Plane extraction for indoor place recognition

In this paper, we present an image based plane extraction method well suited for real-time operations. Our approach exploits the assumption that the surrounding scene is mainly composed by planes disposed in known directions. Planes are detected from a single image exploiting a voting scheme that takes into account the vanishing lines. Then, candidate planes are validated and merged using a region grow- ing based approach to detect in real-time planes inside an unknown in- door environment. Using the related plane homographies is possible to remove the perspective distortion, enabling standard place recognition algorithms to work in an invariant point of view setup. Quantitative Ex- periments performed with real world images show the effectiveness of our approach compared with a very popular method

Water–Demand Management in the Kingdom of Saudi Arabia for Enhancement Environment

The purpose and the goal of the paper is growing substantially and that is being met through the available scarce and dwindling water resources. The kingdom of Saudi Arabia (KSA) faces an acute water shortage due to arid climate and absence of permanent lakes and rivers. Ever-increasing imbalances are usually met by increasing water supplies, whereas the concepts of water-demand management have not been given due importance and weight age. Meeting the rapidly rising demand with scarce and depleting resources remains the critical issue. The goal of this paper is showing; how Geographical Information Systems(GIS) can be used to support infrastructure  planners and analyst  on a local area. This paper places emphasizes on the urgency of adopting conservation and water-demand management initiatives to maintain demand supply relationship and achieve an acceptable balance between water needs and availability. The kingdom places emphasis on the shift from supply development to demand management to use of critical and non-renewable water resources efficiently. The paper suggests that the water-use-efficiency (WUE) in various sectors can be enhanced and improved in the kingdom. The paper presents an overview of the country’s water resources and issues related to water. Some possible conservation and remedial measures particularly in the agricultural sector-the largest and most inefficient user of water have been suggested. The objective of this paper is to safeguard and conserve this precious natural resource through environmental friendly technologies for the future generations to come. It is presumed that water resources can be managed on sustainable basis by devising and employing environmental friendly technologies including water conservation measures. The usefulness of these measures can be supplemented through the vibrant and viable extension and education initiatives and capacity building programs. In this work, three sets applications of GIS models have been produced. The geodatabase of district  areas  in Saudi Arabia including these layers of  Area, Subarea, Cites, water in land, water area, land cover, roads, rail roads, elevations. Keywords: Water Demand, Water Resources, GIS, Highway Street, XML Schema

An Assessment of Rewards and Motivation Strategies as Predictors of Employee Job Satisfaction in the Banking Industry in Kenya

This study sought to assess the prediction effect of reward and Motivation strategies on job satisfaction in the Commercial Banks in Kenya. The sample of the study comprised of 78 respondents being 28 managerial staff and 50 line employees drawn from Commercial Banks in Western Kenya. Data was collected by use of questionnaires and interview schedule. Statistically quantitative data was analyzed using descriptive as well as inferential statistics. Study findings revealed a statistically significant relationship between employee reward and job satisfaction and a significant relationship between employee motivation and job satisfaction. Findings of this study have provided vital and relevant information to stakeholders in the banking industry in Kenya and beyond on how reward and motivation strategies can be harnessed to bring about employee job satisfaction for improved organizational performance. The study has also stretched the frontiers of knowledge on the relationship between employee motivation and resultant occupational attitudes. Keywords: Rewards, Motivation, Job Satisfaction, Commercial Bank

Understanding the stumbling blocks of Italian higher education system:A process mining approach

Nowadays universities strive to continuously enhance their educational programs to improve both the quality and quantity of their graduates. This is a sensitive problem, especially for Italian universities where only 30% of the students enrolled at the university succeed in graduating within a year after the normal duration of the study plan. Over the last few years, the Italian Ministry of University and Education has introduced several indicators to assess students’ careers and help universities identify possible criticality in their study programs. However, these indicators only provide a high-level overview of the graduation process without providing insights into students’ failure. To address this issue, in this work, we propose to model a study program as a process and exploit process analysis techniques to assess students’ performance. These techniques allow delving into students’ careers, thus enabling the investigation of their failures and delays. The findings obtained by applying our approach to the Bachelor program of an Italian university allowed us to determine common bottlenecks that seem to have an impact on students’ graduation time. Moreover, we were able to determine and compare the career paths of successful and late students. The insights gathered by our analysis can be used to support university personnel in delving into factors causing some exams to be a bottleneck, as well as to determine potential improvements in the overall curricula.</p

Understanding the stumbling blocks of Italian higher education system:A process mining approach

Nowadays universities strive to continuously enhance their educational programs to improve both the quality and quantity of their graduates. This is a sensitive problem, especially for Italian universities where only 30% of the students enrolled at the university succeed in graduating within a year after the normal duration of the study plan. Over the last few years, the Italian Ministry of University and Education has introduced several indicators to assess students’ careers and help universities identify possible criticality in their study programs. However, these indicators only provide a high-level overview of the graduation process without providing insights into students’ failure. To address this issue, in this work, we propose to model a study program as a process and exploit process analysis techniques to assess students’ performance. These techniques allow delving into students’ careers, thus enabling the investigation of their failures and delays. The findings obtained by applying our approach to the Bachelor program of an Italian university allowed us to determine common bottlenecks that seem to have an impact on students’ graduation time. Moreover, we were able to determine and compare the career paths of successful and late students. The insights gathered by our analysis can be used to support university personnel in delving into factors causing some exams to be a bottleneck, as well as to determine potential improvements in the overall curricula.</p

Isolation of Methicillin-Resistant Coagulase-Negative Staphylococcus (MRCoNS) from a fecal-contaminated stream in the Shenandoah Valley of Virginia

Staphylococcus is comprised of 41 known species, of which 18 can colonize humans. Despite the prevalence of infectious Staphylococcus within hospital settings and agriculture, there are few reports of Staphylococcus in natural bodies of water. A recent study by the US Food and Drug Administration found substantial contamination of poultry and other meats with Staphylococcus. We hypothesized that intensive farming of poultry adjacent to streams would result in contaminated runoff, resulting in at least transient occurrence of Staphylococcus spp. in stream waters and sediments. In this study, we sought to determine whether Staphylococcus occurs and persists within Muddy Creek, a stream located in Hinton, Virginia that originates at the Appalachian Mountains of Virginia and runs through various agricultural fields and adjacent to a poultry processing plant in the central Shenandoah Valley. Five different Staphylococcus spp. were detected in water and sediment from Muddy Creek. Mannitol Salt Agar (MSA) was used to isolate eleven Staphylococcus from both water and sediment. These isolates were Gram-positive, catalase-positive, and oxidase-negative cocci that were capable of fermenting mannitol. In addition, a method for screening putative staphylococci species from stream water and sediment was developed. Ten out of the eleven tested isolates were oxacillin resistant (now used to identify phenotypic methicillin-resistance) using a Kirby Bauer disc diffusion test. Furthermore, the isolates were susceptible to trimethoprim/sulfamethoxazole, tetracycline, and gentamicin while two of the isolates were resistant to erythromycin. Additionally, the BOX-PCR repetitive sequence fingerprinting method verified the presence of nine different strains among the isolates. Sequencing of the 16S rRNA gene identified five of the isolates as Staphylococcus equorum. The Biolog identification protocol further identified the remaining isolates as Staphylococcus xylosus, Staphylococcus lentus, Staphylococcus succinus, and Staphylococcus sciuri. Finally, polymerase chain reaction amplification (PCR) confirmed that ten of the eleven isolates harbored the mecA gene known to confer methicillin-resistance. Overall, the occurrence of coagulase-negative staphylococci (MRCoNS) in stream water and sediment represents a potential environmental and human health concern

Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision Farming

An effective perception system is a fundamental component for farming robots, as it enables them to properly perceive the surrounding environment and to carry out targeted operations. The most recent approaches make use of state-of-the-art machine learning techniques to learn an effective model for the target task. However, those methods need a large amount of labelled data for training. A recent approach to deal with this issue is data augmentation through Generative Adversarial Networks (GANs), where entire synthetic scenes are added to the training data, thus enlarging and diversifying their informative content. In this work, we propose an alternative solution with respect to the common data augmentation techniques, applying it to the fundamental problem of crop/weed segmentation in precision farming. Starting from real images, we create semi-artificial samples by replacing the most relevant object classes (i.e., crop and weeds) with their synthesized counterparts. To do that, we employ a conditional GAN (cGAN), where the generative model is trained by conditioning the shape of the generated object. Moreover, in addition to RGB data, we take into account also near-infrared (NIR) information, generating four channel multi-spectral synthetic images. Quantitative experiments, carried out on three publicly available datasets, show that (i) our model is capable of generating realistic multi-spectral images of plants and (ii) the usage of such synthetic images in the training process improves the segmentation performance of state-of-the-art semantic segmentation Convolutional Networks.Comment: Submitted to Robotics and Autonomous System

Semantic Annotation of Classification Data for KDD Support Services

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Perbandingan Algoritma K-Nearest Neighbor dan Support Vector Machine Untuk Pemberian Rekomendasi Pemilihan Sekolah Lanjutan (Studi Kasus Siswa Kelas IX MTs Nurul Anwar)

Pendidikan merupakan bidang yang paling penting dalam perkembangan suatu bangsa. Dalam rangka mewujudkan tujuan dari pendidikan nasional secara optimal maka setiap siswa perlu menempuh jenjang pendidikan formal setidaknya sampai siswa menempuh Sekolah Lanjutan Tingkat Atas (SLTA) Sejalan dengan hal ini maka setamat SLTP setiap siswa kelas IX seharusnya melanjutkan pendidikan ke SLTA (SMK/SMA/MA/). Siswa kelas IX yang menempuh jenjang pendidikan SLTP sudah pasti akan dihadapkan dengan masalah memilih sekolah lanjutan, baik sekolah menengah umum maupun kejuruan. Memilih sekolah lanjutan menjadi faktor penting karena berkaitan dengan masa depan siswa. Salah satu pemodelan yang bisa digunakan untuk menentukan rekomendasi pemilihan sekolah lanjutan yaitu dengan Data Mining.Pemanfaatan teknik data mining diharapkan dapat membantu dalam Menentukan rekomondasi pemilihan sekolah lanjutan. Pada penelitian ini membandingkan teknik klasifikasi dari kinerja metode K-Nearst Neighbor dan Support VectorMachine.Atribut yang digunakan terdiri dari Nilai UNBK, Minat Siswa, dan Saran BK. Dengan menggunakan masing-masing data training dan data testing sebanyak 35 data. Hasil dari penelitian yang dilakukan, berdasarkan dari nilai akurasinya Support Vector Machine lebih tinggi yaitu 97,1% dibandingkan dengan K-Nearst Neighbor yaitu 85,7% .Hasil akhir dari penelitian ini adalah metode Support Vector Machine lebih baik digunakan dari pada metode K-Nearst Neighbor

488 Candidacy for heart transplantation in adult congenital heart disease patients: a single-centre, retrospective, cohort study

Abstract Aims End-stage heart failure (HF) is the leading cause of death in adult congenital heart disease (ACHD) population. Heart transplantation (HTx) improves prognosis in ACHD end-stage HF but candidacy evaluation, referral pattern, and correct listing timing are not fully elucidated in this population. To evaluate factors associated to refusal from Htx in ACHD patients with end-stage HF referred for HTx evaluation. Methods and results This retrospective cohort study enrolled consecutive ACHD patients considered for HTx in our institution between 2014 and 2020 and patients undergone HTx between 2000 and 2013. Refusal from HTx served as primary study endpoint. Between 2014 and 2020, 46 ACHD patients were evaluated for HTx, 14 ACHD patients underwent HTx between 2001 and 2013. The main indication to HTx in patients with single ventricle physiology was Fontan failure, while in patients with systemic left ventricle and systemic right ventricle physiology, it was systemic ventricular dysfunction. We compared clinical, anatomical and demographic data of 41 patients accepted for transplantation with 15 patients refused after screening. Risk factors for refusal were: coexistence of multiple high risk features [odds ratio (OR): 3.6; 95% confidence interval (CI): 1.1–12.9; P 0.048]; anatomical factors (OR: 14.5; 95% CI: 3.1–68.4; P 0.001), out-of-centre ACHD/HTx program referral (OR: 5.3; 95% CI: 1.5 to 19.0; p 0.01). Survival in patients accepted for HTx was significantly higher than survival in patients declined from HTx with landmark comparison at 20, 40 and 60 months of 87%, 78%, and 72% vs. 70%, 59%, and 20%, respectively. HTx refusal identifies a high risk ACHD patient subgroup (hazard ratio for overall mortality: 3.1; 95% CI: 1.1–8.3; P 0.02). Conclusions In our study risk factors for refusal from HTx are adverse anatomical features, coexistence of multiple conventional HTx high risk factors and out-of-centre referral. ACHD patients refused from HTx present shorter time to death. Efforts to increase HTx candidacy and to reduce referral delay in tertiary centre are strongly necessary for this growing population