Previous studies suggest that when the residents of an area have lower educational attainment or income levels, residents are less likely to be exposed to the concepts of alternative stormwater management such as green infrastructures or have financial resources to install them. Therefore, fewer green infrastructures will be located in areas with higher poverty levels and lower educational attainment levels. However, previous studies failed to address that some socioeconomically disadvantaged areas are highly prone to hydrological disasters, such as hurricanes and flooding. Geographical specificity may illustrate very different research results than what scholars have previously studied. This study fills the gap in the literature by focusing on areas that desperately need these structures because of its low elevation and adjacent location to hurricane prone coastal areas. Within Houston, Texas the socioeconomically disadvantaged have more green infrastructures than those of higher educational and income levels do. We argue that the pattern of green infrastructure development in Houston is not so much due to the residents’ interest and concern for the environment, but it is more likely the result of past flood events. We will discuss our study results with the historical perspective as well as the federal and local government’s effort.

The controversial War on Drugs launched by Rodrigo Roa Duterte, 16th President of the Republic of the Philippines, has been the thrust of the Davao-based strongman’s campaign and arguably the centerpiece of his presidency. Despite noble intentions and ambitious attempts to wipe the Philippines clean of narcotics trafficking, this ‘war’ has become a polarizing topic from plenary hall debates down to street corner conversations. This study posed questions of whether the drug war would have positive or negative implications to peace and order, as well as gauging its impact to communities who were directly or indirectly affected by its implementation. It is the aim of this study to present statistical validation on the effectiveness of the implementation of the war on drugs of the President through the Philippine Drug Enforcement Agency (PDEA) as the lead government agency. Variables such as awareness, efficiency and transparency were among the key indicators assessed by the selected Barangay officials in Quezon City particularly those which have been tagged as seriously affected barangays by illegal drugs. Crime rates from the Philippine National Police (PNP) in the National Capital Region (NCR) from 2014-2018 were also assessed to determine if the drug war effectively lowered crime rates in the Metro. The city of Quezon was the primary locale utilized by the proponents of the study as the records of the Philippine Drug Enforcement Agency (PDEA) has identified this part of the Metropolis to be one of the of the most affected by illegal drugs. To gather pertinent data and partial and conclusive information, this geographic location provided then the most robust and rich information needed to come up with findings that may be necessary in determining the most effective way of curtailing the proliferation of illegal drugs in the Philippines. The triangulation approach was utilized to provide a multi- perspective and multi- sourced data rendering the research more reliable, verifiable and factual through questionnaire method, interview and data analysis.
Data revealed satisfactory results which is a clear validation that the communities covered in this study were properly informed on the procedural and legal aspects attendant to the implementation of the drug war. On the other hand, gathered data clearly manifested that certain programs that encourage individuals in the community to divulge knowledge on illegal drug activities were not effectively disseminated. Also, information on the litigation of drug personalities were not substantially published through the media. Moreover, it was also proven that there is no direct link between the implementation of the drug war and crime rates in the National Capital region, given the comparison of crime volume before and during the implementation of the war on drugs. This study vividly presents the strong and weak points of the war on drugs program of the current administration, hence, a powerful anchor towards the continuous improvement of its processes through proper implementation and timely evaluation.

Nowadays, the implementation of electronic data, such as electronic medical records systems (EMRS) in the hospital, is quite common. Information technology combines with Artificial Intelligence to deploy robots in the home, the office, the street, and the air, which can make our lives more comfortable and convenient. However, simultaneously, robots can be a threat to people’s life, property, and privacy. This means the interaction of information technology and robots in property and living things will become more common and more complicated. According to the rules of Civil Law, humans have rights and privileges to defend themselves against physical harms, and the invasion of privacy or property. Thus, this article examines the related debates and when, under R.O.C. law, humans may use force against robots, or may sue for damages to protect themselves, their privacy, and their property. As to suffering physical harm, for example, if the office robot rolls over your foot, can you (the injured) sue an injured (the office robot) in a claim for recovery? May you push the robot away to reduce the harm and contend self-defense? Secondly, when a service robot discloses one’s financial information or health information, can one sue the robot for the invasion of privacy? Thirdly, if a driverless car hits your house or a robo-advisor gives improper investment advice, can the homeowner or the investor sue for recovery? Alternatively, do robots have rights under civil law, such as a right not to be harmed? Some allege that it is “in fact only the proxy for rights of the robots’ owner.” In addition, the most significant issue is: what rules and criteria should we put into place to make the resolution of the above questions fairer to all concerned? This article addresses all the above related issues a provides solutions.

This study applied the modified Cournot duopoly model proposed by Buehler & Schmutzler (2008) and Milliou & Pavlou (2013) to analyze and compare the economic efficiency of separation model and vertical integration model. For the industry of downstream R&D, this model examines the effect of market size and product substitutability on the economy output. We establish the following results: (i) the benefits of downstream firm on vertical integration will increase when the product substitutability is lower. (ii) While the market size becomes bigger, the benefit is further enhanced this conclusion. Integration firm will promote investment in R&D to cause intimidation effect. (iii) When the product substitutability is higher to a certain degree, the benefits will also increase. Thus, highly degree of product homogeneity and highly degree of product heterogeneity are more suitable for vertical integration. In the long run, the industry which will extend or increase in demand suggests to merger as early as possible.

Lane detection algorithm is key to advanced driver assistance systems to reduce the number of traffic accidents caused by driver’s negligence. However, the lane feature extraction in most lane detection algorithms is prone to error in challenging conditions, such as high curvature, strong backlighting, low contrast night, and heavy rain, rendering unreliable detection. This work proposes a robust lane detection algorithm based on binary line segment filter for lane feature extraction. The algorithm combines the median local threshold and line segment detector to extract the lane features by binary line segment filter. After correct lane feature extraction, a Hough transform with sliding window and an optimized random sample consensus parabola fitting are applied to detect lanes. Experiments show that the proposed algorithm outperforms the previous work in achieving correct detection rate at 95% in real-time applications of challenging conditions. .

Aim of the research is to screening of the antagonistic bacteria from surface of the sea fan corals of Junceella juncea and Subergorgia suberosa (Pallas 1977) from Gulf of Mannar Coast, Southeast coast of India. Overall, 126 epibiotic bacteria were isolated and tested their antagonistic efficacy against six potent pathogens, such as Shigella dysentriae, Escherichia coli, Klebsiella pneumonia, Staphylococcus aureus, pseudomonas aerogenosa and Candida albicans. The results observed that a total of 20 bacterial isolates were found to be antimicrobial activity. The highest degree zone of inhibition and broad spectrum activity was noticed for the strain JJ109 against C. albicans and E.coli for 5mm and 4mm respectively. So, the molecular taxonomy of the strain JJ109 was done by 16 s r RNA sequences. After the 16 s rRNA sequencing, the phylogenetic construction was carried out and finally, concluded that name of the strain could be Kocuriae marina KMM 3905. Thus, the gorgonian associated or attached bacteria may have a vast array of new natural product compounds with novel bioactivities that can give new drugs against many pathogens. .

Hate speech is a mode of expression of words that attacks a person or a group because of their race, religion, ethnic origin, sexual orientation, and other attributes. Hate speech can be expressed in multiple ways, and it has been increasingly a grave issue since the start of social media. Furthermore, the popularity of cell phones has brought it to unimaginable heights. All major social networks are plagued by some users who like to promote hate speech. Companies like Facebook and Twitter have come under pressure to address this issue. There is still a long way before hate speech can be completely eradicated but it is important to make efforts on the matter. The aim of this research is to identify and integrate the unstructured data of the comments and posts on the platforms of social media that may spread hate speech. We will use a novel framework to effectively integrate the unstructured data with different tools of data sciences. This is important because we believe all social media should be held accountable if there is any hate speech taking place on their platforms. Even though Facebook practices censorship, in this research we still can identify those pages that spread hate speech. Starting from a small set of Facebook pages known to discuss controversial topics, we utilize Network Analysis Techniques to automatically discover other similar pages. Afterward, all recent posts, comments, and the collected pages are then analyzed with Sentiment and Emotion Analysis algorithms. Those posts with severe negativity in the comments are kept and preprocessed before being fed to a K-means clustering algorithm. The result is to clusters all posts into various controversial topics, and then they will be analyzed to pinpoint the offending pages. .

Due to non-performing loans (NPLs) trend to increase, debt collection, a process of pursuing payments of debts from a debtor, become very significant. However, there is still an inadequate number of researches that investigates the problem of debt collection. Therefore, a novel approach for identifying the most potential debtor contact channels is proposed. Our method is based on Naïve Bayes and a concept of the posterior probability. The effectiveness of this method was demonstrated with a result of approximately 75% accuracy and the results show that the accuracy of the Naïve Bayes is equal to that of decision tree while the precision of the Naïve bayes is higher than that of the decision tree. .