Miracle-dz/UniFAR-FTUs huggingface dataset:
Cellular Automata Model for Mixed Traffic Flow with Lane Changing Behavior

Indian cities are seen with predominantly mixed traffic plying on the streets. Modeling the mixed traffic involving vehicles characterised of different speed, length, and width is a challenging issue. Based on the finer cell system of cellular automata (CA) models, this paper proposes to evaluate the mixed traffic behavior with cars and motorcycles for intermediate lane width, which is more common in Indian cities. The maximum car flow is observed (even with the presence of motorcycles) in the results which is higher than the Na-Sch model for cars. This increase is mainly due to the changing behavior. The car flow decreases as the density of the motorcycle increases. Furthermore, the paper proposes to evaluate the effect of lane change behavior on the speed and flow of the traffic stream using the fundamental diagrams of speed flow density curves. The simulation result suggests that lane change probability has little effect on the speed and flow of the traffic stream.
Relationships among carbon isotope composition, growth, and foliar nitrogen in soybean

ABSTRACT Interactions between carbon isotope composition (δ13C), foliar nitrogen concentration (foliar N), and biomass accumulation (growth) merit further investigation in soybean (Glycine max (L.) Merr. ) and other species as these metrics may be valuable for assessing moisture stress and for screening of drought-resistant varieties. To this end, we examined the response of six soybean genotypes to water-deficit stress in a greenhouse study. Two treatments were imposed: low soil moisture (5–10% volumetric water content) and high soil moisture (30–38% volumetric water content). Above-ground biomass accumulation, foliar N, and δ13C were measured at the end of the experiment. Leaf water potential and midday gas exchange (net assimilation, stomatal conductance, and transpiration) were measured multiple times throughout the experiment. All measurements were affected by water-deficit stress. Significant, but weak, positive relationships were found between δ13C and biomass accumulation in both soil moisture treatments. Foliar N was significantly, but weakly, correlated to growth in the high soil-moisture treatment, but not in the low soil-moisture treatment. The data suggest that selection for genotypes with higher δ13C values could result in improved biomass accumulation. The relationship between foliar N and δ13C was negative under high soil-moisture conditions and positive under low soil-moisture conditions. The relationships between δ13C and foliar N could be a highly informative metric to help understand the effects of water-deficit stress and may further indicate whether water or nitrogen acquisition is limiting in a specific environment, which should help in breeding improved soybean cultivars.
Does debt market integration amplify the international transmission of business cycles during financial crises?

The international transmission of real business cycle during financial crisis can differ dramatically depending on the type of debt market integration. We provide a two-country DSGE model distinguishing two transmission channels of financial shocks: (i) balance sheet effect in the short-term debt market, where financially constrained borrowers are readily accessible, and (ii) efficient capital allocation through the long-term debt market, where unconstrained agents participate. Consistent with the model’s prediction, our empirical analysis shows that short-term debt market integration drives business cycle synchronisation during crisis, whereas long-term debt market integration cushions the international transmission of business cycle.
Comparative Preservative Potential of Essential Oils of Ocimum gratissimum from Bambili of the North West and Mbonge of the South West Regions of Cameroon on Scomber scombrus

Essential oils serve as a natural alternative to chemical or synthetic antimicrobials and antioxidants to fight against food borne pathogens or spoilage organisms, inhibiting lipid peroxidation and extending the shelf life of fish and other seafood. This study examines the antibacterial properties Original Research Article Ndasi et al.; AJB2T, 7(1): 20-29, 2021; Article no.AJB2T. 66942 21 of essential oils from leaves of Ocimum gratissimum L. from two localities of the North West and South West of Cameroon on some pathogenic spoilage gram negative and positive bacteria isolated from mackerel, and their antimicrobial and antioxidant effectiveness on the fish quality during preservation for one month at -18 o C. The plant materials were harvested from Bambili, the North West Region of Cameroon and from Mbonge, the South West Region of Cameroon and the essential oils extracted by hydro-distillation using Clevenger-type apparatus. Escherichia coli, Salmonella typhi and Staphylococcus aereus were isolated from mackerel by culture techniques and their susceptibility to the essential oils determined by well diffusion method. Psychrophilic bacteria and Enterobacteraceae counts were used to evaluate the microbiological quality of the fish during storage. Total volatile basic nitrogen and thiobarbituric acid reactive substance assays were used as indices to assess the biochemical quality of the fish during storage. Antibacterial susceptibility test showed that essential oils of O. gratissimum from the North West and South West Regions were active on all the tested microorganisms with different degree.The inhibitory diameters for essential oil from the South West Region were 28.0 mm, 27.2 mm and 26.0 mm while that for essential oil from the Bambili were 24.1 mm, 20.4mm and 21.9 mm for Staphylococcus aereus, Escherichia coli and Salmonella typhi respectively. At the end of storage periods, the values of Psychrotrophs plate counts, total volatile base nitrogen and thiobarbituric acid reactive substances for fish samples treated with essential oil from the Mbonge were 2.71log10cfu/g, 12.88 mgN/100g and 0.88 mgMDA/Kg, while that treated with North West essential oil were 3.00 log10cfu/g, 16.24 mgN/100g and 1.26 mgMDA/Kg respectively. From the obtained results, essential oil of O. gratissimum from the Mbonge was the most effective in preserving Atlantic mackerel.
Surgical Applications of Compliant Mechanisms: A Review

Current surgical devices are mostly rigid and are made of stiff materials, even though their predominant use is on soft and wet tissues. With the emergence of compliant mechanisms (CMs), surgical tools can be designed to be flexible and made using soft materials. CMs offer many advantages such as monolithic fabrication, high precision, no wear, no friction, and no need for lubrication. It is therefore beneficial to consolidate the developments in this field and point to challenges ahead. With this objective, in this article, we review the application of CMs to surgical interventions. The scope of the review covers five aspects that are important in the development of surgical devices: (i) conceptual design and synthesis, (ii) analysis, (iii) materials, (iv) manufacturing, and (v) actuation. Furthermore, the surgical applications of CMs are assessed by classification into five major groups, namely, (i) grasping and cutting, (ii) reachability and steerability, (iii) transmission, (iv) sensing, and (v) implants and deployable devices. The scope and prospects of surgical devices using CMs are also discussed.
The effect of information gap and uncertainty on curiosity and its resolution

ABSTRACT We used a novel missing-letters task to induce curiosity, where participants were shown as a stimulus a nine letter word with some letters missing (2, 4, or 7 missing letters) and asked to complete the word. We found that both information gap (number of letters missing) and participants’ uncertainty regarding the complete word predicted their curiosity to learn the complete word. Participants were later shown the complete word, and their learning satisfaction (measured directly through self-ratings, and indirectly through the affect misattribution procedure) was found to be influenced by the information gap, their familiarity with the word, and whether they had been able to correctly guess the complete word. We proposed a schema verification view of curiosity—people resolve information gaps because they are motivated to verify their prior schema of the environment—to explain our findings and to integrate it with prior work on the topic.
Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques

Early and precisely predicting the students’ dropout based on available educational data belongs to the widespread research topic of the learning analytics research field. Despite the amount of already realized research, the progress is not significant and persists on all educational data levels. Even though various features have already been researched, there is still an open question, which features can be considered appropriate for different machine learning classifiers applied to the typical scarce set of educational data at the e-learning course level. Therefore, the main goal of the research is to emphasize the importance of the data understanding, data gathering phase, stress the limitations of the available datasets of educational data, compare the performance of several machine learning classifiers, and show that also a limited set of features, which are available for teachers in the e-learning course, can predict student’s dropout with sufficient accuracy if the performance metrics are thoroughly considered. The data collected from four academic years were analyzed. The features selected in this study proved to be applicable in predicting course completers and non-completers. The prediction accuracy varied between 77 and 93% on unseen data from the next academic year. In addition to the frequently used performance metrics, the comparison of machine learning classifiers homogeneity was analyzed to overcome the impact of the limited size of the dataset on obtained high values of performance metrics. The results showed that several machine learning algorithms could be successfully applied to a scarce dataset of educational data. Simultaneously, classification performance metrics should be thoroughly considered before deciding to deploy the best performance classification model to predict potential dropout cases and design beneficial intervention mechanisms.
Enzymatic Electrode Design: A Systematic Approach to Enhanced Bioelectrocatalysis

Although they were first discovered more than a century ago, modern enzymatic electrodes used in enzymatic fuel cells and enzymatic amperometric sensors are a rapidly diversifying field, the applications of which have yet to be fully realized. The design parameters that guide the research and industry are also changing as the fundamental mechanisms at the core of the technology become clearer. One of the key design parameters is concerned with how the enzyme catalyst engages the underlying electrode current collector. This one design consideration has led to considerable branching of the field into different fundamental endeavors such as development of enzyme immobilization techniques, research in to the different interfacial electron transfer mechanisms, electrode material characteristics, enzyme orientation in relation to the electrode, and modifications to the electrode to mediate or promote the charge transfer. This work attempts to look at the overall picture of enzyme engagement of the electrode and the subsequent impact of these different characteristics on the overall performance of the electrode. The goal of this work
Unidirectional-bidirectional recurrent networks for cardiac disorders classification

The deep learning approach of supervised recurrent network classifiers model, i.e., recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) are used in this study. The unidirectional and bidirectional for each cardiac disorder (CDs) class is also compared. Comparing both phases is needed to figure out the optimum phase and the best model performance for ECG using the Physionet dataset to classify five classes of CDs with 15 leads ECG signals. The result shows that the bidirectional RNNs method produces better results than the unidirectional method. In contrast to RNNs, the unidirectional LSTM and GRU outperformed the bidirectional phase. The best recurrent network classifier performance is unidirectional GRU with average accuracy, sensitivity, specificity, precision, and F1-score of 98.50%, 95.54%, 98.42%, 89.93%, 92.31%, respectively. Overall, deep learning is a promising improved method for ECG classification.
Development of a Manufacturing Route to Avibactam, a β-Lactamase Inhibitor

Process development work to provide an efficient, robust, and cost-effective manufacturing route to avibactam, a β-lactamase inhibitor is presented herewith. Aspects of this optimization work include the counterintuitive introduction of a protecting group to effect a difficult urea formation and the use of controlled feed hydrogenation conditions to facilitate an elegant one pot debenzylation and sulfation reaction. Overall, the commercial process delivers avibactam in much improved yield with significant reduction in the environmental footprint.
Achievements and Trends in Biocatalytic Synthesis of Specialty Polymers from Biomass-Derived Monomers Using Lipases

New technologies for the conversion of biomass into high-value chemicals, including polymers and plastics, is a must and a challenge. The development of green processes in the last decade involved a continuous increase of the interest towards the synthesis of polymers using in vitro biocatalysis. Among the remarkable diversity of new bio-based polymeric products meeting the criteria of sustainability, biocompatibility, and eco-friendliness, a wide range of polyesters with shorter chain length were obtained and characterized, targeting biomedical and cosmetic applications. In this review, selected examples of such specialty polymers are presented, highlighting the recent developments concerning the use of lipases, mostly in immobilized form, for the green synthesis of ε-caprolactone co-polymers, polyesters with itaconate or furan units, estolides, and polyesteramides. The significant process parameters influencing the average molecular weights and other characteristics are discussed, revealing the advantages and limitations of biocatalytic processes for the synthesis of these bio-based polymers.
Determining accurate hand positions in three-dimensional space using twin-axis electrogoniometer sensors

Electrogoniometers are low price sensors which are easily attached to the body in any environment. Although electrogoniometer sensors are typically used for angular measurements, they can also be used to determine position. This study aimed to accurately determine hand position during the performance of five daily life activities using two electrogoniometer sensors to measure shoulder and elbow angles simultaneously. The measurement of joint angles involves some errors which are divided into intrinsic and extrinsic errors. These errors cause considerable inaccuracies in the estimated hand positions. To overcome this issue, the errors identified are compensated for in two phases, the angular phase and the positional phase, in which a polynomial function and an Elman neural network are used for error compensation, respectively. The derived hand trajectories and the decrease of the root mean square error at every stage of the error compensation, along with the variance accounted for of the Elman networks for each task, show the effectiveness of the proposed method.
DIR/Floortime: Otizm Spektrum Bozukluğunda Bir Erken Müdahale Modeli

In this review, the theoretical foundations, practice steps, basic struc-ture, practical information for the practitioner, and related studies about DIR/Floortime model which is delivered as an early intervention for children especially in the early childhood period with Autism Spectrum Disorder (ASD) are given. DIR/Floortime is an intervention model that is relationship-based and constructed on individual differences, can be used by both professionals and families. Floortime is the intervention method of a model and basically, it is aimed to develop a child’s functional emotional developmental capacities and increase them to typical development level by semi-structured plays with the child. Effective Floortime sessions are created by following the child, creating a con-tinuous circle of communications, and ongoing approximately 20-25 minutes lasting playtimes. There was no research investigating the ef-fect of DIR/Floortime on children with ASD in our country. Studies that are conducted out of our country have been analyzed and it is seen that DIR/Floortime is an effective intervention method especially if it is used in early childhood. It is expected that this model is used more systematically, and more research is being made to search for its effectiveness. It is aimed that this review will also contribute to the theoretical foundations of research which will be conducted in our country.
Optimization of Favourable Test Path Sequences Using Bio-Inspired Natural River System Algorithm

Testing of software requires a great amount of time and effort. The tester's main aim is to design optimized test sequences with a minimum amount of time, effort, and with less redundancy. Testers have used artificial intelligence meta-heuristic algorithms for optimization of test sequences. The model-driven approach is helpful in the generation of test sequences at early designing phase only. The model-driven approach uses UML diagram to represent the system's behavior and design test cases for the system at design stage of software development life cycle. The proposed approach uses natural river system for optimizing favourable non-redundant test path sequences using UML activity diagrams and sequence diagrams. The implementation of proposed approach has been done using python and results show that the proposed approach provides full coverage of test paths with less redundant test nodes compared to other meta heuristic algorithms.
Ecological modeling based on benthic foraminifera from Tupilipalem coast, southeast coast of India: Implications for ecological trajectory

Landsat 8 OLI/TIRS data of Tupilipalem coast demonstrations the periodic closure of lagoon mouth over a brief period affecting the ecological setting and the faunal diversity and distribution. The coastal ecosystem includes a significant metal pollutant due to anthropogenic activities. This study documents forty-nine numbers of benthic foraminiferal species and establishes its relationship with heavy metal concentrations (Fe, Mn, Cr, Cu, Ni, Pb, Zn and Cd). The factor loading matrix generated using factor analysis grades the positive and negative correlations among the heavy metals species. The results of geoaccumulation index, enrichment factor, and pollution load index, construes the seasonal differences in metal concentrations and prevalence of higher concentrations of metals in the sediments. Besides, this paper discusses the impact of coastal morphology on heavy metal concentrations coupled with ecology and distribution of benthic foraminifera.
Onco-Receptors Targeting in Lung Cancer via Application of Surface-Modified and Hybrid Nanoparticles: A Cross-Disciplinary Review

Lung cancer is among the most prevalent and leading causes of death worldwide. The major reason for high mortality is the late diagnosis of the disease, and in most cases, lung cancer is diagnosed at fourth stage in which the cancer has metastasized to almost all vital organs. The other reason for higher mortality is the uptake of the chemotherapeutic agents by the healthy cells, which in turn increases the chances of cytotoxicity to the healthy body cells. The complex pathophysiology of lung cancer provides various pathways to target the cancerous cells. In this regard, upregulated onco-receptors on the cell surface of tumor including epidermal growth factor receptor (EGFR), integrins, transferrin receptor (TFR), folate receptor (FR), cluster of differentiation 44 (CD44) receptor, etc. could be exploited for the inhibition of pathways and tumor-specific drug targeting. Further, cancer borne immunological targets like T-lymphocytes, myeloid-derived suppressor cells (MDSCs), tumor-associated macrophages (TAMs), and dendritic cells could serve as a target site to modulate tumor activity through targeting various surface-expressed receptors or interfering with immune cell-specific pathways. Hence, novel approaches are required for both the diagnosis and treatment of lung cancers. In this context, several researchers have employed various targeted delivery approaches to overcome the problems allied with the conventional diagnosis of and therapy methods used against lung cancer. Nanoparticles are cell nonspecific in biological systems, and may cause unwanted deleterious effects in the body. Therefore, nanodrug delivery systems (NDDSs) need further advancement to overcome the problem of toxicity in the treatment of lung cancer. Moreover, the route of nanomedicines’ delivery to lungs plays a vital role in localizing the drug concentration to target the lung cancer. Surface-modified nanoparticles and hybrid nanoparticles have a wide range of applications in the field of theranostics. This cross-disciplinary review summarizes the current knowledge of the pathways implicated in the different classes of lung cancer with an emphasis on the clinical implications of the increasing number of actionable molecular targets. Furthermore, it focuses specifically on the significance and emerging role of surface functionalized and hybrid nanomaterials as drug delivery systems through citing recent examples targeted at lung cancer treatment.
Small Scale Vapor Pressure Deficit Changes Influence on Transpiration

The combined effects of climate change and population growth will undoubtedly affect the water resources upon which communities rely. It is therefore important to understand water’s complete journey on Earth in order to assess the effects of possible drought on various ecosystems, and initiate studies to formulate solutions. Plants are an important part of our ecosystem, and plant transpiration strongly influences the amount of water vapor in the air. The purpose of this project was to observe how transpiration rates of canopy and vine species are affected by environmental variables. If we can better understand how various environmental variables affect transpiration, we may be able to better understand what could happen to plants during a prolonged drought caused by climate change.