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Bioregenerative Life Support Systems (BLSS) are a key facet of plans for long term habitats on other celestial bodies. With the Artemis program headed to the moon and SpaceX pushing towards Mars the roadmap is about 10–20 years. Therefore it is vital to consider how BLSS will work with the traditional physicochemical Environmental Control and Life Support Systems (ECLSS). Biological systems are complicated. Their inputs and outputs cannot be turned on and off the way physicochemical systems can. Therefore the accurate monitoring and prediction of these systems is fundamental to integrating the BLSS into the life support system as a whole. This paper will summarize the history of BLSS research, the system requirements for integrating a BLSS into the ECLSS, and the research needed to meet these requirements. As the world looks towards the future of humans living on other celestial bodies, there is a lot of work to do to support keeping them safe and healthy. Bioregenerative Life Support Systems have potential to provide massive support, if they are effectively integrated with the physicochemical systems. • Explores the systems requirements of physicochemical and bioregenerative systems. • Provides updated equivalent system mass estimates using LED lighting technologies. • Calculates return on investment time for bioregenerative food production. • Suggests future research directions for bioregenerative life support systems (BLSS). [ABSTRACT FROM AUTHOR]

The present study uses K-means++ clustering to assess the financial performance patterns of renewable or clean energy companies using the Return on Equity (ROE) decomposition. It also explores the relationship between ESG and financial performance and discovers a positive relationship between ROE and ESG. The ESG-ROE relationship becomes negative or insignificant when businesses are grouped based on ROE change. The findings suggest that firm-specific heterogeneity affects the ESG-ROE relationship and long-term financial performance. Hence, it emphasizes the significance of the internal and external environment in determining firms' ESG and financial success. [ABSTRACT FROM AUTHOR]

The article presents the methodology of applying factor analysis. The purpose of the research is to examine the applicability of factor analysis in the assessment of factors determining the return on equity of farms in the European Union, aimed at reducing the correlated variables without losing the information contained therein regarding the return on equity. The research used data published in the European FADN (Farm Accountancy Data Network). Factor analysis was performed with the R statistical program, ver. 4.2.2. On the basis of the conducted research, 4 factors were identified, into which 15 variables were classified. The factors were given the following names: Farm size and production potential (factor 1), Income and ability to self-finance operations (factor 2), Effects of long-term investment and financial decisions (factor 3) and Investment activity (factor 4). The first factor includes: Labor, Area, Subsidies, S_loans, Inputs, Output, Current_assets and Size. To the second: Income, Cash_flow 2 and Cash_flow 2. The third factor includes Fixed_assets and LM_loans, while the fourth one includes Net_inv and Gross_inv. The use of factor analysis allowed the reduction of variables and the identification of factors determining the return on equity of farms from European Union countries. [ABSTRACT FROM AUTHOR]

Email marketing is the practice of sending commercial emails to a targeted audience. Email marketing currently routinely produces a significant ROI (return on investment) in the marketing sector. However, a significant research question in email marketing is how to gather subscriber data and deliver emails just to interested customers. Faced with the explosion in the volume of information, companies are becoming increasingly aware of the structural challenges of Big Data. Collecting and processing large amounts of heterogeneous data in real time is indeed invaluable. In the era of Big Data, the analysis and exploitation of data, by email marketing specialists, is now essential to remain competitive. In this paper, we review the impact of Big Data on email marketing activities and we identify the techniques, frameworks and data types that were adopted. Therefore, our contribution to this work is the proposed conceptual framework for email marketing campaign practice based on big data analytics. [ABSTRACT FROM AUTHOR]

• Source-grid-load-storage multi-flexible resources are incorporated into the flexible planning model considering flexible ramp capacity. • Various flexibility resource investment priorities and potential were evaluated concerning flexibility resource characteristics. • The impact of renewables penetration levels on cost performance and the benefits of the flexibility resource portfolio allocation are quantitatively assessed. With rapidly increasing levels of renewable energy penetration, flexibility resources play an ever more critical role in the future power system. This paper describes a two-stage stochastic mixed integer second order cone (MISOC) method for flexibility resource planning (VFRP) considering source-grid-load-storage interactions. Considering renewable energy penetration levels and flexibility resource characteristics, this study quantifies the extent to which the share of renewable energy generation affects cost performance, and assesses the priority and potential of investing in flexibility resources. Moreover, the multi-type flexibility resource portfolio allocation method and energy storage only approach are compared in terms of socio-economic benefits. A multi-cut Benders-based approach is used for decomposing large-scale stochastic expansion planning problems to reduce the computational burden. Numerical simulation tests on the modified energy test systems I39G20 and F213G20 verified the correctness and effectiveness of the proposed flexible resource planning models and solution algorithms. The simulation results show that retrofit of coal-fired units for better flexibility has a higher investment return. Still, energy storage presents a tremendous investment prospect and potential. Finally, the multi-type flexibility resource portfolio allocation method is more cost-effective than an energy storage only approach. [ABSTRACT FROM AUTHOR]

Measuring the limitations of transitioning to 100% renewable energy systems partly equates with the capability of quantifying associated net-energy production. To foresee future possibilities, an advanced systemwide energy-return-on-investment (EROI) model was constructed. Driven by the thorough consideration of the existing weaknesses of EROI, the model integrates the concept of energy statistics, life cycle assessment techniques and energy system modelling in order to overcome the underlying primary energy quality issues, boundary condition problems and energy system design related uncertainties that compromises comparability of EROI during the energy transition. This model was applied to the Israeli power system that presents a unique opportunity to examine the vulnerability of depending on limited resources and the connection of EROI to system design. This study demonstrates the dependency of EROI on the energy transition path, system design requirements expressed via interactive linkages of curtailment, variable renewable energy penetration and storage system design, and resource diversity. Achieving a very high variable renewable energy penetration by 2050 with solar photovoltaics carries a risk of falling below an EROI value of 10, such vulnerability can be largely minimised by well-conducted manoeuvring of the system design and resource diversification. Despite the large uncertainty of this study, the need for a multicriteria designing technique is out of the question. • An advanced systemwide EROI model suitable for studying transitions is presented. • EROI trend depends on transition paths and system design differences. • EROI bottoms out for system designed without curtailment. • Very high solar photovoltaics penetration of up to 100% carries sustainability risk. • Multicriteria system design and resource diversification can circumvent the risk. [ABSTRACT FROM AUTHOR]

Arid forests in the American West contend with overly dense stands and a need to reduce fuels and restore more natural fire regimes. Forest restoration efforts include fuel treatments, such as thinning and prescribed burning, that can reduce ground and ladder fuels. Restoration and fuel treatments have emerged as leading wildfire risk-reduction strategies in the American West, yet little is known about the cost-effectiveness of such programs. To evaluate forest restoration and fuel treatment benefits and costs, we conducted a meta-analysis of benefit-cost ratios for restoration benefit types documented in the literature for Western U.S. dry mixed conifer forests at risk of uncharacteristic wildfires. A total of 120 observations were collated from 16 studies conducted over the last two decades, with benefits ranging from enhanced ecosystem services to extensively avoided wildfire costs. Significant variation in the value of restoration and fuel treatment benefit types was found, indicating that restoration benefits differ in value based on societal importance. Overall, 17 individual benefit types were aggregated to show that in the most valuable and at-risk watersheds, every dollar invested in forest restoration can provide up to seven dollars of return in the form of benefits and provide a return-on-investment of 600%. [ABSTRACT FROM AUTHOR]

Numerous studies have affirmed that artificial intelligence (AI) can effectively enable energy savings in factories. However, there is currently a lack of explicit research that identifies the energy-saving effects of AI methods as compared to the conventional practices employed in factories, which involve the replacement of equipment with high energy efficiency (non-AI method), with respect to variance in the effectiveness. Thus, this ignited the motivation for this study, wherein the research team developed a "Universal AI workflow for energy saving" which adopted a standard workflow of "learning, Modeling, and Prediction." Additionally, a three-year research project was launched to conduct empirical tests in ten factories, comparing the efficacy differences between AI and Non-AI methods. In 2021, research team established the energy consumption baselines for each factory through data collection; the entirety of 2022 was dedicated to AI integrations; and energy-saving benefits were evaluated in 2023. During the research period, each factory also implemented other non-AI energy-saving methods which were juxtaposed with AI approaches for comparison. Our study results showed that average AI-enabled energy savings across the ten factories amounted to 106,124 kWh/yr, while average energy savings from non-AI methods amounted to 1,231,625 kWh/yr. Despite the inferior energy-saving results, the AI methods had an average return on investment (ROI) of 0.46 years while non-AI methods had an average ROI of 6.22 years, indicating that AI methods could recover the investment costs through energy savings in less than one year. It is also worth noting that the ten factories produced a diverse range of products encompassing wafers, steel, and foods, but the AI methods used for energy saving employed the same workflow. By contrast, the non-AI methods necessitated distinct workflows and detailed engineering adjustments. It can be inferred from these empirical findings that AI techniques for achieving energy-saving control in factories can be integrated using standardized processes. In summary, AI methods can facilitate standardized workflows in factories and offer substantial energy-saving benefits that can typically cover investments within a year. AI represents a highly viable energy conservation option for factories seeking to maintain uniformity in product processes and realize efficient ROIs. • Conducted case studies across ten factories. • Compared the energy-saving effects of implementing AI versus traditional hardware upgrades (non-AI methods). • The annual energy savings from AI were less than one-tenth of those achieved through non-AI methods. • However, the ROI of AI-based energy savings significantly outperformed non-AI methods. • AI methods can facilitate standardized workflows in factories, making it more convenient to implement energy savings. [ABSTRACT FROM AUTHOR]

[Display omitted] • A solventless recovery of grease from sewage sludge was designed. • A very high yield of grease recovery was achieved (up to 70 wt%). • Final dewatered sludge resulted in 15 % less than the amount conventionally obtained. • A price of 800–900 € per ton of grease would guarantee the economic sustainability. For the first time, the recovery of lipids from urban sewage sludge was investigated through a solvent-free process based on elementary operations validated on a pilot scale (2 tons per hour). Sedimented sewage sludge was dewatered through centrifugation, acidified (and in the case treated with hydrogen peroxide), heated to 80 °C and centrifuged to obtain three different streams: i) raw grease, ii) exhausted aqueous phase, and iii) dewatered sludge. The recovery yield of grease from sludge could reach a very high value (70 wt%), depending essentially on the initial lipid content in sewage sludge, which was monitored and found to range between 1.1 and 6.6 wt% (13–25 wt% with respect to TS). The use of H 2 O 2 played a crucial role in the recoverability of lipids and the relevant pureness in free fatty acids content (>89 wt%). In addition, the total solids content in the exhausted aqueous phase and the final residual sludge were sensibly affected by using H 2 O 2. Final dewatered sludge had a very high content of solids (TS 31.0 ± 2.3 wt%) and resulted in 15 % less of the amount of sludge conventionally obtained from anaerobic digestion. According to these figures, for medium wastewater treatment plants, a selling price of 800–900 Euro per ton of grease would guarantee a return on investments within three years of operations. This simple procedure represents a quick and proficient way to treat sewage sludge, turning it into a valuable feedstock for fine chemicals, in agreement with Circular Bioeconomy principles. [ABSTRACT FROM AUTHOR]

Making long-term investment decisions is possible using the portfolio approach. In this respect, there are classical (e.g., Markowitz model) and non-classical (e.g., fundamental model) approaches. The key element in the construction of models is not only the measurement of risk and rate of return, but also the method of selecting assets (companies) to the portfolio. The method of selecting assets often determines the direction of the investor's investment strategy. The portfolio construction and asset selection processes remain an open question. The aim of the study is to assess the effectiveness of portfolio investment using the concept of fundamental strength. The results of the study indicate that the fundamental approach, depending on the portfolio construction procedure used, gives different effects, but allows for risk diversification in times of crisis [ABSTRACT FROM AUTHOR]

• An optimal dispatch model for DN with 5G BSs based on cooperative game is established. • An adaptive aggregation method for 5G BSs is proposed. • Electrical coupling degree and communication service similarity are introduced for 5G BSs aggregation. • A cost allocation interval based on marginal benefit and investment return is constructed. Leveraging the dispatchability of 5G base station energy storage (BSES) not only enables the mobile network operator (MNO) to gain additional revenue, but also facilitates the integration of renewable energy sources in distribution network (DN). However, since BSES and DN are owned by different stakeholders, integrating BSES into DN operations poses significant challenges. In this regard, this paper proposes a DN optimal dispatch model that incorporates the adaptive aggregation of 5G base stations (BSs) through a cooperative game framework. Firstly, the dispatchability of BSESs is analyzed and modelled. Considering it is difficult to dispatch every single unit optimally, an adaptive aggregation model of 5G BSs is established, where the electrical coupling degree and the communication service similarity are taken as comprehensive metrics. On this basis, an optimal dispatch model of DN based on cooperative game is constructed, where the total operational costs of 5G BSs and DN are considered as the characteristic function. The number of 5G BS clusters and the aggregating results are adjusted adaptively during optimization. The optimal aggregation of 5G BSs is achieved using the Affinity Propagation (AP) clustering algorithm. Furthermore, to solve the optimal dispatch model of the DN with enhanced computational efficiency, the particle swarm optimization algorithm integrated with second-order cone programming (PSO-SOCP) is employed. After dispatching, the benefit allocation between MNO and distribution system operator (DSO) is conducted using the Shapley value method and the Equal Profit Method to obtain an entire range of allocation results. Finally, simulations are carried out with results proving the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In 2014, FERC adopted a new theory of capital markets for the purpose of setting authorized returns-on-equity: that all observed dividends and interest rates were anomalous and therefore unreliable indicators of the returns necessary for investors to hold the stocks and bonds of transmission owners. The new theory, used to justify higher authorized ROEs, rejected evidence of fundamental conditions in capital markets and explicitly adopted arguments by investor-owned utilities regarding unreliable markets. After a judicial remand, FERC abandoned the new theory, but consumers in New England and the Midwest were arbitrarily charged hundreds of millions of dollars over several years due to unjust and unreasonable rates for transmission service. [ABSTRACT FROM AUTHOR]

MDA focuses on productivity and maintainability, OMG shows that the expected gains from adopting MDA are reduced operating costs and development time, increased return on investment and new technologies rapid integration into an existing system. MDA's goal is to express the functional and implementation specifications of an application on a given platform independently. In this paper, we present a case study of course management in a department, illustrated by a class diagram and then we generate the code of classes using the enterprise architect software. This paper proposes also an approach in accord with MDA and IFML standards, our approach is about graphical user interfaces (GUI) automatic generation, starting from the business process model on CIM level, to the generation of graphic interfaces code on PSM model by applying MDA model transformations. [ABSTRACT FROM AUTHOR]

The influence of internet financial news on financial markets has been verified from multiple perspectives. This paper examines whether the emotional tendency of COVID-19 news affect financial markets. Based on constructing the sentiment dictionary of COVID-19 news, we measure the sentiment indicators of COVID-19 news, and then study the dynamic relationship between the emotional tendency of COVID-19 news and the rate of return in financial markets. Compared with the news before the outbreak of COVID-19, our results show that the emotional tendency of COVID-19 news does affect the rate of return in the stock and bond markets. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to address STM through an unconventional but pragmatic angle to help optimize efficient compliance governance. This paper proposes using antitrust mechanisms in space as a pragmatic and utilitarian tool for sustainable purposes with regards to STM within a soaring space ecosystem. In the context of accelerated space commercialization and privatization, having a new space antitrust framework at the helm of such transition might indeed prove to be a flexible yet decisive tool into shaping the future of STM and ensuring perennial protection of higher space principles which are enshrined in the Outer Space Treaty and form the essence of space law. On one hand, examples of antitrust key components include fair competition while, on the other hand, higher ethical principles of space law include non-discrimination and benefits sharing. Furthermore, in between these two extremes, security and commerce both rely, respectively on non-harmful interference and competitiveness. To navigate through all these factors, a new space antitrust framework might indeed prove strategic and beneficial to incentivizing the creation of an adaptive, polycentric and action-oriented governance mechanism with great resonance among the commercial new space players and reaffirm the importance of sustainable space traffic management before return on investment, while still making a profit in the long run. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the outcomes of planned oocyte cryopreservation patients most likely to have a final disposition.Design: Retrospective cohort study of all patients who underwent at least 1 cycle of planned oocyte cryopreservation between Jan 2005 and December 2009.Setting: Large urban University-affiliated fertility center PATIENT(S): All patients who underwent ≥1 cycle of planned oocyte cryopreservation in the study period.Intervention(s): None MAIN OUTCOME MEASURE(S): Primary outcome was the disposition of oocytes at 10-15 years. Secondary outcomes included thaw/warming types, laboratory outcomes, and live birth rates. Outcomes and variables treated per patient.Result(s): A total of 231 patients with 280 cycles were included. The mean age at the first retrieval was 38.2 years (range 23-45). A total of 3,250 oocytes were retrieved, with an average of 10 metaphase II frozen/retrieval. To date, the oocytes of 88 patients (38.1%) have been thawed/warmed, 109 (47.2%) remain in storage, 27 (11.7%) have been discarded, and 7 (3.0%) have been transported elsewhere. The return rate (patients who thawed/warmed oocytes) was similar by Society for Assisted Reproductive Technology age group. The mean age of patients discarding oocytes was 47.4 years (range, 40-57). Of the 88 patients who thawed/warmed oocytes, the mean age at the time of thaw/warming was 43.9 years (range, 38-50) with a mean of 5.9 years frozen (range, 1-12). Nine patients (10.2%) thawed/warmed for secondary infertility. A total of 62.5% of patients created embryos with a partner, and 37.5% used donor sperm. On average, 14.3 oocytes were thawed/warmed per patient, with 74.2% survival (range, 0%-100%) and a mean fertilization rate of 68.8% of surviving oocytes. Of 88 patients, 39 (44.3%) planned a fresh embryo transfer (ET); 36 of 39 patients had at least 1 embryo for fresh ET, and 11 had a total of 14 infants. Forty-nine of 88 patients (55.7%) planned for preimplantation genetic testing for aneuploidy, with a mean of 4.2 embryos biopsied (range, 0-14) and a euploidy rate of 28.9%. Of the 49 patients, 17 (34.7%) had all aneuploidy or no embryos biopsied. Twenty-four patients underwent a total of 36 single euploid ET with 18 live births from 16 patients. Notably, 8 PGT-A patients had a euploid embryo but no ET, affecting the future cumulative pregnancy rate. Overall, 80 patients with thaw/warming embryos had a final outcome. Of these, 20 had nothing for ET (arrested/aneuploid), and of the 60 who had ≥1 ET, 27 had a total of 32 infants, with a live birth rate of 33.8% (27/80).Conclusion(s): We report the final outcomes of patients most likely to have returned, which is useful for patient counseling: a utilization rate of 38.1% and a no-use rate of 58.9%, similar across age groups. Further studies with larger cohorts as well as epidemiologic comparisons to patients currently cryopreserving are needed. [ABSTRACT FROM AUTHOR]

This paper documents a computational form of the Intelligent Portfolio Theory for investing and trading in stock markets with two multi-factor models and two trend following trading strategies. The Intelligent Portfolio Theory for stock investment goes beyond the classical portfolio theory by using multi-factor models for stock selection and quantitative trading strategies instead of buy-and-hold. In China A-share market, a special multi-factor Model 1 is developed targeting at the food and beverage sector, which is made up of 7 effective and non-redundant factors selected from 20 candidate factors representing 6 categories: valuation, technology, size, profitability, growth ability, and solvency. A more general multi-factor Model 2 targeting at all the stocks in the whole stock market is also developed, which is constructed as a rule-based system integrating fundamental and technical analysis inspired by the empirical approach of CANSLIM. The multi-factor models are used to forecast or rate the future return of each stock in the sector, and then a small number of stocks are selected to form a portfolio. Each selected stock in the portfolio is then traded using a trend following trading strategy. Two strategies are developed, Strategy 1 with the high-low price channel breakout, and Strategy 2 with the parabolic stop and reverse indicator. The first intelligent portfolio trading system integrates Model 1 with Strategy 1, and is tested on an in-sample data of 7 years and also on an out-of-sample data of 2 years. The second system couples Model 2 with Strategy 2 and is tested on a data of 2 years. Both intelligent portfolio trading systems generated significantly useful performance in terms of annual returns and maximum drawdown. The test results confirmed the effectiveness of the Intelligent Portfolio Theory with proposed multi-factor models and trading strategies. [ABSTRACT FROM AUTHOR]

The issues of SMART services and solutions, their implementation and operation are presented mainly as a topic of large cities, where a large number of residents, potential customers are concentrated. This argument is logical, as a high concentration of population is one of the parameters to guarantee the return on investment of such projects. We think that the topic of SMART solutions should not be a significant priority only for large cities. For this reason, we decided to analyze the needs of small cities in terms of implementing SMART solutions, identify problems as well as requirements to speed up this process. We collected data for our analysis in the Slovak Republic, which with its structure of cities and municipalities is a suitable candidate for such research. In the process of data collection, we addressed request for information more than 2,744 small towns and municipalities with less than 5,000 inhabitants, while 547 representatives of towns and municipalities responded to the questionnaire. The results of the research show an interesting and unambiguous finding. Small towns and rural areas also want to be SMART. In the research, we identified several, not simple problems that need to be solved for the successful implementation of such goals. [ABSTRACT FROM AUTHOR]

In the traditional portfolio models, the return rates of risky assets are usually defined as random variables or fuzzy variables. However, when some events such as financial crises or wars occur that can cause instability in financial markets, the results of these assumptions are not always satisfactory. Therefore, some scholars try to use uncertain variables to express the return rates. In this paper, a two-factor uncertain portfolio problem under optimistic value criteria is studied, where we use the optimistic value instead of expected value to describe the investment return and study the impact of economic circumstance factor and corporation's specificity factor on the return rates. Firstly, a two-factor optimistic value-variance-entropy model with background risk is constructed, in which the total investment return consists of the risky asset return and the background asset return. Then, considering the investors with two kinds of risk preferences, a three-step method is applied to convert the bi-objective optimization model. Moreover, a moment estimation method is used to process the expert-estimated data on the return rates. Finally, a numerical simulation is presented for showing the applicability of our models and solution method. • We use optimistic value to represent the return rather than expected value. • The multiple factors and background risk are considered simultaneously. • Three-step method is used to convert the model into two single-objective models. [ABSTRACT FROM AUTHOR]

Hydrogen is one of the potential candidates to replace fossil fuels to meet net zero emissions target. This study reports a detailed techno-economic and environmental assessment of hydrogen production through ammonia decomposition. The case study is based on a multiple catalytic packed bed reactor with intermediate heating system. Aspen plus® and MATLAB® were linked to evaluate economic and environmental impact of the process. The process parameter like furnace temperature, flue gas recirculation, ammonia decomposition temperature, market ammonia supply pressure, ammonia decomposition pressure, hydrogen purification unit's pressure and equivalence ratio, and economic parameters of capital expenditure (CAPEX) and operating expenditure (OPEX) were considered. The overall thermal efficiency of the developed process is found to be 79%. The levelized cost of hydrogen (LCOH) is estimated and found to be 6.05 USD/kg of H 2 based on CAPEX and OPEX. A major contribution of up to 62.2% to LCOH comes from the price of feed Ammonia. Based on 25-year plant life with 10% discounted rate the plant is economically viable, with a return on investment of 23.7%, in a payback period of 3.58 years. Global warming potential of the process is also carried out. [Display omitted] • Techno-economic and environmental assessment on ammonia decomposition was carried out. • Overall thermal efficiency of H 2 production from NH 3 decomposition process is 79%. • LCOH of H 2 production from NH 3 decomposition process is 6.05 USD/kg of H 2. • Ammonia price alone contributes up to 62.2% of LCOH. • GWP analysis of H 2 production from NH 3 decomposition process is 0.66 kg of CO 2 /kg of H 2. [ABSTRACT FROM AUTHOR]

This study aimed to construct an accounting method for the domestic value-added rate for exports by considering environmental costs and using a developing country, China, as an example, to measure the "positive" and "negative" economic benefits obtained by industrial sectors linking to the global value chains (GVCs), namely the comprehensive rate of return. On this basis, the impact of industrial intelligence on the comprehensive rate of return of the GVCs is further explored. When environmental costs are considered, the comprehensive rate of return of China's industrial sectors linking to the GVCs is reduced by approximately 15%. Industrial intelligence is conducive to improving the comprehensive return rate of China's participation in GVCs. And the accumulation of human capital and the improvement of the competitiveness of export products are important mechanisms for industrial intelligence to affect the comprehensive rate of return of the GVCs. The improvement of industrial intelligence level in pollution-intensive industries has the most obvious promotion effect on the comprehensive rate of return of GVCs, followed by capital and technology-intensive industries, while labor-intensive industries are not sensitive to this effect. • This study calculates the comprehensive return rate of China's participation in GVCs. • The comprehensive return rate is reduced by approximately 15%. • Intelligence development is conducive to improving the comprehensive rate of return. • Human capital and the competitiveness of export products are important mechanisms. [ABSTRACT FROM AUTHOR]

Analyzing the financial benefit of marketing is still a critical topic for both practitioners and researchers. Companies consider marketing costs as a type of investment and expect this investment to be returned to the company in the form of profit. On the other hand, companies adopt different innovative strategies to increase their value. Therefore, this study aims to test the impact of marketing investment on firm value and systematic risk. To do so, data related to four Arabic emerging markets during the period 2010-2019 are considered, and firm share price and beta share are considered to measure firm value and systematic risk, respectively. Since a firm's ownership concentration is a determinant factor in firm value and systematic risk, this variable is considered a moderated variable in the relationship between marketing investment and firm value and systematic risk. The findings of the study, using panel data regression, indicate that increasing investment in marketing has a positive effect on the firm value valuation model. It is also found that the ownership concentration variable has a reinforcing role in the relationship between marketing investment and firm value. It is also disclosed that it moderates the systematic risk aligned with the monitoring impact of controlling shareholders. This study provides a logical combination of governance-marketing dimensions to interpret performance indicators in the capital market. [ABSTRACT FROM AUTHOR]

In recent years, Enterprise Collaboration Systems (ECS) have increasingly been introduced as core components of the digital workplace. Organizations have a range of different goals that they want to achieve when they implement a new collaboration software. In our paper, we argue that traditional controlling indicators with a focus on delivery dates and costs are not suitable to measure the return on investment and the benefit that a user company can achieve from the digital support of work. Conventional indicators provide limited value because they cannot be used to directly attribute the contribution of a digital collaboration project to the achievement of corporate goals. The research described in this paper is part of a long-term university-industry research initiative and the findings contribute to a broader understanding of the metric-based analysis of benefits from ECS use in organizations. We present a method for developing and applying "metrics profiles" to ECS, which makes it possible to analyze the realization of benefits over time. The method was tested and evaluated using non-reactive ECS data from an operational collaboration platform that has been in operation for more than seven years. [ABSTRACT FROM AUTHOR]

IT-business alignment has been the focus for top managers in the last two decades. Literature reported IT-business alignment as one of the key enablers for the success of IT in an organization. Given its importance, this study aimed at exploring the reported benefits of IT-business alignment, the proposed methodologies and the challenges in implementing these models. To do so, a systematic literature review was conducted to find answers to these questions. This study reveals that applying IT in an appropriate and timely way, in harmony with business strategies, goals and needs can result in plenty of benefits for an organization. These benefits include enhanced cooperation among members, competitive advantage, performance enhancement, high return on investment, facilitation of processes and growth among others. In fact, IT today is not only a tool but has become a business enabler delivering values for organization and this implies that huge investment on IT must be well managed and controlled in order to align IT with business. Also, the study reviewed some proposed methodologies or models that have contributed in achieving sustained alignment between business and IT. Lastly, the study reveals that, despite the inevitable benefits, there are still some difficulties or challenges organizations faced in establishing an atmosphere of honesty and humility between IT and business. These challenges are lack of effective communication, low return on IT investments, lack of the necessary skills to adapt to changes in technology or conflicting priorities. [ABSTRACT FROM AUTHOR]

Telecommunication network infrastructures in Africa and the Middle East regions, are deployed and operated in challenging environments that are highly scattered particularly in rural areas. Moreover, considerable number of cell sites are located in areas difficult to access. Furthermore, low income in rural areas does not allow a fast return on investment since the cost of deployment and operation of a cell site is considerable. These issues lead to a difficult human resource management, particularly, in the assignment of technicians to cell site for maintenance purpose. In this paper, an optimized scheme for costs of maintenance operations on cell sites is proposed. We used the k-means clustering algorithm for allocating field technician to a pool of cell sites. Moreover, to alleviate the k-means sensitivity to initialization, we proposed an initialization method that is based on the geometry of a sphere. We conducted series of experiments with sample of thousands of cell towers from OpenCellID and the results demonstrate the effectiveness of the proposal. [ABSTRACT FROM AUTHOR]

In this paper, a probabilistic form of the portfolio selection problem is established in which the uncertainty of risky assets is considered through a probabilistic optimization problem. To this end, by taking seven portfolios of Shanghai stock as a case study, the mean and standard deviation of daily return values are calculated based on five years of real data. The optimal values corresponding to each random case were then stored as a comprehensive database of system responses. Then, by sorting the resulting optimal values from best to worst, the exceedance probabilities of return and risk rankings were calculated for each portfolio and presented in the form of probabilistic pie charts. The results showed that the portfolio with the highest deterministic rate of return has the highest probability of getting the best return ranking as well. However, since the probability of risk in all cases was calculated, the probability of each portfolio to place in the lower rankings (i.e., ranking 2–7) could also be discussed. Additionally, to check the convergence of the model, the probability values calculated by the Monte Carlo method against the sample size were plotted to ensure the accuracy of the final answer. Eventually, by generating Gaussian random noise and importing it into the model input, probability changes were calculated to assess the robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The basic goal of the company's operation is to build its value. There are many measures, which makes value an ambiguous category. The value of the company is interested in its stakeholders, who can use the information about the value in various ways to achieve their goals, e.g. investing, managing the company, etc. The purpose of the article is to examine the relationship between the value of the company, its fundamental strength and rates of return. An important element of the study is to indicate the possibility of using the occurrence of the relationship between value and fundamental strength of the company's stakeholders. The survey results show that fundamental strength influences the shaping of the enterprise value. [ABSTRACT FROM AUTHOR]

This study aims to identify the trend in the financial performance of renewable energy companies and to outline the renewable energy policies impacting their performance. The study is conducted on major renewable energy companies of India and US for the study period of 2015-2019. The financial performance of these companies is analyzed by using return on equity (ROE) decomposition and the trend in their performance is identified by utilizing k-means cluster of machine learning algorithm. Based on their ROE performance, five clusters are formed for Indian companies and four for US companies. It is observed that the performance of the companies in the renewable sector is heavily influenced by the government policies and for these companies to succeed, it is important that the government sets out policies which motivate these companies to invest more for clean energy development. [ABSTRACT FROM AUTHOR]

Degree apprentices (DAs) undertake an exciting, alternative route to academic qualifications, combining real employment with University study. Here, we discuss their education in 'Quality Improvement' at WMG, focusing on the development of statistical expertise in Year 2 of their programme to procure real improvements in manufacturing and service operations. We show how the preparation for these in Year 1 and the further extension of these in Year 3, interleaved with work-based projects, ensure that DAs can make a significant impact during their apprenticeships. This provides a rapid return on investment for employers, and recognition and faster career progression for DAs. [ABSTRACT FROM AUTHOR]

• A new uncertain risk measure is defined for the modeling of investment risk. • An uncertain portfolio optimization model is formulated. • A method is presented for finding the set of analytic Pareto optimal solutions. Portfolio optimization problem is concerned with choosing an optimal portfolio strategy that can strike a balance between maximizing investment return and minimizing investment risk. In many cases, the return rate of risky asset is neither a random variable nor a fuzzy variable. Then, it can be described as an uncertain variable. But, the existing works on uncertain portfolio optimization problem fail to find an analytic solution of optimal portfolio strategy. In this paper, we define a new uncertain risk measure for the modeling of investment risk. Then, an uncertain portfolio optimization model is formulated. By introducing a new variable, we transform it into an equivalent bi-criteria optimization model. Then, we derive a method for the construction of the set of analytic Pareto optimal solutions. Finally, a numerical simulation is carried out to show the applicability of the proposed model and the convenience of finding the analytic solution. [ABSTRACT FROM AUTHOR]

With the development of financial market, people invest their savings into different financial products to increase return. However, financial market is an extremely complex system, where investment opportunities and risk often coexist. For investors, it is necessary to take effective measures to avoid risk while obtaining considerable return. In recent decades, actively managing investment in the form of a portfolio is widely accepted by investors. In this paper, we propose a new three-objective Sortino ratio-expected shortfall-turnover rate (SR-ES-TR) portfolio optimization (PO) model. The Sortino ratio is used to measure the risk-adjusted rate of return. The expected shortfall is used to measure the tail risk of a portfolio. Portfolio liquidity risk is characterized by turnover rate. We constructed a portfolio of ten stocks and solved the SR-ES-TR model by the novel MOPSO-SODE algorithm. Compared with other four multi-objective algorithms, the better performance and validity in solving SR-ES-TR model by MOPSO-SODE algorithm have been verified. • Established a three-objective SR-ES-TR portfolio optimization model with liquidity ratio. • Designed a MOPSO-SODE algorithm based on the MOPSO framework. • Verified the effectiveness of the MOPSO-SODE algorithm in solving SR-ES-TR model. [ABSTRACT FROM AUTHOR]

Anecdotal evidence suggests widespread discrimination against community college transfer students, despite their prevalence among bachelor's degree holders in the US. I send out fictitious job applications and conduct a labor market audit study to examine such discrimination. All applicants have bachelor's degrees, and a random subset are transfer students who attended community colleges for their first two years of college. I find that, for accounting jobs, community college experience significantly reduces callback rates and undermines the value of graduating from selective four-year colleges. In comparison, sales and marketing firms do not exhibit signs of discrimination. • Audit study shows a lower callback rate for community college transfer students. • Accounting firms discriminate against community college transfers vs non-transfers. • The discrimination undermines valuation of college selectivity. • Sales and marketing firms do not exhibit signs of discrimination. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive system for assessing contaminant emissions, including the dominant contaminant reduction effects, recessive contaminant reduction effects, and contaminant reduction efficiency indexes. The system is used to evaluate the linkage changes in contaminant emissions reduction across different sectors, the ease of regulation, and the returns on emissions reduction efforts. The study's results demonstrate that firstly, COVID-19 initially reduced contaminant emissions across almost all sectors, although this effect waned over time. Secondly, applying conventional regulatory measures to sectors with high reductions in contaminant emissions may not yield the expected results. Thirdly, while some sectors may be more difficult to regulate, they may offer a higher return on investment in terms of emissions reduction. Based on the above results, the paper advocates for timely measures to consolidate environmental gains, prevent rebounding of contaminant emissions, prioritize supervision and management of easily regulated sectors, prioritize upgrading of cleaner production in sectors with high reduction efficiency indices, and increase investment in environmental technology. • Created a new comprehensive system for assessing contaminant emission changes. • Quantified the unique linkage changes of contaminant emissions during COVID-19. • Developed new models to reveal contaminants' regulatory difficulty and return. • The innovative reduction effects of four key contaminant indicators were measured. • Applied WHEM firstly in research across multiple areas of contaminant emissions. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) design validation is primarily aimed at responding quickly to serious design errors. A prompt response to BIM request for information (RFI) can significantly enhance the BIM return on investment (ROI). Although prior research has identified additional factors to consider while assessing the benefits of BIM coordination, it was not possible to conduct an economic assessment. Therefore, this study fills this gap by conducting an economic assessment of the introduction of priority policies in BIM coordination using priority queue model. The findings demonstrate that the introduction of priority policies can impact the optimal BIM staffing and the engineers' waiting cost for high-priority RFIs. We quantify the BIM ROI that can be improved by introducing the priority policy in BIM coordination, using time and cost metrics derived from the queue model. • The impact of introducing a priority policy on BIM design validation has been evaluated. • The proposed framework has been validated using data from two real projects where BIM was applied. • The effectiveness of BIM design validation is quantitatively analyzed through a priority queueing model. • The improved BIM ROI is quantified through the optimal number of BIM staff and the engineer's waiting avoidence cost. [ABSTRACT FROM AUTHOR]

The Optical Network is considered an important asset to any telecom operator. One of the most critical issues to the operators is how they can maximize the Return of Investments (ROI) by optimizing the operational costs in the optical network. In this paper for the first time we propose an Intelligent Universal Platform (IUF) to manage and optimize the operational tasks in the optical network. the proposed platform drives the supervision of the network to the automation, and is formed by the integrations between four models, the 1'st one is the energy model and built according to the variations in the power consumptions and it proposes the recommended actions to optimize the energy consumption by swapping the cooling system between two system types (air-conditions and fan units) according to the temperature degrees, also the model learns from the variations in the heat dissipation according to the busy and the idle modules and it can transform the unused hardware to the sleeping mode, the 2'nd model is the fault localizations and is built by the practical experiments of the faults recovery and forms its expert system to predict the mean time to repair (MTTR), the 3'rd model is the network performance and is built by monitoring the optical signal to noise ratio (OSNR) and provides the recommended corrective actions to keep the same level of the performance, the 4'th model is the configuration management and it suggests the best routes in the network. The paper studied four use cases in two situations As-Is and To-Be, the cases are about the energy consumptions, the fault locations, the circuit creations and finally the variation in the signal to noise ratio. the results shows that by using the machine learning in our platform the time of the fault location is reduced from 40 min to 3 min, the efforts to create one circuit is reduced by 30.87%, the number of the complaints are reduced by 30% per year, and the response time to the complains is decreased from 55 min to 5 min. This indicates that in the near future the machine techniques will play a significant role in monitor, detect, localize faults, and finally optimize the resources of optical network, all without human intervention. [ABSTRACT FROM AUTHOR]

In the past few years, there has been increasing awareness regarding the significance of the Green Innovation Strategy (GIS) in the academic and practical fields. Hence, it becomes important to determine the correlation between the GIS and the Corporate Financial Performance (CFP). This study attempted to determine the dynamic correlation between the GIS and the CFP, with regards to the firm size. For this purpose, this study has collected data for 163 international automotive firms, from the CSRHub database, for the period ranging between 2011 and 2017. Furthermore, we also used the dynamic panel data system, i.e., the Generalised Method of Moment (GMM) method, for estimating this relationship. The empirical results indicated that the GIS positively affected the CFP. Interestingly, we also uncovered that the firm size moderated the negative correlation between the GIS and the CFP. The small-sized firms showed higher green innovation investments return than the larger-sized firms, which indicated that these smaller firms were more prone to seek variation and visibility, for accessing better resources. Furthermore, due to the extensive scrutiny of the stakeholders, these small firms could generate higher profits. The implications for managers and the theories in this regard are then discussed. • The relationship between firm-level green innovation strategy and corporate financial performance is investigated. • Green innovation strategy interacts with firm size to affect organisational performance. • The paper controlled for endogeneity that emerges from unobservable heterogeneity of firms using the dynamic method of moments estimator. • Green innovation strategy shows significant positive effect on corporate financial performance. • The small-sized firms shown GIS than the larger-sized firms, which more inclined to pursue variation and visibility, for accessing better resources. [ABSTRACT FROM AUTHOR]

In order to improve performance, company allocates significant amount of expenditures for IT investment. Regardless IT productivity paradox phenomenon, the companies certainly expect investment that have been made able to gain various benefits to the company. This research aims to compare five method of IT investment evaluation and find the closest accurate method according to selected literatures. Analysis was done by comparing: i) tangible and intangible ii) advantage iii) disadvantage iv) object and v) how to use. The result showed that Return on Investment (ROI), and Cost Benefit Analysis (CBA) focus on tangible. While Multi Criteria Decision Making (MCDM) focus on intangible. The method must justify tangible and intangible to get accurate result. Information Economics (IE) and Real Options Analysis (ROA) focus on both tangible and intangible. From both methods, IE was the closest accurate method because it could calculate tangible, quasi-tangible, and intangible, and also calculates the value of an investment based on enhanced ROI, business factors, and risk criteria. [ABSTRACT FROM AUTHOR]

Off-grid groundwater cotton irrigators using traditional technologies have had few options to reduce water extraction costs. New options of blending of Alternating Current and Direct Current through an all-in-one soft start converter/controller offer opportunities to lower costs, diversify fuel sources and continuous off-grid power in remote locations. Diesel energy can be replaced by renewable fuel sources through installing an electric submersible pump powered by solar photovoltaic in combination with a diesel generator. An investment analysis using a discounted cashflow over 25 years showed a 23% internal rate of return and payback over 5 years. Benefits of installing a renewable fuel source included lower greenhouse gas emissions per bale of cotton produced and less labour to monitor and service traditional diesel irrigation plant. Emissions calculations reveal a reduction of 26%, or 35 kg CO 2 e per cotton lint bale, and an abatement of 2665 t/CO 2 e for the life of the project. The cost of emissions abatement under the Renewable Energy Target to displace almost one million litres of diesel was found to be $31 t CO 2 e. • A microgrid solution reducing energy costs and emissions is analysed for an irrigation pump in NSW, Australia. • The microgrid includes photovoltaic panels and a diesel generator used to power a submersible pump 24 h a day. • The analysis indicates a 5-year payback and 23 per cent internal rate of return for the investment. • Emissions are reduced by 26 per cent (per 227 kg cotton lint bale) equating to 2665 t/CO 2 e abatement over the project life. [ABSTRACT FROM AUTHOR]

People's focus on either financial benefit or ecological benefit makes decisions on unconventional oil and gas extraction hard. This paper combines the energy return ratio with the financial return ratio through a comprehensive analysis model, which is more parsimonious and more objective than other comprehensive analysis models. The model was applied to analyze the comprehensive energy/financial efficiency of seven sample unconventional petroleum (oil and gas) companies in North America. Among them, four are oil sands operating companies with the largest oil sands production and three are shale oil and shale gas operating companies with the largest number of drilled but uncompleted shale wells, and complete available data. Results of our analysis indicate that during the most recent seven years the selected companies' energy return ratio and financial return ratio of unconventional oil and gas extraction operations show obviously different tendencies as a result of oil price fluctuations. However, their comprehensive energy/financial efficiency indicators show no significant trend, which was different from the cases for either one of the individual indicators. We demonstrated that a comprehensive indicator combining both energy and financial efficiency indicators could be more accurate than either one of them individually, to measure the sustainability and the true value of a company or business unit, recognizing both economic and biophysical value. We concluded by suggesting that the energy return on investment indicator and the comprehensive efficiency indicator both be disclosed and audited along with financial and commodity reserves metrics. Such a summary statistic will be more useful for investors and public policy analysts than the various energy efficiency statistics buried in the Global Reporting Initiative (GRI) reports voluntarily produced by various companies. We argue that this summary statistic would provide incentives for companies to innovate and to improve efficiency as well as meet public policy objectives in energy and environment even when the commodity price makes it easier to meet financial objectives. Image 1021 • Energy return metric is affected less than financial one by price volatility. • Tendencies for energy return ratio and financial return ratio diverge. • Combined financial and energy ratio provides more accurate information. • Formal audited report of energy and financial efficiency indicators is recommended. • Energy and financial efficiency reporting improves fossil fuel company performance. [ABSTRACT FROM AUTHOR]

Financial literacy has become an interesting topic recently, given the global trends and the need for individuals to take responsibility for their personal finances and, in particular, for their retirement planning. In this paper we investigate financial literacy of adults in Bosnia and Herzegovina. Our survey questionnaire is based on the OECD/INFE questionnaire, with some adjustments to national specificities. The method of research is the online survey. Looking at the results, our research overall shows rather low level of financial literacy of Bosnian citizens. In particular, low level of understanding and skills related to basic concepts, such as compound rate of return and diversification, indicate that there are many aspects of financial knowledge that could be improved. Financial knowledge and behavior of our sample is better positioned than the average in other countries. Due to worse attitude, the overall level of financial literacy is lower than the average in other countries. In addition, the analysis showed that attitudes towards the money are worse than the attitudes of citizens in other countries. Looking at at different levels of income, we notices no statistically significant difference in behavior and attitudes within different groups. The low level of financial literacy emphasizes the importance of introducing financial education at an earlier age, since the lack of financial education can cause people to start saving later in life, thus limiting required retirement goals. As a result, it is unlikely to achieve an optimal balance between current consumption during work and future consumption as individuals retire. In addition, the lack of information on the distribution of risk and return on various investments can lead to the misallocation of their pension portfolios. [ABSTRACT FROM AUTHOR]

In recent years, innovation and R&D activities have been one of the most important factors affecting the growth strategies of the companies and their financial performance. However, it is the fundamental elements of sustainable economic growth, innovative activities have strategic decision-making characteristics due to their effects on efficiency and market value. The aim of this paper is to investigate the effects of R&D expenditures on the financial performance of 62 production companies listed in Borsa Istanbul in the period of 2008-2017 by using panel data methods. We used return on assets (ROA), return on equity (ROE) as a proxy to measure company performance, and to measure R&D expenditures we considered the R&D intensity and R&D expenditures over total sales as explanatory variables. The empirical findings of this study support the existence of a positive interaction between R&D expenditures and financial performance. [ABSTRACT FROM AUTHOR]

• The designs of vapor-recompression distillation columns for the ethylene-ethane separation with high or low pressures in the auxiliary condenser are compared. • High pressure reduces refrigeration cost in the condenser but increases compressor cost. • The high-pressure design has 11% lower total energy cost but requires more capital investment. • The return on investment is quite attractive. A recent paper studied the separation of ethylene and ethane using several types of distillation systems in which cryogenic cooling is required. The economics of a conventional column using a very low-temperature expensive refrigerant in the condenser were compared with several configurations using process compressors. The alternative configurations involve the pressure in the auxiliary condenser and the fraction of the column overhead vapor sent through the compressor. Low pressure in the condenser requires a very low-temperature expensive refrigerant but has lower compressor costs. The purpose of this paper is to show that all of the vapor from the column should be compressed and then split, with some going to the reboiler and the rest going to an auxiliary condenser that operates at high pressure so that a higher-temperature less-expensive refrigerant can be used. [ABSTRACT FROM AUTHOR]

• Return on investments in land restoration can be increased with an understanding of land potential. • High returns on land degradation avoidance are likely on high value land with low resilience. • Analysis requires an understanding of degradation resistance, resilience. • It also requires understanding likely changes in land values driving future land use. • Ignoring these factors may result in negative returns or even increased degradation. The response hierarchy of "Avoid > reduce > reverse" is increasingly acknowledged as the best strategy for prioritizing actions designed to address land degradation at hectare to national scales. This hierarchy is based on the assumption that the economic return on investment (ROI) will usually be higher for actions that help avoid degradation than for those required to restore already degraded land. While a useful first step, the hierarchy fails to account for how differences in land potential, defined as its potential to sustainably generate ecosystem services, may affect the ROI of actions at each level of the response hierarchy. The objective of this paper is to present a strategy for improving ROI at the landscape scale and above by systematically applying a more holistic understanding of land potential to the identification and prioritization of land investments. This objective is addressed in three sections. The first section explains how the potential short- and long-term resistance and resilience of the land can be used together with its potential productivity to prioritize actions designed to avoid, reduce and reverse degradation. In the second section we explain how this prioritization can be further optimized based on an understanding of degradation risk as indicated by the land's potential to generate relatively high short-term profits under management systems that are likely to increase degradation, or result in degradation of restored land. This potential, and land managers' perception of it, depend on a wide variety of factors including markets, infrastructure, and access to technology. Together these first two sections provide a framework for increasing ROI, while reducing the risk of failure at hectare to national scales. In the final section we briefly describe the Land-Potential Knowledge System (LandPKS), a modular mobile app that makes it possible for virtually anyone with a smartphone to make the land potential determinations necessary to apply the framework described in the first two sections. [ABSTRACT FROM AUTHOR]

Social lending or peer to peer lending (p2p lending) has emerged as a viable digital platform where lenders and borrowers can do business without the involvement of financial institutions. P2p lending has gained significant momentum recently, with some platform has reached billion-dollar loan circulation. However, p2p lending platforms are not free from any form of risks. A higher return on investment for investor comes with a risk of the loan and interest not being repaid. For this purpose, this research proposes a tree-based classification method for predicting whether a loan will go bad or default before the loan is approved. The high dimensionality of the dataset needs to be processed and chosen carefully. This paper proposes a Binary PSO with SVM to perform feature selection for the dataset and Extremely Randomized Tree (ERT) and Random Forest (RF) as the classifiers. In this research, BPSOSVM-ERT and BPSOSVM-RF are compared with several performance metrics. The experimental results show BPSOSVM can produce subset of features without decreasing the performance from the original features and ERT can outperform RF in several performance metrics. [ABSTRACT FROM AUTHOR]

Investigating the seasonality of disease incidences is very important in disease surveillance in regions with periodical climatic patterns. In lieu of the paradigm about disease incidences varying seasonally in line with meteorology, this work seeks to determine how well simple epidemic models can capture such seasonality for better forecasts and optimal futuristic interventions. Once incidence data are assimilated by a periodic model, asymptotic analysis in relation to the long-term behavior of the disease occurrences can be performed using the classical Floquet theory, which explains the stability of the existing periodic solutions. For an illustrative case, we employed infected-recovered models with an infection rate of a single period and that of commensurate periods to assimilate weekly dengue incidence data from the city of Jakarta, Indonesia, which we present in their raw and moving-average-filtered version. To estimate the infection rate of a single period, eight optimization schemes were assigned returning magnitudes of the rate that vary insignificantly across schemes. Three schemes were assigned to estimate the infection rate of commensurate periods based on three different sets of periods used. Each scheme involving commensurate periods gives better fitting than that involving only a single period. The computation results combined with the analytical results indicate that if the disease surveillance in the city does not improve, then the incidence will raise to a certain positive orbit and remain cyclical. [ABSTRACT FROM AUTHOR]

Abstract The aim of this study is to analyze whether managers, practitioners and individual investors could obtain higher risk-adjusted returns by allocating their investments to funds that integrate specific levels of socially responsible (SR) criteria in their portfolios. This is achieved by comparing the performance of mutual funds according to their SR characteristics: environmental, governance, social, and sustainability attributes. For a large sample of 3920 equity SR mutual funds around the world, performance is measured using a multifactor model that incorporates relevant benchmarks according to the fund investment objectives, and using Carhart's (1997) methodology to measure mutual fund performance persistence. In general, fund performance is not significant, the average being negative and close to zero. Funds achieving relatively high levels of SR attributes in their portfolios seem to experience overall worse performances. This evidence is, however, mainly driven by the behavior of worst-performing funds. Moreover, investing in the previous best SR funds could lead investors to greater overall returns in most areas and levels of SR attributes considered. This evidence highlights the role of managers in enhancing the returns of a portfolio with a well-defined SR investment policy. Therefore, there is no incompatibility between pursuing higher ethical (and sustainable) values as well as greater financial performances from investments—provided managers have the skills necessary to choose the right SR funds. Highlights • We compare the performance of socially responsible (SR) funds. • Funds with high SR attributes experience overall worse performances. • Investing in the previous best SR funds leads to greater overall returns. • The evidence highlights managers' role for designing SR investment policies. • Ethical and sustainable values are compatible with greater performances. [ABSTRACT FROM AUTHOR]

Abstract The process of "transitioning" to adulthood for youth with disabilities has long been recognized to be an important but understudied public policy concern. This paper evaluates the labor market effects of Virginia's school-to-work vocational evaluation program, PERT. Using a unique panel data set containing more than a decade of labor market and service information, we provide the first-ever assessment of the long-term employment impacts of a transitioning program for youth with disabilities. Overall, the estimated effects are substantial: PERT has an estimated median quarterly rate of return of nearly 30%. [ABSTRACT FROM AUTHOR]

Abstract Gasification is one of the most promising technologies for conversion of biomass into power generation due to its tremendous potential for improving the efficiency of energy conversion and reducing the cost of electricity (COE). In this study, the techno-economic feasibility of distributed power plants via wheat/corn straw gasification in China was investigated, and an economic model was established using a basic discounted cash flow analysis to estimate economic performance of the power plants. The effects of key variables (such as scales, feedstock cost, electricity prices and run time etc.) on economic performance were analyzed, and the results showed that plant scale and straw cost are the most influential parameters on the plant economic performance. It is estimated that a plant with a capacity of 5 MWe can be the optimal option for agricultural straw gasification for distributed power generation, the COE is 0.402 CNY/kWh, and SO 2 , NOx and dust emission are 2.5, 2.0 and 0.038 g/kWh, respectively. The net present value (NPV) and the annual average of return on investment (ROI) of the plant are 85.9 million CNY and 49.7%, respectively, with a high discount of 0.12 at a current feed-in tariff (0.75 CNY/kWh) for biomass to power in China, suggesting a good economic feasibility and market competitiveness. The deployment of agricultural residues resources gasification to distributed power generation displacing coal-fired power to supply electricity with rural area shows a significant potential in pollutants emission reduction and coal saving. Biomass gasification for distributed power generation serves as a sustainable technique for utilization of agricultural resources in practice, and would be widely applied in the near future supported by renewable energy strategies of Chinese government. Graphical abstract Image 1 Highlights • Techno-economic feasibility of distributed crop straw gasification to power plants was analyzed • Agricultural residues cost associated with power plant scale was investigated • Wheat/rice straw gasification to power plant with 5 MWe is more suitable for distributed power generation • A sustainability analysis was performed on wheat/rice straw gasification to power plant • The models and methods on feasibility evaluation of biomass utilization can be applied for other biomass resources and in different areas [ABSTRACT FROM AUTHOR]