responsibility in educational work, but also tensions between personal identity and the work context, reflected in the perception of low recognition and the importance of institutional leadership. The teachers show a critical and reflective view of their work, influenced by childhood experiences that impact their conceptions of educational justice and equity. To conclude, the common characteristics of professional identity in higher education highlight the need to integrate them into professional development. The article advocates for the design of more personalized training programs and the promotion of continuous learning among peers as key elements to strengthen this identity and improve pedagogical practice.

Sex Trafficking in Asia: A Review from Business and Organisational Perspectives

The sex trafficking industry has evolved into a persistent and globally prevalent problem, notably in Asia. It is fundamentally a business-oriented and profit-driven activity. Political, social, and economic issues influence the modus operandi of sex traffickers. The majority of existing studies on sex trafficking in Asia are studied from several viewpoints, including human rights, feminists, and migration, with the least emphasis on the traffickers’ economic and organisational issues. Therefore, this paper will discuss the root causes of sex trafficking in Asia and examine it from a business economic and organisational standpoint, by analysing extensive literature and examining primary and secondary sources. According to these sources, three important factors contribute to the crime of sex trafficking: the supply of potential victims for exploitation in the source countries, the constant demand for trafficked victims in the destination countries, and the presence of organised crime groups acting as intermediaries in the market-driven business enterprise.

Resilience and Self-Care in Patients with Heart Failure: A Cross-Sectional Study

Background: A key component of managing heart failure, self-care can significantly affect patient outcomes. Patients may find the constant and intricate demands of self-care stressful, and they may need to show resilience to manage their symptoms. Objectives: The present study aimed to identify resilience and self-care among patients with heart failure. Methodology: The design of the present study was cross-sectional. The research population consisted of heart failure patients visiting the cardiac hospitals and admitted to the cardiac ward. A convenience sampling method was used to select 200 patients with heart failure. The data were collected from April 6th, 2023 to June 29th, 2023 about the participants’ demographic variables, Self-Care of Heart Failure Index (SCHFI V6.2 English), and the Connor-Davidson Resilience Scale (CD-RISC). The data were analyzed using descriptive statistics (percentage, frequency, standard deviation, mean score) for the demographic variables. To assess reliability, Cronbach’s alpha was used. Results: The present study showed that the majority of sample (n=185, 92.5%) had a medium level of self-care in symptom perception (Mean±SD= 28.86±16.32), self-care maintenance (Mean±SD= 34.2±15.63), and self-care management (Mean±SD= 35.52±17.25). The research findings showed a strong negative association between patients' resilience and self-care (p<0.001). There was a statistically significant association between self-care and participants’ marital status (p=0.02). Self-care was greater among those who were married. Conclusion: The present findings showed that patients with heart failure had a moderate level of self-care and resilience. There were associations between resilience and self-care in heart failure patients. The variables that may mediate or affect self-care need to be well-recognized by the research population

Quantum sensing with tunable superconducting qubits: optimization and speed-up

Abstract Sensing and metrology are crucial in both fundamental science and practical applications. They meet the constant demand for precise data, enabling more dependable assessments of theoretical models’ validity. Sensors, now a common feature in many fields, play a vital role in applications like gravity imaging, geology, navigation, security, timekeeping, spectroscopy, chemistry, magnetometry, healthcare, and medicine. The advancements in quantum technologies have sparked interest in employing quantum systems as sensors, offering enhanced capabilities and new possibilities. This article describes the optimization of the quantum-enhanced sensing of magnetic fluxes with a Kitaev phase estimation algorithm based on frequency tunable transmon qubits. It provides the optimal flux biasing point for sensors with different qubit transition frequencies and gives an estimation of decoherence rates and achievable sensitivity. The use of 2- and 3-qubit entangled states are compared in simulation with the single-qubit case. The flux sensing accuracy reaches 10 − 8 ⋅ Φ 0 and scales inversely with time, which proves the speed-up of sensing with high ultimate accuracy.

Molten‐Salt‐Assisted Synthesis of Carbon‐Doped BN Nanosheets for the Sensitive Detection of Paracetamol

AbstractHigh‐performance sensors are in constant demand for health care and environmental protection. In this study, a sensitive paracetamol (PCM) sensor was designed by modifying glassy carbon electrode (GCE) with carbon‐doped boron nitride (BCN/GCE) nanosheets. The BCN nanosheets were synthesized using a molten‐salt method, which ensured the uniformity of size distribution. X‐ray diffraction and transmission electron microscopy results suggested that the carbon‐doping hardly changed the crystal structure and morphology of hexagonal boron nitride (HBN). However, the carbon‐doping can reduce the bandgap, increase the specific surface area and improve the electrical conductivity, which is attributed to the formation of abundant point defects in the BN crystal. These point defects modulate electronic structure and surface activity, and further improve the electrocatalytic properties of BN. The BCN nanosheets exhibited outstanding electrocatalytic activity in the oxidation of paracetamol (PCM). With the help of square wave voltammetry (SWV) method, the BCN/GCE demonstrated linear current response towards PCM covering the concentrations of 0.05–70 μM and 70–200 μM with a detection limit (LOD) of 0.0086 μM. The developed method also showed excellent selectivity and high stability. In addition, the BCN/GCE sensor worked well when it was applied to quantify PCM in actual samples.

Problems and prospects for employment of young Russian teachers

The article raises the problems related to the peculiarities of building a professional career of graduates with pedagogical education. Despite the constant demand in the labor market, educational organizations are in a situation of chronic shortage of teaching staff. The aim of the article is to analyze the difficulties and challenges faced by young teachers not only during employment, but also during the period of adaptation in an educational institution. Based on a large array of statistical data, as well as sociological studies conducted by the research teams of Institute of Sociology of FCTAS RAS and RSUH, risk factors and channels of leaving the labor market of young teachers-professionals shown. The article poses, firstly, the problem of insufficiently meaningful choice and, as a consequence, motivation of future teachers during their enrollment in the university, the formation of “professional ballast” of students unmotivated for the profession of teacher. Secondly, the problem of lack of necessary vacancies, insufficient salary, uncomfortable working conditions. Thirdly, the problem of adaptation to work in an educational institution: the lack of stress resistance, professionalism, communication competence, responsibility and initiative. It is concluded that in order to attract young teachers to schools it is necessary to take thoughtful measures to retain motivated people in the profession, starting from the university stage and ending with the creation of favorable conditions for their career development in the workplace.

An FPGA-Based YOLOv5 Accelerator for Real-Time Industrial Vision Applications.

The You Only Look Once (YOLO) object detection network has garnered widespread adoption in various industries, owing to its superior inference speed and robust detection capabilities. This model has proven invaluable in automating production processes such as material processing, machining, and quality inspection. However, as market competition intensifies, there is a constant demand for higher detection speed and accuracy. Current FPGA accelerators based on 8-bit quantization have struggled to meet these increasingly stringent performance requirements. In response, we present a novel 4-bit quantization-based neural network accelerator for the YOLOv5 model, designed to enhance real-time processing capabilities while maintaining high detection accuracy. To achieve effective model compression, we introduce an optimized quantization scheme that reduces the bit-width of the entire YOLO network-including the first layer-to 4 bits, with only a 1.5% degradation in mean Average Precision (mAP). For the hardware implementation, we propose a unified Digital Signal Processor (DSP) packing scheme, coupled with a novel parity adder tree architecture that accommodates the proposed quantization strategies. This approach efficiently reduces on-chip DSP utilization by 50%, offering a significant improvement in performance and resource efficiency. Experimental results show that the industrial object detection system based on the proposed FPGA accelerator achieves a throughput of 808.6 GOPS and an efficiency of 0.49 GOPS/DSP for YOLOv5s on the ZCU102 board, which is 29% higher than a commercial FPGA accelerator design (Xilinx's Vitis AI).

Predicting resistance of bearing steels to surface initiated fatigue: Application to hybrid contact

The constant demand for improved energy efficiency in lubricated rolling/ sliding contacts, such as in rolling bearings, is generally addressed through increased power density and the use of low-viscosity (i.e., thin-film) lubricants. In these conditions, mixed lubrication regime can dominate with intensive asperity interactions between two contacting surfaces, thus increasing the risk of surface-initiated fatigue (e.g., in the form of micropitting). In the present study, the influence of steel mechanical properties on the micropitting accumulation in a hybrid contact (steel versus Si3N4 ceramics of different roughness) has been experimentally and theoretically investigated. It was found that higher roughness of Si3N4 surface could increase the risk of micropitting on the steel component. However, despite formation of some mild micropitting, the bearing steels tested here show good performance. For better understanding of the relationship between the mechanical properties of bearing steels and micropitting resistance, a new theoretical framework is introduced. The new micropitting model, based on the finding that the nucleation of a micropit is driven by (i) local accumulation of the plastic strain in mixed rough contact, and (ii) reduction of the total energy by debonding of the plastically deformed zone is introduced. The new micropitting model was shown to match with experiments across the range of test conditions. Micropitting performance maps based on the mechanical properties of steels (yield strength, work hardening and fracture toughness) have been obtained. The new theory has implications not only for predicting micropitting but also for predicting adhesive wear resistance of different steels.

Valuation of Memory Effect of Fuzzy EOQ Model with Constant Demand Rate

Valuation of Memory Effect of Fuzzy EOQ Model with Constant Demand Rate

Seaweeds and Their Secondary Metabolites: A Promising Drug Candidate With Novel Mechanisms Against Cancers and Tumor Angiogenesis.

Cancer continually remains a severe threat to public health and requires constant demand for novel therapeutic drug candidates. Due to their multi-target orientation, lesser toxicity, and easy availability, natural compounds attract more attention from current scientific research interest than synthetic drug molecules. The plants and microorganisms produce a huge variety of secondary metabolites because of their physiological diversification, and the seaweeds occupy a prominent position as effective drug resources. Seaweeds comprise microscopic or macroscopic photosynthetic, multicellular, eukaryotic marine algae that commonly inhabit the coastal regions. Several molecules (such as polysaccharides, lipids, proteinaceous fractions, phenolic compounds, and alkaloids) are derived from seaweeds, and those small molecules are well attractive and more effective in cancer research programs. Their structural variation, derivative diversity, and quantity vary with seaweed species and geographical origin. Their smaller molecular weight, unique derivatives, hydrophobicity, and degree of sulfation are reported to be causes of their crucial role against different cancer cells in vitro. Several reports showed that those compounds selectively discriminate between normal and cancer cells based on receptor variations, enzyme deficiency, and structural properties. The present review aimed to give a concise explanation regarding their structural diversity, extractability, and mechanism of action related to their anti-cancer activities based on recently published data.

Investigating the Impacts of Autonomous Vehicles on the Efficiency of Road Network and Traffic Demand: A Case Study of Qingdao, China.

Rapid urbanization has led to the development of intelligent transport in China. As active safety technology evolves, the integration of autonomous active safety systems is receiving increasing attention to enable the transition from functional to all-weather intelligent driving. In this process of transformation, the goal of automobile development becomes clear: autonomous vehicles. According to the Report on Development Forecast and Strategic Investment Planning Analysis of China's autonomous vehicle industry, at present, the development scale of China's intelligent autonomous vehicles has exceeded market expectations. Considering limited research on utilizing autonomous vehicles to meet the needs of urban transportation (transporting passengers), this study investigates how autonomous vehicles affect traffic demand in specific areas, using traffic modeling. It examines how different penetration rates of autonomous vehicles in various scenarios impact the efficiency of road networks with constant traffic demand. In addition, this study also predicts future changes in commuter traffic demand in selected regions using a constructed NL model. The results aim to simulate the delivery of autonomous vehicles to meet the transportation needs of the region.

POLITICAL STABILITY IN UKRAINE UNDER MARTIAL LAW: TESTING THE INSTITUTIONAL APPROACH

The article is dedicated to analysing the risks to political stability in Ukraine in the context of the full-scale war for national self-preservation. The outcomes of the war will determine the future and potential of the Ukrainian nation as a full-fledged political entity on the international and global stages. The author assumes that the Ukrainian political system is unstable, and the risks of unconventional events increase due to the restriction and blocking of political participation by the broader population. The goal of the article is to test and verify the principles of the institutional approach, as explicated by S. Huntington, in the realities of Ukrainian society. The institutional approach is used as the theoretical and methodological basis. The method of index construction is applied to empirical data. The author has constructed three indices and twelve sub-indices. The obtained indicators are informative and illustrative, and they are promising for comparative and longitudinal studies. The author concludes that political instability is inherent in the Ukrainian political system, which is linked to the modernization challenge in the form of constant demands from citizens to the political authorities. The practical significance of the article lies in the recommendations to the authorities regarding the establishment of communication bodies, more active responses to popular demands, combating speculation and disinformation, and institutionalizing political activity directed at improving the situation of the military. The article is empirical in nature and focuses on analysing the social reality of 2020. The article is limited in its relevance and relativity to the conditions of modern wartime Ukrainian society. Further research may be directed at analysing the functioning of political institutions in wartime conditions using the examples of Israel and Armenia.

СОВРЕМЕННЫЕ ВИДЫ БЕЗГЛЮТЕНОВОГО СЫРЬЯ: ПЕРСПЕКТИВЫ ПРИМЕНЕНИЯ В ТЕХНОЛОГИИ МУЧНЫХ КОНДИТЕРСКИХ ИЗДЕЛИЙ

The purpose of the study is to identify new technological solutions in the field of flour confectionery development. The growth of a range of specialized products under sanction restrictions is of particular importance. Flour confectionery products, including various types of cookies, wafers, muffins, cakes, occupy a certain place in the diet and are in constant consumer demand, and therefore the development of new compositions and technologies for gluten-free products is a relevant area of research. The work provides systematized data on modern trends in the development of gluten-free flour confectionery products in terms of composition, preparation, properties, quality of finished products and their nutritional value. Functional ingredients such as proteins, dietary fiber, minor compounds used to improve product properties, including sensory profile, are discussed. Research was based on the methods of searching, analyzing and systematizing scientific papers and materials in the electronic databases Google Scholar, PubMed, Scie¬nce Direct, Scopus, eLibrary.ru, which consider modern trends in the development of technologies in the field of gluten-free flour confectionery products. The search was conducted over the past five years. New approaches to modern technologies for designing gluten-free flour confectionery products; the main types of flour used in production; types of additional raw materials to ensure sensory characteristics and increase the nutritional value of finished products were identified. It has been established that most papers on gluten-free products include sensory analysis. The practical significance of the work lies in helping scientists and manufacturers to determine current trends and promising areas of research in the field of developing specialized products.

Integration of a Hydrogen Storage Cavern into the Carbon2Chem® Project

AbstractUnderground salt caverns allow large hydrogen storage capacities at low specific costs. Such storage is required to balance fluctuating hydrogen supply and constant demand of large consumers as a methanol plant from the Carbon2Chem® project. The geological and technical feasibility for developing up to 168 Mio. m3 (n.c.) or 595 GWh of working gas capacity until 2040 at a location in the Lower Rhine Bay is described. Various options for realization as well as a novel process for cavern leaching parallel to hydrogen storage operation are evaluated. Further work is required including geological exploration and optimization of brine production. The main hurdles for investment decisions are the political uncertainties on the hydrogen storage market design and on the ramp‐up of the hydrogen market.

Noise-reduction techniques for 1H-FID-MRSI at 14.1 T: Monte Carlo validation and in vivo application.

Proton magnetic resonance spectroscopic imaging (1H-MRSI) is a powerful tool that enables the multidimensional non-invasive mapping of the neurochemical profile at high resolution over the entire brain. The constant demand for higher spatial resolution in 1H-MRSI has led to increased interest in post-processing-based denoising methods aimed at reducing noise variance. The aim of the present study was to implement two noise-reduction techniques, Marchenko-Pastur principal component analysis (MP-PCA) based denoising and low-rank total generalized variation (LR-TGV) reconstruction, and to test their potential with and impact on preclinical 14.1 T fast in vivo 1H-FID-MRSI datasets. Since there is no known ground truth for in vivo metabolite maps, additional evaluations of the performance of both noise-reduction strategies were conducted using Monte Carlo simulations. Results showed that both denoising techniques increased the apparent signal-to-noise ratio (SNR) while preserving noise properties in each spectrum for both in vivo and Monte Carlo datasets. Relative metabolite concentrations were not significantly altered by either method and brain regional differences were preserved in both synthetic and in vivo datasets. Increased precision of metabolite estimates was observed for the two methods, with inconsistencies noted for lower-concentration metabolites. Our study provided a framework for how to evaluate the performance of MP-PCA and LR-TGV methods for preclinical 1H-FID MRSI data at 14.1 T. While gains in apparent SNR and precision were observed, concentration estimations ought to be treated with care, especially for low-concentration metabolites.