Miracle-dz/UniFAR-FTUs huggingface dataset:
An Approach to Identify Diseases in Betel Leaf Using Deep Learning Techniques

Despite its many benefits, such as vitamin C, thiamine, niacin, riboflavin, and carotene, we fail to preserve betel vine leaves against bacteria, resulting in a substantial crop loss worldwide. Because of the high level of damage, identifying leaf diseases are essential. To classify betel leaf diseases, several deep learning methods such as VGG16, VGG19, Resnet50, AlexNet, and InceptionV3 were applied using transfer learning, as well as some traditional machine learning approaches such as SVM and Logistic Regression. The dataset contains 5800 individual images of betel leave with two diseases (Stem Leaf and Bacterial Leaf Spot) that were captured using a mobile phone camera and preprocessed using Contrast limited adaptive histogram equalization (CLAHE) and Gaussian mixture model (GMM). Finally, the InceptionV3 reached the maximum accuracy of 94.83 % among the rest of the implemented algorithms, on an unseen test set with a total of 658 images.
Fuzzy least square support vector machines for regression

The conception of fuzzy membership is introduced into least square support vector machines(LSSVMs),which overcomes the disadvantage that LSSVMs are so sensitive to outliers in training samples. And then fuzzy least square support vector machines(FLSSVMs) are proposed based on support vector domain description(SVDD). Data samples in the feature space are described and the smallest enclosing hypersphere is obtained.The fuzzy membership value to each sample point is determined according to the distance of each sample from the center of the hypersphere,which can reduce the effect of outliers.Numerical results show that the predictive precision of the proposed method is higher than that of SVMs and LSSVMs without decreasing the training speed.
Realistic models for detection of neuronal currents with magnetic resonance imaging

A great challenge facing brain research is to “see” neurons in action at high spatial and temporal resolution in the living human brain. While existing non-invasive techniques, such as electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI), have either poor spatial or temporal resolution, neuronal current MRI (nc-MRI) may hold the potential to revolutionize cognitive neuroscience by imaging neuronal activity at high temporal and spatial resolutions. However, the implementation of nc-MRI using existing instrumentation is yet to be convincingly demonstrated. In this project, I investigated the feasibility of nc-MRI via computer simulations. To allow realistic neuronal current simulations, the laminar cortex model (LCM) was first developed. The LCM incorporates the laminar architecture of the cerebral cortex into a continuum cortex model (previously developed by Wright et al.) to simulate the collective activity of cortical neurons. As validations, the LCM has been used to simulate the local field potentials (LFP) of the primary visual cortex. The LCM produced spontaneous LFPs exhibited frequency-inverse (1/f) power spectrum behaviour. The LCM also captured the fundamental as well as the high order harmonics under intermittent light stimulation. To model neuronal currents, I decomposed the neuronal activity simulated by the LCM into action potentials and postsynaptic potentials. The geometries of dendrites and axons were generated dynamically to account for neuronal morphology diversity. Magnetic fields produced by action potentials and postsynaptic potentials were calculated for the cases of spontaneous and stimulated cortical activity, from which the nc-MRI signal was determined. The MRI signal magnitude change was found to be below currently detectable levels (< 0.1 part-per-million), but signal phase change was potentially detectable (in the order of 0.1 milli-radian). Furthermore, nc-MRI signals were sensitive to temporal and spatial variations in neuronal activity and independent of the intensity of neuronal activation. Synchronous neuronal activity produces large phase changes, up to 1 milliradian, and the signal phase oscillated with neuronal activity. Based on the computer simulation results, I proposed to image oscillatory neuronal currents using a multi-echo spin echo (MESE) and a synchronised multi-echo gradient recalled echo (MEGRE) sequences. A MESE sequence can accumulate phase changes for multiple neuronal activity oscillation periods through applying radio-frequency (RF)
Ocular Surface Changes in Treatment-Naive Thyroid Eye Disease

Objective: To investigate the association of meibomian gland dysfunction (MGD) and ocular surface exposure with tear film instability in untreated thyroid eye disease (TED) patients. Methods: A cross-sectional study of TED patients from September 2020 to September 2022 was conducted. Ocular surface parameters included ocular surface disease index (OSDI), tear meniscus height (TMH), non-invasive tear break-up time (NITBUT), partial blinking rate, lipid layer thickness (LLT), meibomian gland dropout (meiboscore), Schirmer’s test, and corneal punctate epithelial erosions (PEE). Ocular surface exposure was assessed by the margin reflex distances of the upper and lower eyelid (MRD1 and MRD2), the amount of exophthalmos, lateral flare, and lagophthalmos. Results: In total, 152 eyes from 76 TED patients (64 females and 12 males, age 42.99 ± 12.28 years) and 93 eyes from 61 healthy controls (51 females and 10 males, age 43.52 ± 17.93 years) were examined. Compared with control eyes, TED eyes had higher OSDI, TMH, LLT, and PEE; shorter NITBUT; and worse meiboscore (all p < 0.05). They also had larger amounts of exophthalmos, longer MRD1, more lateral flare, and lagophthalmos. Multivariate analysis identified an association of the tear film instability with lagophthalmos (β = −1.13, 95%CI: −2.08, −0.18) and severe MGD in the lower eyelid (β = −5.01, 95%CI = −7.59, −2.43). Conclusions: Dry eye in TED is mainly manifested as evaporative dry eye disease. Severe lower eyelid MGD and worse lagophthalmos were significantly associated with tear film instability in treatment-naive TED patients.
Optimized FLPID using TLBO algorithm: Applied to Quadrotor (UAV) system

In this paper, to control the quadrotor system, an optimized Fuzzy Logic PID controller employed Teaching Learning Based optimization algorithm (FLPID-TLBO) is suggested. In order to evaluate the performances of this controller, a comparison study between the FLPID-TLBO and a Fuzzy Logic PID (FLPID) controller is performed. Using the dynamic model of the quadrotor, several simulations are conducted under MATLAB software. The outcomes attained show that the suggested FLPID-TLBO controller is efficient in convergence speed and tracking accuracy.
Forecasting the Sunspot Cycle

SUMMARY The problem of forecasting the development of the well-known sunspot cycle of approximately 11 years from observations made early in the cycle is considered. Each cycle is described by a member of a family of curves with different parameters for each cycle. Forecasts are obtained using this model and these forecasts are combined with forecasts obtained from an autoregressive model. Forecasting the time of maximum sunspot activity and the maximum sunspot number is also discussed.
Design and Implementation of a Sine-Wave Inverter using Microcontroller with Modified Pulse Width Modulation Technique

Due to constantly increasing demand and generation gap, the people of Bangladesh are constantly facing load shedding. Additionally, because of rising fuel prices in the international market, the cost of power generation is increasing. This research proposes a cheap but effective modified sine wave inverter for photovoltaic systems. The harmonics performance of this proposed model is much better than traditional push pull inverter. A feedback system is included for low voltage recovery in output. This system also employs a high voltage protection system, low voltage protection system, overload protection system, low battery protection system, etc for the inverter’s safety purpose. From the simulation result, it is seen that the total harmonics distortion (THD) of this inverter is around 56.11% but by inserting a small LCL filter, this THD value reduces to 0.08%. A single-phase laboratory prototype is then developed to ensure the simulation results are accurate and the proposed topology can be implemented successfully.
Diagnosis and Management of Cerebral Venous Thrombosis Due to Polycythemia Vera and Genetic Thrombophilia: Case Report and Literature Review

(1) Background: Cerebral venous and dural sinus thrombosis (CVT) rarely appears in the adult population. It is difficult to diagnosis because of its variable clinical presentation and the overlapping signal intensities of thrombosis and venous flow on conventional MR images and MR venograms. (2) Case presentation: A 41-year-old male patient presented with an acute isolated intracranial hypertension syndrome. The diagnosis of acute thrombosis of the left lateral sinus (both transverse and sigmoid portions), the torcular Herophili, and the bulb of the left internal jugular vein was established by neuroimaging data from head-computed tomography, magnetic resonance imaging (including Contrast-enhanced 3D T1-MPRAGE sequence), and magnetic resonance venography (2D-TOF MR venography). We detected different risk factors (polycythemia vera-PV with JAK2 V617F mutation and inherited low-risk thrombophilia). He was successfully treated with low-molecular-weight heparin, followed by oral anticoagulation. (3) Conclusions: In the case of our patient, polycythemia vera represented a predisposing risk factor for CVT, and the identification of JAK2 V617F mutation was mandatory for the etiology of the disease. Contrast-enhanced 3D T1-MPRAGE sequence proved superior to 2D-TOF MR venography and to conventional SE MR imaging in the diagnosis of acute intracranial dural sinus thrombosis.
The problematics of describing periphrastic predication : between word and image

The article discusses selected problems connected with description of complex predicates (CPred), i.a. , the still controversial issue of criteria applied to differentiate and classify elements of this category, their status in lexical-grammatical system and their relationship with synthetic entities belonging to the class of VERBUM. What makes CPreds interesting is the fact that they have been encountered in various languages (e.g. ispytywat’ voschiscenije (Russian) = miec // odczuc podziw // zachwyt (Polish) = to feel admiration (for) (English); polucat’ pomosc (Russian) = otrzymac // dostac pomoc (Polish) = to receive help (from sb) (English); zaviazywat’ družbu (Russian) = nawiazac przyjaźn // zaprzyjaźnic sie (Polish) = to strike up a friendship // to become friends (English), etc.). This situation calls for a search for complex solutions, also with the aid of new linguistic theories. Especially the cognitivist thesis about the prototypical character of lexical-grammatical categories allows to classify Cpreds as a typologically diversified group of complex entities (characterised by varied degrees of fixedness) functioning within the class of VERBUM understood as a gradated and polycentric category. Such an approach allows for differentiation of several types (structural models) of Cpreds, i.e. entities representing a peripheral group of verbs and differing with respect to structure, lexical composition and degree of ‘fixedness’ of their meanings, and, as a result, with respect to their global content: 1) [VCOP + NKONKR // Nabstr // Adj // Adv] (standard nominal predicates); 2) [VMOD + VINF] + . (modal predicates); 3) [VFAZ +V // NA] + . (phase-aspectual predicates); 4) [VGENER + NA // NE // Nabstr] + . (verbo-nominal analytisms); 5) [VMETAF // METAPRED + Nabstr // NA // NE ] + . (periphrastic predicates as part of verbal phraseology).
Automatic Pulp and Teeth Three-Dimensional Modeling of Single and Multi-Rooted Teeth Based on Cone-Beam Computed Tomography Imaging: A Promising Approach With Clinical and Therapeutic Outcomes

Background Cone-beam computed tomography (CBCT) imaging offers high-quality three-dimensional (3D) acquisition with great spatial resolution, given by the use of isometric voxels, when compared with conventional computed tomography (CT). The current literature supports a median reduction of 76% (up to 85% reduction) of patients’ radiation exposure when imaged by CBCT versus CT. Clinical applications of CBCT imaging can benefit both medical and dental professions. Because these images are digital, the use of algorithms can facilitate the diagnosis of pathologies and the management of patients. There is pertinence to developing rapid and efficient segmentation of teeth from facial volumes acquired with CBCT. Methodology In this paper, a segmentation algorithm using heuristics based on pulp and teeth anatomy as a pre-personalized model is proposed for both single and multi-rooted teeth. Results A quantitative analysis was performed by comparing the results of the algorithm to a gold standard obtained from manual segmentation using the Dice index, average surface distance (ASD), and Mahalanobis distance (MHD) metrics. Qualitative analysis was also performed between the algorithm and the gold standard of 78 teeth. The Dice index average for all pulp segmentation (n = 78) was 83.82% (SD = 6.54%). ASD for all pulp segmentation (n = 78) was 0.21 mm (SD = 0.34 mm). Pulp segmentation compared with MHD averages was 0.19 mm (SD = 0.21 mm). The results of teeth segmentation metrics were similar to pulp segmentation metrics. For the total teeth (n = 78) included in this study, the Dice index average was 92% (SD = 13.10%), ASD was low at 0.19 mm (SD = 0.15 mm), and MHD was 0.11 mm (SD = 0.09 mm). Despite good quantitative results, the qualitative analysis yielded fair results due to large categories. When compared with existing automatic segmentation methods, our approach enables an effective segmentation for both pulp and teeth. Conclusions Our proposed algorithm for pulp and teeth segmentation yields results that are comparable to those obtained by the state-of-the-art methods in both quantitative and qualitative analysis, thus offering interesting perspectives in many clinical fields of dentistry.
Large eddy simulation of methane/air lifted flame with hot co-flow

ABSTRACT The lifted flame with hot co-flow can represent typical combustion features in the practical systems with recirculation of the combustion product. In this study, the methane/air lifted flame was simulated by large eddy simulation. Regarding the special stabilization mechanism in the lifted flame, two different one-step methane oxidation mechanisms were used: (i) the conventional mechanism widely used in engineering simulations and (ii) a modified mechanism considering the effects of the equivalence ratio. By comparing the simulation results with the experimental data, both mechanisms could predict the liftoff phenomenon; however, the simulation using the modified mechanism provided more reasonable results.
Smart Shopping Carts to Increase Healthier Food Purchase: A Conjoint Experiment

: Shopping carts, in general, should be suitable for carrying smart technology in the retail store environment. Also, a smart shopping cart can present verbal motivating stimuli to increase healthier food purchases. A conjoint experiment was used to test with a hypothetical purchasing task for young consumers (n=91) the potential of motivating stimulus on smart shopping carts to influence healthier purchases when buying frozen pizza. The results show a positive impact for all stimuli stemming from the smart shopping cart, three of which were health-based. This shows that stimuli revealing dynamic and personalized data through smart technology in a physical grocery retail setting have the potential to outperform traditional brand statements. Our conjoint experiment increased young consumers’ likelihood of choosing a healthier frozen pizza. This result demonstrates that verbal stimuli on smart shopping carts can function as motivating augmentals on young adult consumers’ healthier food purchases and are in line with the market positioning and customer-service focus of many retailers and brands today, emphasizing a social marketing standing.
Skin Microbiome in Prurigo Nodularis

Prurigo nodularis (PN) is a chronic condition characterized by the presence of nodular lesions accompanied by intense pruritus. The disease has been linked to several infectious factors, but data on the direct presence of microorganisms in the lesions of PN are scarce. The aim of this study was to evaluate the diversity and composition of the bacterial microbiome in PN lesions by targeting the region V3-V4 of 16S rRNA. Skin swabs were obtained from active nodules in 24 patients with PN, inflammatory patches of 14 patients with atopic dermatitis (AD) and corresponding skin areas of 9 healthy volunteers (HV). After DNA extraction, the V3-V4 region of the bacterial 16S rRNA gene was amplified. Sequencing was performed using the Illumina platform on the MiSeq instrument. Operational taxonomic units (OTU) were identified. The identification of taxa was carried out using the Silva v.138 database. There was no statistically significant difference in the alpha-diversity (intra-sample diversity) between the PN, AD and HV groups. The beta-diversity (inter-sample diversity) showed statistically significant differences between the three groups on a global level and in paired analyses. Staphylococcus was significantly more abundant in samples from PN and AD patients than in controls. The difference was maintained across all taxonomic levels. The PN microbiome is highly similar to that of AD. It remains unclear whether the disturbed composition of the microbiome and the domination of Staphylococcus in PN lesions may be the trigger factor of pruritus and lead to the development of cutaneous changes or is a secondary phenomenon. Our preliminary results support the theory that the composition of the skin microbiome in PN is altered and justify further research on the role of the microbiome in this debilitating condition.
Nonlinear Effects Induced by Interactions among Functional Groups of Bacteria and Fungi Regulate the Priming Effect in Malagasy Soils

The priming effect (PE) occurs when fresh organic matter (FOM) supplied to soil alters the rate of decomposition of older soil organic matter (SOM). The PE can be generated by different mechanisms driven by interactions between microorganisms with different live strategies and decomposition abilities. Among those, stoichiometric decomposition results from FOM decomposition, which induces the decomposition of SOM by the release of exoenzymes by FOM-decomposers. Nutrient mining results from the co-metabolism of energy-rich FOM with nutrient-rich SOM by SOM-decomposers. While existing statistical approaches enable measurement of the effect of community composition (linear effect) on the PE, the effect of interactions among co-occurring populations (non-linear effect) is more difficult to grasp. We compare a non-linear, clustering approach with a strictly linear approach to separately and comprehensively capture all linear and non-linear effects induced by soil microbial populations on the PE and to identify the species involved. We used an already published data set, acquired from two climatic transects of Madagascar Highlands, in which the high-throughput sequencing of soil samples was applied parallel to the analysis of the potential capacity of microbial communities to generate PE following a 13C-labeled wheat straw input. The linear and clustering approaches highlight two different aspects of the effects of microbial biodiversity on SOM decomposition. The comparison of the results enabled identification of bacterial and fungal families, and combinations of families, inducing either a linear, a non-linear, or no effect on PE after incubation. Bacterial families mainly favoured a PE proportional to their relative abundances in soil (linear effect). Inversely, fungal families induced strong non-linear effects resulting from interactions among them and with bacteria. Our findings suggest that bacteria support stoichiometric decomposition in the first days of incubation, while fungi support mainly the nutrient mining of soil’s organic matter several weeks after the beginning of incubation. Used together, the clustering and linear approaches therefore enable the estimation of the relative importance of linear effects related to microbial relative abundances, and non-linear effects related to interactions among microbial populations on soil properties. Both approaches also enable the identification of key microbial families that mainly regulate soil properties.
Infinite Order U-statistics

A large class of estimators that is useful in the study of statistical inference is the class of U-statistics. A U-statistic is an extension of the concept of a sample mean; a U-statistic is the average over all possible evaluations of a function, called a kernel, of a random sample. In this paper, we extend this estimator by considering kernels whose order may depend upon the sample size and call the resulting estimator an infinite order U-statistic (IOUS). Certain basic properties, such as almost sure consistency, asymptotic normality and large sample interval estimates, are shown to hold. The extension to kernels of large order raises certain computational issues which are addressed via resampling techniques. To argue that the extension is practicable, several examples of IOUSs are provided. In some examples we write a known statistic, such as Nelson's hazard function estimator, as an IOUS. This representation automatically provides information about the bias of the estimator. Further, this type of representation suggests a natural truncation of para- meters that can be written as certain infinite sums.
Performance Modeling and Analysis of Design Patterns for Microservice Systems

The adoption of design patterns in the microservice architecture and cloud-native development scope was recently reviewed to investigate the industry practice. Interestingly, when considering performance-related aspects, practitioners focus on specific metrics (e.g., the time taken to handle requests) to identify sources of performance hindrance. This paper investigates a subset of seven design patterns that industrial practitioners indicate as relevant for system performance. We are interested to quantify the impact of these patterns while considering heterogeneous workloads, thus supporting software architects in understanding the root causes of performance issues. We use queuing networks to build the performance models of the seven design patterns and extract quantitative insights from model-based performance analysis. Our performance models are flexible in their input parameterization and reusable in different application contexts. We find that most design patterns confirm the expectation of practitioners, and our experimental results assess the identified performance gains and pains. One design pattern (i.e., Gateway Offloading) shows the peculiar characteristic of contributing to performance pains in some cases, leading to novel insights about the impact of design patterns in microservice systems.