Renewable energy policy around the globe

Renewable energy systems (RESs) have an inherent quality of achieving independence Vis a Vis useful energy harnessing for self or localized use. At larger scale the renewable energy system allows the power generation and distribution without a significant harm to the environment. Propagation and proliferation of RESs and the Renewable Energy Technology (RET), collectively demand an effective policy making infrastructure to be in place to ensure their penetration locally and globally. Renewable Energy Policies have a great impact on how readily these systems are adopted by the investors and market, how efficient these systems are which is dependent on the research and development (R&D) of RESs, how effectively the country or region copes with the scourge of environmental harm with the use of RESs. Renewable Energy Policies also have an impact on how costly these technologies are and how these technologies fiscally benefit common man, investors and industries. Efficiency enhancement, stability and intelligent management of grids integrated with RES are also renewable policy dependent. This paper thoroughly and critically examines the importance of renewable energy policy. not just at domestic or national level but also at global level. It discusses in detail the core ingredients of renewable energy policy which were adopted in the past, which are being adopted now and which should be adopted in future. This paper also discusses policy, business and financial models; importance of tariff formulation, incentives and subsidies and a few factors which may pose a risk to the development and adoption of RESs. At the end a few suggestions have also been made which, if adopted, would help promote the RESs proliferation across the globe at a rapid pace and would ensure energy security, sustainable development and environmental conservation.

The power of visual analytics and language processing to explore the underlying trend of highly popular song lyrics

The number of songs are increasing at a very high rate around the globe. Out of the songs released every year, only the top few songs make it to the billboard hit charts .The lyrics of the songs place an important role in making them big hits combined with various other factors like loudness, liveness, speech ness, pop, etc. The artists are faced with the problem of finding the most desired topics to create song lyrics on. This problem is further amplified in selecting the most unique, catchy words which if added, could create more powerful lyrics for the songs. We propose a solution of finding the bag of unique evergreen words using the term frequency-inverse document frequency (TF-IDF) technique of natural language processing. The words from this bag of unique evergreen words could be added in the lyrics of the songs to create more powerful lyrics in the future.

Hermite-Hadamard-Fejér type inequalities for co-ordinated harmonically convex functions via Katugampola fractional integrals

The aim of this paper is to establish the Hermite-Hadamard-Fejér type inequalities for co-ordinated harmonically convex functions via Katugampola fractional integral. We provide Hermite-Hadamard-Fejér inequalities for harmonically convex functions via Katugampola fractional integral in one dimension.

Qualitative analysis of a mathematical model of divorce epidemic with anti-divorce therapy

Marriage is the living together of two persons as husband and wife. Separation and Divorce are the frontier challenges facing the existence of stable family system. In this paper, we construct an epidemiological model of divorce epidemic using standard incidence function as force of marital disunity. The study examines qualitatively that the two equilibra (divorce-free and endemic equilibrium point) are globally stable by Lyapunov functions. Numerical results reveal that, anti-divorce protocols and reconciliation can jointly stabilize marriages, and Married cases that survive divorce epidemic in 30 years period of marriage (twice the survival period of separation) cannot break again.

Internet’s handprint

In this decade there will an unprecedented growth of generated data, computations, instructions, and operations. This growth may not compromise clean air, clean water and a sustainable energy and material usage, but rather facilitate these prerequisites for flora and fauna. There are many indications (expected trends and estimates) showing that the Internet Sector will be able to provide solutions to other Sectors such as Buildings, Transportation and Industry which will help reduce the total global consumption of energy and materials. For instance, products are replaced by virtual services e.g. by using e-readers instead of paperbacks, and transportation is avoided by online shopping or Internet meetings. This is more resource and energy efficient than before and entire sectors, like transport, industry, and agriculture can be optimized. Internet may foster new sustainable lifestyles which can lower the affluence despite certain rebound effects. The underlying idea is that e.g. human-related global greenhouse gas (GHG) supply can be significantly halted if existing and developing ICT Solutions are used in other sectors (and in the Internet infrastructure itself) to cause a handprint. Such solutions include products-sold-as-services, smart Grid and smart metering. Compared to earlier approaches, the 2020 transformative effects on smart work, land use and smart circularity are included in the discussion, as well as consequential LCA modelling. Internet’s handprint will be 4-7 times its footprint in 2030. The handprint is highly dependent e.g. on how large share of the buildings can adopt smart metering and the product to service rate. Internet will in itself use intelligent ICT solutions as well as neuromorphic, reversible and superconducting computing as well as nanophotonics to mitigate its own material and energy use. However, more importantly the intelligent ICT solutions should be used in the rest of the society to reach efficiency goals. Power saving is a highly efficient strategy for cost reduction in the Internet Sector itself and beyond.

Variation of radiofrequency intensities from the DTTV-transmitter in Kampala Metropolitan; Uganda

This paper presents the variation of radiofrequencies intensities from the DTTV-transmitter in Kampala Metropolitan for the sub 700 MHz (470-694 MHz) and the 700 MHz (694-790 MHz) frequency bands. The results of this study showed that though all the measurement locations from the transmitter have a good reception of DTTV signals, their radiofrequency intensities varied at the different points on the same measurement location at a constant distance from the DTTV transmitter. The study further showed that there is a general decrease in the radiofrequency intensities for the sub 700 MHz frequency band and a slight general increase in the radiofrequency intensities for the 700 MHz frequency band. This research revealed that the measured Reference Signal Received Power (RSRP) values for all the measurement locations where within the IEEE and FCC recommended values for any DTTV signal reception.

A model of dual latency compartments for the transmission dynamics of COVID-19 in Oyo state, Nigeria

In this study, a mathematical model of dual latency compartments is developed to investigate the transmission dynamics of COVID-19 epidemic in Oyo state, Nigeria. The model consists of non-pharmaceutical control strategies which include the use of face masks, social-distancing and impact of mass-media on the spread of novel coronavirus in the state. Results indicate control reproduction number \(R_C = 1.4\) with possibilities of high constructive influence of mass-media. Thus, at the fitted values of \(\sigma _f = 0.1,\; \sigma _d = 0.1,\;\sigma _m = 0.6\), the peak of the COVID-19 epidemic is attained after 59,217 infectious quarantined individuals and 328,440 infectious but not quarantined individuals have contracted COVID-19 in about 439 and 443 days respectively from the date of the first incidence. Therefore, efforts on mass-media with programs that can inform the people on effective use of face masks, social-distancing and other safety measures can aid reduction of reproduction number to a value below 1 necessary for eradication of the disease.

Cebyšev inequalities for co-ordinated \(QC\)-convex and \((s,QC)\)-convex

In this paper, we establish some new Cebyšev type inequalities for functions whose modulus of the mixed derivatives are co-ordinated quasi-convex and \(\alpha \)-quasi-convex and \(s\)-quasi-convex functions.

Best proximity point of modified Suzuki-Edelstein-Geraghty type proximal contractions

In this paper, we introduce the notion of modified Suzuki-Edelstein-Geraghty proximal contraction and prove the existence and uniqueness of best proximity point for such mappings. Our results extend and unify many existing results in the literature. We draw corollaries and give illustrative example to demonstrate the validity of our result.

A holistic comparison between deep learning techniques to determine Covid-19 patients utilizing chest X-Ray images

Novel coronavirus likewise called COVID-19 began in Wuhan, China in December 2019 and has now outspread over the world. Around 63 millions of people currently got influenced by novel coronavirus and it causes around 1,500,000 deaths. There are just about 600,000 individuals contaminated by COVID-19 in Bangladesh too. As it is an exceptionally new pandemic infection, its diagnosis is challenging for the medical community. In regular cases, it is hard for lower incoming countries to test cases easily. RT-PCR test is the most generally utilized analysis framework for COVID-19 patient detection. However, by utilizing X-ray image based programmed recognition can diminish the expense and testing time. So according to handling this test, it is important to program and effective recognition to forestall transmission to others. In this paper, author attempts to distinguish COVID-19 patients by chest X-ray images. Author executes various pre-trained deep learning models on the dataset such as Base-CNN, ResNet-50, DenseNet-121 and EfficientNet-B4. All the outcomes are compared to determine a suitable model for COVID-19 detection using chest X-ray images. Author also evaluates the results by AUC, where EfficientNet-B4 has 0.997 AUC, ResNet-50 has 0.967 AUC, DenseNet-121 has 0.874 AUC and the Base-CNN model has 0.762 AUC individually. The EfficientNet-B4 has achieved 98.86% accuracy.