An integrated multiple layer perceptron-genetic algorithm decision support system for photovoltaic power plant site selection

There is a need for non-renewable energy sources in generation of power for almost every domestic and commercial purposes. This source of energy helps in the development of a country. Because of the increasing usage of the fossil fuels and depletion of these resources, our focus has been shifted towards the renewable sources of energy like solar, water and wind. Therefore, in the present scenario, the usage of renewable sources has been increasing rapidly. Selection of a solar power plant (SPP) requires environmental factor, local terrain, and local weather issues. Thus, a large amount of investment is required for installation. Multi-criteria decision making (MCDM) is a method that identifies one in choosing the best sites among the other proposed options. This paper gives a detailed study of optimal ranking of SPP site using analytical hierarchy process (AHP), multiple layer perceptron (MLP) neural network trained with back propagation (BP) algorithm and genetic algorithm (GA). Three SPP sites of India were considered and various important criteria like local weather, geographical location, and environmental factors are included in our study as SPP site selection is a multi-criteria problem. A precise comparison of these three methods is listed in this paper.

Global Multi-Source Information Fusion Management and Deep Learning Optimization for Tourism

The purpose is to solve the problems of sparse data information, low recommendation precision and recall rate and cold start of the current tourism personalized recommendation system. First, a context based personalized recommendation model (CPRM) is established by using the labeled-LDA (Labeled Latent Dirichlet Allocation) algorithm. The precision and recall of interest point recommendation are improved by mining the context information in unstructured text. Then, the interest point recommendation framework based on convolutional neural network (IPRC) is established. The semantic and emotional information in the comment text is extracted to identify user preferences, and the score of interest points in the target location is predicted combined with the influence factors of geographical location. Finally, real datasets are adopted to evaluate the recommendation precision and recall of the above two models and their performance of solving the cold start problem.

IP Geolocation through Reverse DNS

IP Geolocation databases are widely used in online services to map end-user IP addresses to their geographical location. However, they use proprietary geolocation methods, and in some cases they have poor accuracy. We propose a systematic approach to use reverse DNS hostnames for geolocating IP addresses, with a focus on end-user IP addresses as opposed to router IPs. Our method is designed to be combined with other geolocation data sources. We cast the task as a machine learning problem where, for a given hostname, we first generate a list of potential location candidates, and then we classify each hostname and candidate pair using a binary classifier to determine which location candidates are plausible. Finally, we rank the remaining candidates by confidence (class probability) and break ties by population count. We evaluate our approach against three state-of-the-art academic baselines and two state-of-the-art commercial IP geolocation databases. We show that our work significantly outperforms the academic baselines and is complementary and competitive with commercial databases. To aid reproducibility, we open source our entire approach and make it available to the academic community.

Influence of Ancient Mesopotamian Aesthetics of Gardens/Parks and Water Installations on the Development of Landscape Architecture

Based on the information from fundamental historical sources, the oldest origins of civilization can be found in the river valleys of the Tigris and the Euphrates. Throughout Mesopotamia important routes of trade and migration of peoples followed, which influenced the emergence and development of one of the world’s first urban-type civilizations. It was here that the Sumerian civilization evolved, leading to a major cultural and technological breakthrough. Their widely used irrigation canals influenced not only the landscape, but also the entire ecological, economic and political systems of the time, water being a particularly important factor in this civilization. The oldest known gardens have also expanded here, and the Hanging Gardens of Babylon still fuel people's imagination. Due to its unique geographical location, the region has had a profound impact on the surrounding nations, and it is no accident that the Assyrian Imperial Parks of Northern Mesopotamia exerted a great influence on the civilizations that followed. Undoubtedly, ancient Mesopotamia occupies a fundamental place in the development of garden art. With the growing use of roof gardens and the use of plants in modern architectural constructions as an extremely important tool for composition, it is worth exploring more closely the origins of this landscape-relevant process.

A novel weather parameters prediction scheme and their effects on crops

Weather forecast is significantly imperative in today’s smart technological world. A precise forecast model entails a plentiful data in order to attain the most accurate predictions. However, a forecast of future rainfall from historical data samples has always been challenging and key area of research. Hence, in modern weather forecasting a combo of computer models, observation, and knowledge of trends and patterns are introduced. This research work has presented a fitness function based adaptive artificial neural network scheme in order to forecast rainfall and temperature for upcoming decade (2021-2030) using historical weather data of 20 different districts of Karnataka state. Furthermore, effects of these forecasted weather parameters are realized over five major crops of Karnataka namely rice, wheat, jowar, maize, and ragi with the intention of evaluation for efficient crop management in terms of the passing relevant messages to the farmers and alternate measures such as suggesting other geographical locations to grow the same crop or growing other suitable crops at same geographical location. A graphical user interface (GUI) application has been developed for the proposed work in order to ease out the flow of work.

السودان و صراع المصالح الدولية : رؤية استراتيجية

massive and varied natural resources which are available in strategic quantities, beside the strategic geographical location , in a way to study the impact of this situation on the National security. The objective of stduy is to determine strategic vision in order to achieve the National strategic interests and the best way to deal with International interests conflict. The most important findings of the study are : ■ The necessity of the national strategic planning as an important factor to achieve the strategic power needed to manage the international interests conflict. ■ The necessity of developing a National mechanism to take Care of state strategic plan

Molecular Characterization of Severe Acute Respiratory Syndrome Coronavirus 2 Isolates From Central Inner Sardinia

BackgroundThe SARS-CoV-2 pandemic stimulated an outstanding global sequencing effort, which allowed to monitor viral circulation and evolution. Nuoro province (Sardinia, Italy), characterized by a relatively isolated geographical location and a low population density, was severely hit and displayed a high incidence of infection.MethodsAmplicon approach Next Generation Sequencing and subsequent variant calling in 92 respiratory samples from SARS-CoV-2 infected patients involved in infection clusters from March 2020 to May 2021.ResultsPhylogenetic analysis displayed a coherent distribution of sequences in terms of lineage and temporal evolution of pandemic. Circulating lineage/clade characterization highlighted a growing diversity over time, with an increasingly growing number of mutations and variability of spike and nucleocapsid proteins, while viral RdRp appeared to be more conserved. A total of 384 different mutations were detected, of which 196 were missense and 147 synonymous ones. Mapping mutations along the viral genome showed an irregular distribution in key genes. S gene was the most mutated gene with missense and synonymous variants frequencies of 58.8 and 23.5%, respectively. Mutation rates were similar for the S and N genes with one mutation every ∼788 nucleotides and every ∼712 nucleotides, respectively. Nsp12 gene appeared to be more conserved, with one mutation every ∼1,270 nucleotides. The frequency of variant Y144F in the spike protein deviated from global values with higher prevalence of this mutation in the island.ConclusionThe analysis of the 92 viral genome highlighted evolution over time and identified which mutations are more widespread than others. The high number of sequences also permits the identification of subclusters that are characterized by subtle differences, not only in terms of lineage, which may be used to reconstruct transmission clusters. The disclosure of viral genetic diversity and timely identification of new variants is a useful tool to guide public health intervention measures.

Critical medical ecology and intersectionality perspectives in schistosomiasis prevention and control in selected communities in Mindanao, the Philippines

Schistosomiasis japonica remains a public health concern in many areas of the Philippines. Periodic Mass Drug Administration (MDA) to at-risk populations is the main strategy for morbidity control of schistosomiasis. Attaining MDA coverage targets is important for the reduction of morbidity and prevention of complications due to the disease, and towards achieving Universal Health Care. The study employed a qualitative case study design. Key informant interviews and focus group discussions were conducted to provide in-depth and situated descriptions of the contexts surrounding the implementation of MDA in two selected villages in known schistosomiasis-endemic provinces in Mindanao in the Philippines. Data analysis was done using the Critical Ecology for Medical Anthropology (CEMA) model coupled with the intersectionality approach. It was found that within various areas in the CEMA model, enabling as well as constraining factors have been encountered in MDA in the study settings. The interplay of income class, geographical location, gender norms and faith-based beliefs may have led to key populations being missed during the conduct of MDA in the study sites. The constraints faced by the target beneficiaries of MDA, as well as programme implementers, must be addressed to enhance service delivery and to control morbidity due to schistosomiasis. Improving compliance with MDA also requires a holistic, integrated approach to addressing barriers to participation, which are shaped by wider socio-political and power structures.

TrackInk: An IoT-Enabled Real-Time Object Tracking System in Space

Nowadays, there is tremendous growth in the Internet of Things (IoT) applications in our everyday lives. The proliferation of smart devices, sensors technology, and the Internet makes it possible to communicate between the digital and physical world seamlessly for distributed data collection, communication, and processing of several applications dynamically. However, it is a challenging task to monitor and track objects in real-time due to the distinct characteristics of the IoT system, e.g., scalability, mobility, and resource-limited nature of the devices. In this paper, we address the significant issue of IoT object tracking in real time. We propose a system called ‘TrackInk’ to demonstrate our idea. TrackInk will be capable of pointing toward and taking pictures of visible satellites in the night sky, including but not limited to the International Space Station (ISS) or the moon. Data will be collected from sensors to determine the system’s geographical location along with its 3D orientation, allowing for the system to be moved. Additionally, TrackInk will communicate with and send data to ThingSpeak for further cloud-based systems and data analysis. Our proposed system is lightweight, highly scalable, and performs efficiently in a resource-limited environment. We discuss a detailed system’s architecture and show the performance results using a real-world hardware-based experimental setup.

Characterization of Tensile Properties of Cola lepidota Fibers

Plant fibers are being increasingly explored for their use in engineering polymers and composites, and many works have described their properties, especially for flax and hemp fibers. Nevertheless, the availability of plant fibers varies according to the geographical location on the planet. This study presents the first work on the mechanical properties of a tropical fiber extracted from the bast of Cola lepidota (CL) plant. After a debarking step, CL fibers were extracted manually by wet-retting. The tensile properties are first identified experimentally at the fibers scale, and the analysis of the results shows the great influence of the cross-section parameters (diameter, intrinsic porosities) on these properties. Tensile properties of CL fibers are also predicted by the impregnated fiber bundle test (IFBT). At this scale of bundles, a hackling step, which reduces shives and contributes to the parallelization of the fibers within bundles, improves tensile properties predicted by IFBT. The comparison with the properties of plant fibers given in the literature shows that CL fibers have tensile properties in the same range as kenaf, flax or hemp fibers.