Assessing Large-Scale Power Relations among Locations from Mobility Data

The pervasiveness of smartphones has shaped our lives, social norms, and the structure that dictates human behavior. They now directly influence how individuals demand resources or interact with network services. From this scenario, identifying key locations in cities is fundamental for the investigation of human mobility and also for the understanding of social problems. In this context, we propose the first graph-based methodology in the literature to quantify the power of Point-of-Interests (POIs) over its vicinity by means of user mobility trajectories. Different from literature, we consider the flow of people in our analysis, instead of the number of neighbor POIs or their structural locations in the city. Thus, we modeled POI’s visits using the multiflow graph model where each POI is a node and the transitions of users among POIs are a weighted direct edge. Using this multiflow graph model, we compute the attract, support, and independence powers . The attract power and support power measure how many visits a POI gathers from and disseminate over its neighborhood, respectively. Moreover, the independence power captures the capacity of a POI to receive visitors independently from other POIs. We tested our methodology on well-known university campus mobility datasets and validated on Location-Based Social Networks (LBSNs) datasets from various cities around the world. Our findings show that in university campus: (i) buildings have low support power and attract power ; (ii) people tend to move over a few buildings and spend most of their time in the same building; and (iii) there is a slight dependence among buildings, even those with high independence power receive user visits from other buildings on campus. Globally, we reveal that (i) our metrics capture places that impact the number of visits in their neighborhood; (ii) cities in the same continent have similar independence patterns; and (iii) places with a high number of visitation and city central areas are the regions with the highest degree of independence.

Remote sensing and geophysical studies for groundwater exploration in Osmania University campus, Hyderabad, India: A case study

With the increasing resolution of satellite sensors, it is possible to fruitfully exploit the special advantages of image analysis for a wide range of geological environments. With this view, a LISS-III and PAN merged image of the 1600 acre (approximately 6.5 sq km) Osmania University (OU) campus taken from IRS-ID in the month of May (a fairly representative month in terms of minimum annual drainage/vegetation cover) was acquired. The image was then digitally processed and visually interpreted for potential groundwater resource regions. Since occurrence of groundwater in crystalline rocks, the host rocks for the entire Hyderabad region, is generally associated with secondary porosity, the accent was on determining and establishing lineaments of considerable surface extent. This was then augmented with maps of subsurface features as obtained from geophysical studies for the southern part of 0 U campus and available bore well/open well information. Subsequently, information from the three sources was integrated for a better understanding of the geological situation and the interrelationship of its various constituents to determine possible locations of groundwater resources. The significant findings comprised the identification of three major dykes, two running E-W and the third running NE-SW. A major N-S linear exposure of granitic rocks, as also several criss-crossing fractures in the southern side of the campus, along with the prevailing drainage pattern for the entire campus area were mapped. Based on these findings and supporting geophysical/hydrogeological data, a geological/lithological map of Osmania University campus was prepared and prospective groundwater zones have been identified.

Enlightenment of Intelligent Construction in the Construction of First-Class University Campus

In September 2017, the Ministry of Education, Ministry of Finance, and the National Development and Reform Commission jointly announced the first batch of “double first-class” construction universities, aiming to improve China’s education level and strengthen the country’s core competitiveness. In addition to first-class teaching and first-class scientific research, first-class universities should also have first-class campuses. The campus of a first-class university should have a complete range of infrastructure, scale, and function to meet the requirements of school-running and scientific research; it should also advocate sustainable development, which includes the concept of energy-saving, smart, and green. The traditional production methods and management models of the construction industry are far from being able to meet the needs of the construction and development of first-class campuses. Digital, intelligent, and informatized construction models are the only way for current campus construction.

Diversity and abundance of pollinators in different winter crops at Sher-e-Bangla Agricultural University Campus, Dhaka

The study was conducted at Sher-e-Bangla Agricultural University campus to document the diversity and abundance of pollinators of five commonly grown winter crops, viz., mustard, coriander, niger, black cumin and linseed from October 2020 to February 2021. A total number of fifteen species was identified under eleven genera, nine subfamilies, nine families and seven super families. The study revealed that honey bees, halictids, nymphalids, coccinellids, butterflies and dipterans of genera Apis, Halictus, Lasioglossum, Aglais, Coccinella, Pieris, Eurema, Musca, Syrphid and Calliphora belonging to the families Apidae, Halictidae, Nymphalidae, Coccinellidae, Pieridae, Muscidae, Syrphidae and Calliphoridae, respectively were present in the field. The species diversity was high in mustard with 15 species while it was low in linseed (5 species). The honey bees, Apis sp. and sweat bees, Halictus sp. were common pollinators of all five oilseed crops, while the housefly, Musca domestica was specific to mustard. The honey bee, Apis sp. was predominant among all the insect pollinators in five oilseed crops. The Species Richness (SR), Shannon-Weaver index (H’), Community dominance and Question of similarity indices were applied to determine the diversity and abundance of pollinators. J. Biodivers. Conserv. Bioresour. Manag. 2021, 7(1): 33-42

University spaces for cooperation strengthening pro-innovative attitudes of academic community

The article and its undertaken matter are part of the discussion on the pro-innovative nature of a university – featuring examples of (mainly educational) activities which boost innovation on the university campus and beyond, and referring to trends occurring in the analysed areas. The presented content has been limited to the social dimension of innovation, which among other things, changes the way of perceiving the world and interpersonal relations, also in terms of organization of workspaces and (co)working methods. The purpose of the article is also to draw attention to the participation of students, academic teachers in various activities (also pro-innovative ones) taking place within a non-traditional social infrastructure. Problem analysis was based on literature studies, including reports on research on innovation as well as on the author’s professional experience related to new spaces of education – and an attempt to answer the question: “Can cooperation within the described spaces strengthen pro-innovative attitudes, including the attitudes of future pedagogues?”

Containment of a multi-index B.1.1.7 outbreak on a university campus through a genomically-informed public health response

The first cluster of SARS-CoV-2 cases with lineage B.1.1.7 in the state of Michigan was identified through intensive university-led surveillance sampling and targeted sequencing. A collaborative investigation and response was conducted by the local and state health departments, and the campus and athletic medicine COVID-19 response teams, using S-gene target failure screening and rapid genomic sequencing to inform containment strategies. A total of 50 cases of B.1.1.7-lineage SARS-CoV-2 were identified in this outbreak, which was due to three coincident introductions of B.1.1.7-lineage SARS-CoV-2, all of which were genetically distinct from lineages which later circulated in the broader community. This investigation demonstrates the successful implementation of a genomically-informed outbreak response which can be extended to university campuses and other settings at high risk for rapid emergence of new variants.

Air Quality Measurement Using an IoT Network: a Case Study

Context: The evaluation of air quality in Colombia is localized; it does not go beyond determining whether the level of the polluting gas at a specific point of the monitoring network has exceeded a threshold, according to a norm or standard, in order to trigger an alarm. It is not committed to objectives as important as the real-time identification of the dispersion dynamics of polluting gases in an area, or the prediction of the newly affected population. From this perspective, the presence of polluting gases was evaluated on the university campus of Escuela Colombiana de Ingeniería Julio Garavito, located north of the city of Bogotá, and the affected population was estimated for the month of October, 2019, using the Kriging geostatistical technique. Method: This study is part of the design and construction of an auxiliary mobile station that monitors and reports complementary information (CO and SO2 gases) to that provided by the Guaymaral meteorological station, located in the north of Bogotá. This information is transmitted through an IoT network to a server, where a database is created which stores the information on polluting gases reported by the 14 stations of the Bogotá air quality monitoring network, the information sent by the auxiliary station, and the statistical information of the population present on the university campus. Pollutant gas data and population information recorded from October 1st to 31st, 2019, are the input for data analysis using the Kriging interpolation method and predicting the affected population on said campus. Results: There is a particulate matter concentration of 29 µg/m3 of PM10 in the coliseum and 12,6 µg/m3 of PM2,5 in building G, in addition to 9,8 ppb of O3 in building I, 14,9 ppb of NO2 in that same building, 0,79 ppb of CO in building C, and 0,65 ppb of SO2 also in building C, thus allowing to infer, according to the Bogotá air quality index, a favorable air quality for a population of 2.131 people who visited the campus university during the aforementioned period. Conclusions: The correct integration of the data in the web server and their analysis, carried out in the R language, allowed determining the approximate indicators of the polluting factors around Escuela Colombiana de Ingeniería Julio Garavito. Additionally, to determine the affected population, these indicators were correlated with the information on the registered population that entered the campus during the period under study. Based on the results obtained, it was concluded that the air quality on the campus of Escuela Colombiana de Ingeniería Julio Garavito is favorable, and that 2.131 people benefited daily from these conditions.