Length-weight relationship: eight species of Cyprinidae from river Panjkora, Khyber Pakhtunkhwa, Pakistan

Seven hundred and twenty four fish specimens were captured from March to September 2016. The materials used in the current study were cast nets, hand nets. Eight cyprinid fish species were studied for their length-weight relationships. Parameter b in the LWR was 3.03, 3.06, 3.02, 2.29, 2.82, 3.43, 2.73 and 2.47 for Schizothorax plagiostomus, Schizothorax esocinus, Racoma labiata, Tor putitora, Barilius vagra, Garra gotyla, Puntius ticto and Arassius auratus respectively. Current study is the first attempt on the LWRs of cyprinid species, provide a baseline approach for conservation and /management of local fish fauna of economic importance.

Developing a Cross-lingual Semantic Word Similarity Corpus for English–Urdu Language Pair

Semantic word similarity is a quantitative measure of how much two words are contextually similar. Evaluation of semantic word similarity models requires a benchmark corpus. However, despite the millions of speakers and the large digital text of the Urdu language on the Internet, there is a lack of benchmark corpus for the Cross-lingual Semantic Word Similarity task for the Urdu language. This article reports our efforts in developing such a corpus. The newly developed corpus is based on the SemEval-2017 task 2 English dataset, and it contains 1,945 cross-lingual English–Urdu word pairs. For each of these pairs of words, semantic similarity scores were assigned by 11 native Urdu speakers. In addition to corpus generation, this article also reports the evaluation results of a baseline approach, namely “Translation Plus Monolingual Analysis” for automated identification of semantic similarity between English–Urdu word pairs. The results showed that the path length similarity measure performs better for the Google and Bing translated words. The newly created corpus and evaluation results are freely available online for further research and development.

An Enhanced Approach for Sentiment Analysis Using Association Rule Mining

In today’s world social networking platforms like Facebook, YouTube, twitter etc. are a great source of communication for internet users and loaded with large number of emotions, views and opinions of the people. Sentiment analysis is the study of attitudes, emotions and opinions of the people and is also known as opinion mining. Sentiment analysis is used to find the opinion i.e. negative or positive about a particular subject. In this paper an Enhanced sentiment analysis approach is presented by using the Association rule mining i.e. Apriori and machine learning approach such as Support Vector Machine. The Enhanced approach is compared with the baseline approach, on accuracy, precision, recall, and F1-score measures. The Enhanced approach for sentiment analysis is implemented using the R programming language. The Enhanced approach shows better performance in comparison to the baseline approach. Keyword: Sentiment Analysis, Opinion Mining, Support Vector Machine, Association Rule Mining, Machine Learning

Robot Anticipation Learning System for Ball Catching

Catching flying objects is a challenging task in human–robot interaction. Traditional techniques predict the intersection position and time using the information obtained during the free-flying ball motion. A common pain point in these systems is the short ball flight time and uncertainties in the ball’s trajectory estimation. In this paper, we present the Robot Anticipation Learning System (RALS) that accounts for the information obtained from observation of the thrower’s hand motion before the ball is released. RALS takes extra time for the robot to start moving in the direction of the target before the opponent finishes throwing. To the best of our knowledge, this is the first robot control system for ball-catching with anticipation skills. Our results show that the information fused from both throwing and flying motions improves the ball-catching rate by up to 20% compared to the baseline approach, with the predictions relying only on the information acquired during the flight phase.

Indexing Baselines: Determining Physiological Stress in Rescued Orphaned Koala Joeys Under Rehabilitation by Comparing Faecal Cortisol Levels Between Healthy and Impaired Individuals

Koala (Phascolarctos cinereus) is an iconic folivorous marsupial native to the sclerophyll forests and woodlands of Australia. Due to the ever-changing habitat, this species is highly vulnerable to anthropogenic factors such as habitat loss and fragmentation, and this is reflected in the increasing number of injured and/or diseased koalas over the years. The majority of adult koalas admitted at wildlife hospitals are deceased, either due to natural causes, or have to be euthanized. Thus, orphaned koala joeys constitute a substantial number of wildlife rescues, and mortality is also prevalent in koala joeys being hand-reared/rehabilitated, with little knowledge about the causes of such high rates of mortality. Wildlife hospitals/rehabilitation centres are inherently stressful, and although the hypothalamic–pituitary–adrenal (HPA) axis plays a vital role in mediating the stress endocrine function (by producing glucocorticoids such as cortisol), there are no studies quantifying glucocorticoids in koala joeys. To contribute to this dearth of research, we sampled a total of seven individuals residing at Port Macquarie Koala Hospital and noted their clinical information. Faecal samples were collected from all seven koala joeys during routine cage cleaning. In total, 123 faecal samples were collected, processed and analysed for cortisol using enzyme-immunoassay (EIA). We used the iterative baseline approach to determine baseline and peak concentrations of FCM in koala joeys. Baseline concentrations ranged between 14.11 ng/g – 51.10 ng/g (healthy – sick), whereas, peak FCM concentrations ranged between 25.65 ng/g – 56.58 ng/g (healthy – sick). There was a significant difference (p < 0.05) between FCM concentrations of healthy and impaired individuals. Healthy individuals displayed relatively consistent FCM concentrations, whereas, diseased individuals displayed a significant increase in FCM concentrations over time. Our study provides the first record of baseline and peak FCM concentrations in rescued koala joeys with their associated clinical condition. Future studies can use the iterative baseline approach to determine FCM concentration in wild koala joeys that can serve as a baseline to compare glucocorticoid levels of rescued joeys.

Predicting the Potential Distribution of Non-Native Mammalian Species Sold in the South African Pet Trade

The pet trade is one of the main pathways of introduction of several mammals worldwide. In South Africa, non-native mammalian species are traded as pets, and so far, only four of these species are considered invasive. We used a list of 24 companion mammalian species compiled from a previous study. We selected a subset of 14 species for species distribution modeling (SDM) based on their trade popularity, invasion history and potential economic and socio-economic impacts. We aimed to estimate their potential distribution using their distribution records. Our SDM indicated that climate in South Africa was suitable for most traded species. However, commonly and easily available species had the broadest areas of suitable climates, such as house mice (Mus musculus) and Norwegian rats (Rattus norvegicus). In addition, the model with a human footprint suggested a high risk of invasion for Norwegian rats but less for house mice distribution. This assessment suggests the need of strict trade regulations and management strategies for pet mammals with broader suitability, which are already invasive, and most available for sale. In addition, our results provide a baseline approach that can be used to identify mammalian pet species with a potential risk of invasion so that urgent preventive measures can be implemented.

Constructing an Imaginary Wall in the Ocean: A Technical Cartography Study on the Delimitation of Maritime Boundaries in the Provinces of South Sumatra and Bangka Belitung Islands Based on GIS

Marine management areas in Indonesia can be claimed 12 nautical miles from the coastline by regional governments, according to Law 23 of 2014. However, in reality, there are many provinces whose distances to other provinces are fewer than 2 × 12 nautical miles, necessitating that they be delimited fairly and proportionally. The provinces of South Sumatra and Bangka Belitung Islands are such an example. The absence of clear boundaries drawn on the national map of Indonesia was the fundamental problem and focus of this study, owing to the need for delimitation of regional sea boundaries. The delimitation method used to obtain the median line was the equidistance principle using the basepoint to basepoint approach and baseline to baseline, within the consideration of the coastline proportion. Small islands are taken into account as a highly influencing factor and cause of the deviation from the pure median line. The median line results based on the basepoint to basepoint approach showed an area of the ocean as large as 7426.24:5973.41 km2, considering the coastline proportion. Meanwhile, with the baseline to baseline approach, resulting area was 7430.65:5956.13 km2 (South Sumatra:Bangka Belitung Islands). The equidistance principle is a comprehensive method for calculating the median line, as shown in this research.

Significant Permission Identification for Machine Learning Based Android Malware Detection

The dreadful rate of growth of malicious apps has become a significant issue that sets back the prosperous mobile scheme. A recent report indicates that a brand new malicious app for golem is introduced each ten seconds. To combat this serious malware campaign, we'd like a scalable malware detection approach that may effectively and expeditiously determine malware apps. varied malware detection tools are developed, together with system-level and network-level approaches. However, scaling the detection for an outsized bundle of apps remains a difficult task. during this paper, we tend to introduce SIGPID, a malware detection system supported permission usage analysis to address the speedy increase within the range of golem malware. rather than extracting and analyzing all golem permissions, we tend to develop 3-levels of pruning by mining the permission information to spot the foremost important permissions that may be effective in identifying between benign and malicious apps. SIGPID then utilizes machine-learning based mostly classification ways to classify totally different families of malware and benign apps. Our analysis finds that solely twenty two permissions square measure important. we tend to then compare the performance of our approach, victimisation solely twenty two permissions, against a baseline approach that analyzes all permissions. The results indicate that once Support Vector Machine (SVM) is employed because the classifier, we are able to bring home the bacon over ninetieth of preciseness, recall, accuracy, and F-measure, that square measure concerning constant as those created by the baseline approach whereas acquisition the analysis times that square measure four to thirty two times but those of victimisation all permissions. Compared against alternative progressive approaches, SIGPID is more practical by sleuthing ninety three.62% of malware within the information set, and 91.4% unknown/new malware samples. Keywords: SIGPID (Significant Permission Identification), SVM(Support Vector Machine), Android, Malware, Benign, Data pruning

Exploration of Support Structure Design for Additive Manufacturing at a Major OEM: a Case Study

The support structures required in many forms of additive manufacturing are often seen as waste that is tolerated as necessary. In metal additive processes, cost is frequently reduced by minimizing the amount of support structures needed to produce a part so that in turn, material use is decreased. However, there still exists the challenge of generating parts that are not deformed by the stresses created in the process. In this case study, support structures were leveraged to address deformation. A part was printed via direct metal laser melting with supports with a high grouping density in areas of high anticipated deformation in order to stiffen the part to prevent deformation. Then, they were printed again with a low grouping density to allow the part to relax and reduce stress. Combinations of support strategy and leaving supports on during post processing were used to investigate the effects of keeping or removing the supports in post-print operations such as surface treatment. The two optimized support strategies saw a lower deformation than the baseline approach to supports, and the releasing strategy was closest to the reference solid model with a 26% reduction in average deformation. The results suggest that the support structures in additively manufactured parts have a different impact on the part than the original intent of the supports to simply alleviate a process requirement. The support structures should be used to impact the final part geometry.

Cognitive throughput and working memory raw scores consistently differentiate resilient and vulnerable groups to sleep loss

Study Objectives Substantial individual differences exist in cognitive deficits due to sleep restriction (SR) and total sleep deprivation (TSD), with various methods used to define such neurobehavioral differences. We comprehensively compared numerous methods for defining cognitive throughput and working memory resiliency and vulnerability. Methods 41 adults participated in a 13-day experiment: 2 baseline, 5 SR, 4 recovery, and one 36h TSD night. The Digit Symbol Substitution Test (DSST) and Digit Span Test (DS) were administered every 2h. Three approaches (Raw Score [average SR performance], Change from Baseline [average SR minus average baseline performance], and Variance [intraindividual variance of SR performance]), and six thresholds (±1 standard deviation, and the best/worst performing 12.5%, 20%, 25%, 33%, 50%) classified Resilient/Vulnerable groups. Kendall’s tau-b correlations compared the group categorizations’ concordance within and between DSST number correct and DS total number correct. Bias-corrected and accelerated bootstrapped t-tests compared group performance. Results The approaches generally did not categorize the same participants into Resilient/Vulnerable groups within or between measures. The Resilient groups categorized by the Raw Score approach had significantly better DSST and DS performance across all thresholds on all study days, while the Resilient groups categorized by the Change from Baseline approach had significantly better DSST and DS performance for several thresholds on most study days. By contrast, the Variance approach showed no significant DSST and DS performance group differences. Conclusion Various approaches to define cognitive throughput and working memory resilience/vulnerability to sleep loss are not synonymous. The Raw Score approach can be reliably used to differentiate resilient and vulnerable groups using DSST and DS performance during sleep loss.