Minimal Investment Research Articles

SOLAR RADIATION SIMULATION DEVICE FOR INVESTIGATION OF THERMAL AND PHOTOVOLTAIC PANELS

The paper presents the design and execution of a solar radiation simulation device, which ensures the adequate operation of thermal and photovoltaic panels by using light sources which have a spectrum close to the one produced by sun at minimum investment costs. The equipment is used for the experimental tracking of the behaviour of photovoltaic panels and solar thermal panels for both short and longer time intervals. The solar radiation simulation device has been developed to provide the radiation necessary for the evaluation of the efficiency and the heating of the solar panels exposed to sun. A system consisting of six OSRAM Ultravitalux 300W high-pressure lamps has been adopted, offering an optimized solution, both qualitatively and economically. The main steps achieved during its execution were: design, pre-dimensioning, verification, final dimensioning, execution and testing. According to the final tests, it was concluded that the proposed device can provide the quantitative and qualitative parameters required for solar radiation simulation in case of laboratory tests.

On hedge fund inceptions in a competitive market

This paper examines hedge fund inceptions in a competitive market. Using the recursive demeaning estimation method, we find hedge fund inceptions are positively related to market competition and market condition, but negatively related to investor sentiment and style performance. This implies hedge fund managers actively seek arbitrage and inception opportunities, when market conditions are recovering with competition heated up. We also find new hedge funds tend to open in a less popular investment area when investor sentiment is lower and/or it has greater potential to grow in the future. Furthermore, funds, launched in a more competitive market when market condition is poorer, tend to perform better in the subsequent months, with multi-strategies hedge funds showing strong results. Important determinants include minimum investment value, initial fund size and redemption fee. Our findings support private investors when selecting new hedge funds for investment.

DART: diagnostic-CT-enabled planning: a randomized trial in palliative radiation therapy (study protocol)

Palliative radiotherapy (PRT) is an effective treatment for managing symptoms of advanced cancer. At least half of all radiation treatments are delivered with palliative intent, aimed at relieving symptoms, such as pain or shortness of breath. Symptomatic patients must receive PRT quickly, therefore expeditious treatment planning is essential. Standard radiation planning requires a dedicated CT scan acquired at the cancer centre, called a 'CT simulation', which facilitates treatment planning (i.e. tumor delineation, placement of radiation beams and dose calculation). However, the CT simulation process creates a bottleneck and often leads to delays in starting treatment. Other researchers have indicated that CT simulation can be replaced by the use of standard diagnostic CT scans for target delineation and planning, which are normally acquired through the radiology department as part of standard patient workup. The goals of this feasibility study are to assess the efficacy, acceptability and scalability of diagnostic-CT-enabled planning, compared to conventional CT simulation planning, for patients receiving PRT to bone, soft tissue and lung disease. This is a randomized, phase II study, with 33 PRT patients to be randomized in a 1:2 ratio between conventional CT simulation (Arm 1), and the diagnostic CT enabled planning workflow (Arm 2). Patients will be stratified by treatment target volume (bone and soft tissue metastasis vs. primary or metastatic intrathoracic disease targets). The primary endpoint is the amount of time the patient spends at the cancer centre. Secondary endpoints include efficacy (rate of plan deliverability and rate of plan acceptability on blinded dose distribution review), stakeholder acceptability (based on patient and clinician perception of acceptability questionnaires) and scalability. This study will investigate the efficacy, acceptability and scalability of a "sim-free" PRT pathway compared to conventional CT simulation. The workflow may provide opportunity for resource optimization by using pre-existing diagnostic imaging and requires minimal investment due to its similarity to current PRT models. It also offers potential benefit to patients by eliminating an imaging procedure, reducing the amount of time spent at the cancer centre, and expediting time to treatment. Clinicaltrials.gov identifier: NCT05233904. Date of registration: February 10, 2022; current version: 1.4 on April 29, 2022.

A multi-objective optimization of FCL and DOCR settings to mitigate distributed generations impacts on distribution networks

Despite providing increased reliability and power quality in meeting the load demand, the integration of DGs has imposed challenges on the protection system. High penetration of DGs changes the overall network impedance and increases the fault current level in the network. Consequently, some buses are exposed to critical conditions, violate the circuit breakers (CB) handling capacity, and affect the existing relay coordination settings. To mitigate these adverse impacts, fault current limiters (FCL) can be a potential solution to minimize the short circuit current within permissible switchgear-rated limits. Due to its expensive cost, it is crucial to ensure the optimal FCL placement and sizing, and at the same time maintain the effectiveness of the protection performance during various network operation states. This research formulates FCL and directional overcurrent relay (DOCR) settings as a combined protection coordination scheme optimized by a multi-objective hybrid optimization technique. The proposed formulation aims to determine the minimal FCL sizing with minimum investment cost to satisfy the relay coordination constraints at high fault-level buses, irrespective of DG locations and network operating state. User-defined relay characteristics (UDC) are employed to intensify the DOCR coordination performance and obtain minimum operating time, where the conventional inverse relay constants (A&B) are optimized with (TSM & Ipu). The combined UDC relay settings and FCL problem formulation solution attained by MO-hybrid optimization can lessen the escalated electromagnetic stresses, costly switchgear replacement, and relay maloperation. The performance of the proposed technique is assessed by implementing it on the radial (IEEE-33 Bus) and meshed (IEEE 30-Bus) DNs with optimized FCL sizing and relay settings. The optimal results demonstrate the effectiveness of the proposed technique in maintaining the relay coordination performance in the presence of DGs and FCL under an ON/OFF grid connection.

Application of photovoltaic panels in electric vehicles to enhance the range

There is a significant increase in the number of alternative energy sources and electric vehicles. Therefore, there is a growing need for new technical solutions to increase the distance that an electric vehicle can travel on a single charge. The aim of this study is to assess the possibility of mileage increasing of an electric vehicle by means of commercially available solar energy technologies that require minimal investment. The considered electric car can be recharged from solar panels mounted on its roof during parking stages. Photovoltaic modules can contribute to the vehicle's propulsion or energize its accessories, such as ventilation, air conditioner, heated passenger seats, interior lighting. The results demonstrate feasibility of the proposed solutions for both cases with and without sun-tracking adjustments of solar panels. The calculations show that the vehicle-integrated photovoltaic panels can provide energy for up to 6.32% of the range on a full charge of the battery during the sunniest summer months and up to 1.16% of the range during the least sunny winter months, for the given conditions.

Industry 4.0 IoT Cloud Machinery Control with Arduino Sensor System for AI Powered Applications

Abstract: Industrial automation has historically been finished with wired structures. Wireless versions swiftly rising in commercial communique because of the extended ease of setting up with wireless structures. In addition to the capacity wired structures are bulky. Industrial automation structures are used for circumstance monitoring, control programs, and the mobile workforce. Common to all commercial automation programs are the necessities for international availability of components, coexistence among wired and wireless technologies, lifetime, security, and interoperability. The wireless generation is hastily rising to fulfill those needs. Industry 4.0 is revolutionizing the manner organizations manufacture, enhance and distribute their products. Manufacturers are integrating new technologies, together with the Internet of Things (IoT), cloud computing and analytics, and AI and machine learning into their manufacturing centres and in the course of their operations. This digital technology results in improved automation, predictive maintenance, self-optimization of techniques enhancements and, above all, a brand-new degree of efficiencies and responsiveness to clients now no longer formerly possible. Using high-tech IoT devices in smart factories ends in better productiveness and progressed best. Replacing manual inspection enterprise models with AIpowered visible insights reduces production mistakes and saves cash and time. With minimum investment, quality control personnel can set up a smart phone linked to the cloud to monitor production processes from simply anywhere. By making use of machine learning algorithms and equipping themselves with the right tools now, such as rugged IT equipment, enterprise resource planning and manufacturing execution systems, manufacturers can create an IT infrastructure that allows them to capitalize on these exciting new technologies at the earliest opportunity.

Studying flowers in 3D using photogrammetry.

Flowers are intricate and integrated three-dimensional (3D) structures predominantly studied in 2D due to the difficulty in quantitatively characterising their morphology in 3D. Given the recent development of analytical methods for high-dimensional data, the reconstruction of flower models in three dimensions represents the limiting factor to studying flowers in 3D. We developed a floral photogrammetry protocol to reconstruct 3D models of flowers based on images taken with a digital single-lens reflex camera, a turntable and a portable lightbox. We demonstrate that photogrammetry allows a rapid and accurate reconstruction of 3D models of flowers from 2D images. It can reconstruct all visible parts of flowers and has the advantage of keeping colour information. We illustrated its use by studying the shape and colour of 18 Gesneriaceae species. Photogrammetry is an affordable alternative to micro-computed tomography (micro-CT) that requires minimal investment and equipment, allowing it to be used directly in the field. It has the potential to stimulate research on the evolution and ecology of flowers by providing a simple way to access 3D morphological data from a variety of flower types.

Unrelated Parallel Machine Scheduling with Job and Machine Acceptance and Renewable Resource Allocation

In this paper, an unrelated parallel machine scheduling problem with job (product) and machine acceptance and renewable resource constraints was considered. The main idea of this research was to establish a production facility without (or with minimum) investment in machinery, equipment, and location. This problem can be applied to many real problems. The objective was to maximize the net profit; that is, the total revenue minus the total cost, including fixed costs of jobs, job transportation costs, renting costs of machines, renting cost of resources, and transportation costs of resources. A mixed-integer linear programming (MILP) model and several heuristics (greedy, GRASP, and simulated annealing) are presented to solve the problem.

Optimized strategy valorizing unautoclaved cottonseed hull as ruminant alternative feeds via solid-state fermentation: Detoxifying polyphenols, restraining hazardous microflora and antibiotic-resistance gene hosts

Cottonseed hull (CSH), typical agro-industrial byproduct, is widely valorized as ruminant alternative feeds, but many constraints including extensive anti-nutritional factors and indigenous hazardous microflora are still reasonably unsolved. This study proposed a novel strategy that valorizes unautoclaved CSH as ruminant alternative feeds via solid-state fermentation (SSF), where feasibility and microbial mechanism were roundly investigated. The SSF of raw CSH (R group, proposed in this study) was compared with autoclaved CSH (M group, prevalent strategy) based on physicochemical properties, chemical functionalities, and superficial structures. Compared to M group, R group though has exhibited near detoxification effect of polyphenols (81.7% free gossypol (FG) and 62.2% condensed tannin (CT), both below threshold values) after SSF, yet the accumulation of crude protein and fat has been higher (109.3 and 51.0 g/kg dry matter, respectively). Metagenomic analyses indicated the indigenous hazardous microorganisms and antibiotic-resistance genes were prevented from amplification in R group. More importantly, Pleurotus was the keystone contributor to functional gene sets of degrading polyphenols (FG and CT) and lignin during SSF in R group; furthermore, CAZyme gene sets associated with polysaccharide degradation were primarily derived from bacterial communities ( Cellulomonas , and Stenotrophomonas , et al.). Ultimately, microbial SSF of raw CSH can be a feasible strategy with minimum facility investment and energy consumption, thus expediting CSH valorization in feedstock industries and the sustainable bio-economy in developing countries. • SSF valorizing raw cottonseed hull (CSH) as ruminant alternative feeds is feasible. • This strategy facilitates the sustainable bio-economy. • Polyphenols content declined to safe threshold during SSF. • SSF is resilient to animal pathogens and the transfer of antibiotic resistance genes. • Functional gene set about polyphenol and lignin degradation is traced from Pleurotus .

Increased efficiency of hydrokinetic turbines through the use of an obstacle on the channel bottom

Nowadays, the growing demand for energy and the current shift towards the eradication of fossil fuels has led to continuous research into and development of alternative clean energies. Among these, one of the least investigated is the extraction of hydrokinetic energy in hydraulic channels. This article highlights an investigation focused on improving the performance of a hydrokinetic turbine installed in a hydraulic channel through the use of an obstacle on the channel bottom, thus providing a solution involving minimum investment and without the need for maintenance. It was found through a series of tests carried out in a hydrodynamic water tunnel that the power produced increases with the height of the obstacle. In addition, a validated numerical model facilitated the completion of the research by studying the influence of the height of the obstacle on the flow rate that passes through the turbine rotor. The latter representing the key factor in the generation of power together with the forces actuating on the blades. Under conditions analogous to those of the literature examples (with the turbine fully submerged), a power increase of about 25% was obtained in the tests.

Data science and machine learning in the IIoT concepts of power plants

Smart power plants are no longer just futuristic ideas but are rapidly becoming a reality, and for this to be achievable, an application of artificial intelligence in Industrial Internet of Things (IIoT) concepts is necessary. This paper presents the place and role of Data Science (DS) and Machine Learning (ML) on edge, fog, and cloud levels of vertical IIoT concepts of power plants. A comprehensive functional analysis of edge, fog, and cloud levels has been done. Data analyzing, preprocessing, and processing are described on all levels separately. Limitations in signal conversion and data preprocessing at the edge level (edge computing), data processing and analysis at the fog level (fog computing), and information processing at the application and cloud levels (high-end and cloud computing) are described in detail. ML algorithms have been selected based on the particular management and control level. The proposed concepts represent management and maintenance improvements with minimal investment and avoidance of production downtime.

A novel relatively linear hybrid Three-Level strategy for retrofitting heat exchanger networks

This paper proposes a three-level straightforward retrofitting approach for heat exchanger networks (HENs) using a genetic algorithm (GA) coupled with linear programming (LP) and integer linear programming (ILP) formulations. At the first level, the GA produces HEN structures. The local total annual cost (TAC) of these HENs is computed at the second level, consisting of LP, ILP, and an external search loop. LP model is formulated based on the minimum utility consumption, whereas ILP determines the minimum investment cost of implementing potential changes to the existing network to turn it into a GA-generated network. The external search loop is also used to handle the stream splitting ratios. Eventually, the final optimal TAC of the retrofitted network is determined by applying a correction procedure at the third level to the second level results. The results demonstrate that this approach could reduce the retrofitted network's TAC by about 0.5%-20% compared to others.

Identifying, Analyzing and Recommending Parameters and their Weightage for the Design of an Educational Public Experience

With the passage of time, the life in this world is getting faster and public spaces are the only spaces of interaction, relief and relaxation in urban areas. Under such circumstances, these spaces can be used as a medium for educating the public regarding various things. People can be taught many things of importance to them as well as for welfare of the community through many means and this paper attempts to explore parameters in architectural and urban design which can be used as a medium for educating the public. Further study has been done to find sub-parameters which can suggest pinpointed interventions to narrate stories and teach people through public spaces. Furthermore, statistical research on survey results has been conducted to understand the impact of these sub parameters upon the public. The data has been analyzed to find the optimal direction of research and development of certain parameters for more efficient learning spaces in public areas. Principal Component Analysis (PCA) has been carried out to identify parameters and their weightage for investing resources for the best quality of educational public spaces with minimum investment of resources. Interpretation of statistical research reveals interesting facts on which parameters should attract more expenditure in current designs and which should attract more research for future development to maximize development of public educational experiences.

PUBLIC-PRIVATE PARTNERSHIP AS A TOOL FOR INCREASING THE INVESTMENT ATTRACTIVENESS OF THE REGION (BY THE EXAMPLE OF THE MOSCOW REGION)

The Institute of public-private partnership (PPP) is studied both within the framework of economic and legal science, but most of the studies on this topic were conducted even before the adoption of the Law on Public-Private Partnership. In this regard, they cannot be considered relevant. To date, many problems of regulating the PPP institute remain unresolved. For example, the content of the Law on Public-Private Partnership requires improvement. The improvement of the PPP institute should be carried out on the basis of objective indicators related to the effectiveness of the projects being implemented and the level of development of the relevant legal relations in certain regions. The relevance of the research of the institute of public-private partnership is justified by its significant role in the modernization of the economy, the active development of relevant legal relations in the Russian Federation, the presence of a number of problematic aspects of the functioning of this institute. Public-private partnership is one of the forms of investment activity, as the implementation of relevant projects increases the technical level of production, the competitiveness of regional products, reduces the burden on regional and local budgets, improves the quality and accessibility of services provided to the population, which has a positive effect on investment potential and reduces investment risks. The Moscow Region consistently leads the Rating of investment attractiveness of Russian regions. Its investment potential is characterized as a potential with maximum opportunities and with minimal investment risks. To improve the implementation of public-private partnership in the Moscow region, it is necessary to propose the implementation of a mechanism for direct investment in startups and small and medium-sized businesses in the Russian market.

Pengaruh pengetahuan investasi, manfaat motivasi, dan modal minimal investasi terhadap keputusan investasi di Pasar Modal

This study aims to determine the effect of investment knowledge, investment benefits, investment motivation and minimal capital on investment decisions by students. The population in this study is data from students majoring in accounting for the 2018 class from several universities in the city of Palembang with the help of the slovin method. While the sample data used were 85 respondents who came from students majoring in accounting. The data collection method used in this study is a qualitative method in which researchers distribute questionnaires that will be filled out by respondents using SPSS version 25 test equipment, Investment Benefits and Investment Motivation have no effect on Investment Decisions. While the Minimum Investment Capital affects investment decisions

PENGARUH PENGETAHUAN PASAR MODAL SYARIAH DAN MODAL MINIMAL TERHADAP MINAT BERINVESTASI SAHAM (Studi Kasus Mahasiswa FEBI UIN Jambi)

This study aims to determine the effect of Islamic capital market knowledge and minimum capital on interest in investing in shares of FEBI UIN STS Jambi students. The research method is quantitative, and the method of data analysis is the coefficient of determination test. The research sample was 92 samples of FEBI UIN STS Jambi students from the 2018-2019 class, using a Likert scale questionnaire. The results showed that Islamic capital market knowledge (X1) had a significant positive effect on interest in investing in shares (Y), which means Ho is rejected and Ha is accepted. Minimum investment capital (X2) shows that there is a significant positive effect on interest in stock investment (Y), which means Ha is accepted and Ho is rejected. X1 and X2 have a significant effect on the Y variable. It is known that the significance value (X1) and (X2) simultaneously has an effect on (Y) of 0.000 < 0.005 and the value of Fcount > Ftable (60.279 > 3.099). Then the keyword is Ha is accepted and Ho is rejected. This shows that the higher the knowledge of the Islamic capital market and the minimum affordable capital, the higher the interest of students to invest.

Determinan Penyerapan Tenaga Kerja Di Indonesia Periode 2016-2020

Indonesia has natural resources and a high population, which motivates the Indonesian people to improve their life in a sustainable manner. However, if there is an increase in population, it is not balanced with the absorption of labor. Then there will be problems that will occur in that area. Meanwhile, the general condition of employment in Indonesia for the 2016-2020 period experienced very volatile growth and this can basically be influenced, namely through external and internal. This study seeks to find out the truth whether the influence of GRDP, inflation, minimum wages, investment and education on labor absorption by quantitative research and using secondary data. The data collection technique uses literature studies and documentation located at the Central Statistics Agency (BPS) for each province in Indonesia. Then the data that has been collected will be tested and analyzed using panel data regression techniques. The conclusion from the results of this study is that the GRDP variable has a positive and insignificant effect on employment in Indonesia for the 2016-2020 period. Then the inflation and education variables have a negative and insignificant effect on labor absorption in Indonesia for the 2016-2020 period. In contrast to the minimum wage and investment variables, the results of the analysis in this study are positive and significant for each variable on labor absorption in Indonesia for the 2016-2020 period.

Optimization of configuration for home micro-grid cogeneration system based on Wind-PV/T-PEMFC

Residential energy consumption has increased rapidly with the improvement of living standards of Chinese households, in particular, heating demand in North China has a direct impact on the haze. The integration of renewable energy with the combined heat and power (CHP) technology provides an effective method to solve the household energy consumption and environmental impacts, but the construction, capacity, and cost of the system seriously affect the economy of the system, environmental protection, and operational reliability. Therefore, a family-oriented small-scale combined heat and power system was designed based on wind, photovoltaic, fuel cell, and hydrogen storage. The scheduling strategy of heat determined by power, meeting the requirements of all electrical loads and partial heat loads, and the minimum investment for the whole life cycle of 20 years was used as the objective function to design the system. Combined with the actual environmental conditions and thermoelectric load, an adaptive particle swarm optimization algorithm was improved for solving optimal allocation. The system was analyzed through thermal and power conditions, economy, the reliability of meeting the load, and other evaluation criteria in the optimal system configuration, the prediction, and evaluation of the prospects and development of the system provide basis and reference for the family cogeneration micro-grid applications.

Is this the end of the road for bio-inspired road construction materials?

The global road network spans 64.3million km and is of huge significance for the social and economic development. The level of investment in road construction and maintenance is high, e.g. EU €44billion/year (2019), China €614.7billion/year (2019) and US €94billion/year (2019). Despite the level of investment, there has been minimal investment in the development of new asphalt technologies, particularly when compared with R&D investment in other industries, such as the automotive industry. Despite the limited investment, there have been some innovations in asphalt technology. For the past 20 years, researchers have developed bio-inspired asphalt technology, self-healing and bio-binders and have applied them to asphalt pavements. This research has emerged as a response to global warming and the need to reduce both carbon emissions and reliance on oil in asphalt technology. This paper charts the development of two bio-inspired technologies and considers their significance in relation to the need to reduce carbon emissions and oil dependence (in line with the UN strategic goals, specifically: SDG 9, 11 and 12). This paper considers the potential benefits of bio-inspired technologies and outlines the current barriers to their further development. This paper aims to begin a conversation with stakeholders on how to speed up the acceptance of bio-inspired asphalt technologies and their adoption in road design, construction and maintenance. Or is it the case that we have reached the end of the road for bio-inspired road construction materials?

Energy, exergy, economic, environmental (4E) and dynamic analysis based global optimization of chemical looping air separation for oxygen and power co-production

Chemical looping air separation (CLAS) is a promising oxygen generation technology due to its energy conservation nature. It can be integrated with power generation system (PGS) to improve energy efficiency by utilizing high waste heat of CLAS system. This work presents a global optimization strategy for revamping the CLAS-PGS system, in which the comprehensive performance of the system is evaluated by a novel energy, exergy, economic, environmental, and dynamic (4E-1D) analysis method. First, two original after-burner reheating (ABR) and mixed heating (MH) technologies are proposed to optimize the CLAS-HI-PGS scheme. Second, all the schemes are simulated to provide steady-state simulation results for global 4E analysis, and optimized by genetic algorithm (GA) with minimum capital investment cost (CIC) as target to further improve energy efficiency of CLAS-PGS. Finally, the dynamic behaviors of the schemes are investigated with effective control schemes designed to ensure safe and stable operation of CLAS-PGS. In order to measure additional losses caused by disturbance, the 4E analysis is also combined with dynamic analysis. The results show that the scheme combing MH with CLAS-PGS (CLAS-MH-PGS) displays best, proving the effectiveness of this proposal and providing a new way for CLAS-PGS industrial application.