Search

Perkembangan teknologi masa sekarang banyak berkembang di berbagai bidang termasuk bidang penyedia kos. Banyak situs – situs penyedia kos yang dapat memudahkan pengguna dari luar kota mencari kos tempat mereka belajar ataupun kerja. Tetapi banyak pengguna merasa kesusahan dalam mencari kos yang terjangkau dikarenakan berbagai hal. Situs penyedia kos yang diteliti adalah situs Mamikos.com dan Papikost.com. Penelitian dilakukan untuk mencari tingkat usability dalam kedua website tersebut. Untuk mengetahui tingkat usabilitynya, metode Webuse digunakan dalam penelitian ini yang memiliki 24 pertanyaan yang dibagi berdasarkan empat aspek yaitu Konten, Organisasi & Keterbacaan, Navigasi dan Link, Desain Tampilan Pengguna, dan Performa & Keefektifan. Penelitian ini juga menggunakan skala Likert dari 1 samapi 5 agar mendapatkan penilaian yang akurat. 24 pertanyaan tentang Mamikos.com dan Papikost.com disebarkan ke berbagai responden dan didapatkan hasil bahwa tingkat usability pada Mamikos.com lebih tinggi dibandingkan Papikost.com. Kata kunci: Mamikos.com; Papikost.com; Skala Likert; Usability; Webuse.

The circular economy encourages the production and consumption of sustainable embankment geomaterials and their blends utilizing recycled waste materials in roads, railway tracks, airfields, and underground structures. Geomaterials comprising high-plastic soft expansive clay pose excessive settlement during cyclic traffic/railway/airfield loading resulting in uneven geometry of overlying layers. This paper demonstrates multiobjective optimized improvement of expansive clay (C) geotechnical characteristics by cost-effective agro-wastes additives at microlevel (by 3% to 12% rice husk ash, i.e., RHA), nanolevel (by 0.6% to 1.5% rice husk derived green nano-SiO2, i.e., NS), and synergistic micro to nanolevel (NS-RHA). The swell potential, resilient modulus (MR), initial elastic modulus (Es), unconfined compressive strength (UCT), and California bearing ratio (CBR) of C and its blends were determined. The chemical characterization of C and its blends were conducted through Fourier transform infrared spectroscopy (FTIR) and optical microscopic tests. The outcome of this study depicted that the cost ratio for the optimized composite, i.e., (1.2% NS-9% RHA)/(9% RHA) is 1.22 whereas stiffness ratio MR (NS-RHA)/MR(RHA) and Es (NS-RHA)/Es(RHA) and strength ratio UCT(NS-RHA)/UCT(RHA) and CBR(NS-RHA)/CBR(RHA) were found to be 2.0, 1.64, 2.17, and 2.82, respectively. FTIR revealed the chemical compatibility between C, RHA, & NS from durability perspective. Cost-stiffness results of this study can be applied by geotechnical experts to economize the green stabilization of C by use of agro-waste for sustainable development.

Upon its recurrent nosocomial infections and that it is multidrug resistant pathogen, after its isolation from both patients and hospital environment and its macroscopic, microscopic and biochemical characterization Acinetobacter baumannii was detected using PCR technique, through the amplification of blaOXA51like gene, in addition to the detection of transposable elements coding genes. At the molecular detection 20 as a positive results of our study, the results of PCR reaction explain the presence of blaOXA51like gene in all isolates (98%) and these results for transposable element (TEM-1 beta-lactames, Tnp-R, Tnp-A26, Tnp-A), Tnp-R were positive result in (8) eight of A. baumannii isolate while another transpose Tnp-A26, Tnp-A, TEM-1 beta-lactames were negative results for all A. baumannii isolate. These eight positive isolates were transmitted from A. baumannii to A. pittii by fadH2 gene which proved as a receptor gene for transposon, which represented as a resistance gene to be confirmed by A. pittii sequencing. [ABSTRACT FROM AUTHOR]

Background and aim: The impacted tooth is defined as a condition where the tooth fails to erupt into the dental arch. It could be extracted with a procedure called odontectomy. The most common complication of this procedure is infection by aerobic as well as anaerobic bacteria, and antibiotics could treat this complication. This study aimed to identify the type of bacteria found on the wound of odontectomy patients and the antibiotics sensitivity pattern in RSUD Arifin Achmad Riau. Material and methods: Patient specimens after odontectomy were collected from wound swabs. All specimens were then cultured and identified for the type of bacteria, and an antibiotic sensitivity test was done by Kirby-Bauer method according to the Clinical and Laboratory Standard Institute (CLSI). Results: This study found that the most dominant population patient odontectomy is female (66.67%), 15-55 years old, graduated from senior high school (63.33%), the third molar as the affected tooth (86.67%), and located in the lower jaw (100%). Gram-positive bacteria (52%) followed by Alpha-hemolytic Streptococcus (40.74%), Staphylococcus aureus .22,22%) and Coagulase Negative Staphylococci (37.04%). Gram-negative bacteria (48%) followed by Klebsiella sp. (56%), Enterobacter sp. (32%), Pseudomonas sp. (8%), and Escherichia coli (4%). The antibiotics with the highest sensitivity were levofloxacin, meropenem, chloramphenicol, gentamicin, sulphamethoxazole, amikacin, ceftazidime, fosfomycin, and ciprofloxacin. All bacteria were resistant to ampicillin and metronidazole. Conclusions: Klebsiella sp and Alpha-hemolytic Streptococcus are the most dominant bacteria found in the wound of odontectomy patients. Antibiotics recommended for post-odontectomy wounds are Carbapenem and Quinolones.

Acute ischemic stroke (AIS) is a life-threatening complication of COVID-19. This study aims to compare anticoagulant or antiplatelet administration on mortality cases in patients with COVID-19 and AIS. To know the mortality rate in COVID-19 patients with AIS after anticoagulants or antiplatelets therapies. We searched PubMed, ScienceDirect, and Google Scholar for a retrospective cohort study of anticoagulant or antiplatelet effects on mortality cases in COVID-19 and AIS patients. The retrospective cohort was screened using our eligibility criteria, and quality was assessed using the Newcastle Ottawa Scale. Heterogeneity was assessed using the I2 test, and publication bias was evaluated using a funnel plot. All analyses were performed using Review Manager 5.4. Seven retrospective cohort studies involving 58 patients (38 of whom received anticoagulant therapy) met the inclusion criteria. Our combined analysis showed that anticoagulation versus antiplatelet therapy in COVID-19 patients with AIS on the forest plot chart did not significantly affect mortality (OR: 0.9 95% CI 0.42-1.91 I2=0 %). The study showed no significant difference in the incidence of death between anticoagulants or antiplatelet agents to COVID-19 patients with AIS.

A major concern related to the healthcare industry is uniformity in healthcare delivery. There is considerable variation in the diagnosis and treatment of patients depending on the experience and expertise of the doctors. Information technology can play a major role in addressing this issue. Research investigating the use of data-driven approaches and knowledge-driven clinical pathways to achieve uniformity in the delivery of healthcare is ongoing. Specifically, the integration of data and knowledge-driven approaches can be used to ensure uniformity in the delivery of patient care, thus avoiding inappropriate variance. The data-driven approach can utilize the bulk of medical data being generated. The knowledge-driven approach in the form of clinical pathways incorporates evidence-based care. In this context, knowledge and data-driven computerized physician order entry (CPOE) systems are gaining importance in healthcare delivery systems. This paper proposes a knowledge and data-driven framework for a CPOE system. This work is based on a knowledge base populated with disease quadruples, each of which comprises a list of symptoms, tests, results, and medications for a particular disease. The data used in the proposed system are obtained from two datasets, namely, the MIMIC (Medical Information Mart for Intensive Care) and the Disease-Symptom Knowledge Database, which is based on the operational data of New York-Presbyterian Hospital (NYPH). We combined both datasets using the common attribute of disease to generate data with more attributes to aid decision making. This was performed with the help of specialists and clinical knowledge. The resulting patient data are further integrated with clinical pathways before the extraction of disease quadruples. The novelty of this work lies in the extraction of disease quadruples from the integration of patient data with clinical pathways. The list dynamically ranks each element of the quadruple based on its association score to facilitate the generation of prescription order sets for the CPOE. The effectiveness of the proposed system in providing uniform patient care delivery has been validated by experts. The proposed system can significantly improve patient safety and the quality of healthcare delivery due to the integration of data-driven capability with clinical pathways.

This paper aims to develop an evolutionary deep learning based hybrid data driven approach for short term load forecasting (STLF) in the context of Bangladesh. With the lapse of time, the power system is getting complex. Penetration of intermittent renewable energy (RE) into the grid, changing prosumer load pattern with the need of demand side management (DSM) has thrown a challenge for dynamic power system operation and control. Load forecasting plays a significant role in this dynamic operation and control. In addition, it directly affects the future planning of network expansion, unit commitment and economic energy mix for power market. Day ahead short-term forecasting is very crucial for day to day operation. As such, various conventional and modified methods have been used over time for short-term prediction. Nevertheless, the existing approaches like age old statistical methods, artificial intelligence (AI), machine learning (ML), deep learning (DL) techniques alone cannot provide effective accuracy all the time. Hence, an integrated genetic algorithm (GA)-bidirectional gated recurrent unit (Bi-GRU) hybrid data driven technique (GA-BiGRU) is proposed in this work. The developed method is validated in Bangladesh power system (BPS) network by providing day ahead forecasting of electrical load of the whole country. Besides, the performance of the prediction model is compared with some existing approaches such as long short-term memory network (LSTM), gated recurrent unit (GRU) and integrated genetic algorithm-gated recurrent unit (GA-GRU) in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The outcome gives an indication of better forecasting accuracy of proposed GA-BiGRU evolutionary DL technique compared to others.

Introduction: Convolutional neural networks (CNNs) have maintained their dominance in deep learning methods for human action recognition (HAR) and other computer vision tasks. However, the need for a large amount of training data always restricts the performance of CNNs. Method: This paper is inspired by the two-stream network, where a CNN is deployed to train the network by using the spatial and temporal aspects of an activity, thus exploiting the strengths of both networks to achieve better accuracy. Contributions: Our contribution is twofold: first, we deploy an enhanced spatial stream, and it is demonstrated that models pre-trained on a larger dataset, when used in the spatial stream, yield good performance instead of training the entire model from scratch. Second, a dataset augmentation technique is presented to minimize overfitting of CNNs, where we increase the dataset size by performing various transformations on the images such as rotation and flipping, etc. Results: UCF101 is a standard benchmark dataset for action videos, and our architecture has been trained and validated on it. Compared with the other two-stream networks, our results outperformed them in terms of accuracy.

Tempat pemrosesan akhir (TPA) sampah secara open dumping dapat ditemukan di Pulau Nusa Lembongan, Provinsi Bali. Sampah yang ditimbun dengan proses open dumping cenderung menghasilkan dampak lingkungan, salah satu dampak tersebut adalah air lindi. Untuk mencegah dampak lingkungan maka diperlukan pengolahan lindi, salah satunya adalah dengan kolam stabilisasi. Tujuan dari studi ini adalah mengetahui luas lahan yang dibutuhkan dan prediksi effluent air limbah yang dikeluarkan dari perencanaan kolam stabilisasi. Perencanaan ini dilakukan dengan menggunakan data sekunder dan pengolahan data secara deskriptif dari kajian literatur. Perhitungan debit air limbah dari data intensitas hujan memperlihatkan debit air lindi dapat mencapai 4.579,2 liter⁄hari dengan kualitas air lindi yang cenderung biodegradable. Unit yang dibutuhkan untuk mencapai standar minimum kualitas lindi adalah bak ekualisi, kolam anaerobik, kolam fakultatif, kolam maturase, dan anaerobic baffle reactor (ABR). Total kebutuhan lahan dari unit tersebut adalah 0,013 ha. Sedangkan kualitas effluent berdasarkan parameter utama biochemical oxygen demand (BOD) adalah 9,8 mg/L, dimana baku mutu yang disyaratkan adalah 100 mg/L.

The aim of this study was to compare the effects of Half-Somersault and Brandt-Daroff exercises on dizziness, fear of fall, and quality of life in patients with posterior canal benign paroxysmal positional vertigo (PC-BPPV). This study was conducted from July 2020 to November 2020. A total of 20 patients were enrolled in the study, through sealed envelope method, and assigned to two groups, A and B (10 in each). Vestibular activity and participation measure and Fall Efficacy Scale (FES) were used. The mean age of the patients was 36.70±11.58 years. There was a significant (p=0.05) difference between the Vestibular activities and participation measure (VAP) score at post intervention and Fall Efficacy Scale (FES) (p

Anaerobic digestion of dairy cow manure (DCM) is constrained by a low methane production of animal manure. A method to overcome that is by co-digestion DCM and food industry by-product. This study investigated the process performance anaerobic co-digestion of DCM and tofu cake (TC) at different volatile solid (VS) mixing ratios. The treatments were partial substitutions of DCM with TC by 5%, 10%, and 15% (w/w) in reactors T2, T3, and T4 respectively, while T1 was served as control. Co-substrate of DCM and TC gave a positive effect (P < 0.05) on methane production by 24.23, 34.74, and 52.51% respectively for T2, T3, and T4 compared to the control reactor. Low total volatile fatty acids, ammonia nitrogen concentration, stable methane production and neutral pH values of all digested slurries indicate that TC is suitable to increase methane production of DCM up to a DCM/TC ratio of 2.92:1, in terms of VS.

Age-related macular degeneration (AMD) represents a leading cause of blindness worldwide. Neovascular AMD (nAMD) is a subtype of AMD most frequently treated with intravitreal anti-vascular endothelial growth factor (aVEGF) injections, which has allowed for patients to maintain vision that would have otherwise been lost. However, the need for frequent intravitreal injections for optimal results poses a risk for undertreatment in nAMD patients due to the high treatment burden associated with current aVEGF therapy. Many novel agents and pathways are being explored and targeted for less burdensome treatment options, one of which is the ranibizumab port delivery system (PDS). The PDS is a surgically implanted, refillable device that allows for the sustained release of ranibizumab, a widely used aVEGF agent, into the vitreous cavity. Positive results non-inferior to monthly ranibizumab injections in both phase II and phase III clinical trials allowed for FDA approval of the device with refill intervals of 6 months, which represents the longest approved treatment interval to date for nAMD therapy. This article reviews the need for a durable nAMD treatment option in real-world practice, the clinical trial and extension study data for the PDS, the risk of adverse events and safety profile of the PDS and the potential clinical role of the PDS in answering the real-world needs of nAMD treatment. In addition, other pipeline sustained-treatment modalities are discussed in the context of ongoing clinical trials.

In real-time electricity markets, locational marginal prices (LMPs) can be determined by solving multi-interval economic dispatch problems to manage inter-temporal constraints (i.e., ramp rates). Under the current practice, the LMPs for the immediate interval are binding, while the prices for the subsequent intervals are advisory signals. However, a generator may miss the opportunity for higher profits, and compensatory uplift payments are needed at the settlement. To address these issues, the “temporal locational marginal pricing (TLMP)” that augments LMP by incorporating multipliers associated with generators’ reported ramp rates was developed. It was demonstrated that it would result in zero uplift payments, showing great potential as a good pricing scheme. Numerical examples also showed that “the generators had incentives to reveal their ramp rates truthfully”. In this paper, the incentive compatibility of TLMP with respect to ramp-rate reporting is discussed. Our idea is to develop numerical examples to investigate whether reporting the true ramp rates is the best option for generators. The results indicate that TLMP is not incentive compatible, and there are market-clearing scenarios where not reporting true ramp rates may be beneficial.

Lignocellulosic biomass needs attention as an alternative energy source to overcome the adverse impacts of fossil fuels. Diversified Galiyat forests of Lower Himalaya may represent the potential source of lignocellulose degrading microbiota, particularly the lytic bacteria. Therefore, soil and wood samples were collected from different sites of Nathiagali and Thandiani of Galiyat forests. The soil samples collected were clayey, with a pH between 6.7 and 7.0, and with an organic matter of 2.8%–2.9% in Nathiagali and 2.1%–2.2% in Thandiani. The soils were enriched with more diversified cultivable bacteria (9 Log CFU/g) than the respective wood samples (7.4–8.6 Log CFU/g). Out of 90 bacteria, 22 isolates were efficient for cellulose degradation, 14 for xylanase activity, and 10 for laccase production. Cluster analysis showed that lignocellulolytic bacteria were grouped based on the sample medium (soil–wood) rather than the sampling site (Thandiani–Nathiagali). Efficient bacteria were also sequenced, and we found that cellulase production was prevalent in Pseudomonas spp. while laccase activity was diverse among taxonomically varied bacteria. Moreover, Stenotrophomonas sp. TS2B1 performed the best for corncob xylose degradation. Overall, the results suggest that Galiyat forests represent diverse lignocellulolytic microbial populations which should be further evaluated for applications in lignocellulosic waste management and for potential consequent fuel production.

The sudden outbreak of the novel Coronavirus infectious disease (COVID-19) resulted in significant challenges to global health systems. One of the primary challenges is rapid, reliable, and accurate detection of the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) virus among the suspected COVID-19-infected individuals. At present, quantitative real-time PCR (qRT-PCR) is a widely used diagnostic method. However, it requires expensive instruments and expertise in the interpretation of results. These constraints reflect the significant need for the development of alternative diagnostic options.This study will validate the use and efficiency of the reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) assay as a potential alternative for the detection of COVID-19. For this purpose, a cohort of 297 suspected COVID-19 patients was tested using both the RT-LAMP assay and the conventional RT-PCR method. For the RT-LAMP assay, three genes (orf-1ab, N, and S) were identified as the target sites for the detection of COVID-19. Based on a comparative assessment, 117 out of 124 positive COVID-19 cases were observed using the RT-LAMP technique with an overall 91.45% sensitivity. Interestingly, where a consensus on 163 individuals free of SARS-Cov-2 was observed, RT-LAMP specificity was 90%. Based on these findings, the robustness of the technique, and the reduced dependency on expensive instrumentation, RT-LAMP-based COVID-19 detection is strongly recommended as a potential alternative assay.

Objective: To assess the utility and validation of the Surgical Apgar Score (SAS) in predicting postoperative complications of hip fractures. Methods: This prospective observational study included patients who received operations for hip fractures from 1st March 2017 to 30th June 2018 at the Department of Orthopedic Surgery, Liaquat National Hospital and Medical College. Patients were followed at the outpatient department, and complications and mortality were recorded through phone calls. The predictability of SAS for postoperative complications was assessed. Results: SAS≤4 was found as a significant predictor for postoperative pulmonary (P=0.008) and cardiac complications (P=0.042) as well as blood transfusion required to optimize postoperative hemoglobin (P=0.03) in the patients with hip fractures. Conclusions: SAS provides reliable feedback information about patients' postoperative risk during the surgery. Hip fracture patients with scores≤4 should be monitored for major complications both during the hospital admission and after the discharge.

Infrastructure-less (sometimes known as ad-hoc) networking paradigm is very appealing and potentially shaping its future into almost all emerging networks (i.e., IoT, wireless sensor networks, vehicular ad-hoc networks, emergency, and tactical radio networks, etc.). However, when conventional networking protocols are used, such networks often perform poorly, mainly because of interference within the network and limited network throughput. Recent advancements in wireless communications have enabled full-duplex (FD) operation by suppressing self-interference, which can theoretically double the network throughput. However, conventional medium access control (MAC) protocols like carrier-sense multiple access with collision avoidance (CSMA/CA) favor half-duplex (HD) operation and fail to benefit from FD transmission opportunities. This article proposes a novel hybrid MAC protocol for full-duplex ad-hoc networks. The proposed MAC combines time division multiple access (TDMA) and IEEE 802.11 distributed coordination function (DCF) strengths in chains of time-slotted contention-based control frames and collision-free data frames. The aim is to fully utilize FD transmission opportunities to increase network throughput. The proposed protocol modifies request-to-send (RTS) and clear-to-send (CTS) frames in IEEE 802.11 DCF MAC to form FD-RTS/CTS frames. These frames are used to enable collision-free FD communications among neighbors. The proposed scheme mitigates conventional MAC issues like hidden-node problem (HNP) and exposed-node problem (ENP). It also allows concurrent FD data transmissions in a collision-free manner. The model is generic and can be applied to any ad-hoc wireless network. In this article, the design is applied to a single channel ad-hoc network with FD transceivers. We compared the proposed design with IEEE 802.11 CSMA/CA protocol with both HD and FD transceivers. The simulation results show a 30% gain in throughput, reduced latency, and fairness among participating wireless nodes.

A massive amount of medical data is available in healthcare industry, which can be utilized to extract useful knowledge. A Clinical Decision Support System (CDSS) is used to improve patient’s safety by minimizing medical errors. Heart disease is one of the major chronic maladies even in todays’ world. Many researchers have employed different data mining techniques to predict heart disease. The objective of proposed framework is to improve the accuracy of heart disease prediction. In this paper, an ensemble based voting scheme is proposed to efficiently predict heart disease. Four benchmark heart disease datasets from UCI repository have been utilized for experimentation and evaluation. The performance of the proposed ensemble is compared with individual classifiers as well as with five different ensemble schemes using various parameters in order to show the effectiveness of the proposed ensemble scheme. The evaluation of results shows that the proposed ensemble scheme has better average accuracy (83%) as compared to other ensemble schemes as well as individual classifiers.

Deep learning-based classification and detection algorithms have emerged as a powerful tool for vehicle detection in intelligent transportation systems. The limitations of the number of high-quality labeled training samples makes the single vehicle detection methods incapable of accomplishing acceptable accuracy in road vehicle detection. This paper presents detection and classification of vehicles on publicly available datasets by utilizing the YOLO-v5 architecture. This paper’s findings utilize the concept of transfer learning through fine tuning the weights of the pre-trained YOLO-v5 architecture. To employ the concept of transfer learning, extensive data sets of images and videos of the congested traffic patterns were collected by the authors. These datasets were made more comprehensive by pointing various attributes, for instance high- and low-density traffic patterns, occlusions, and different weather circumstances. All of these gathered datasets were manually annotated. Ultimately, the improved YOLO-v5 structure becomes accustomed to any difficult traffic patterns. By fine-tuning the pre-trained network through our datasets, our proposed YOLO-v5 has exceeded several other traditional vehicle detection methods in terms of detection accuracy and execution time. Detailed simulations performed on the PKU, COCO, and DAWN datasets demonstrate the effectiveness of the proposed method in various challenging situations.

This paper includes the behaviour of RC column, using the steel strength data employed by Rafi et al. (2014). Eccentric short columns are studied for this purpose, both tension and compression controlled sections, are analysed considering the current design practice of Pakistan. Three cross sections were analysed using different steel percentages against load-moment interaction and the strength analyses. Concrete strength is also varied in this analysis. The load moment interaction diagrams were observed in major and minor axes and strength analysis is made for compression controlled and tension controlled sections. In this analysis it is observed that a section designed as a tension controlled is giving brittle failure at certain limit which should be avoided. Considering this scenario, several random cross sections are analysed, strength reduction factors for eccentric and pure axial columns are computed. Conclusions are made on behalf of this analysis for different types of column design.

Mobile communication has become a dominant medium of communication over the past two decades. New technologies and competitors are emerging rapidly and churn prediction has become a great concern for telecom companies. A customer churn prediction model can provide the accurate identification of potential churners so that a retention solution may be provided to them. The proposed churn prediction model is a hybrid model that is based on a combination of clustering and classification algorithms using an ensemble. First, different clustering algorithms (i.e. K-means, K-medoids, X-means and random clustering) were evaluated individually on two churn prediction datasets. Then hybrid models were introduced by combining the clusters with seven different classification algorithms individually and then evaluations were performed using ensembles. The proposed research was evaluated on two different benchmark telecom data sets obtained from GitHub and Bigml platforms. The analysis of results indicated that the proposed model attained the highest prediction accuracy of 94.7% on the GitHub dataset and 92.43% on the Bigml dataset. State of the art comparison was also performed using the proposed model. The proposed model performed significantly better than state of the art churn prediction models.

Microalgal biomass provides a renewable source of biofuels and other green products. However, in order to realize economically viable microalgal biorefinery, strategic identification and utilization of suitable microalgal feedstock is fundamental. Here, a multi-step suboptimal screening strategy was used to target promising microalgae strains from selected freshwaters of the study area. The resulting strains were found to be affiliated to seven closely-related genera of the family Scenedesmaceae, as revealed by both morphologic and molecular characterization. Following initial screening under upper psychrophilic to optimum mesophilic (irregular temperature of 14.1 to 35.9°C) cultivation conditions, superior strains were chosen for further studies. Further cultivation of the selected strains under moderate to extreme mesophilic cultivation conditions (irregular temperature of 25.7 to 42.2°C), yielded up to 74.12 mgL-1day-1, 19.96 mgL-1day-1, 48.56%, 3.34 μg/mL and 1.20 μg/mL, for biomass productivity, lipid productivity, carbohydrate content, pigments content and carotenoids content respectively. These performances were deemed promising compared with some previous, optimum conditions-based reports. Interestingly, the fatty acids profile and the high carotenoids content of the studied strains revealed possible tolerance to the stress caused by the changing suboptimal cultivation conditions. Overall, strains AY1, CM6, LY2 and KL10 were exceptional and may present sustainable, promising feedstock for utilization in large-scale generation of green products, including biodiesel, bioethanol, pigments and dietary supplements. The findings of this study, which exposed promising, eurythermal strains, would expand the current knowledge on the search for promising microalgae strains capable of performing under the largely uncontrolled large-scale cultivation settings.

Feature selection is the process of identifying the most relevant features from the given data having a large feature space. Microarray datasets are comprised of high-quality features and very few samples of data. Feature selection is performed on such datasets to identify the optimal feature subset. The major goal of feature selection is to improve the accuracy by identifying a minimal feature subset. For this purpose, the proposed research focused on analyzing and identifying effective feature selection algorithms. A novel framework is proposed which utilizes different feature selection methods from filters, wrappers, and embedded algorithms. Furthermore, classification is then performed on selected features to classify the data using a support vector machine (SVM) classifier. Two publically available benchmark datasets are used, i.e., the Microarray dataset and the Cleveland Heart Disease dataset, for experimentation and analysis, and they are archived from the UCI data repository. The performance of SVM is analyzed using accuracy, sensitivity, specificity, and f-measure. The accuracy of 94.45% and 91% is achieved on each dataset, respectively.

Introduction: This study aimed to evaluate minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of lemon fruit (Citrus limon) extract in inhibiting Salmonella typhi growth in vitro. Methods: This research was categorized as a laboratory experimental study. Lemon fruit (Citrus limon) extract was prepared with concentration as follows: 100.000 ppm, 50.000 ppm, 25.000 ppm, 12.500 ppm, 6.250 ppm, 3.125 ppm, 1.562 ppm, 781 ppm, and 390 ppm. Dilution tests with Mueller-Hinton broth medium were performed to determine the MIC. After 24 hours of incubation, isolated Salmonella typhi inside the tube was inoculated back in MacConkey agar plate medium to determine the MBC. Replications were conducted 3 times according to Federer’s formula. Results: MIC of lemon fruit (Citrus limon) extract to Salmonella typhi was determined at 3.125 ppm. Meanwhile, MBC was determined at 6.250 ppm. Conclusion: This study showed the potential antimicrobial effect of lemon fruit (Citrus limon) extract against Salmonella typhi in-vitro. Further studies are still needed to determine its efficacy and safety in vivo and also its full antibacterial spectrum.