Mapping Time-Space Brickfield Development Dynamics in Peri-Urban Area of Dhaka, Bangladesh

Due to the high demand for cheap construction materials, clay-made brick manufacturing has become a thriving industry in Bangladesh, with manufacturing kilns heavily concentrated in the peripheries of larger cities and towns. These manufacturing sites, known as brickfields, operate using centuries-old technologies which expel dust, ash, black smoke and other pollutants into the atmosphere. This in turn impacts the air quality of cities and their surroundings and may also have broader impacts on health, the environment, and potentially contribute to global climate change. Using remotely sensed Landsat imagery, this study identifies brickfield locations and areal expansion between 1990 and 2015 in Dhaka, and employs spatial statistics methods including quadrat analysis and Ripley’s K-function to analyze the spatial variation of brickfield locations. Finally, using nearest neighbor distance as density functions, the distance between brickfield locations and six major geographical features (i.e., urban, rural settlement, wetland, river, highway, and local road) were estimated to investigate the threat posed by the presence of such polluting brickfields nearby urban, infrastructures and other natural areas. Results show significant expansion of brickfields both in number and clusters between 1990 and 2015 with brickfields increasing in number from 247 to 917 (total growth rate 271%) across the Dhaka urban center. The results also reveal that brickfield locations are spatially clustered: 78% of brickfields are located on major riverbanks and 40% of the total are located in ecologically sensitive wetlands surrounding Dhaka. Additionally, the average distance from the brick manufacturing plant to the nearest urban area decreased from 1500 m to 500 m over the study period. This research highlights the increasing threats to the environment, human health, and the sustainability of the megacity Dhaka from brickfield expansion in the immediate peripheral areas of its urban center. Findings and methods presented in this study can facilitate data-driven decision making by government officials and city planners to formulate strategies for improved brick production technologies and decreased environmental impacts for this urban region in Bangladesh

Thwarting ICMP low-rate attacks against firewalls while minimizing legitimate traffic loss

© 2013 IEEE. Low-rate distributed denial of service (LDDoS) attacks pose more challenging threats that disrupt network security devices and services. Such type of attacks is difficult to detect and mitigate. In LDDoS attacks, attacker uses low-volume of malicious traffic that looks alike legitimate traffic. Thus, it can enter the network in silence without any notice. However, it may have severe effect on disrupting network services, depleting system resources, and degrading network speed to a point considering them as one of the most damaging attack types. There are many types of LDDoS such as application server and ICMP error messages based LDDoS. This paper is solely concerned with the ICMP error messages based LDDoS. The paper proposes a mechanism to mitigate low-rate ICMP error message attacks targeting security devices, such as firewalls. The mechanism is based on triggering a rejection rule to defend against corresponding detected attack as early as possible, in order to preserve firewall resources. The rejection rule has certain adaptive activity time, during which the rule continues to reject related low-rate attack packets. This activity time is dynamically predicted for the next rule activation period according to current and previous attack severity and statistical parameters. However, the rule activity time needs to be stabilized in a manner in order to prevent any additional overhead to the system as well as to prevent incremental loss of corresponding legitimate packets. Experimental results demonstrate that the proposed mechanism can efficiently defend against incremental evasion cycle of low-rate attacks, and monitor rejection rule activity duration to minimize legitimate traffic loss

Social media bot detection with deep learning methods: a systematic review

Social bots are automated social media accounts governed by software and controlled by humans at the backend. Some bots have good purposes, such as automatically posting information about news and even to provide help during emergencies. Nevertheless, bots have also been used for malicious purposes, such as for posting fake news or rumour spreading or manipulating political campaigns. There are existing mechanisms that allow for detection and removal of malicious bots automatically. However, the bot landscape changes as the bot creators use more sophisticated methods to avoid being detected. Therefore, new mechanisms for discerning between legitimate and bot accounts are much needed. Over the past few years, a few review studies contributed to the social media bot detection research by presenting a comprehensive survey on various detection methods including cutting-edge solutions like machine learning (ML)/deep learning (DL) techniques. This paper, to the best of our knowledge, is the first one to only highlight the DL techniques and compare the motivation/effectiveness of these techniques among themselves and over other methods, especially the traditional ML ones. We present here a refined taxonomy of the features used in DL studies and details about the associated pre-processing strategies required to make suitable training data for a DL model. We summarize the gaps addressed by the review papers that mentioned about DL/ML studies to provide future directions in this field. Overall, DL techniques turn out to be computation and time efficient techniques for social bot detection with better or compatible performance as traditional ML techniques

Phytochemical, Nutritional and Pharmacological Potentialities of Amaranthus spinosus Linn. : A review

Amaranthus spinosus has long been cultivated in tropical and subtropical areas of the world, especially in South Asia. It is well accepted by the people for its nutritional, pharmacological, phytochemical, and therapeutic functions in the human body. Tender stems, leaves, shoots, grains and sometimes the whole part of A. spinosus are eaten by humans or fed to farm animals, which contain carbohydrates, proteins, fats, fibers, vitamins, minerals and many other phytochemicals. This review aims to represent the nutritional and pharmacological activities of A. spinosus. To have a better understanding, we have discussed the nutritional status of A. spinosus, its available phytochemicals and their functional properties. Further, we demonstrated the potentiality of A. spinosus in various disease condition by discussing its functional activities, which includes antioxidant, antidiabetic, immuno-modulatory, hematological, gastrointestinal, anti-inflammatory, diuretic, antimicrobial, antimalarial, anti-ulcer, antipyretic, and antigenic activity. The availability of various important phytochemicals along with their functional properties make Amaranthus spinosus valuable for pharmaceuticals and nutraceuticals industry

Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal

In this paper, we propose a novel contrastive learning based deep learning framework for patient similarity search using physiological signals. We use a contrastive learning based approach to learn similar embeddings of patients with similar physiological signal data. We also introduce a number of neighbor selection algorithms to determine the patients with the highest similarity on the generated embeddings. To validate the effectiveness of our framework for measuring patient similarity, we select the detection of Atrial Fibrillation (AF) through photoplethysmography (PPG) signals obtained from smartwatch devices as our case study. We present extensive experimentation of our framework on a dataset of over 170 individuals and compare the performance of our framework with other baseline methods on this dataset.Comment: 10 pages, 4 figures, Preprint submitted to Journal of Computers in Biology and Medicin

Assessment of heavy metals concentration in water and Tengra fish (Mystus vittatus) of Surma River in Sylhet region of Bangladesh

The study was carried out to assess the concentration of heavy metals in water and Tengra fish (Mystus vittatus) of the Surma River, the largest water basin ecosystem covering the north-eastern parts of Bangladesh. Water and Tengra fish (M. vittatus) samples were collected from a total of six sampling stations in which three sampling stations were in Sylhet district and the rest three were in Sunamganj district. Samples were collected from February 2017 to June 2017 on a monthly basis. Water and Tengra fish (M. vittatus) samples were analyzed for the detection of heavy metals viz., lead (Pb), chromium (Cr) and cadmium (Cd) concentrations. Atomic absorption spectrophotometry was used for the detection of heavy metals after digestion of the samples. Pb and Cr were detected from both water and Tengra fish (M. vittatus) samples collected from all the six sampling stations of Sylhet and Sunamganj district. But, Cd was not found both in water and Tengra fish (M. vittatus) during the study period. This study concluded that the detected concentrations of metals (Pb and Cr) in the studied Tengra fish (M. vittatus) muscles were accepted by the international legislation limits and are safe for human consumption. But in water, Pb is the only metal that potentially poses the ecological risk to the water body as it exceeds the acceptance level recommended by World Health Organization (WHO). Consequently, close monitoring of metals pollution of the Surma River is recommended with a view to minimizing the health risk of the population that depend on the river for their water and fish supply

Quality assessment and shelf-life of processed tilapia (Oreochromis niloticus) fish sticks: Laboratory based study

Customers prefer tilapia (Oreochromis niloticus), one of the most popular freshwater fish species farmed in Bangladesh, because of its flavor and affordable market pricing. This study aimed to develop value-added tilapia fish sticks and evaluate the quality changes, shelf life, and storage stability of the developed tilapia fish sticks in order to investigate the possibilities of better utilizing low-value tilapia fish and to satisfy consumers' growing demand for quality ready-to-eat food products. For this regard, storage characteristics in room (28ºC) and refrigerator (5ºC) temperatures were assessed in terms of microbiological, chemical, proximate, and sensory attributes. The moisture, lipid, protein, and ash contents of the fish sticks were observed to be 56.23±0.62, 7.62±0.27, 26.01±0.39, and 2.93±0.23%, respectively, at fresh condition. As storage time increased, it was discovered that ash content at room temperature increased while moisture, lipid, and protein levels steadily declined. On the other hand, it was discovered that at refrigeration temperatures, ash and fat content increased while moisture and protein content decreased. Compared to fish sticks held at ambient temperature, changes in the proximate composition of fish sticks stored in a refrigerator were found to be more stable. TVB-N was initially measured as 12.38±0.45 mg/100 g. After 24 hours of room storage, the TVB-N value exceeded the acceptable level; however, after 72 hours of refrigeration, it did not exceed the acceptable limit and was deemed fit for consumption. TPC was observed in fresh fish sticks as 3.74±0.31 Log CFU/g. In 48 hours at room temperature, the bacterial load of tilapia fish sticks increased sharply (p<0.05) during the course of the storage period and went above the microbiological threshold for fishery products (7 Log CFU/g of flesh). The bacterial growth trend was slower and, after 72 hours, was within the permitted limit at refrigerated storage temperature. All fresh products had the highest initial sensory ratings. At ambient temperature, all of the products sensory qualities significantly declined with time (p<0.05), however at refrigeration temperature, the product was determined to be more stable. The overall acceptability score assessed for appearance, flavor, taste, and texture was within acceptable limits for up to 24 hours at room temperature, but not for 72 hours at refrigeration temperature. According to the study's findings, tilapia fish sticks have a very limited shelf life at room temperature (28°C), only lasting around 24 hours, whereas they can last up to 72 hours at 5°C in the refrigerator

BayesBeat: A Bayesian Deep Learning Approach for Atrial Fibrillation Detection from Noisy Photoplethysmography Data

The increasing popularity of smartwatches as affordable and longitudinal monitoring devices enables us to capture photoplethysmography (PPG) sensor data for detecting Atrial Fibrillation (AF) in real-time. A significant challenge in AF detection from PPG signals comes from the inherent noise in the smartwatch PPG signals. In this paper, we propose a novel deep learning based approach, BayesBeat that leverages the power of Bayesian deep learning to accurately infer AF risks from noisy PPG signals, and at the same time provide the uncertainty estimate of the prediction. Bayesbeat is efficient, robust, flexible, and highly scalable which makes it particularly suitable for deployment in commercially available wearable devices. Extensive experiments on a recently published large dataset reveal that our proposed method BayesBeat substantially outperforms the existing state-of-the-art methods.Comment: 8 pages, 5 figure

Multidrug Resistance in Cancer: Understanding Molecular Mechanisms, Immunoprevention and Therapeutic Approaches

Cancer is one of the leading causes of death worldwide. Several treatments are available for cancer treatment, but many treatment methods are ineffective against multidrug-resistant cancer. Multidrug resistance (MDR) represents a major obstacle to effective therapeutic interventions against cancer. This review describes the known MDR mechanisms in cancer cells and discusses ongoing laboratory approaches and novel therapeutic strategies that aim to inhibit, circumvent, or reverse MDR development in various cancer types. In this review, we discuss both intrinsic and acquired drug resistance, in addition to highlighting hypoxia- and autophagy-mediated drug resistance mechanisms. Several factors, including individual genetic differences, such as mutations, altered epigenetics, enhanced drug efflux, cell death inhibition, and various other molecular and cellular mechanisms, are responsible for the development of resistance against anticancer agents. Drug resistance can also depend on cellular autophagic and hypoxic status. The expression of drug-resistant genes and the regulatory mechanisms that determine drug resistance are also discussed. Methods to circumvent MDR, including immunoprevention, the use of microparticles and nanomedicine might result in better strategies for fighting cancer