Co-combustion modeling of rice husk and plastic bag as energy source in Indonesia

This study was conducted to obtain a model combustion characteristics of rice husk and plastic bags as a energy source. The characteristics modeling using Autodesk Mechanical Desktop, Gambit, and Ansys - Fluent software. Maximum temperature of gas in the grate bed was obtained of about 1,710 K, in the furnace of about 1,670 K, and the average temperature in the furnace of about 1,086 K. The flue gas CO2, CO, and H2O in the furnace was obtained maximum of about 0.336% (3,360 ppm), 0.305% (3,050 ppm), and 0.132% (1,320 ppm), respectively. It was concluded that the co-combustion characteristics model of RH90 + PB10 produces temperature that meets the needs of a trap on the boiler and flue gas produces a small that is safe for the environment. Thus, it can be the basis for the development of utilization as fuel in the power plant

Efficient two-stage cryptography scheme for secure distributed data storage in cloud computing

Cloud computing environment requires secure access for data from the cloud server, small execution time, and low time complexity. Existing traditional cryptography algorithms are not suitable for cloud storage. In this paper, an efficient two-stage cryptography scheme is proposed to access and store data into cloud safely. It comprises both user authentication and encryption processes. First, a two-factor authentication scheme one-time password is proposed. It overcomes the weaknesses in the existing authentication schemes. The proposed authentication method does not require specific extra hardware or additional processing time to identity the user. Second, the plaintext is divided into two parts which are encrypted separately using a unique key for each. This division increases the security of the proposed scheme and in addition decreases the encryption time. The keys are generated using logistic chaos model theory. Chaos equation generates different values of keys which are very sensitive to initial condition and control parameter values entered by the user. This scheme achieves high-security level by introducing different security processes with different stages. The simulation results demonstrate that the proposed scheme reduces the size of the ciphertext and both encryption and decryption times than competing schemes without adding any complexity

Validation of an electrogoniometry system as a measure of knee kinematics during activities of daily living

Purpose: The increasing use of electrogoniometry (ELG) in clinical research requires the validation of different instrumentation. The purpose of this investigation was to examine the concurrent validity of an ELG system during activities of daily living. Methods: Ten asymptomatic participants gave informed consent to participate. A Biometrics SG150 electrogoniometer was directly compared to a 12 camera three dimensional motion analysis system during walking, stair ascent, stair descent, sit to stand, and stand to sit activities for the measurement of the right knee angle. Analysis of validity was undertaken by linear regression. Standard error of estimate (SEE), standardised SEE (SSEE), and Pearson’s correlation coefficient r were computed for paired trials between systems for each functional activity. Results: The 95% confidence interval of SEE was reasonable between systems across walking (LCI = 2.43 °; UCI = 2.91 °), stair ascent (LCI = 2.09 °; UCI = 2.42 °), stair descent (LCI = 1.79 °; UCI = 2.10 °), sit to stand (LCI = 1.22 °; UCI = 1.41 °), and stand to sit (LCI = 1.17 °; UCI = 1.34 °). Pearson’s correlation coefficient r across walking (LCI = 0.983; UCI = 0.990), stair ascent (LCI = 0.995; UCI = 0.997), stair descent (LCI = 0.995; UCI = 0.997), sit to stand (LCI = 0.998; UCI = 0.999), and stand to sit (LCI = 0.996; UCI = 0.997) was indicative of a strong linear relationship between systems. Conclusion: ELG is a valid method of measuring the knee angle during activities representative of daily living. The range is within that suggested to be acceptable for the clinical evaluation of patients with musculoskeletal conditions

IMPROVEMENT OF YIELD AND QUALITY OF ROSELLE (HIBISCUS SABDARIFFA L.) PLANT BY USING NATURAL SOURCES OF PHOSPHORUS AND POTASSIUM IN CALCAREOUS SANDY SOILS

Two separate field experiments were conducted during 2013 and 2014 to study the effects of natural sources of phosphorus and potassium fertilizers as rock phosphate (RP) and feldspar (F) with or without biofertilizers comparing with chemical fertilizer (PK) on growth, yield and quality characteristics of roselle (Hibiscus sabdariffa L.) plant. The first experiment included phosphorus treatments (at different levels of rock phosphate; 150, 200 and 250 kg/fed) and the second one included potassium treatments (at different levels of feldspar; 250, 350 and 450 kg/fed), each comparing with recommended dose of chemical PK. The obtained results revealed that co-inoculation of PDB (Bacillus megaterium var. phosphaticum) and KDB (Bacillus mucilaginosus) in conjunction with direct application of rock phosphate at rates of 200 and 250 kg/fed and feldspar at rates of 350 and 450 kg/fed respectively, into the soil significantly increased the growth characteristics under the study (plant height, number of branches/plant, fresh and dry weight/plant,) along with yield (number of fruits/plant, fresh weight of fruit/plant, fresh and dry weight of sepals/plant, weight of seeds g/plant and weight of dry sepals kg/fed) comparing to chemical PK and other treatments. The highest growth and yield were obtained from plants treated with 200 kg/fed rock phosphate plus PDB in the first experiment and 350 kg/fed feldspar plus KDB in the second experiment. While PK treatment resulted in the highest acidity % and total anthocyanin content of dry sepals as compared to all the other treatments in the first and second seasons. Generally, the results suggest that the use of biofertilizer with rock phosphate or with feldspar are economical, environmental friendly and have potential to improve roselle yield and quality

Influence of Melissa officinalis essential oil and its formulation on Typhlodromips swirskii and Neoseiulus barkeri (Acari: Phytoseiidae)

The toxicity of Melissa officinalis L. essential oil and its formulation (Melissacide) were evaluated against eggs and females of two predatory phytoseiid mites, Typhlodromips swirskii (Athias Henriot) and Neoseiulus barkeri (Hughes), using direct spray. Results indicate that both tested materials were potent on predatory females than egg stage. Typhlodromips swirskii was proved to be more sensitive to the oil and formulation than N. barkeri. Females mortality were (62-100%) in T. swirskii, and (46-69%) in N. barkeri, when both predatory mites were sprayed with LC50 and LC90 of the oil and Melissacide reported on Tetranychus urticae Koch. Females of both predators were suffered from reduction in food consumption when sprayed with two sublethal concentrations of Melissacide, while insignificant differences reported in daily number of eggs deposited by females of T. swirskii, when sprayed with its LC25 value of Melissacide and control

Automated server-side model for recognition of security vulnerabilities in scripting languages

With the increase of global accessibility of web applications, maintaining a reasonable security level for both user data and server resources has become an extremely challenging issue. Therefore, static code analysis systems can help web developers to reduce time and cost. In this paper, a new static analysis model is proposed. This model is designed to discover the security problems in scripting languages. The proposed model is implemented in a prototype SCAT, which is a static code analysis Tool. SCAT applies the phases of the proposed model to catch security vulnerabilities in PHP 5.3. Empirical results attest that the proposed prototype is feasible and is able to contribute to the security of real-world web applications. SCAT managed to detect 94% of security vulnerabilities found in the testing benchmarks; this clearly indicates that the proposed model is able to provide an effective solution to complicated web systems by offering benefits of securing private data for users and maintaining web application stability for web applications providers

QoS Categories Activeness-Aware Adaptive EDCA Algorithm for Dense IoT Networks

IEEE 802.11 networks have a great role to play in supporting and deploying of the Internet of Things (IoT). The realization of IoT depends on the ability of the network to handle a massive number of stations and transmissions, and to support Quality of Service (QoS). IEEE 802.11 networks enable the QoS by applying the Enhanced Distributed Channel Access (EDCA) with static parameters regardless of existing network capacity or which Access Category (AC) of QoS is already active. Our objective in this paper is to improve the efficiency of the uplink access in 802.11 networks; therefore we proposed an algorithm called QoS Categories Activeness-Aware Adaptive EDCA Algorithm (QCAAAE) which adapts Contention Window (CW) size, and Arbitration Inter-Frame Space Number (AIFSN) values depending on the number of associated Stations (STAs) and considering the presence of each AC. For different traffic scenarios, the simulation results confirm the outperformance of the proposed algorithm in terms of throughput (increased on average 23%) and retransmission attempts rate (decreased on average 47%) considering acceptable delay for sensitive delay services.Comment: 17 pages, 10 figure

Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair

A large body of the literature of automated program repair develops approaches where patches are generated to be validated against an oracle (e.g., a test suite). Because such an oracle can be imperfect, the generated patches, although validated by the oracle, may actually be incorrect. While the state of the art explore research directions that require dynamic information or rely on manually-crafted heuristics, we study the benefit of learning code representations to learn deep features that may encode the properties of patch correctness. Our work mainly investigates different representation learning approaches for code changes to derive embeddings that are amenable to similarity computations. We report on findings based on embeddings produced by pre-trained and re-trained neural networks. Experimental results demonstrate the potential of embeddings to empower learning algorithms in reasoning about patch correctness: a machine learning predictor with BERT transformer-based embeddings associated with logistic regression yielded an AUC value of about 0.8 in predicting patch correctness on a deduplicated dataset of 1000 labeled patches. Our study shows that learned representations can lead to reasonable performance when comparing against the state-of-the-art, PATCH-SIM, which relies on dynamic information. These representations may further be complementary to features that were carefully (manually) engineered in the literature