Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Overlapping Block Processing of Cancer Genes Data for Earlier Prediction of Breast Cancer Diseases using Regression Algorithms

Earlier prediction of breast cancer through genes is a challenging task. Existing methods use gene expression in large datasets for the classification and identification of breast cancer based on genes. In this paper, the Bag of Words (BoW) method is applied to a large data set based on block processing to predict cancer at an earlier stage with non-overlapping based block processing of gene data. Based on blocks of gene data, a gene's frequency count is calculated. For frequency calculation, BoW techniques with different gene data lengths are used. The frequency value calculated from the bag-of-words provides the input for machine learning based prediction. Regression techniques such as Multiple Linear Regression (MLR), Simple Linear Regression(SLR) and Logistic Regression (LR) are used for the prediction of breast cancer using non-overlapping based block processing of large data. The LR technique's prediction accuracy is about 93.5 % which is very high compared to other methods. High accuracy in the prediction of cancer genes at an early stage is achieved.
Formative Measurement of Multidimensional SERVQUAL On Internet Service Providers: The Nexus of Marketing Reaction

The quality of service offered by Internet service providers continues to interest academics and practitioners, especially with the incorporation of artificial intelligence in customer service. Therefore, this study aims to empirically substantiate the multidimensionality as a shaper of service quality of internet service providers. Besides, the study aims to establish the nexus between service quality, customer value, trust, and commitment. Furthermore, it examines the nexus between customer value and trust, as well as trust and customer commitment. Quantitative methods were used to answer the research objectives. Empirical data was collected using an online questionnaire distributed to 398 customers in three major cities in Indonesia using a purposive sampling technique. The results of this analysis provide evidence that service quality positively impacts customer trust, value, and commitment. Moreover, it was also found that customer value positively and significantly influences customer trust. Finally, trust was found to positively and significantly influence customer commitment. The study also established that internet network speed, troubleshooting, quality of web information and guidance, protection, and privacy are critical dimensions that determine the service quality of internet service providers.
Enzymatic Biofuel cell with Silver-leaf Bioelectrodes for Energy Harvesting from Glucose

The performance and properties of the silver leaf enzymatic biofuel cell are ideal for use with various power sources. The corner of the silver electrode region, the silver leaf attached to a double-sided (10 mm × 10 mm) adhesive tape, and the electrical connections will all be visible with copper tape. In a range of operating modes, including cyclic voltammetry (CV), linear sweep voltammetry (LSV), and chronoamperometry (CA), the electrode that was intended to function as both an anode and a cathode performed as anticipated. These methodologies were applied for the efficiency assessment. This disposable electrode configuration can withstand an open-circuit voltage of 300 mV and generate an 84 μW/cm2 power density. This innovative method makes it possible to use inexpensive electrodes in microelectronic devices in a flexible manner.
Image Forensics of Compressed Image on Social Media with Lightweight Deep Learning

The widespread use of forged images on social media often involves compressing them into the JPEG format to save bandwidth or storage. For automatic detection of these forgeries, a classification system is used. However, this system is designed based on image data that is not always compressed. To effectively classify forgeries on social media, the learning data should include both fake and original images compressed to the JPEG format. Deep learning offers a solution to detect image forgery, but its high processing demands are a challenge. This study employs the lightweight ShuffleNet v2 architecture to mitigate this, further optimizing feature extraction by substituting the activation function with the FReLU activation funnel. Our preliminary research utilized JPEG-compressed images to create a relevant dataset. The test aims to gauge the efficacy of the modified ShuffleNet v2 in spotting fake images compressed via the Facebook application.
Traction Locomotive Incipient Fault Analysis using Wavelet based Particle Swarm Optimization

The present work aspires to pursue a qualitative research on the loosening of brushes in Indian induction motor locomotives, observing the traction transformer input current. At the initial step, the locomotive has been built in programming and the current input to the transformer has been documented at normal condition. Brushes have been gradually loosened and the transformer inputs have again been recorded. Both the currents have been assessed using a novel Wavelet Transform (WT) coefficient statistical analysis based Particle Swarm Optimization (PSO) Technique. From the computed Skewness, Kurtosis and RMS values, global bests have been disintegrated and optimization has been done in PSO plane to assess different conditions in the engine associated with an incipient brush fault. Here multi-objective PSO (MOPSO) has been used for the assessment and decision making. With the outcome of the refinement, a credo has been developed at the end to segregate the faulty traction engine from the healthy one. The technique used here has been validated in offline mode with satisfactory outcome.
Enhancing Natural Language Processing with LDA-BERT Integration: A Comprehensive Review of Methodologies, Applications, and Future Directions

Natural Language Processing (NLP) plays a critical role in deciphering unstructured text data. This review paper explores the integration of Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT) in various NLP tasks. We examine how this hybrid model enhances traditional NLP methodologies, particularly addressing the limitations of the kMeans clustering algorithm and expanding to diverse applications like sentiment analysis and topic discovery. Through an analysis of recent studies, the paper highlights the versatility and effectiveness of LDA-BERT in handling complex data structures, mitigating outlier influences, and adapting to varied data densities. The lack of standardized benchmarks for evaluating these models and their diverse application contexts are discussed as key challenges. The review emphasizes the necessity for future research to develop more uniform evaluation frameworks to better assess the effectiveness of LDA-BERT models across different NLP tasks.
A Review of Image and Point Cloud Fusion-Based 3D Object Detection for Autonomous Driving

With the application of multi-sensor fusion technologies in object detection, Self-driving automobiles have received more and more attention. The complementary advantages of different types of sensors (camera, LIDAR) can provide accurate and reliable environment perception for autonomous driving systems. We categorize the 3D object identification methods based on image and point cloud fusion into early, intermediate, and late, focusing on the camera and LIDAR combination in autonomous driving, furthermore, the characteristics of various methods and the performance of different models are analyzed and compared. Finally, the possible future opportunities and challenges for 3D object detection are evaluated.
Gluten Free Bread in a Diet of Celiacs

Celiac disease is activated by the consumption of some storage proteins present in cereals, which are usually classified as prolamins, as they are soluble in 70-90 % alcohol [1,2]. Wheat prolamins, called gliadins, together with another storage protein glutelin, could form a complex called gluten, which is responsible for rheological and mechanical properties of the dough and wheat based food. In this form the protein could be isolated from various species of the genus Triticum (common wheat, spelt, durum, kamut), as well as triticale. Among other prolamins, which could be dangerous for people with celiac disease, there are secalins, from rye, and hordeins from barley (both from Triticae tribe). Grains of grasses from other subfamilies then Pooideae, do not contain prolamins with similar activity. Even oats, which is in Aveneae tribe, is considered as a safe source of protein for most of celiacs, especially as it contains low amounts of prolamins [3,4,5], however immunological reactions could be observed for some patients [6,7] or some oats varieties [8,9,10]. Among other effects celiac disease could cause problems with absorption of nutrients including micro and macroelements and vitamins. The removal of wheat, barley and rye from the diet requires changes of eating habits, as these cereals often constitute the basis of daily nourishment, which could be a problem for some patients [11]. Products which have to be removed include traditional bakery products, pasta, groats, flour based sauces and soups, and many confectionary products and snacks [12,13].
False - Face Challenge Response Authentication Automated Test Using Anthropomorphic Images

Captcha is the reverse Turing test done by the machine to verify the human online automatically. This proposed work presents a method to use anthropomorphic (human outer cover morphed with monkey images) images for designing captchas. A set of false – face (morphed) images displayed in the grid with another group of images where the generated human-like pictures need to be as close as possible to human faces but not entirely. Such anthropomorphic captcha secure high score than other captchas while differentiating the human and machine. In the second level, clicked images are replaced by obfuscated, deformed, distorted and noised text images to decrease the machine recognition rate remarkably, compared to previous work while human recognition increased likewise. In addition to that, deep neural network model (proposed False-face model) trained with animal, human, morphed and alphabet images in a powerful way to classify the images. These images are tested with existing face classification algorithms like DeepFace, FaceNet, and TBE-CNN. This algorithm could not produce significant success in the prediction of anthropomorphic images. It helps to improve web security significantly while using this proposed captcha design.
Cirquent Calculus Deepened

Cirquent calculus is a new proof-theoretic and semantic framework, whose main distinguishing feature is being based on circuit-style structures (called cirquents), as opposed to the more traditional approaches that deal with tree-like objects such as formulas, sequents or hypersequents. Among its advantages are greater efficiency, flexibility and expressiveness. This article presents a detailed elaboration of a deep-inference cirquent logic, which is naturally and inherently resource conscious. It shows that classical logic, both syntactically and semantically, can be seen to be just a special, conservative fragment of this more general and, in a sense, more basic logic—the logic of resources in the form of cirquent calculus. The reader will find various arguments in favour of switching to the new framework, such as arguments showing the insufficiency of the expressive power of linear logic or other formula-based approaches to developing resource logics, exponential improvements over the traditional approaches in both representational and proof complexities offered by cirquent calculus (including the existence of polynomial size cut-, substitution-and extension-free cirquent calculus proofs for the notoriously hard pigeonhole principle), and more. Among the main purposes of this article is to provide an introductory-style starting point for what, as the author wishes to hope, might have chances to become a new line of research in proof theory—a proof theory based on circuits instead of formulas.
A Lightweight Deep Recurrent Q-Learning Technique for Autonomous Wildfire Surveillance

We study the problem of wildfire surveillance using autonomous unmanned aerial vehicles (UAVs). The objective of the UAVs is to find the maximum number of locations that are under fire, assuming that the UAVs can share their observations. We propose a deep recurrent Q-learning technique that uses these observations to make decisions for the robots, i.e., where to move next. The prohibitively large state space underlying the decision policy motivates a neural network approximation, but prior work used only convolutional layers to extract spatial fire information from the current observations. Our network also incorporates a recurrent module to capture temporal information from the history of observations. Experiments involving two simulated fixed-wing aircraft feature a more realistic physics-based wildfire propagation model than the discrete wildfire models of prior work. Results show that our proposed technique uses about 20 times less memory than the approach of prior work, while performing comparably in terms of finding the fire's locations.
Evaluating scaling models in biology using hierarchical Bayesian approaches

Theoretical models for allometric relationships between organismal form and function are typically tested by comparing a single predicted relationship with empirical data. Several prominent models, however, predict more than one allometric relationship, and comparisons among alternative models have not taken this into account. Here we evaluate several different scaling models of plant morphology within a hierarchical Bayesian framework that simultaneously fits multiple scaling relationships to three large allometric datasets. The scaling models include: inflexible universal models derived from biophysical assumptions (e.g. elastic similarity or fractal networks), a flexible variation of a fractal network model, and a highly flexible model constrained only by basic algebraic relationships. We demonstrate that variation in intraspecific allometric scaling exponents is inconsistent with the universal models, and that more flexible approaches that allow for biological variability at the species level outperform universal models, even when accounting for relative increases in model complexity.
Machine Learning Models can help Early Disease Detection in Medicinal Plants

Medicinal plants are susceptible to a variety of pests and diseases. One of the most difficult tasks in agriculture is early diagnosis of diseases. If pathogens are not detected early on, they can have a significant influence on overall yield, resulting in lower income of farmers. However, as technology has advanced, the effectiveness and precision/accuracy of recognizing plant diseases has improved. This paper discusses the comparison between the traditional methods and modern techniques of machine learning for early disease detection. The difficulties of recognizing plant disease and pests, and also the benefits in early disease detection using machine learning pest detection methods are discussed. The review includes the opportunities for enhancement in the current plant recognition databases/systems.
Intrinsic Isolation in SIW-based MIMO Antenna for 5G mmWave Applications

In this paper, a compact, low profile two port wideband Multiple input Multiple output (MIMO) antenna based on substrate integrated waveguide (SIW) functioning is proposed for millimeterWave (mmWave) applications. The structure includes two elliptical shaped patch radiators responsible for wide bandwidth, positioned side by side. For better impedance matching, each antenna element is fed by a steeped impedance transmission line. This antenna is unique as it exploits MIMO technology with SIW benefits in order to improve isolation as well as quality of transmission. According to simulated results, the proposed MIMO antenna can cover the n259band of the mmWave. Maximum isolation between ports is 22 dB without any additional isolation structure, and the envelope correlation coefficient (ECC) in the band of interest is less than 0.02. The proposed antenna may be used for small portable devices defined for mmWave communication in 5GNR networks.
The impact of traditional use on vultures in South Africa

Summary Use of vultures is an important component of traditional medicine, particularly in South Africa and there is evidence to suggest that traditional use is at least partly responsible for the rapid decline of vulture populations in this country. Until very recently, little information on the extent of the trade in animal parts, particularly vultures, for traditional medicine was available. The secretive and illegal nature of vulture use makes it extremely difficult to obtain reliable data on amounts and turnovers of species traded, which is essential to assess potential impacts on species populations. Research confirms that vultures are used in the traditional medicine industry for a range of purposes, but are believed to be most effective for providing clairvoyant powers, foresight and increased intelligence. The main drivers of demand for these uses are betting and gambling, for improved business success, and intelligence in school children. Vulture is also prescribed by traditional healers for various ailments, including headaches. It is estimated that 160 vultures are sold per annum in eastern South Africa, with some 59,000 consumption events of vulture pieces. The total annual value of sales of vultures to end consumers in eastern South Africa (excluding the costs of vultures as input costs) is estimated at R1,2 million ($US 120k). Various species of vulture are used for traditional medicine; there is no distinct species preference. Vultures traded in the eastern South African markets are harvested by vulture hunters from a range of formally protected and unprotected natural areas in KwaZulu-Natal, Eastern Cape, Lesotho and southern Mozambique. They are killed using shotguns, poisons or traps. Poisoning is the most destructive and widely used method of harvesting as large numbers of vultures are usually killed in one poisoning event. An estimated 1,250 vulture traders, hunters and traditional healers in eastern South Africa are involved in the vulture trade. With small vulture populations and poor breeding success, the current trade in vultures is not sustainable at the present harvest levels. The implication for people using or trading in vultures is that the benefits currently enjoyed will not be available in 15 to 30 years time.
Reinforcement Learning-Based Swarm Control for UAVs in Static and Dynamic Multi-Obstacle Environments

In this paper, a reinforcement learning-based method for controlling the swarm of unmanned aerial vehicles (UAVs) in complex environments with static and dynamic obstacles has been presented. The method in this paper utilizes double deep Q-network (DDQN) algorithm to train the action of virtual leader in UAV swarm, which enables the UAVs to effectively navigate through complex environments while avoiding collisions. Besides, the artificial potential field method is also applied on each individual UAV to deal with emergency. The effectiveness of the proposed approach is evaluated by simulations in complex scenario.
Changes in soil organic matter over 70 years in continuous arable and ley–arable rotations on a sandy loam soil in England

The sequestration in soil of organic carbon (SOC) derived from atmospheric carbon dioxide (CO2) by replacing arable crops with leys, has been measured over 70 years on a sandy loam soil. The experiment was designed initially to test the effect of leys on the yields of arable crops. A 3‐year grazed grass with clover (grass + clover) ley in a 5‐year rotation with arable crops increased percentage organic carbon (%OC) in the top 25 cm of the soil from 0.98 to 1.23 in 28 years, but with little further increase during the next 40 years with all‐grass leys given fertilizer nitrogen (N). In this second period, OC inputs were balanced by losses, suggesting that about 1.3% OC might be near the equilibrium content for this rotation. Including 3‐year lucerne (Medicago sativa) leys had little effect on %OC over 28 years, but after changing to grass + clover leys, %OC increased to 1.24 during the next 40 years. Eight‐year leys (all grass with N or grass + clover) in 10‐year rotations with arable crops were started in the 1970s, and after three rotations %OC had increased to ca. 1.40 in 2000–2009. Over 70 years, %OC declined from 0.98 to 0.94 in an all‐arable rotation with mainly cereals and to 0.82 with more root crops. Applications of 38 t ha−1 farmyard manure (FYM) every fifth year increased %OC by 0.13% by the mid‐1960s when applications ceased. Soil treated with FYM still contained 0.10% more OC in 2000–2009. Changes in the amount of OC have been modelled with RothC‐26.3 and estimated inputs of C for selected rotations. Little of the OC input during the 70 years has been retained; most was retained in the grazed ley rotation, but 9 t ha−1 only of a total input of 189 t ha−1. In other rotations more than 98% of the total OC input was lost. Despite large losses of C, annual increases in OC of 4‰ are possible on this soil type with the inclusion of grass or grass + clover leys or the application of FYM, but only for a limited period. Such increases in SOC might help to limit increases in atmospheric CO2.
Automatic generation of opaque constants based on the k-clique problem for resilient data obfuscation

Data obfuscations are program transformations used to complicate program understanding and conceal actual values of program variables. The possibility to hide constant values is a basic building block of several obfuscation techniques. For example, in XOR Masking a constant mask is used to encode data, but this mask must be hidden too, in order to keep the obfuscation resilient to attacks. In this paper, we present a novel technique based on the k-clique problem, which is known to be NP-complete, to generate opaque constants, i.e. values that are difficult to guess by static analysis. In our experimental assessment we show that our opaque constants are computationally cheap to generate, both at obfuscation time and at runtime. Moreover, due to the NP-completeness of the k-clique problem, our opaque constants can be proven to be hard to attack with state-of-the-art static analysis tools.
Insulation of Enhancer-Promoter Communication by a Gypsy Transposon Insert in the Drosophila cut Gene: Cooperation between Suppressor of Hairy-wing and Modifier of mdg4 Proteins

ABSTRACT The Drosophila mod(mdg4) gene products counteract heterochromatin-mediated silencing of thewhite gene and help activate genes of the bithorax complex. They also regulate the insulator activity of the gypsy transposon when gypsy inserts between an enhancer and promoter. The Su(Hw) protein is required for gypsy-mediated insulation, and the Mod(mdg4)-67.2 protein binds to Su(Hw). The aim of this study was to determine whether Mod(mdg4)-67.2 is a coinsulator that helps Su(Hw) block enhancers or a facilitator of activation that is inhibited by Su(Hw). Here we provide evidence that Mod(mdg4)-67.2 acts as a coinsulator by showing that some loss-of-functionmod(mdg4) mutations decrease enhancer blocking by a gypsy insert in the cut gene. We find that the C terminus of Mod(mdg4)-67.2 binds in vitro to a region of Su(Hw) that is required for insulation, while the N terminus mediates self-association. The N terminus of Mod(mdg4)-67.2 also interacts with the Chip protein, which facilitates activation of cut. Mod(mdg4)-67.2 truncated in the C terminus interferes in a dominant-negative fashion with insulation in cut but does not significantly affect heterochromatin-mediated silencing ofwhite. We infer that multiple contacts between Su(Hw) and a Mod(mdg4)-67.2 multimer are required for insulation. We theorize that Mod(mdg4)-67.2 usually aids gene activation but can also act as a coinsulator by helping Su(Hw) trap facilitators of activation, such as the Chip protein.