Value-Based Caching in Information-Centric Wireless Body Area Networks.

We propose a resilient cache replacement approach based on a Value of sensed Information (VoI) policy. To resolve and fetch content when the origin is not available due to isolated in-network nodes (fragmentation) and harsh operational conditions, we exploit a content caching approach. Our approach depends on four functional parameters in sensory Wireless Body Area Networks (WBANs). These four parameters are: age of data based on periodic request, popularity of on-demand requests, communication interference cost, and the duration for which the sensor node is required to operate in active mode to capture the sensed readings. These parameters are considered together to assign a value to the cached data to retain the most valuable information in the cache for prolonged time periods. The higher the value, the longer the duration for which the data will be retained in the cache. This caching strategy provides significant availability for most valuable and difficult to retrieve data in the WBANs. Extensive simulations are performed to compare the proposed scheme against other significant caching schemes in the literature while varying critical aspects in WBANs (e.g., data popularity, cache size, publisher load, connectivity-degree, and severe probabilities of node failures). These simulation results indicate that the proposed VoI-based approach is a valid tool for the retrieval of cached content in disruptive and challenging scenarios, such as the one experienced in WBANs, since it allows the retrieval of content for a long period even while experiencing severe in-network node failures

Crystallization Mechanism and Thermal Stability of Se98 In2-χ Snχ (χ=0.0.5,1,1.5) Semiconducting Glasses

Results of Differential Scanning Calorimeter (DSC) under non isothermal condition on Se98 In2-χ Snχ (χ=0.0.5,1,1.5) chalcogenide glasses have been reported and discussed. ln the glassy region, the dependence of the glass transition temperature Tg on the heating rate or obey a power law, Tg=To{ɑ}ʏ ,and the glass transition activation energy decreases with the addition of Sn. The crystal growth kinetics has been investigated using Kissinger, Gao et.al, and Ozawa equations. Results indicate that the crystallization activation energy decreases and the crystallization ability is retarded, due to the formation of cross-linked structure, with the addition of Sn. Besides to that the crystal growth is found to occur in 2-dimensions. Investigation of thermal stability through the calculations of the temperature difference TC-Tg, S-parameter, Hruby number, crystallization rate factor and the enthalpy released during the crystallization process, indicates that Se98 In0.5 Sn1.5 glass is thermally most stable in the composition range of stud

Thermal Conductivity, Thermal Diffusivity and Specific Heat Of Se98 In2-χSnχ (χ=0,0.5,1,1.5) Semiconducting Glasses

Measurements of thermal conductivity (λ) and thermal diffusivity (χ) of Se98 In2-χSnχ(χ=0,0.5,1,1.5) semiconducting glasses have been presented in this paper. The measured values of both λ and χ have been used to determine the specific heat per unit volume (pcp) of these glasses and in the Composition range of investigation. Both λ and χ are found to increase systematically with the addition of Sn. This compositional dependence behavior of λ and χ is attributed to the replacements of the original structural units by Se-Sn units. These new structural units increase the cohesive energy of the system and account for the observed increase in λ and χ. The type of bond, iono-covalent, which Sn makes with Se as it is incorporated in Se-In-Sn glass, is in support of our results

Food and feeding habit of chapila (Gudusia chapra)

Abundance of diatom (Bacillariophyceae) in the plankton population and the dietary role of it in chapila (Gudusia chapra) in pond were studied. A total of 25 genera of phytoplankton belonging to Bacillariophyceae (7), Chlorophyceae (11), Cyanophyceae (5) and Euglenophyceae (2) and 9 genera of zooplankton belonging to Crustacea (3) and Rotifera (6) were recorded from the water. Among the phytoplankton, highest abundance of Chlorophyceae was observed, and Bacillariophyceae, Cyanophyceae and Euglenophyceae ranked the second, third and fourth position in the planktonic population, respectively. Among the zooplankton, Rotifera was recorded as the most dominant group and Crustacea as the least one. From the gut content analysis, 4 groups of phytoplankton consisting of 33 genera of plankton were identified and recoded [sic] of which 25 belonging to phytoplankton and 8 belonging to zooplankton. This study reveals that the Chlorophyceae and Cyanophyceae were the most dominant food items of chapila. Bacillariophyceae (diatom) and Euglenophyceae were less important and Crustacea and Rotifera were the least important in the diet of Chapila. The present investigation showed that chapila appeared to be a plankton feeder with a preference for phytoplankton to zooplankton. Electivity analysis showed that the fish avoided zooplankton and strongly selected phytoplankton. In the gut contents of fish, Chlorophyceae was positively and Bacillariophyceae (diatom) was negatively selected throughout the experimental period, in the pond water

Comparative economic analysis of pond fish production in Mymensingh and Jessore Districts, Bangladesh

The study was conducted to determine the cost, return and relative profitability of pond fish production of Mymensingh and Jessore districts. A total of 75 ponds were selected on the basis of purposive random sampling technique from 7 villages under 2 Upazila (Trishal and Gouripur) of Mymensingh districts and 8 villages under 4 Upazila (Monimmpur, Jhikorgacha, Chowgacha and Sadar) of Jessore district. It was found that per hectare per year gross cost of pond fish production in Mymensingh and Jessore were Tk 333457.75 and Tk 54327.74, while gross return were Tk 434131.16 and Tk. 96640.00 and net return were Tk 100673.41 and Tk. 42312.26, respectively. The findings of this study revealed that the pond fish production in Jessore district was more profitable than that of Mymensingh district. Cobb-Douglas production function was applied to realize the specific effect of the factors on pond fish production. Out of six variables included in the function three variables had positive impact on return from pond fish production, in Mymensingh district but five variables had positive impact on return from pond fish production in Jessore distric

Enhancement Punching Shear in Flat Slab Using Mortar Infiltrated Fiber Concrete

In this paper, improving the punching shear of slab column connection using mortar infiltrated fiber concrete is studied. Eight specimens of reinforced concrete slabs identical in dimension and reinforcement were tested, six of them were casting with hybrid concrete (normal strength concrete and mortar infiltrated fiber concrete) and two specimens were cast with normal strength concrete as control specimens. All specimens were tested under vertical loading. The mortar infiltrated fiber concrete was cast monolithically with the normal strength concrete at different thickness at one and a half times of the effective depth (1.5d) at the center of the slab, once at all the thickness of cross section of the slab and the others at half thickness either tension or compression face of the slabs all cases cast with two types of fiber. The vertical load was applied upward through a square column with a dimension of (100 mm). In all slabs, no failure in mortar infiltrated fiber concrete was observed. The test results showed that the use of mortar infiltrated fiber concrete improves the punching shear strength for some cases according to the type of fibers and the location of casting mortar infiltrated fiber concrete in slabs. The enhancement in punching shear strength due to using mortar infiltrated fiber concrete at 1.5d square shape (265 mm) ranged from 4% to 46% compared with the control specimens

BaitBuster-Bangla: A Comprehensive Dataset for Clickbait Detection in Bangla with Multi-Feature and Multi-Modal Analysis

This study presents a large multi-modal Bangla YouTube clickbait dataset consisting of 253,070 data points collected through an automated process using the YouTube API and Python web automation frameworks. The dataset contains 18 diverse features categorized into metadata, primary content, engagement statistics, and labels for individual videos from 58 Bangla YouTube channels. A rigorous preprocessing step has been applied to denoise, deduplicate, and remove bias from the features, ensuring unbiased and reliable analysis. As the largest and most robust clickbait corpus in Bangla to date, this dataset provides significant value for natural language processing and data science researchers seeking to advance modeling of clickbait phenomena in low-resource languages. Its multi-modal nature allows for comprehensive analyses of clickbait across content, user interactions, and linguistic dimensions to develop more sophisticated detection methods with cross-linguistic applications

Identifying Crisis Response Communities in Online Social Networks for Compound Disasters: The Case of Hurricane Laura and Covid-19

Online social networks allow different agencies and the public to interact and share the underlying risks and protective actions during major disasters. This study revealed such crisis communication patterns during hurricane Laura compounded by the COVID-19 pandemic. Laura was one of the strongest (Category 4) hurricanes on record to make landfall in Cameron, Louisiana. Using the Application Programming Interface (API), this study utilizes large-scale social media data obtained from Twitter through the recently released academic track that provides complete and unbiased observations. The data captured publicly available tweets shared by active Twitter users from the vulnerable areas threatened by Laura. Online social networks were based on user influence feature ( mentions or tags) that allows notifying other users while posting a tweet. Using network science theories and advanced community detection algorithms, the study split these networks into twenty-one components of various sizes, the largest of which contained eight well-defined communities. Several natural language processing techniques (i.e., word clouds, bigrams, topic modeling) were applied to the tweets shared by the users in these communities to observe their risk-taking or risk-averse behavior during a major compounding crisis. Social media accounts of local news media, radio, universities, and popular sports pages were among those who involved heavily and interacted closely with local residents. In contrast, emergency management and planning units in the area engaged less with the public. The findings of this study provide novel insights into the design of efficient social media communication guidelines to respond better in future disasters