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  • As the pressures of climate change begin to bite particularly hard in the developing world and in regions on the margins of extreme environments, water security is becoming increasingly difficult for many people. A review of rainwater harvesting technologies, some of which date back centuries is reported in the International Journal of Water. The review offers a range of solutions to the problem of safely harvesting rainwater for domestic and agricultural irrigation, for grey-water applications such as laundry, and even for obtaining and treating water to bolster the drinking water supply.

    Raseswari Pradhan of the Department of Electrical Engineering at VSSUT Burla and Jaya Prakash Sahoo of Central University both in Odisha, India, recognise that there is a vast array of solutions to harvesting and storing water that might incorporated into a city-based strategy for water supply in the modern smart city. Even though many of the approaches are superficially simple, they have been tried and tested over many years. Of course, any approach undertaken will require that the ethos of rainwater harvesting be adopted widely and wholeheartedly. Any approach taken does not preclude mismanagement and nor does it ensure that it will rain.

    The team's survey of the various different approaches offers a roadmap for architects and planners in arid regions of the world who can ensure the maximum benefit is gained when the rain does fall.

    Pradhan, R. and Sahoo, J.P. (2021) 'Smart rain water harvesting techniques', Int. J. Water, Vol. 14, Nos. 2/3, pp.141-166.
    DOI: 10.1504/IJW.2021.123065

  • Artificial intelligence can be used to predict the onset of diabetes mellitus given sufficient patient information according to work published in the International Journal of Business Intelligence and Systems Engineering.

    Shahid Mohammad Ganie, Majid Bashir Malik, and Tasleem Arif of the BGSB University in Rajouri, India, point out that there are millions of people around the world with the complex metabolic condition diabetes mellitus. Many people who have not yet been diagnosed with the condition may well have health issues of which they are unaware that have set them on a course to developing this serious and potentially life-changing health problem.

    Fundamentally, diabetes is associated with high blood sugar concentration over a prolonged period of time. Almost one in ten of us has some form of diabetes. Left untreated it can cause organ damage and even death. There are three forms of diabetes mellitus. Type 1, insulin-dependent diabetes, is an autoimmune dysfunction where the pancreas does not produce sufficient insulin to control blood sugar. It usually develops in childhood or adolescence. Type 2, non-insulin-dependent diabetes, begins with insulin resistance and often proceeds to insufficient insulin. It can affect almost anyone at almost any age. It is most often associated with obesity. Type 3, gestational diabetes, affects women during pregnancy, causing hyperglycemia.

    A fourth condition is also noted, pre-diabetes, where genetics, hormonal dysfunction, or exposure to certain exogenous chemicals or other factors ultimately lead to an increase in insulin production.

    Type 1 diabetes is treated with controlled injections of insulin. Type 2 can be managed by weight loss, improved diet and exercise, and the avoidance of tobacco products. Type 3 usually resolves after childbirth but for some mothers, having this condition is a risk factor for her or even her child later developing Type 2 diabetes.

    The team has trained various algorithms with relevant data associated with diabetes risk and demonstrated that one of them, the gradient boosting classifier, outperformed all others and offered a prediction accuracy of more than 92 percent when tested against known cases.

    Ganie, S.M., Malik, M.B. and Arif, T. (2021) 'Early prediction of diabetes mellitus using various artificial intelligence techniques: a technological review', Int. J. Business Intelligence and Systems Engineering, Vol. 1, No. 4, pp.325–346.
    DOI: 10.1504/IJBISE.2021.122759

  • There are millions of people around the world who now use remote computer systems to store and even process their files. There are many providers offering cloud services in a highly competitive market. In order to differentiate the companies have to offer something innovative or unique to putative customers. One such strategy is to have a two-stage pricing offer. However, this might come at a cost to the company and other users in terms of reduced security and privacy breaches where malicious third parties can create free, unvalidated accounts and use those to exploit loopholes in the system, such as faults and bugs. The issues are discussed in detail in the International Journal of Sensor Networks.

    Mengdi Yao of Wuhan University of Science and Technology and Donglin Chen of Wuhan University of Technology, both in Wuhan, China, explain that a two-stage approach to pricing can entice new customers but comes with the aforementioned risk. They have now devised a two-stage pricing approach that greatly reduces the risk from malicious third parties to other users during the free trial phase. The work addresses both risk-neutral and risk-averse users to devise two approaches within the strategy.

    Fundamentally, the team has shown five characteristics of the two-stage pricing strategy. The first, is that for a fixed-term free trial, the cloud security risk coefficient is low. Secondly, if the free trial period is flexible, then cloud security risk is low and subsequent profit increases at first but then decreases with longer free periods. Thirdly, as the security coefficient rises, so the free trial period can be shortened. Fourthly, if the security risk is low, then improvements in quality will boost profits. Finally, for risk-averse users, a shorter free period and better-perceived value will ultimately lead to greater profits.

    The team points out that planning and marketing at cloud storage companies must consider the above, especially in the face of competitors who may not offer two-stage pricing at all.

    Yao, M. and Chen, D. (2022) 'Two-stage pricing strategy for personal cloud storage: free trial and the cloud security risk', Int. J. Sensor Networks, Vol. 39, No. 1, pp.56–66.
    DOI: 10.1504/IJSNET.2022.122980

  • Research into neural networks could lead to a way to identify unexpected and potentially hazardous interactions between different medications being taken at the same time. Details are provided in the International Journal of Data Mining and Bioinformatics.

    When a patient is taking several medications simultaneously, there is always the risk that any one of those drugs might interact with another and either inhibit or enhance its activity beyond that which is required for the prescribed benefits. Similarly, one drug may interfere with the normal processing in the body, and specifically, the liver of another drug being taken at the same time, leading to a drug circulating in the bloodstream for longer. Either way, drug-drug interactions can cause side effects that are not seen when any of the given drugs is taken individually.

    Serena Rajakumar, G. Kavitha, and I. Sathik Ali of the Department of Information Technology, at the B.S.A. Crescent Institute of Science & Technology in Chennai, Tamil Nadu, India, point out that there is a vast literature on drug-drug-interactions but extracting the requisite information from countless disparate sources is an almost impossible task, especially under the daily time pressures in a healthcare setting. There are some databases that include drug-drug interactions for many common pharmaceuticals that are easier to search, but these are themselves compiled manually and do not offer a complete picture of all possible interactions and effects.

    Healthcare practitioners prescribing multiple drugs for complex conditions in their patients do not necessarily have time to plough through databases. A deep learning algorithm based on a trained neural network could be used to quickly and precisely reveal potentially risky drug-drug interactions without the need for a manual search. The team has demonstrated how their system can automatically extract information from the biomedical literature discussing drug behaviour and then compile a new growing database of potentially troublesome interactions.

    The team adds that the current approach does not yet reveal whether any given drug-drug interaction is antagonistic or synergistic, that step will be taken in a future iteration of the algorithm. For the time being, that distinction will need to be considered by the healthcare professional alerted to any interaction by the present system.

    Rajakumar, S., Kavitha, G. and Ali, I.S. (2021) 'Extraction of drug-drug interaction information using a deep neural network', Int. J. Data Mining and Bioinformatics, Vol. 25, Nos. 3/4, pp.181–200.
    DOI: 10.1504/IJDMB.2021.122855

  • An efficient neural network can now take a series of music files as input and define them quickly by genre and style, thanks to work published in the International Journal of Web Services. Such a system could be a boon to music streaming services that hope to offer their users an effective recommendation system to allow them to access novel music they may enjoy as much as their old favourites.

    Many millions of people listen to music through online streaming or download services on their computers, smart devices and mobile phones rather than selecting a plastic disc from a collection to be played on a dedicated machine. As such, there are many aspects of the enjoyment and recommendation of new music that can utilise the vast repositories of information found online as well as the connectivity of online communities. However, for a system to be able to automate recommendations to users, there is an inherent need for each piece of music to be appropriately tagged with respect to genre, style, tempo, and other such characteristics.

    Jagendra Singh of the School of Computer Science Engineering and Technology at Bennett University in Greater Noida, India, has tested the system against six types of music, including jazz, hip-hop, electronic, rock, classical, and folk and found it to be effective. The algorithm performs even better when the spectrographic frequency of the sounds and the time sequence pattern are incorporated as variables into their hybrid recommendation system.

    While it is inevitable that word-of-mouth recommendations among music fans will persist, the diversity and density of music now available to so many people online means that music can reach new audiences, more quickly. Moreover, the desires of music fans keen to seek out novelty quickly without waiting for a friend or contact to discover the next greatest hit for them could be served well by algorithmic recommendation systems.

    Singh, J. (2022) 'An efficient deep neural network model for music classification', Int. J. Web Science, Vol. 3, No. 3, pp.236–248.
    DOI: 10.1504/IJWS.2022.122991

  • An analysis of the micro-blogging updates from investors posted on the social media site Twitter, offers an insight into the personality traits that are most closely linked to investment success. The research, published in the journal Global Business and Economics Review, suggests that successful investors predominantly exhibit two personality traits: emotional stability and openness. An additional finding is that all investors, successful or otherwise, have low agreeableness and do not exhibit extraversion.

    Agreeableness and extraversion are two of the so-called Big Five personality traits. Added to those we have openness, conscientiousness, and neuroticism. They are a common archetype system for exploring personality. Individuals can have any combination of those five traits, each being positioned on a spectrum from a complete absence of said trait to predominance of that trait. So, a person might be an agreeable person who is mildly neurotic, strongly conscientious, extroverted, and a great deal of openness. Some traits are likely to be found to be strongly present with others.

    R. Ramprakash and C. Joe Arun of the Loyola Institute of Business Administration (LIBA) in Chennai, India, selected a group of successful investors active on Twitter and performed an analysis of their tweets using linguistic inquiry and word count (LIWC) software. They hoped to reveal from their tweets common personality traits among successful investors. The work might provide clues to the inner workings of a world that always seems to be governed by whim and vagueries and does not succumb easily to analyses that might open up ways in which to predict how investments might rise and fall in a given time period. Opportunity and risk seem to be unknowable quantities, but insights into the personality of investors might add useful knowledge.

    The researchers explain that while most research has focused on measuring the investment performance of individual investors and comparing that with their personality traits, the present study provides an interesting insight into the existing literature by identifying successful investors and observing their dominant personality trait, which, in turn, lead to specific behaviour.

    Ramprakash, R. and Arun, C.J. (2022) 'A study of the tweets of successful investors in order to identify their personality', Global Business and Economics Review.
    DOI: 10.1504/GBER.2022.10043146

  • The out-moded aphorism – "Behind every great man is a good woman" – might be brought clumsily up-to-date by writing instead that "Alongside every great person is a great partner". Indeed, writing in the International Journal of Entrepreneurship and Small Business, researchers from Israel have investigated the impact of a life partner on the social capital of entrepreneurs.

    Ben Bulmash of the Faculty of Technology Management at the Holon Institute of Technology in Holon, suggests that psychological capital is a concept of growing importance in the world of entrepreneurial business. In this world challenges and uncertainties are ever-present and perhaps increasingly so in the wake of the COVID-19 pandemic, civil unrest, climate change, and war-mongering. There are three psychological components to psychological capita, which might be thought of as a state of mind rather than a character trait: optimism, pessimism, and self-esteem. How, asks Bulmash, are these three traits affected by the presence, support or otherwise of a life partner?

    In studying entrepreneurial activities and the world of business, the focus is often on product design, marketing strategy, financial planning, and technological aspects of the business. The right blend can lead to success. That said, previous studies have shown that positive psychological capital can lead to business longevity and success. A focus on the entrepreneur's life may, however, be just as important a factor. Bulmash now has found, as one might expect to some degree, that low levels of support from an entrepreneur's life partner lead to what might be referred to as the least favourable mental states.

    "Difficult and unsupportive relationships are detrimental to business success, possibly more so in the early stages of a business, when uncertainty is high and results not immediate," Bulmash writes. It is important when trying to understand entrepreneurial activity to understand that the entrepreneur's life and life partner can play a significant role in predicting the trajectory of their business.

    Bulmash, B. (2022) 'At the heart of things: the impact of life-partners on entrepreneurs' psychological capital', Int. J. Entrepreneurship and Small Business, Vol. 45, No. 4, pp.476–488.
    DOI: 10.1504/IJESB.2022.122658

  • Older adults with or without health problems could continue to live independent lives as far as is practical with the use of smart technologies, such as wearable sensors, and internet-connected monitoring systems that can alert remote carers to acute problems, such as a sudden downturn in health metrics, a fall, or other issues, as soon as they arise.

    Writing in the International Journal of Engineering Systems Modelling and Simulation, a team from India provides a user perspective on such ambient assisted living systems. Ashish Patel and Jigarkumar Shah of Pandit Deendayal Energy University in Gandhinagar, India, explain that AAL systems must offer carers timely and detailed information when the older adult's environment or personal conditions change from their normal to a new normal that represents a risky situation has arisen or their health has suddenly declined. There are numerous wearable and situational monitoring devices that can report room and body temperature, air quality, whether a person is mobile, seated, or has fallen, and other such variables.

    The team has surveyed AAL system users to get an insider perspective on how well these systems might work. An effective AAL system must offer continuous monitoring but also security and privacy to allow vulnerable or older adults to live independently in their preferred home. It does not offer a complete approach to care, of course, but augments the caring environment for that adult offered by relatives, friends, and professionals, depending on the person's needs and choices.

    The researchers present a framework and a practical approach to a hybrid AAL system that brings together personal monitoring devices and environmental monitoring devices with a view to improving the health standards of an older person living alone. The framework takes into account the person's needs and desires rather than simply defining the requisite technological setup. The team points out that in order to incorporate the person's emotional state in such a hybrid system, there must be a certain level of compromise when it comes to their privacy, as the monitoring software and thence the carers who are there to respond to alerts from the system will have some insight into the person's inner life in order that an appropriate response can be made in a timely manner.

    Patel, A. and Shah J. (2022) 'Towards enhancing the health standards of elderly: role of ambient sensors and user perspective', Int. J. Engineering Systems Modelling and Simulation, Vol. 13, No. 1, pp.96–110.
    DOI: 10.1504/IJESMS.2022.122739

  • We live in the information age, you might say. More than 2.5 quintillion bytes* of data are generated around the globe every day. Managing that data is impossible and yet we make use of huge chunks of it in many disparate and sometimes unimaginable ways. Extracting knowledge from repositories and databases, the big data, can lead to a better understanding of natural and non-natural phenomena in climate change, economics, medicine, and beyond.

    Predictive analysis is key to making intelligent decisions based on such big data, according to researchers writing in the International Journal of Engineering Systems Modelling and Simulation. However, there are problems that must be addressed especially when such big data exists in the cloud.

    Krishna Kumar Mohbey and Sunil Kumar of the Central University of Rajasthan in Ajmer, India, consider the impact of big data in this context. They point out that one of the biggest issues facing those who would work with big data is that while some of it may well be structured, much of it is only semi-structured, and vast amounts are entirely unstructured.

    The storage, management, and analysis of all of this data is one of the greatest challenges facing computing today. While cloud computing provides many of the tools needed in a distributed way and to some extent has revolutionized information and communications technology (ICT), there remains a long road ahead before we can truly cope with big data fully.

    However, distributed storage and massive parallel processing of big data in the cloud could provide the foundations on which the future of big data and predictive analysis might be built. The team reviews many of the current approaches that use historical data and machine learning to build predictions about the outcomes of future scenarios based on contemporary big data sources. The team points to where research might take us next in the realm of big data and warns of the possible dead-ends.

    "The key aim is to transform the cloud into a scalable data analytics tool, rather than just a data storage and technology platform," the team writes. They add that now is the time to develop appropriate standards and application programming interfaces (APIs) that enable users to easily migrate between solutions and so take advantage of the elasticity of cloud infrastructure.

    Mohbey, K.K. and Kumar, S. (2022) 'The impact of big data in predictive analytics towards technological development in cloud computing', Int. J. Engineering Systems Modelling and Simulation, Vol. 13, No. 1, pp.61–75.
    DOI: 10.1504/IJESMS.2022.122732

    *2.5 quintillion bytes is about 1 million terabytes. A general household computer might have a 1 terabyte hard drive these days, so that's data maxing out the storage capacity of about 2,500,000 computers every day.

  • Machine learning algorithms can be used to make accurate forecasts about changes in population, according to research published in the International Journal of Data Science. The work demonstrates that the best of the available algorithms trained on historical data works better than conventional demographic modeling based on periodic census data.

    Fatih Veli Sahinarslan, Ahmet Tezcan Tekin, and Ferhan Çebi of the Department of Management Engineering at Istanbul Technical University, in Istanbul, Turkey, have compared the predictive power of various algorithms – extreme gradient boosting, CatBoost, linear regression, ridge regression, Holt-Winters, exponential, autoregressive integrated moving average (ARIMA) and prophet prediction model. They trained the algorithms using 1595 different demographic indicators from 262 countries recorded between 1960 and 2017. Indicators include age and gender distribution, labour force, education, birthplace, birth and death rates, and migration statistics.

    Their demonstration to predict the population of Turkey for the year 2017 proved the value of the algorithmic approach over traditional modeling. Understanding population dynamics and forecasting how a population might change in years to come is a critical part of policymaking and planning for healthcare, education, housing, transport, and infrastructure. Ten-year census cycles are useful, but they do not give a fine-grained account of a changing population, especially in the light of changes in life expectancy, migration, war, political upheaval, and pandemics, where the character of a population might change radically on a much shorter timescale.

    The researchers suggest that machine learning algorithms, ensemble regression models in particular, can offer a "better estimate" of the future population of a country. They are able to do so because they can reduce the number of factors that otherwise make it difficult to make an estimate and also through analysis of any uncertainties in the demographic data.

    "Machine learning algorithms on population estimation will make an essential contribution to…the planning of national needs and pave the way for more consistent social, economic, and environmental decisions," the team concludes.

    Sahinarslan, F.V., Tekin, A.T. and Çebi, F. (2021) 'Application of machine learning algorithms for population forecasting', Int. J. Data Science, Vol. 6, No. 4, pp.257–270.
    DOI: 10.1504/IJDS.2021.122770

News

World Review of Entrepreneurship, Management and Sustainable Development is now an open access-only journal

We are pleased to announce that the World Review of Entrepreneurship, Management and Sustainable Development is now an Open Access-only journal. All accepted articles submitted from 1 June 2022 onwards will be Open Access, and will require an article processing charge of US $1500.

International Journal of Information and Communication Technology is now an open access-only journal

We are pleased to announce that the International Journal of Information and Communication Technology is now an Open Access-only journal. All accepted articles submitted from 24 May 2022 onwards will be Open Access, and will require an article processing charge of US $1500.

International Journal of Economics and Business Research is now an open access-only journal

We are pleased to announce that the International Journal of Economics and Business Research is now an Open Access-only journal. All accepted articles submitted from 10 May 2022 onwards will be Open Access, and will require an article processing charge of US $1500.

New Editor for International Journal of Rapid Manufacturing

Prof. Sheng-feng Qin from Northumbria University in the UK has been appointed to take over editorship of the International Journal of Rapid Manufacturing.

New Editor for International Journal of Artificial Intelligence and Soft Computing

Prof. Gai-Ge Wang from the Ocean University of China has been appointed to take over editorship of the International Journal of Artificial Intelligence and Soft Computing.