Tail-biting convolutional codes extend the classical zero-termination convolutional codes: Both encoding schemes force the equality of start and end states, but under the tail-biting each state is a valid termination. This paper proposes a machine-learning approach to improve the state-of-the-art decoding of tail-biting codes, focusing on the widely employed short length regime as in the LTE standard. This standard also includes a CRC code. First, we parameterize the circular Viterbi algorithm, a baseline decoder that exploits the circular nature of the underlying trellis. An ensemble combines multiple such weighted decoders, each decoder specializes in decoding words from a specific region of the channel words' distribution. A region corresponds to a subset of termination states; the ensemble covers the entire states space. A non-learnable gating satisfies two goals: it filters easily decoded words and mitigates the overhead of executing multiple weighted decoders. The CRC criterion is employed to choose only a subset of experts for decoding purpose. Our method achieves FER improvement of up to 0.75dB over the CVA in the waterfall region for multiple code lengths, adding negligible computational complexity compared to the circular Viterbi algorithm in high SNRs.

Regeneration of lost tail is of great importance to lizards. Anolis carolinensis, a green lizard, is capable of regenerating its tail efficiently after autotomy. Hence, it is considered as a model organism in regeneration study. A. carolinensis shed its tail in order to distract the predator’s attention and thus makes a way to escape. Restoring of the amputated tail takes several days and the mechanism is currently clearly understood. Although save its life, tail regeneration is associated with the impairment of several vital functions in Anoles. In addition, various differences have been observed between original and regenerated tail in terms of mechanism and structure. To date, very little work has been conducted on tail autotomy and regeneration at molecular and genetic level. The genes responsible for regeneration in anoles are identified recently. These genes are evolutionarily conserved through all tetrapod vertebrates. They are, however, in a state of ‘switched-off’ in other vertebrates including humans. Consequently, a throughout study of these so called ‘switched-off’ genes may provide a way of restoring lost organs in human, and thus could revolutionize the modern medical science.

This research highlights the importance of error-correcting codes in ensuring secure and efficient data transmission over noisy channels. It discusses the application of Convolutional codes and the Viterbi algorithm for error detecting and correcting in applications where the data rate is high and the error probability is relatively low. This research extends beyond previous studies by conducting a comparative analysis of Convolutional codes, the Viterbi algorithm, and alternative coding methods utilized in satellite communication systems, by evaluating their effectiveness and efficiency relative to other coding approaches like BCH, LDPC, and turbo codes. The evaluation encompasses key performance metrics including error correction capabilities, computational complexity, and robustness against various error types. Additionally, practical implementation factors such as power consumption, computational requirements, and compatibility with existing hardware are considered. The analysis sheds light on the advantages and disadvantages of each coding method, offering insights into why convolutional codes and the Viterbi algorithm are particularly suitable for GPS systems.

Fluidized beds are pivotal in the process industry and chemical engineering, with Computational Fluid Dynamics (CFD) playing a crucial role in their design and optimization. Challenges in CFD modeling stem from the scarcity or inconsistency of experimental data for validation, along with the uncertainties introduced by numerous parameters and assumptions across different CFD codes. This study navigates these complexities by comparing simulation results from the open-source MFIX and OpenFOAM, and the commercial ANSYS FLUENT, against experimental data. Utilizing a Eulerian-Eulerian framework and the kinetic theory of granular flow (KTGF), the investigation focuses on solid-phase properties through the classical drag laws of Gidaspow and Syamlal-O’Brien across varied parameters. Findings indicate that ANSYS Fluent, MFiX, and OpenFOAM can achieve reasonable agreement with experimental benchmarks, each showcasing distinct strengths and weaknesses. The study also emphasizes that both the Syamlal-O’Brien and Gidaspow drag models exhibit reasonable agreement with experimental benchmarks across the examined CFD codes, suggesting a moderated sensitivity to the choice of drag model. Moreover, the analysis were also carried out for 2D and 3D simulations, revealing that the dimensional approach impacts the predictive accuracy to a certain extent, with both models adapting well to the complexities of each simulation environment. The study highlights the significant influence of restitution coefficients on bed expansion due to their effect on particle-particle collisions, with a value of 0.9 deemed optimal for balancing simulation accuracy and computational efficiency. Conversely, the specularity coefficient, impacting particle-wall interactions, exhibits a more subtle effect on bed dynamics. This finding emphasizes the critical role of carefully choosing these coefficients to effectively simulate the nuanced behaviors of fluidized beds.

Ticks drive a wide diversity of pathogens to a great variety of hosts, including humans. We conducted a tick surveillance study in northwestern Spain between 2014 and 2019. Ticks were removed from people and were identified. Tick numbers, species, development stages, evolution over time, seasonal and geographical distribution, and epidemiological characteristics of people bitten by ticks were studied. We collected ticks from 8143 people. Nymphs of Ixodes ricinus were the most frequently collected. Rhipicephalus bursa, Rhipicephalus sanguineus sensu lato (s.l), Hyalomma marginatum, Hyalomma lusitanicum, Dermacentor marginatus, Dermacentor reticulatus and Haemaphysalis punctata were also found, with adults as the main stage. Hyalomma spp. and R. bursa have been progressively increasing over time. Although bites occurred throughout the year, the highest number of incidents were reported from April to July. The distribution patterns of the tick species were different between the north and the south of the region, which was related to cases detected in humans of the pathogens they carried. Adult men were more likely to be bitten by ticks than women. Ticks were most frequently removed from adults from the lower limbs, while for children they were mainly attached to the head. Epidemiological surveillance is essential given the increase in tick populations in recent years, mainly of species potentially carrying pathogens causing emerging diseases in Spain, such as the Crimean Congo Hemorraghic Fever (CCFH).

Quantum computing (QC) has opened the door to advancements in machine learning (ML) tasks that are currently implemented in the classical domain. Convolutional neural networks (CNNs) are classical ML architectures that exploit data locality and possess a simpler structure than fully-connected multi-layer perceptrons (MLPs) without compromising the accuracy of classification. However, the concept of preserving data locality is usually overlooked in the existing quantum counterparts of CNNs, particularly for extracting multi-features in multidimensional data. In this paper, we present a multidimensional quantum convolutional classifier (MQCC) that performs multidimensional and multi-feature quantum convolution with average and Euclidean pooling and thus adapting the CNN structure to a variational quantum algorithm (VQA). Average pooling is based on the quantum Haar transform (QHT) and Euclidean pooling is based on partial quantum measurement. The experimental work was conducted using multidimensional data to validate the correctness and demonstrate the scalability of the proposed method utilizing both noisy and noise-free quantum simulations. We evaluated the MQCC model with reference to reported work on state-of-the-art quantum simulators from IBM Quantum and Xanadu using a variety of standard ML datasets. The experimental results showed favorable characteristics of our proposed techniques compared to existing work with respect to a number of quantitative metrics such as the number of training parameters, cross-entropy loss, classification accuracy, circuit depth, and quantum gate count.

The article shows materials to the question about algebraic features of the genetic code and about the dictatorial influence of the DNA and RNA molecules on the whole organism. Presented results testify in favor that the genetic code is an algebraic code related with a wide class of algebraic codes, which are a basis of noise-immune coding of information in communication technologies. Structural features of the genetic systems are associated with hypercomplex double (or hyperbolic) numbers and with bisymmetric doubly stochastic matrices. The received results confirm that represented matrix approaches are effective for modeling genetic phenomena and revealing the interconnections of structures of biological bodies at various levels of their organization. This allows one to think that living organisms are algebraically encoded entities where structures of genetic molecules have the dictatorial influence on inherited structures of the whole organism. New described algebraic approaches and results are discussed.

With the increasingly stringent airworthiness standards, the noise generated during the rotorcraft flight is gradually attracting people’s attention. It widely operated helicopters at low altitudes because of their maneuverability. The way to reduce the noise caused by the complex airflow of the helicopter rotor system has progressively become a hot topic for researchers. Using a hybrid acoustic analysis method, this paper investigates the improvement of the noise and thrust of the helicopter’s tail rotor through the tail rotor structural parameters. For the basic model, the turbulence simulation is performed using an incompressible detached eddy simulation (DES) method, and the Lighthill acoustic analog equation is calculated using the finite element method (FEM). We verified the accuracy of the method through wind tunnel tests. We chose a series of structural parameters for sound simulation and fluid simulation calculations. The results indicate that the modified tail rotor noise reduced by 16.5 dBA and the total thrust increased by 19.9% from the prototype model. This work can enhance the duct tail rotor design to improve aerodynamic and aeroacoustic performance.

Polar coding gives rise to the first explicit family of codes that provably achieve capacity with efficient encoding and decoding for a wide range of channels. Recently, Arikan presented a new polar coding scheme, which he called polarization-adjusted convolutional (PAC) codes. At short blocklengths, such codes offer a dramatic improvement in performance as compared to CRC-aided list decoding of conventional polar codes. PAC codes are based primarily upon the following main ideas: replacing CRC codes with convolutional precoding (under appropriate rate profiling) and replacing list decoding by sequential decoding. Simulation results show that PAC codes, resulting from the combination of these ideas, are close to finite-length lower bounds on the performance of any code under ML decoding. One of our main goals in this paper is to answer the following question: is sequential decoding essential for the superior performance of PAC codes? We show that similar performance can be achieved using list decoding when the list size $L$ is moderately large (say, $L \geq 128$). List decoding has distinct advantages over sequential decoding in certain scenarios, such as low-SNR regimes or situations where the worst-case complexity/latency is the primary constraint. Another objective is to provide some insights into the remarkable performance of PAC codes. We first observe that both sequential decoding and list decoding of PAC codes closely match ML decoding thereof. We then estimate the number of low weight codewords in PAC codes, and use these estimates to approximate the union bound on their performance. These results indicate that PAC codes are superior to both polar codes and Reed-Muller codes. We also consider random time-varying convolutional precoding for PAC codes, and observe that this scheme achieves the same superior performance with constraint length as low as $\nu = 2$.

Using a detailed atmospheric radiative transfer model, we derive macroscopic relationships of downwelling and outgoing longwave radiation, useful for hydrological practice. We validate them using empirical formulae based on downwelling radiation data, which are in common use in hydrology, as well as satellite data for the outgoing radiation. We use the macroscopic relationships to infer the relative importance of carbon dioxide and water vapour in the greenhouse effect. The results show that the contribution of the former is 4% – 5%, while water and clouds dominate with a contribution of 87% – 95%. The minor effect of carbon dioxide is also confirmed by the small, non-discernible effect of the recent escalation of atmospheric CO2 concentration from 300 to 420 ppm, which is quantified at 0.5% for both downwelling and outgoing radiation. Water and clouds also perform other important functions in climate, such as regulating the heat storage and the albedo, as well as cooling the Earth’s surface though latent heat transfer, with a contribution of 50%. By confirming the major role of water on climate, these results suggest that hydrology should have a more prominent and more active role in climate research.

Yellow fever is an emerging and re-emerging viral disease transmitted through the bites of infective Aedes mosquitoes. Several outbreaks of yellow fever have been documented in southern Ethiopia.Understanding the transmission cycle is pivotal to manage arboviral disease outbreaks. Therefore, the present study aimed to evaluate which species of Aedes mosquitoes contribute to the YF virus transmission and the outbreaks that have occurred, and their behaviors (biting and resting) in the region. Two districts were selected based on previous Yellow Fever (YF) outbreak history. A longitudinal entomological sampling was carried out to collect adult Aedes mosquitoes using human landing catches, mechanical mouth aspirators and pyrethrum spreadsheet collection. Adult mosquito collections were conducted twice a month for six months from February 2019 to July 2020. Identification of mosquito species at the genus level was done using morphological keys and speciation using molecular techniques on 406 Aedes due to limited laboratory resources (primers and probe, reagents shortage) and budget limits. Aedes mosquitoes were pooled and tested for YFV, dengue virus (DENV, serotype 1-4) and chikungunya virus (CHKV) by qPCR. A total of 1689 mosquitoes 93.7% (1582/1689) of Aedes and 6.3% (107/1689) of Culex were collected. Of the total collected mosquitoes, 58.7% (991/1689) were from Ofa study sites whereas the left 41.3% (698/1689) were from Boko Dawula. 93.5% (1579/1689) of Aedes complex were collected during wet season. Most of the mosquitoes were collected during the wet season 97.9% (1653/1689). Of the 1582 Aedes simpsoni complex, 57.7% (913/1582) were from Ofa district and the remaining 42.3% (669/1582) were from Boko Dawula district. Of the 406 Aedes mosquitoes molecularly characterized to the species level, the Aedes simpsoni complex accounted for 99.5% (404/406), while Aedes aegypti found in the Ofa district accounted for only 0.5% (2/ 406). From the 934 Aedes simpsoni tested for viruses and none were positive. The human biting activities of Aedes (Ae.) simpsoni peaked at 8:00 – 9:00 hour and 16:00 – 17:00 hour, mostly outdoors, both within the villages and forests. The leaves of Ensete (E.) ventricosum appear to be ideal resting places for Aedes (Ae.) simpsoni complex. Although the tested Ae. simpsoni complex was negative for arboviruses; morning and afternoon activities of the species coincide with human outdoor activities and may therefore pose the risk of viral infection. The lower dominance of Aedes aegypti indicated that the major responsible vector for the occurrences of previous and current arboviral diseases was due to other mentioned Aedes species. It is of great importance to improve surveillance activities of arboviruses in reservoir hosts and vectors to establish control measures. Furthermore, the origin of bloodmeal and the mosquito’s role in the transmission of arboviral diseases need further study to improve the understanding of this species.

Rice has become the second major food among rural people. It is one of the most nutrient and low-cost food available in Asia. Rice is composed of two major subspecies as Japonica and Indica and it belongs to the Poaceae family. The World Bank predicts that rice demand will grow 51% faster than population growth in 2025. It is high time to develop a model which predicts the type of infected paddy leaf and avoids the loss of grains. In this paper, the proposed hybrid CNN model, three CNN networks with different layer properties are trained parallel. The extracted feature from these CNN networks is combined to provide the classification of the type of infected leaf. Fieldwork was carried and about 340 samples are collected from five different types of classes. The proposed hybrid model was tested with both the fieldwork dataset and also with an available dataset on the internet. The experimental results show that the proposed model outperforms the other CNN network and yields an accuracy value of 98%, a precision value of 95%, and a misclassification rate of 4% respectively.

Although increasing knowledge about biological systems has advanced exponentially in recent decades, it is surprising to realize that the very definition of Life keeps presenting theoretical challenges. Even if several lines of reasoning seek to identify the essence of life phenomenon, most of these thoughts contain fundamental problem in their basic conceptual structure. Most concepts fail to identify necessary and sufficient features to define life. Here, we analyzed the main conceptual framework regarding theoretical aspects supporting life concepts, such as (i) the physical, (ii) the cellular and (iii) the molecular approaches. Based on ontological analysis, we propose that Life should not be positioned under the ontological category of Matter. Yet, life should be better understood under the top-level ontology of “Process”. Exercising an epistemological approach, we propose that the essential characteristic pervading each and every living being is the presence of organic codes. Therefore, we explore theories in biosemiotics in order to propose a clear concept of life as a macrocode composed by multiple inter-related coding layers. Therefore, we suggest a clear distinction between the concept of life and living beings, a distinction that is not evident in theoretical terms. From the proposed concept, we suggest that the evolutionary process is a fundamental characteristic for life’s maintenance but not to its definition. The current proposition opens a fertile field of debate in astrobiology, biosemiotics and robotics.

Glaucoma, a global leading cause of blindness, necessitates accurate segmentation of the optic disc (OD) and optic cup (OC) for effective screening. However, existing segmentation methods based on convolutional neural networks (CNNs) suffer from high computational complexity and long inference times. In this paper, we propose an end-to-end segmentation method for the OD and OC using a lightweight MobileNetv3 as the feature extraction network. Our approach incorporates boundary branches and an adversarial learning network to achieve multi-label segmentation of the OD and OC. We evaluate the proposed method on three publicly available datasets: Drishti-GS, RIM-ONE-r3, and REFUGE. The experimental results demonstrate segmentation accuracies of 0.974/0.900, 0.966/0.875, and 0.962/0.880 for the OD and OC on the respective datasets, while significantly reducing inference time.

In recent years, electroencephalogram (EEG) emotion recognition has emerged as a research hotspot in the field of artificial intelligence (AI). Despite advancements in EEG-based emotion recognition, room for enhancing performance persists due to EEG signal redundancy and limitations in feature extraction, leading to inefficient models and emotional information loss. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification, and 97.69% and 98.05% in ternary classification, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

The digital representation of various signals allows, at the subsequent stages of their transmission, to apply correction codes that provide protection against possible errors arising from the action of interference in the communication channel. At the same time, it is important that, with the required correcting ability, these codes have the maximum possible speed. The article presents the results of calculations for linear codes, showing their really achievable limiting capabilities.

The paper investigates new robust code constructions based on bent functions and spline–wavelet transformation. Implementation of bent functions in codes construction increases the probability of error detection in the data channel and cryptographic devices. Meanwhile, use of spline–wavelets theory for constructing the codes gives the possibility to increase system security from the actions of an attacker. Presented constructions combines spline–wavelets functions and bent functions. Developed robust codes, compared to existing ones, have a higher parameter of maximum error masking probability. Illustrated codes are ensuring the security of transmitted information. Some of the granted constructions were implemented on FPGA.

The closed forms of the non-resonant thermonuclear function in the Maxwell-Boltzmann and Tsallis case with depleted tail are obtained in generalized special functions. The results are written in terms of H-function of two variables. The importance of the results in this paper lies in the fact that the reaction rate probability integrals in Maxwell-Boltzmann and Tsallis cases are not obtained by the conventional method of approximation or by means of a single variable transform technique but by means of a two variable transform method. The Behaviour of the depleted non-resonant thermonuclear functions are examined using graphs. The results in the paper are of much interest to astrophysicists and statisticians in their further work in this area.

Formidable and often seemingly insurmountable conceptual, technical, and methodological challenges hamper the measurement of oxidative stress in humans. For instance, fraught and flawed methods, such as the thiobarbituric acid reactive substances assay kits for lipid peroxidation, rate-limit progress. To advance translational redox research, we present ten comprehensive “cheat codes” for measuring oxidative stress in humans. The cheat codes include analytical approaches to assess reactive oxygen species, antioxidants, biomarkers of oxidative damage and redox regulation. They provide essential conceptual, technical, and methodological information inclusive of curated “do” and “don’t” guidelines. Given the biochemical complexity of oxidative stress, we present a research question-grounded decision tree guide for selecting the most appropriate cheat code (s) to implement in a prospective human experiment. Worked examples demonstrate the benefits of the decision tree-based cheat code selection tool. The ten cheat codes define an invaluable resource for measuring oxidative stress in humans.

The growing concern about climate change and global warming has driven greater involvement of international organizations, countries, companies, and society in general in acting and ensur-ing the life of our planet and the sustainability of future generations. Sustainability and finance are two concepts that are intertwined through sustainable finance, serving as an instrument that guides financial resources towards achieving sustainable development. Sustainability allows companies to identify risks and opportunities, being responsible for measuring their social, envi-ronmental, and economic impact under a multi-stakeholder approach. One of the three main cri-teria of sustainability is corporate governance, and the main corporate governance body is the board of directors, where the composition of the board and its supervision and monitoring func-tions improve the ESG performance. A relevant aspect within the board of directors is the ade-quate balance of gender diversity on the board, becoming an aspect regulated by laws and cor-porate governance code. This article develops the main concepts related to sustainability, sus-tainable finance and corporate governance describing the current situation of these topics and their relationship, creating a conceptual framework that allows identifying topics necessary for further empirical research and the implication of regulation in the scope of ESG issues.

Every protein consists of a linear sequence over an alphabet of $20$ letters/amino acids. The sequence unfolds in the $3$-dimensional space through secondary (local foldings), tertiary (bonds) and quaternary (disjoint multiple) structures. The mere existence of the genetic code for the $20$ letters of the linear chain could be predicted with the (informationally complete) irreducible characters of the finite group $G_n:=\mathbb{Z}_n \rtimes 2O$ (with $n=5$ or $7$ and $2O$ the binary octahedral group) in our previous two papers. It turns out that some quaternary structures of protein complexes display $n$-fold symmetries. We propose an approach of secondary structures based on free group theory. Our results are compared to other approaches of predicting secondary structures of proteins in terms of $\alpha$ helices, $\beta$ sheets and coils, or more refined techniques. It is shown that the secondary structure of proteins shows similarities to the structure of some hyperbolic $3$-manifolds. The hyperbolic $3$-manifold of smallest volume --Gieseking manifold--, some other $3$ manifolds and the oriented hypercartographic group are singled out as tentative models of such secondary structures. For the quaternary structure, there are links to the Kummer surface.

Reinforced concrete (RC) columns are often placed under confinement to increase their strength and ductility. Carbon fiber reinforced polymer (CFRP) materials have recently been recognized as favorable confinement systems. At present, a number of national standards and codes dedicated to the design of concrete components strengthened with CFRP in general and CFRP confinement in particular are available. These sets of rules provide design equations for confined reinforced concrete columns with circular and rectangular cross sections. Most of the standards and codes exhibit significant differences, including the used predictive models limitations, observed effects, and covered loading conditions. In this paper, five international standards and design guidelines are introduced and discussed. The purpose is to present a constructive and critical assessment of the state-of-the-art design methodologies available for CFRP confined RC columns and to discuss effects not previously considered properly. Therefore, some recent research efforts and findings from the Leipzig University of Applied Sciences are also introduced. The obtained data is used for a comparative study of the guideline predictive equations. Furthermore, it is shown that some new findings concerning the rupture strength and the maximum strength plus accompanying axial strain of a CFRP confined column are suitable to improve the current guidelines.

This paper proposes two max-pooling engines, named the RTB-MAXP engine and the CMB-MAXP engine, with a scalable window size parameter for FPGA-based convolutional neural network (CNN) implementation. The max-pooling operation for the CNN can be decomposed into two stages, i.e., a horizontal axis max-pooling operation and a vertical axis max-pooling operation. These two one-dimensional max-pooling operations are performed by tracking the rank of the values within the window in the RTB-MAXP engine and cascading the maximum operations of the values in CMB-MAXP engine. Both the RBM-MAXP engine and the CMB-MAXP engine were implemented using VHSIC Hardware Description Language (VHDL) and verified by simulations. They have been employed for and tested in our CNN accelerator targeting at the CNN model YOLOv4-CSP-S-Leaky for object detection.

Iterative Stochastic decoding is an alternative to standard fixed-point decoding of Low-density parity check codes (LDPC) to reduce inter-node routing. In this paper, we propose a Flexible Field-Programmable Gate Array (FPGA)-Based Stochastic decoding (SD) hardware architecture for LDPC codes in the Fifth-Generation (5G) New Radio (NR) Standard that supports decoding of set of various code rates. This decoder’s runtime flexibility is desirable to switch a better performing code rate automatically based on the channel conditions without the extra time needed for reprogramming of FPGA. An offline design method is implemented to generate the hardware description language (HDL) code description of the decoder for the required code-rate set, which is further synthesized and integrated into the Xilinx Kintex-7 series FPGA board to determine the hardware resource utilisation and processing throughput. The Synopsys design tools were employed during both the simulation and synthesis stages, in combination with TSMC 65-nm CMOS standard cell technology, to facilitate comparative analysis. Compared with state-of-the-art designs, the proposed architecture reduces hardware utilization by up to 26% and achieved energy efficiency by 52%.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

It is well recognised that the mining industry in South Africa is highly rated for its substantial contribution to the country’s economic growth, including employment and infrastructural development. It is also known that mining and ore processing activities potentially pose a severe threat to public health and environmental well-being, in the way operations are carried out, mine wastes are disposed of (in dumps), local communities are relocated, mine management and the mining community in general, perceive established environmental standards and etiquette. This paper examines ethical practices and norms in the South African mining industry, with particular reference to the management of tailings dams. We analyse the modes of articulation of the country’s regulatory instruments for tailings management, and review the corporate social responsibility (CSR) approach of leading mining companies. Despite decades of research and resulting recommendations on tailings management, it is concluded that current legislations are largely ineffective, level of adherence by mine management and the mining community, low, and mechanisms for compliance monitoring, weak. New perspectives on legislative issues for unsolved problems in tailings handling are put forward, and directions for future research, indicated.

Purpose: Intervertebral disc is a leading avascular organ in the body. Hence, it may utilize autophagy and apoptosis to alive in unfavorable condition like stress and mechanical force. But excessive force and stress is a key cause to intervertebral disc degeneration (IVDD). Purpose of this study is to identify, external fixator (EF) and saline injection (SI) may induce autopahgy and apoptosis mediated nucleus pulposus (NP) cells death in rat tail’s disc and which method is better for studying autophagy and apoptosis. Methods: Sixteen 9-week-old male Sprague–Dawley rat tails were treated with 0.9% saline and EF (two-cross kirschner wires) for 6 weeks and 12 weeks as well. Treated discs were dissected for checking out participation of autopahgy and apoptosis to intervertebral disc degradation (IVDD). For identifying them, we performed H&E staining, Masson’s trichrome staining, and Immunohistochemistry (IHC) for LC3, Beclin-1and P62 as well as MMP-2, MMP-3 and TIMP-1.Furthermore, we performed quantitative polymerase chain reaction (qPCR)to check out autophagy related gene expression (beclin-1, LC3 and P62) and apoptosis related gene expression (MMP-2, MMP-3 and TIMP-1) respectively. Results: More insidious nucleus pulposus (NP) cell degeneration was indentified in (EF) group than in (Ctrl) control group. And rate of degradation was elevated with increasing duration of compression, but SI group cannot distinguish the margin of AF and NP cells. Highest along with lateral expression was found for LC3, Beclin-1, and P62 of both groups. Up-regulated and central expression was observed for MMP-2, MMP-3 and TIMP-1 of both groups, although SI group cannot recognize the margin between NP and AF cells. Utmost autophagy related gene expression was identified in EF group than in SI group. Besides, maximum autophagy accumulating materials was found in EF group than in SI group. And it was amplified along with increasing the duration of compression. Highest, apoptotic gene expression was observed in SI group, whereas, EF group showed lowest expression. Conclusion: External fixator (EF) method was better to study autophagy and apoptosis because it enhanced IVDD after compression which is actively linked with autophagy and apoptosis. Degeneration process was conversely proportional along with duration of compression. On contrary, saline injection (SI) could not distinguish AF and NP cell border.

The type of tail of sheep is an important economic trait. However, the candidate genes associated with the tail type are uncertain. The objective of this study was to identify the genetic region and genotype responsible for the tail type phenotype. Here we perform a genome-wide association study (GWAS) in 40 large tailed Han sheep and 40 Altay sheep as case and 40 Tibetan sheep as control. The results indicated that a total 31 genome-wide significant SNPs associated with type of tail traits were detected. For significant SNPS loci, determine its physical location, and screening of candidate genes within section. By combining information of previously reported and annotated biological functional genes, we identified SPAG17, Tbx15, VRTN, NPC2, BMP2 and PDGFD as the most promising candidate genes for type of tail traits. Based on the above identified candidate genes on type of tail traits, we selected BMP2 and PDGFD to conduct the genetic effect analysis in a large Altay sheep and Tibetan sheep population. Rs119 T>C in the exon1 of BMP2 gene and 1 SNPs in the exon4 (rs69 C>A) of PDGFD gene were detected, rs119 that located on exon1 of BMP2 gene was TT genotype in Altay sheep, while with CC genotype in Tibetan sheep. On rs69 of PDGFD gene, Altay sheep with CC genotype, however, Tibetan sheep with AA genotype. These results indicated that the significant associations of SNPs detected in GWAS were indirectly caused by the genetic effects of BMP2 and PDGFD on sheep tail fat deposition.

Parkinson’s Disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assist in recognizing the PD with good accuracy. The decorrelation function reduces the non-linear correlation between features and bias in order to learn bias-invariant features. The Parkinson’s Progression Markers Initiative (PPMI) dataset, referred to as a single scanner imbalanced dataset in this study used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new Decorrelated Convolutional Neural Networks (DcCNN) framework by applying decorrelation-based optimization to Convolutional Neural Networks(CNN). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN model performs significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner invariant features without significantly impacting the performance of a model. The obtained dataset is a multi-scanner dataset which leads to scanner bias due to the differences in acquisition protocols and scanners. The multi-scanner dataset is a combination of two datasets, namely PPMI and FTLDNI - frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed Feature Extraction-DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multi-scanner dataset for differentiating PD from healthy control, which is superior to the DcCNN model trained on a single scanner imbalanced dataset.

In the bioprinting process, controlling the motion of bioprinters involves a computer-aided design (CAD) model, converting that model into g-code, and transmitting the motion commands to the bioprinters. The g-code file contains information about the motion of the axes and can be generated using various software. However, a challenge arises when using g-code between different bioprinters. Therefore, the objective of this study was to propose a reliable method for analyzing the differences in 3D bioprinting devices by parameterizing a crucial variable for extrusion bioprinting: the E-value. Four bioprinters from three different manufacturers were used to investigate the extrusion behavior when subjected to the same E-value.

In this paper, we propose a pre-configured error pattern ordered statistics decoding (PEPOSD) algorithm and discuss its application to short cyclic redundancy check (CRC)-polar codes. Unlike the traditional OSD that changes the most reliable independent symbols, we regard the decoding process as testing the error patterns, like guessing random additive noise decoding (GRAND). Also, the pre-configurator referred from ordered reliability bits (ORB) GRAND can better control the range and testing order of EPs. Offline-online structure can accelerate the decoding process. Additionally, we also introduce two orders to optimize the search order for testing EPs. Compared with CRC-aided OSD and list decoding, PEPOSD can achieve a better trade-off between accuracy and complexity.

Macrophytes and seagrasses play a crucial role with a variety of the functions in the marine ecosystem and respond synchronized-promptly to the changing climate, followed by ecological status. Monitoring of the seagrasses was one of the paramount topics at the marine environment. One of the fast monitoring techniques is the vegetation acoustics with the advantages compared to the other remote sensing techniques. The acoustic method alone is ambitious to sea-truth identities of the backscatterers at the sea. Therefore, a package of computer programs was developed to identify, estimate leaf biometrics (leaf length and biomass) of one of the common seagrasses, Posidonia oceanica. Some troubleshooting in the acoustical data were fixed and then solved to reach the estimates regarding to the matters encountered for the vegetation as well as fisheries and plankton acoustics. One of the trouble was the “Lost” bottom which occurred during the data collection and post-processing due to occurrence of the acoustical noises, reverberations, interferences and intense scatterers suchlike fish schools. Another problem to remove was the occurrence as studying the near-bottom echoes belonging to submerged vegetations such as seagrasses, followed by spurious echoes. Last one was recognition of the seagrass to estimate leaf length and biomass calibrating the sheaths/vertical rhizome of the seagrass and establishing relationships between acoustical units and biometrics, respectively. As a consequence, an autonomous package of the code written in language MatLab was developed to perform all processes, and the name of the package was abbreviated as POSIBIOM, POSIdonia BIOMass. This study presented the algorithms, methodology, acoustics-biometrics relation, and mapping of the biometrics for the first time, and discussed advantages and disadvantages of the package compared to the software purposed for the bottom types, habitat and vegetation acoustic. The future studies were recommended to improve the package.

Asymmetry in the upper and lower tails is an important feature in modeling bivariate distributions. This article focuses on the log ratio between the tail probabilities at upper and lower corners as a measure of tail asymmetry. Asymptotic behavior of this measure at extremely large and small thresholds is explored with particular emphasis on the skew-normal copula. Our numerical studies reveal that, when the correlation or skewness parameters are around at the boundary values, some asymptotic tail approximations of the skew-normal copulas proposed in the literature are not suitable to compute the measure of tail asymmetry with practically extremal thresholds.

Incremental control strategies provide undeniable advantages for controlling Uncrewed Aerial Vehicles (UAVs) due to their reduced model dependency and accurate tracking capacities. This is of particular relevance for hybrid UAVs, specially for tail-sitters, which perform complex manoeuvres when transitioning to and from aerodynamic flight. This paper summarizes the research undertaken in the application of the Incremental Nonlinear Dynamics Inversion (INDI) and Incremental Backstepping (IBKS) to a small tail-sitter UAV. After the application of these control strategies in a simulated scenario, the implementation in a Micro-Controller Unit was validated in a Hardware-in-the-Loop scenario. Following such validation, indoor experimental trials were undertaken in order to evaluate the capacity of these controllers to stabilize and control the real aircraft. The stabilization in the flight trials was successfully implemented and allow to conclude that both INDI and IBKS are control strategies suited to control a tail-sitter in vertical flight, providing accurate tracking capabilities under smooth actuation. Furthermore, it was found that the IBKS is significantly more computationally demanding than the INDI, although having a global proof of stability that is of interest in aircraft control.

The detection and recognition of tables in image-based documents is a challenging and essential task for automated data extraction and anal- ysis. This paper introduces a methodology that utilizes Convolutional Neural Networks (CNNs) to address this problem. By harnessing the ro- bust visual recognition abilities of CNNs, the proposed method effectively detects and extracts tables from image-based documents. Additionally, an Encoder-Dual Decoder (EDD) architecture is employed to extract the textual content from table cells, enabling the transformation of visual data into structured, machine-readable formats. This structured data is then further processed using a Vector Space Model (VSM)-based language modeling technique for query-based table retrieval. The methodology’s accuracy is assessed by analyzing the spatial relationships between the extracted cells and comparing them against a pre-defined table structure. The results provide an evidence to the effectiveness of the proposed ap- proach, with an impressive 85% accuracy rate and a corresponding error rate of 15%. This accurate extraction and recognition of tables from image-based documents can significantly enhance applications in data analysis, information retrieval, and knowledge extraction.

Aiming at solving difficulties related to aero-engine classification and identification, the infrared spectra of aero-engine hot jets are measured by telemetry Fourier transform infrared spectrometer, and a six types aero-engine hot jet spectrum data set is generated. The spectrum data set were randomly sampled at a ratio of 8:1:1 to generate training set, validation set and prediction set. In this paper, a Peak Finding Siamese Convolutional Neural Network (PF-SCNN) is designed to match and classify spectrum data. In the first place, the matching pairs of training set and validation set are made independently with the labels of positive and negative samples creating. The spectrum feature extraction and distance similarity calculation are carried out by the Siamese convolutional neural network (SCNN). When entering the prediction phase, the prediction set and training set are integrated to predict in the trained model. The prediction set labels are ultimately determined by matching them with the training set labels having the highest similarities to ensure accurate predictions. To improve the operation efficiency of the SCNN, a peak finding method is introduced to extract the spectrum peaks. Peak positions of high frequency are counted, while the peak data are extracted. The peak data are used to take the place of the original data for training and prediction. The performance measures are defined as Accuracy, Precision, Recall, Confusion matrix and F1-score, and the prediction accuracy was up to 99%. The experimental results indicate that the proposed PF-SCNN is suitable for our spectral dataset and can complete the task of infrared spectrum classification of aero-engine hot jets.

Antimony (Sb) has gained significance as a critical raw material (CRM) within the European Union (EU) due to its strategic importance in various industrial sectors, particularly in the textile industry for flame retardants and as a component of Sb-based semiconductor materials. Moreover, Sb is emerging as a potential alternative for anodes used in lithium-ion batteries, a key element in the Energy transition. This study focused on exploring the feasibility of identifying and quantifying Sb mineralizations through the spectral signature of soils using reflectance spectroscopy, a non-invasive remote sensing technique, and by employing deep learning algorithms such as Convolutional Neural Networks (CNNs). Common signal preprocessing techniques were applied to the spectral data, and the soils were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Despite achieving high R-squared values, the study faces a significant challenge of generalization of the model to new data. Despite the limitations, this study provides valuable insights into potential strategies for future research in this field.

This study examines the feasibility of using event-related potentials (ERPs) obtained from electroencephalographic (EEG) recordings as biomarkers for long-term memory item classification. Previous studies have identified old/new effects in memory paradigms associated with explicit long-term memory and familiarity. Recent advancements in convolutional neural networks (CNNs) have enabled the classification of ERP trials under different conditions and identification of features related to neural processes at the single-trial level. We employed this approach to compare three CNN models with distinct architectures using experimental data. Participants (N = 25) performed an association memory task while recording ERPs that were used for training and validation of the CNN models. The EEGNET-based model achieved the most reliable performance in terms of precision, recall, and specificity compared with the shallow and deep convolutional approaches. The classification accuracy of this model reached 70% for known items and 62% for unknown items. Good overall accuracy requires a trade-off between recall and specificity and depends on the architecture of the model and the dataset size. These results suggest the possibility of integrating ERP and CNN into online learning tools and identifying the underlying processes related to long-term memorization.

Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by current machine learning techniques because of a lack of its physiological understanding. To investigate the suitability of FC in BCI for the elderly, we propose the decoding of lower- and higher-order FCs using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. Seventeen younger (24.5±2.7 years) and twelve older (72.5±3.2 years) adults were recruited to perform tasks related to hand-force control with or without mental calculation. CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increases the classification accuracy by 88.3% compared to the filter-bank common spatial pattern (FBCSP). LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe depending on task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly.

A plenty of theories on the origin of genetic codes have been proposed so far, yet all ignored the energetic driving force, its relation to the biochemical system, and most importantly, the missing “matchmaker” between proteins and nucleic acids. Here, a new hypothesis is proposed, according to which ATP is at the origin of the primordial genetic code by driving the coevolution of the genetic code with the pristine biochemical system. This hypothesis aims to show how the genetic code was produced e.g. by photochemical reactions in a protocell that derived from a lipid vesicle enclosing various life’s building blocks (e.g. nucleotides and peptides). At extant cell, ATP is the only energetic product of photosynthesis, and is at the energetic heart of the biochemical systems. ATP could energetically form and elongate chains of both polynucleotides and polypeptides, thus acting a “matchmaker” between these two bio-polymers and eventually mediating precellular biochemical innovation from energy transformation to informatization. ATP was not the only one that could drive the formation of polynucleotides and polypeptides, but favored by precellular selection. The protocell innovated a photosynthesis system to produce ATP efficiently and regularly with the aids of proteins and RNA/DNA. The completion of permanently recording the genetic information by DNA marked the dawn of cellular life operated by Darwinian evolution. The ATP hypothesis assumes or supports the photochemical origin of life, shedding light on the origins of both photosynthetic and biochemical systems, which remains largely unknown thus far. Based on ATP hypothesis, virus (like the new coronavirus) could not be the earliest life on Earth, as it has neither biochemical systems nor lipid bilayer membrane that provided relatively isolated environment for the development of protobiochemical reactions, although it owns the genetic code of a cellular life. Virus could not be a bridge between life and non-life, but is an anti-life substance, as it depletes cellular material for its own replication, and then spreads by destroying the host cells. It can be imagined that if cellular life are completely wiped out by the virus, the complete destruction of life on Earth would be inevitable.

Background: Dual function tail tubular proteins (TTP) belonging to the lytic bacteriophages are the interesting group of biologically active enzymes. Surprisingly, apart from their structural function, they are also polysaccharide hydrolyzes destroying bacterial extracellular components. One of the representatives of this group is TTPB from Klebsiella pneumoniae phage – KP32. TTPB hydrolyzes exopolysaccharide (EPS) of Klebsiella pneumoniae and Enterococcus faecalis strain. This depolymerizing feature was associated with the activity to prevent bacterial biofilm formation. TTPB can inhibit biofilm formation by K. pneumoniae, Enterobacter cloacae, Staphylococcus aureus, Enterococcus faecalis and Pseudomonas aeruginosa strains. Moreover, synergistic activity with antibiotic action has been observed, most likely due to depolymerases’ facilitation of contact of antibiotic with bacterial cells. Methods: TTPB was overexpressed in E coli system, purified and tested towards the bacterial strains using agar overlay method. The hydrolytic activity of TTPB was performed using EPSs of K. pneumoniae PCM2713 and E. cloacae ATCC 13047 as the substrates. Next, we determined the reducing sugar (RS) levels in the TTPB/EPS mixtures, regarding the RS amount obtained after acidic hydrolysis. The antibiofilm activity of TTPB has been set down on bacterial biofilm using a biochemical method. Finally, we have demonstrated the synergistic activity of TTPB with kanamycin. Results: For the first time, the hydrolytic activity of TTPB towards bacterial EPSs has been shown. TTPB releases about a half of the whole RS amount of EPSs belonging to K. pneumoniae PCM 2713 and E. cloacae ATCC 13047 strains. 1.12 µM of the phage protein reduces biofilm of both strains by over 60%. Destroying the bacterial biofilm the phage protein improves the antibiotic action increasing kanamycin effectiveness up to four times.

Early diagnosis of paroxysmal atrial fibrillation (PAF) could suggest patients to receive timely interventions in clinical practice. Various PAF onset prediction algorithms might benefit from accurate heart rate variability (HRV) analysis and machine learning classification but to be challenged in real-time monitoring scenarios. The aim of this study is to present an end-to-end deep learning based PAFNet model that integrates a sliding window technique on raw R-R interval of electrocardiogram (ECG) segments to achieve a real-time prediction of PAF onsets. This integration enable the deep convolutional neural network (CNN) to be customized as a light-weight architecture that accommodates the size of sliding windows simply by altering the input layer, specifically its effectiveness in making a new prediction with each new heartbeat. Catering to potential influence of input sizes, three CNN models were trained using 50, 100, and 200 R-R intervals, respectively. For each model, the performance of automated algorithms was evaluated for PAF prediction using a ten-fold cross-validation. As a results, a total of 56,381 PAFN-type and 56,900 N-type R-R interval segments was collected from publicly accessible ECG databases, and a promising prediction performance of the automated algorithm with a 100 R-R intervals was achieved in terms of the sensitivity of 97.12%, the specificity of 97.77%, and the accuracy of 97.45%, respectively. Importantly, the automated algorithm with a sliding windows step of 1 could process one sample in only 23.1 milliseconds and identify the onset of PAF at least 45 minutes in advance. The present results suggest that the sliding window technique on raw R-R interval sequences along by deep learning based algorithms may offers the possibility of providing an accurate, real-time, and end-to-end clinical tool for mass monitoring of PAF.

This article is devoted to the problem of genetically coding of inherited cyclic structures in biological bodies, whose life activity is based on a great inherited set of mutually coordinated cyclic processes. The author puts forward and arguments the idea that the genetic coding system is capable of encoding inherited cyclic processes because it itself is a system of cyclic codes connected with Boolean algebra of logic. In other words, the physiological processes in question are cyclical because they are genetically encoded by cyclic codes. In support of this idea, the author presents a set of his results on the connection of the genetic coding system with cyclic Gray codes, which are one of many known types of cyclic codes. This opens up the possibility of using for modeling inherited cyclic biostructures those algebraic and logical theories and constructions that are associated with Gray codes and have long been used in engineering technologies: Karnaugh maps, Hilbert curve, Hadamard matrices, Walsh functions, dyadic analysis, etc. Additionally, the connections of these constructions with statistical rules of genomic DNAs and binary-genomic numbers are considered.

Motivated by experimental success in designing linear amphiphilic copolymers with selective demicellization capabilities through head-to-tail depolymerization triggered by specific stimuli, we advanced the design paradigm by increasing the number of triggering groups on the copolymer chain through architectural modifications. Utilizing molecular dynamics simulations, we explored the demicellization and cargo release dynamics of linear and miktoarm copolymers, featuring one, two and three hydrophobic block or branches, each capable for head-to-tail depolymerization. Our findings revealed that, under stoichiometric trigger molecule concentrations, miktoarms with three branches exhibited consistently faster depolymerization rates than those with two branches and linear copolymers. Conversely, at constant trigger molecule concentrations, the depolymerization rates of copolymers exhibited more complex behavior influenced by two opposing factors: the excess of trigger molecules, which increased with a decrease in the number of hydrophobic branches or blocks, and simultaneous head-to-tail depolymerization, which intensified with an increasing number of branches. Our study elucidates the intricate interplay between copolymer architecture, trigger molecule concentrations, and depolymerization dynamics, providing valuable insights for the rational design of amphiphilic copolymers with tunable demicellization and cargo release properties.

Tail-sitters aim to combine the advantages of fixed-wing and rotor-craft, but demand a robust and fast stabilization strategy to perform vertical maneuvers and transitions to and from aerodynamic flight. The research conducted in this work intends to assess the performance of nonlinear control strategies to stabilize the attitude of a X-Vert VTOL aircraft when hovering, comparing existing solutions to applications of Nonlinear Dynamics Inversion (NDI) and its incremental version, INDI. Such controllers are implemented and tuned in simulation in order to stabilize a model of the tail-sitter , complemented by estimation methods that allow to feed back the necessary variables. These estimators and controllers are then implemented in a microcontroller, validating them in a Hardware-in-the-Loop (HITL) scenario, with simple maneuvers in vertical flight. Lastly, the developed control solutions are used to stabilize the aircraft in experimental flight, being monitored by a motion capture system. The experimental results allow to validate the model of the X-Vert and compare the effectiveness of the different control solutions in stabilizing it, with the INDI presenting itself as a more robust control strategy, with better tracking capabilities and less actuator demands.

Public transportation scheduling aims to optimize the allocation of transportation resources, enhance transportation efficiency, and increase passenger satisfaction, which is crucial for building a sustainable urban transportation system. As a complement to public transportation, shared bikes provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, such as peak usage periods in the morning and evening and significant fluctuations in demand across different areas, the optimization of shared bike dispatch can better meet user needs, thereby reducing vehicle vacancy rates and increasing operating revenue. Addressing this issue, this paper designs a comprehensive decision-making approach for spatio-temporal demand prediction and bike dispatch optimization. For demand prediction, we designed a T-GCN based bike demand prediction model. In terms of dispatch optimization, we considered factors such as dispatch capacity, distance restrictions, and dispatch costs, and designed an optimization solution based on genetic algorithms. Finally, this paper validates the approach using shared bike operating data, shows that T-GCN can effectively predict short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution verifying the model’s effectiveness. The shared bike dispatch approach proposed in this paper, which combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.

In celIs, microtubule location, length, and dynamics are regulated by a host of microtubule-associated proteins and enzymes that read where to bind and act based on the microtubule “tubulin code,” which is predominantly encoded in the tubulin carboxy-terminal tail (CTT). Katanin is a highly conserved AAA ATPase enzyme that binds to the tubulin CTTs to remove dimers and sever microtubules. We have previously demonstrated that short CTT peptides are able to inhibit katanin severing. Here, we examine effects of CTT sequences on this inhibition activity. Specifically, we examine CTT sequences found in nature, alpha1A (TUBA1A), detyrosinated alpha1A, Δ2 alpha1A, beta5 (TUBB/TUBB5), beta2a (TUBB2A), beta3 (TUBB3), and beta4b (TUBB4b). We find that these natural CTTs have distinct abilities to inhibit, most noticeably beta3 cannot inhibit katanin. Two non-native CTT tail constructs are also unable to inhibit – despite having 94% sequence identity with alpha1 or beta5 sequences. Surprisingly, we demonstrate that poly-E and poly-D peptides are capable of inhibiting katanin significantly. An analysis of the hydrophobicity of the CTT constructs indicates that more hydrophobic polypeptides are less inhibitory than more polar polypeptides. These experiments not only demonstrate inhibition, but also likely interaction and targeting of katanin to these various CTTs when they are part of a polymerized microtubule filament.

Freshwater pond aquaculture is the prevailing fish culture system worldwide, especially in developing countries. Climate change outcomes and inadequate environmental practices challenge its sustainability. This study applies emergy synthesis to assess the environmental performance of freshwater pond aquaculture in Brazil, aiming to identify and propose practices towards sustainability. As a study model, nine semi-intensive lambari farms operating at three levels of management were evaluated: low (LC), moderate (MC) and high (HC) control. Results showed that the main inputs for LC were services (27-46%), feed (7-39%), and water (15-21%), while for the MC and HC farms, they were feed (35-49% and 17-48%, respectively) and services (33-39% and 26-36%, respectively). All farms required more than 60% of their emergy from purchased inputs, resulting in low emergy sustainability index (ESI = 0.1-0.5). Replacing animal protein and oil on diet composition by vegetal sources, using superficial water instead of springwater, increasing juvenile productivity, and controlling pond fertilization can lead all systems to higher efficiency and resilience, increasing sustainability.

The discovery of a simple numerical formula for the projection of all the atomic mass of life-sustaining CONHSP bioatoms leads to the emergence of a set of Nested CODES unifying all the biological, genetic and genomic components by unifying them from bioatoms up to 'to whole genomes. In particular, we demonstrate the existence of a digital meta-code common to the three languages ​​of biology that are RNA, DNA and amino acid sequences. Through this meta-code, genomic and proteomic images appear almost analogous and correlated. The analysis of the textures of these images then reveals a binary code as well as an undulatory code whose analysis on the human genome makes it possible to predict the alternating bands constituting the cariotypes of the chromosomes. The application of these codes to perspectives in astrobiology, in Cancers basic research and the emergence of binary codes and regions of local stability (voting process), whose fractal nature we demonstrate, is illustrated.

This paper introduces a class of symmetric ciphering systems with a finite secret key, which provides ideal secrecy, autonomy in key generation and distribution, and robustness against the probabilistic structure of messages (Ideally Secret Autonomous Robust – ISAR system). The ISAR system is based on wiretap polar codes constructed over an artificial wiretap channel with a maximum secrecy capacity of 0.5. The system autonomously maintains a minimum level of key equivocation by continuously refreshing secret keys without needing additional key generation and distribution infrastructure. Thus, this class of systems generalizes Shannon's ideal and strongly ideal ciphering systems. Additionally, it can transform any stream ciphering system with a finite secret key of known length into an ISAR system without the necessity of knowing and/or changing its algorithm. Therefore, this class of system strongly supports privacy, a critical requirement for contemporary security systems. The ISAR system's reliance on wiretap polar coding for strong secrecy ensures resistance to passive attacks with known plaintext messages. Furthermore, resistance to passive attacks on generated keys follows directly from ideal secrecy and autonomy. The results presented offer an efficient methodology for synthesizing this class of systems with predetermined security margins and a complexity of the order of nlogn, where n is the block length of the applied polar code.

Coconut is an important multipurpose crop wherein different stages of its maturity are used for different products. Traditional approach of its classification use sound formed by tapping the fruit, and the tapper will base its judgment through the sound he heard. The procedure was highly subjective and attempts had been made by other researchers to automate and classify the adulthood of the coconut objectively. Nowadays, deep learning techniques are being utilized to solve classification problems. One such method is Convolutional Neural Network (CNN) that takes raw pixel data, learns to extract features, and ultimately classifies the input. In this study, a portable device was developed that takes audio signals generated from tapping the fruit mechanically. This audio clip was converted to spectrogram and was used as input to a LeNet CNN architecture. We argue that CNN classifier has improved accuracy compared to the non-deep learning system. Our evaluation confirmed that the use of CNN had improved the accuracy of classifying coconut by about 15%.

A convolutional neural network (CNN) is sometimes understood as a black box in the sense that while it can approximate any function, studying its structure will not give us any insights into the nature of the function being approximated. In other terms, the discriminative ability does not reveal much about the latent representation of a network. This research aims to establish a framework for interpreting the CNNs by profiling them in terms of interpretable visual concepts and verifying them by means of Integrated Gradient. We also ask the question, "Do different input classes have a relationship or are they unrelated?" For instance, could there be an overlapping set of highly active neurons to identify different classes? Could there be a set of neurons that are useful for one input class whereas misleading for a different one? Intuition answers these questions positively, implying the existence of a structured set of neurons inclined to a particular class. Knowing this structure has significant values; it provides a principled way for identifying redundancies across the classes. Here the interpretability profiling has been done by evaluating the correspondence between individual hidden neurons and a set of human-understandable visual semantic concepts. We also propose an integrated gradient-based class-specific relevance mapping approach that takes the spatial position of the region of interest in the input image. Our relevance score verifies the interpretability scores in terms of neurons tuned to a particular concept/class. Further, we perform network ablation and measure the performance of the network based on our approach.

Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperparameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed the advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.

In this paper an integrated methodology for the coupling between 1D- and 3D-CFD simulation codes is presented, which has been developed to support the design and calibration of new diesel engines. The aim of the proposed methodology is to couple 1D engine models, which may be available in the early-stage engine development phases, with 3D predictive combustion simulations, in order to obtain reliable estimates of engine performance and emissions for newly designed automotive diesel engines. The coupling procedure features simulations performed in 1D-CFD by means of GT-SUITE and in 3D-CFD by means of Converge, executed within a specifically designed calculation methodology. An assessment of the coupling procedure has been performed by comparing its results with experimental data acquired on an automotive Diesel engine, considering different working points including both part load and full load conditions. Different multiple injection schedules have been evaluated for part-load operation, including pre and post injections. The proposed methodology, featuring detailed 3D chemistry modeling, was proven to be capable to properly assess pollutant formation, specifically to estimate NOx concentrations. Soot formation trend was also well-matched for most of the explored working points. The proposed procedure can therefore be considered as a suitable methodology to support the design and calibration of new Diesel engines, thanks to its ability to provide reliable engine performance and emissions estimations from the early-stage of a new engine development.

Alzheimer's disease (AD) is a progressive and evolving neurodegenerative disease with an insidious onset that can lead to memory loss and cognitive impairment. There is no effective treatment for this disease. However, early diagnosis plays an important role in treatment planning to slow down its progression, as treatment has the greatest impact in the early stages of the disease. Neurological images obtained through different imaging techniques provide powerful information and help diagnose the disease. With the wide application of deep learning techniques in disease diagnosis, especially the prominence of Convolutional Neural Networks (CNNs) in computer vision and image processing, more and more studies are proposing the use of this algorithm for the diagnosis of AD. In this paper, we first systematically introduce the impact of AD on people, detailing the biomarkers, early clinical symptoms, and risk factors of this disease. Secondly, it goes on to detail the development of CNNs, their form, and methods to help diagnose AD. It is proposed that CNNs can help diagnose AD by analyzing medical imaging data, particularly structural brain scans such as magnetic resonance imaging (MRI) and functional scans such as positron emission tomography (PET). Finally, it is concluded that CNNs are of great importance for the diagnosis of AD and that they are likely to play an increasingly important role in the early detection of the disease, the understanding of disease mechanisms, and ultimately, in the development of effective AD therapies and interventions. CNNs are playing an increasingly important role in the Their potential impact on healthcare emphasizes the importance of continued research and innovation in neural networks and medical imaging.

Hydrogen from natural gas reforming can be produced efficiently with a high CO2 capture rate. This can be achieved with oxygen-blown autothermal reforming as core technology combined with pressure-swing adsorption for hydrogen purification and refrigeration-based tail gas separation for CO2 capture and recirculation of residual hydrogen, carbon monoxide and methane. The low-temperature tail gas separation section is presented in detail, and to quantify the exergy efficiency of this section, a detailed exergy analysis is conducted. The irreversibilities in 42 different process components are quantified. In order to provide a transparent verification of the consistency of exergy calculations, the total irreversibility rate is calculated by two independent approaches: Through the bottom-up approach, all individual irreversibilities are added to obtain the total irreversibility rate. Through the top-down approach, the total irreversibility rate is calculated solely by the exergy flows crossing the control volume boundaries. The consistency is verified as the comparison of results obtained by the two methods shows a relative deviation of 4·10-7. The exergy efficiency of the CO2 capture process is calculated based on two different definitions. Both methods give a baseline exergy efficiency of 58.38%, which indicates a high degree of exergy utilisation in the process.

The classification of goods involved in international trade in Brazil is based on the Mercosur Common Nomenclature (NCM). The classification of these goods represents a real challenge due to the complexity involved in assigning the correct category codes especially considering the legal and fiscal implications of misclassification. This work focuses on the training of a classifier based on Bidirectional En-coder Representations from Transformers (BERT) for the tax classification of goods with NCM codes. In particular, this article presents results from using a specific Portuguese Language tuned BERT model as well results from using a Multilingual BERT. Experimental results justify the use of these models in the classification process and also that the language specific model has a slightly better performance.

Several types of soft tissue sarcomas have peripheral infiltrative growth characteristics called tail-like lesions. The efficacy of neoadjuvant therapy for tumors with tail-like lesions has not been elucidated. From 2012 to 2019, we analyzed 36 patients with soft tissue sarcoma with tail-like lesions treated with neoadjuvant therapy, including chemotherapy, radiotherapy, or both. The ef-fect of neoadjuvant therapy on the tail sign was investigated by analyzing the change in tail-like lesions during neoadjuvant therapy and histological responses. The median length of the tail-like lesion reduced from 29.5 mm at initiation to 19.5 mm after neoadjuvant therapy. The extent of shrinkage in tail-like lesion was related to the histopathological responses in the main part. Com-plete disappearance of the tail-like lesion was observed in 12 patients; however, it was not related to achieving a microscopically negative margin. The oncologic outcomes did not significantly differ between cases with complete disappearance of tail-like lesions or not. This study indicated the shrinkage of tail-like lesions did not have significant effect on complete resection or improve-ment of clinical outcomes. A more comprehensive evaluation is needed to elaborate on the surgi-cal strategy.

The Mediator is composed of multiple subunits conserved from yeast to humans and plays a central role in transcription. The tail components are not required for basal transcription but are required for response to different stresses. While some stresses are familiar such as heat, desiccation, and starvation, others are exotic, yet yeast can elicit a successful stress response. MCHM is a hydrotrope that induces growth arrest in yeast. We found that a naturally occurring variation in the Med15 allele, a component of the Mediator tail, altered the stress response to many chemicals in addition to MCHM. Med15 contains two polyglutamine repeats (polyQ) of variable lengths that change the gene expression of diverse pathways. Med15 protein existed in multiple isoforms and its stability was dependent on Ydj1, a protein chaperone. The protein level of the Med15 with longer polyQ tracts was lower and turned over faster than the allele with shorter polyQ repeats. MCHM sensitivity via variation of Med15 was regulated by Snf1 in a Myc tag dependent manner. Tagging Med15 with Myc altered its function in response to stress. Genetic variation in transcriptional regulators magnifies genetic differences in response to environmental changes. These polymorphic control genes are master variators.

The practice of the veterinary profession is regulated by public law and corporate regulations. The most fundamental corporate regulations of veterinary associations, to which membership is a condition of practising the profession, are the codes of veterinary ethics. The codes of ethics contain five basic rules for practising veterinary responsibilities to animals, clients, the public, the veterinary team, and the veterinarians themselves. Veterinarians are often faced with dilemmas about whose interests to protect; this is due to the tasks the profession carries out while protecting the animals of their owners and the public. Ethical principles are a guideline for veterinarians in difficult situations. Depending on the hierarchy of values imposed by codes of ethics, different decisions can be made. In this article, we present the importance that the Polish code of veterinary ethics gives to animal welfare as an obligation of veterinarians towards animals and compare how animal welfare is positioned in the European, American, British and New Zealand codes of veterinary ethics. In contrast to other codes, the Polish code of veterinary ethics does not sufficiently recognise animal welfare as a value to be protected in the practice of the profession.

Using smartphone especially android platform has already got eighty percent market shares, due to aforementioned report, it becomes attacker’s primary goal. There is a growing number of private data onto smart phones and low safety defense measure, attackers can use multiple way to launch and to attack user’s smartphones.(e.g. Using different coding style to confuse the software of detecting malware). Existing android malware detection methods use multiple features, like safety sensor API, system call, control flow structure and data information flow, then using machine learning to check whether its malware or not. These feature provide app’s unique property and limitation, that is to say, from some perspectives it might suit for some specific attack, but wouldn’t suit for others. Nowadays most malware detection methods use only one aforementioned feature, and these methods mostly analysis to detect code, but facing the influence of malware’s code confusion and zero-day attack, aforementioned feature extraction method may cause wrong judge. So, it’s necessary to design an effective technique analysis to prevent malware. In this paper, we use the importance of word from apk, because of code confusion, some malware attackers only rename variables, if using general static analysis wouldn’t judge correctly, then use these importance value to go through our proposed method to generate picture, finally using convolutional neural network to see whether the apk file is malware or not.

This article focuses on generating an alternative in order to identify traffic officers during driving. This research employed the latest You Only Look Once (YOLO) model, using a six-phase methodology: data collection, data preparation involving resizing and labeling, implementation of various filters to avoid overfitting, model training, prediction evaluation, and result interpretation. The YOLO model was applied across three iterations using a dataset of 1862 images. To enhance training efficiency and detection speed, the graphics processing unit (GPU) acceleration was utilized, further enhancing the experimental process. The results of this study revealed that the YOLOv8x variant produced the most promising results. This proposed model attained a remarkable F1 score of 0.95, bolstered by a confidence score of 0.631, with the potential for an increase to 0.80 in confidence without significantly compromising the F1-score. These findings are poised to make a substantial contribution to the broader research landscape, particularly in advancing the effectiveness of detection models for traffic officers.

In the realm of computer vision, semantic segmentation is the task of recognizing objects in images at the pixel level. This is done by performing a classification of each pixel. The task is complex and requires sophisticated skills and knowledge about the context to identify objects’ boundaries. The importance of semantic segmentation in many domains is undisputed. In medical diagnostics, it simplifies the early detection of pathologies, thus mitigating the possible consequences. In this work, we provide a review of the literature on deep ensemble learning models for polyp segmentation and we develop new ensembles based on convolutional neural networks and transformers. The development of an effective ensemble entails ensuring diversity between its components. To this end, we combine different models (HarDNet-MSEG, Polyp-PVT, and HSNet) trained with different data augmentation techniques, optimization methods, and learning rates, which we experimentally demonstrate to be useful to form a better ensemble. Most importantly, we introduce a new method to obtain the segmentation mask which is more suitable for combining transformers in an ensemble. In our extensive experimental evaluation, the proposed ensembles exhibit state-of-the-art performance.

An application of deep convolutional neural network and recurrence plot for financial market movement prediction is presented. Though it is challenging and subjective to interpret its information, the pattern formed by a recurrence plot provide a useful insight into the dy- namical system. We used a recurrence plot of seven financial time series to train a deep neural network for financial market movement predic- tion. Our approach is tested on our dataset and achieved an average of 53.25% classification accuracy. The result suggests that a well trained deep convolutional neural network can learn a recurrence plot and pre- dict a financial market direction.

With the increase of the Artificial Neural Network (ANN), machine learning has taken a forceful twist in recent times. One of the most spectacular kinds of ANN design is the Convolutional Neural Network (CNN). The Convolutional Neural Network (CNN) is a technology that mixes artificial neural networks and up to date deep learning strategies. In deep learning, Convolutional Neural Network is at the center of spectacular advances. This artificial neural network has been applied to several image recognition tasks for decades and attracted the eye of the researchers of the many countries in recent years as the CNN has shown promising performances in several computer vision and machine learning tasks. This paper describes the underlying architecture and various applications of Convolutional Neural Network.

(1) Background: The aim of this study was to compare the survival benefit of radical ante-grade-modular pancreato-splenectomy (RAMPS) with conventional distal pancreato-splenectomy (cDPS) in left-sided pancreatic cancer. (2) Methods: A retrospective propensity score matching (PSM) analysis was conducted on 333 patients who underwent RAMPS or cDPS for left-sided pancreatic cancer at four tertiary cancer centers. The study assessed prognostic factors and com-pared survival and operative outcomes. (3) Results: After PSM, 99 patients were matched in each group. RAMPS resulted in a higher retrieved lymph node count than cDPS (15.0 vs. 10.0, p < 0.001). No significant differences were observed between the two groups in terms of R0 resection rate, blood loss, hospital stay, or morbidity. The 5-year overall survival rate was similar in both groups (cDPS vs. RAMPS, 44.4% vs. 45.2%, p = 0.853), and disease-free survival was also comparable. Multivariate analysis revealed that ASA score, preoperative CA19-9, histologic differentiation, R1 resection, adjuvant treatment, and lymphovascular invasion were significant prognostic factors for overall survival. Preoperative CA19-9, histologic differentiation, T-stage, adjuvant treatment, and lymphovascular invasion were independent significant prognostic factors for disease-free survival. (4) Conclusions: Although RAMPS resulted in a higher retrieved lymph node count, survival outcomes were not different between two groups. RAMPS was a surgical option to achieve R0 resection rather than a standard procedure. Adjuvant treatment was an independent prognostic factor for both overall and disease-free survival.

Deep convolutional neural networks (CNNs) have revolutionised computer vision technology by automatically learning hierarchical representations of image data, leading to state-of-the-art performance in visual recognition tasks. This article presents a comprehensive review of deep CNNs, from their evolution and architectures to the current state-of-the-art research. The review also provides the core concepts and building blocks of CNNs and their concise mathematical representations. Furthermore, it explores applications of deep CNNs in three popular domains, including medical diagnosis, remote sensing, and facial recognition. This review will benefit researchers and practitioners in computer vision and artificial intelligence, as well as industry professionals seeking to leverage the latest advancements in deep learning technologies.

Convolutional neural network (CNN), a class of artificial neural network (ANN) is attracting interests of researchers in all research domain. CNN was invented for computer vision. They have also shown to be useful for semantic parsing, sentence modeling and other natural language processing related tasks. Here in this paper we discuss the basics of CNN models and their scope to provide a reference/baseline to the researchers interested in using CNN models in their research.

Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.

This paper proposes an interpretable geometry solution based on the formal language set of text and diagram. Geometry problems are solved by machines, which still poses challenges in natural language processing and computer vision. Significant progress promotes existing methods in the extraction of geometric formal languages. However, the neglect of the graph structure information in the formal language and the lack of further refinement of the extracted language set can lead to the poor effect of the theorem prediction and affect the accuracy in problem solving. In this paper, the formal language graph is constructed by the extracted formal language set and used to theorem prediction by graph convolutional network. So as to better extract the relationship set of diagram elements, an improved diagram parser was proposed. The test results indicate that the improved method has good results in solving the problem with interpretability geometry.

Autonomous image recognition has numerous potential applications in the field of planetary science and geology. For instance, having the ability to classify images of rocks would allow geologists to have immediate feedback without having to bring back samples to the laboratory. Also, planetary rovers could classify rocks in remote places and even in other planets without needing human intervention. Shu et al. classified 9 different types of rock images using a Support Vector Machine (SVM) with the image features extracted autonomously. Through this method, the authors achieved a test accuracy of 96.71%. In this research, Convolutional Neural Networks(CNN) have been used to classify the same set of rock images. Results show that a 3-layer network obtains an average accuracy of 99.60% across 10 trials on the test set. A version of Self-taught Learning was also implemented to prove the generalizability of the features extracted by the CNN. Finally, one model has been chosen to be deployed on a mobile device to demonstrate practicality and portability. The deployed model achieves a perfect classification accuracy on the test set, while taking only 0.068 seconds to make a prediction, equivalent to about 14 frames per second.

Objectives: In order to solve the problem that most of the existing research focuses on the binary tactile attributes of objects,which ignores the tactile attribute strength and category recognition,an attribute strength and category recognition method based on convolutional neural network matrix-label is proposed. Methods:Firstly,in the data preparation stage,we preprocess the raw data and determine the matrix labels to build the haptic dataset.Secondly,in the feature extraction stage,we fuse the haptic data of two fingers and use the convolutional neural network to extract the attribute strength features.Finally,in the attribute strength and category recognition stage,all channel haptic data is fused to predict the attribute strength and category.Results:We compared with the multi-label convolutional neural network method in terms of elastic strength,hardness strength and category,and compared the attribute strength recognition capabilities of the two methods using novel objects outside the haptic dataset.The results show that the accuracy of the last 20 iterations of the matrix-label method has an average elastic strength of 96.73%,hardness strength of 97.34%,and category of 96.67%.The performance is better.When the Euclidean distance between the prediction of the novel object and the real label is less than 1,the accuracy of the elastic strength is best to reach 100%,and the hardness strength is best to reach 100%.The performance is better. Conclusions:The effectiveness of the method has been verified.Comparing with the convolutional neural network method,our method can effectively recognize the attribute strength and category of objects.

The detection of seismic events at regional and teleseismic distances is critical to Nuclear Treaty Monitoring. Traditionally, detecting regional and teleseismic events has required the use of an expensive multi-instrument seismic array; however in this work, we present DeepPick, a novel seismic detection algorithm capable of array-like performance from a single trace. We achieve this directly, by training our single-trace detector against labeled events from an array catalog, and by utilizing a deep temporal convolutional neural network. The training data consists of all arrivals in the International Seismological Centre Catalog for seven seismic arrays over a five year window from 1 Jan 2010 to 1 Jan 2015, yielding a total training set of 608,362 detections. The test set consists of the same seven arrays over a one year window from 1 Jan 2015 to 1 Jan 2016. We report our results by training the algorithm on six of the arrays and testing it on the seventh, so as to demonstrate the transportability and generalization of the technique to new stations. Detection performance against this test set is outstanding. Fixing a type-I error rate of 1%, the algorithm achieves an overall recall rate of 73% on the 141,095 array beam picks in the test set, yielding 102,394 correct detections. This is more than 4 times the 23,259 detections found in the analyst-reviewed single-trace catalogs over the same period, and represents an 8dB improvement in detector sensitivity over current methods. These results demonstrate the potential of our algorithm to significantly enhance the effectiveness of the global treaty monitoring network.

Current knowledge of the RNA world indicates two different genetic codes being present throughout the living world. In contrast to non-coding RNAs that are built of repetitive nucleotide syntax, the sequences that serve as templates for proteins share – as main characteristics – a non-repetitive syntax. The differences in their syntax structure is coherent with the difference of the functions they represent. Whereas non-coding RNAs build groups that serve as regulatory tools in nearly all genetic processes, the coding sections represent the evolutionarily successful function of the genetic information storage medium. The DNA genomes themselves are rather inactive, whereas the non-coding RNA domain is highly active, even as non-random genetic innovation operators. This indicates that repetitive syntax is the essential pre-requisite for RNA interactions to install variable RNA-group-identities, whereas the non-repetitive syntax serves as a stable conservation tool for successful selection processes out of RNA-groups cooperation and competition. The interaction opportunities of RNA loops with repetitive syntax are higher than with non-repetitive ones. Interestingly, these two genetic codes resemble the function of all natural languages, i.e., (a) everyday language use for organization and coordination of biotic group behavior, and (b) artificial (instrumental) language use for conservation of blueprints for complex protein-body constructions.

Celiac disease is a gluten-sensitive immune-mediated enteropathy of the small intestine that occurs in genetically predisposed individuals. Abnormal immune response results in mucosal inflammation, villous atrophy, and crypt hyperplasia. This study was a proof-of-concept exercise that used a convolutional neural network to classify hematoxylin and eosin (H&E) histological images of celiac disease, normal small intestine control, and non-specified duodenal inflammation; 7294, 11,642, and 5966 images, respectively. The trained network classified celiac disease images with high performance (accuracy 99.7%, precision 99.6%, recall 99.3%, F1-score 99.5%, and specificity 99.8%). Interestingly, when the same network (already trained for the 3 class images), analyzed duodenal adenocarcinoma (3723 images), the new images were classified as duodenal inflammation in 63.65%, small intestine control in 34.73%, and celiac disease in 1.61% of the cases. Finally, when the network was retrained using the 4 histological subtypes of images, all performance parameters were above 99% for celiac disease. In conclusion, the convolutional neural network (CNN)-based deep neural system was able to classify medical histological images with high performance. Narrow artificial intelligence (AI) is designed to perform tasks that typically require human intelligence, but it operates within limited constraints and is task specific.

Creating intelligent systems capable of recognizing emotions is a difficult task, especially when looking at emotions in animals. This paper describes the process of designing a “proof of concept” system to recognize emotions in horses. This system is formed by two elements, a detector and a model. The detector is a fast region-based convolutional neural network that detects horses in an image. The model is a convolutional neural network that predicts the emotions of those horses. These two elements were trained with multiple images of horses until they achieved high accuracy in their tasks. 400 images of horses were collected and labeled to train both the detector and the model while 80 were used to validate the system. Once the two components were validated, they were combined into a testable system that would detect equine emotions based on established behavioral ethograms indicating emotional affect through head, neck, ear, muzzle and eye position. The system showed an accuracy of between 69% and 74% on the validation set, demonstrating that it is possible to predict emotions in animals using autonomous intelligent systems. Such a system has multiple applications including further studies in the growing field of animal emotions as well as in the veterinary field to determine the physical welfare of horses or other livestock.

Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits. For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis can influence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludes that proposed model attains an accuracy of 89.59% and MSE of 0.1041 which outperform well other baseline models.

Nowadays smartphone utilization for disease diagnosis and remote health care applications has become promising due to their ubiquity. Here, a novel convolutional neural network method for detecting keratoconus that is wholly implemented on a smartphone is proposed. The proposed method provides accurate detection of over 72.9% for all stages of keratoconus. Preliminary results indicate 90%, 83%, 64% and 52% detection rate for severe, advanced, moderate and mild stages of disease, respectively.

In recent years, the field of deep learning achieved considerable success in pattern recognition, image segmentation and may other classification fields. There are a lot of studies and practical applications of deep learning on images, video or text classification. In this study, we suggest a method for changing the architecture of the most performing CNN models with the aim of designing new models to be used as stand-alone networks or as a component of an ensemble. We propose to replace each activation layer of a CNN (usually a ReLu layer) by a different activation function stochastically drawn from a set of activation functions: in this way the resulting CNN has a different set of activation function layers.

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

Epilepsy is a neurological disorder that affects approximately 1% of the world's population. To diagnose and estimate the occurrence of epilepsy, the analysis of recorded brain activity is performed by a neurologist, which is not only time-consuming and tedious but also occasionally accompanied by human error. Therefore, in recent decades, researchers have aimed to unravel an approach for designing and building an automated method for diagnosing and estimating the occurrence of epilepsy. Accordingly, the present study proposed two new-fangled ways based on brain signals and a convolutional neural network (CNN). Moreover, this research implements a CNN with a sequential three-layer structure. Numerous experiments were performed, and the accuracy of estimating epilepsy using the developed methods was achieved at 95% without feedback and 97% with feedback. The proposed methods were proven to be more accurate than the previous techniques and can be employed as a physician's assistant once entering the field of operation.

Docking the tail of lambs is a standard husbandry procedure and is achieved through several techniques including clamps, hot or cold knives and latex rings, the last of which is the most popular. All tail docking methods cause acute pain which can be reduced by application of local anesthetic, however precise anatomical injection for optimal efficacy requires considerable skill. This pen trial evaluated the ability of local anesthetic delivered with a dual function ring applicator / injector to alleviate acute tail docking pain. Thirty ewe lambs were assigned to one of three treatment groups (n = 10 per group): ring plus local anesthetic (Ring LA), ring only (Ring) and sham handled control (Sham). Lambs were videoed and behavior categorized every 5 minutes for the first hour and every 10 min for the subsequent 2 hours after treatment. There was a significant effect (p &lt; 0.001) of treatment on total active pain related behaviors in the first hour, with Ring lambs showing higher counts compared to Ring LA or Sham. Ring lambs also displayed a significantly higher count of combined abnormal postures (p &lt; 0.001) than Ring LA or Sham lambs. Delivery of 1.5ml of 2% lignocaine via the dual action device abolished abnormal behaviors and signs of pain in Ring LA lambs. However, lambs in the Ring LA group spent less time attempting to suckle compared to Ring and Sham lambs, suggesting that some residual discomfort remained.

In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view including drone and non-drone targets. The analysis of the received signals allows multiple targets to be distinguished because of the different reflection patterns. The proposed framework consists of four processes including signal processing, cloud points clustering, target tracking, and target recognition. Signal processing translates the raw signals into spare cloud points. These points are merged into several clusters, each representing a single target. Target tracking estimates the new locations of each detected target. A novel convolutional neural network model is developed for drone and/or non-drone targets feature extraction and recognition. For performance evaluation, a dataset collected with an IWR6843ISK mmWave sensor by Texas Instruments is used for training and testing the convolutional neural network. The proposed recognition model achieves an accuracy of 98.4% for one target and 98.1% for two targets.

Seawater density is an important physical property in oceanography that affects the accuracy of calculations such as gravity fields and tidal potentials, and the calibration of acoustic and optical oceanographic sensors. In related studies, constant density values are frequently used, which can introduce significant errors. Therefore, this study employs a basic convolutional neural network model to construct a comprehensive model showing the seawater density distribution across the globe. The model takes into account depth, latitude, longitude, and month as inputs. Numerous real seawater datasets were used to train the model, and it has been shown that the model has an absolute mean error and root mean square error of less than 2 kg/m3 in 99% of the test set samples. The model effectively demonstrates the influence of input parameters on the distribution of seawater density. In this paper, we present a newly developed global model for distributing seawater density which is both comprehensive and accurate, surpassing previous models. The utilization of the model presented in this paper for estimating seawater density can minimize errors in theoretical ocean models and serve as a foundation for designing and analyzing ocean exploration systems.

Handwriting is a form of biometric behavioral characteristic with evident individual distinctiveness. With the surge of the deep learning trend and the demand for forensic identification, handwriting identification has become one of the focal points of research in the field of pattern recognition. The research on handwriting identification in major world languages has reached a mature stage. However, there is still a notable lack of relevant research in the field of Mongolian handwriting identification, despite the fact that Mongolian is used by over 4 million people in China. This paper embarks on an initial exploration of Mongolian handwriting identification by constructing a convolutional neural network named MWInet-12. In this paper, the model evaluation experiments were conducted using a dataset comprising 156,372 samples contributed by 125 authors from the MOLHW dataset. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio. The final results of the experiments reveal impressive accuracy on the test set, achieving a top-1 accuracy of 89.60% and a top-5 accuracy of 97.53%. Furthermore, through comparative experiments involving Resnet, Fragnet, and GRRNN models, this paper establishes that the proposed model yields the most favorable results for Mongolian handwriting identification.

Near Earth Asteroids represent potential threats to human life because their trajectories may bring them in the proximity of the Earth. Monitoring these objects could help predict future impact events, but such efforts are hindered by the large numbers of objects that pass through the Earth’s vicinity. Additionally, there is also the problem of distinguishing asteroids from other objects in the night sky, which implies sifting through large sets of telescope image data. Within this context, we believe that employing machine learning techniques could greatly improve the detection process by sorting out the most likely asteroid candidates to be reviewed by human experts. At the moment, the use of machine learning techniques is still limited in the field of astronomy and the main goal of the present paper is to study the effectiveness of deep CNNs for the classification of astronomical objects, asteroids in this particular case, by comparing some of the well-known deep convolutional neural networks, including InceptionV3, Xception, InceptionResNetV2 and ResNet152V2. We have applied transfer learning and fine-tuning on these pre-existing deep convolutional networks and from the results that we have obtained one can see the potential of using deep convolutional neural networks in the process of asteroid classification. The InceptionV3 model has the best results in the asteroid class, meaning that by using it, we loose the least number of valid asteroids.

Personality is the most critical feature that tells us about an individual. It is the collection of the individual’s thoughts, opinions, emotions and more. Personality detection is an emerging field in research and Deep Learning models have only recently started being developed. There is a need for a larger dataset that is unbiased as the current dataset that is used is in the form of questionnaires that the individuals themselves answer, hence increasing the chance of unconscious bias. We have used the famous stream-of-consciousness essays collated by James Pennbaker and Laura King. We have used the Big Five Model often known as the five-factor model or OCEAN model. Document-level feature extraction has been performed using Google’s word2vec embeddings and Mairesse features. The processed data has been fed into a deep convolutional network and a binary classifier has been used to classify the presence or absence of the personality trait. Hold- out method has been used to evaluate the model, and the F1 score has been used as the performance metric.

Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis [1-7]. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 Tera-OPS (TOPS - operations per second), generating convolutions of images of 250,000 pixels with 8-bit resolution for 10 kernels simultaneously — enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition.

This paper presents a study and implementation of a convolutional neural network to identify and recognize humpback whale specimens from the unique patterns of their tails. Starting from a dataset composed of images of whale tails, all the phases of the process of creation and training of a neural network are detailed – from the analysis and pre-processing of images to the elaboration of predictions, using TensorFlow and Keras frameworks. Other possible alternatives are also explained when it comes to tackling this problem and the complications that have arisen during the process of developing this paper.

Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperpa-rameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.

Weeds are unwanted and invasive plants that proliferate and compete for resources such as space, water, nutrients, and sunlight, affecting the quality and productivity of the desired crops. Weed detection is crucial for the application of precision agriculture methods and for this purpose machine learning techniques can be used, specifically convolutional neural networks (CNN). This study focuses on the search for CNN architectures and technology used to detect and identify weeds in different crops; 61 articles applying CNN architectures and technology were analyzed in the last five years (2019-2023). The results show the used of different devices to acquire the image for training, such as digital cameras, smartphones, and drone cameras. Additionally, the YOLO family and algorithms are the most widely adopted architectures, followed by VGG, ResNet, Faster R-CNN, AlexNet, and MobileNet, respectively. This study provides an update on CNNs that will serve as a starting point for researchers wishing to implement these weed detection and identification techniques.

In recent years, Edge Intelligence (EI) has emerged, combining edge computing with AI, specifically deep learning, to run AI algorithms directly on edge devices. In practical applications, EI faces challenges related to computational power, power consumption, size, and cost, with the primary challenge being the trade-off between computational power and power consumption. This has rendered traditional computing platforms unsustainable, making heterogeneous parallel computing platforms a crucial pathway for implementing EI. In our research, we leveraged the Xilinx Zynq 7000 heterogeneous computing platform, employed High-Level Synthesis (HLS) for design, and implemented two different accelerators for LeNet-5 using loop unrolling and pipelining optimization techniques. The experimental results show that when running at a clock speed of 100 MHz, the PIPELINE accelerator, compared to the UNROLL accelerator, experiences an 8.09% increase in power consumption but speeds up by 14.972 times, making the PIPELINE accelerator superior in performance. Compared to the CPU, the PIPELINE accelerator reduces power consumption by 91.37% and speeds up by 70.387 times, while compared to the GPU, it reduces power consumption by 93.35%.This study provides two different optimization schemes for edge intelligence applications through design and experimentation, and demonstrates the impact of different quantization methods on FPGA resource consumption. These experimental results can provide a reference for practical applications, thereby providing a reference hardware acceleration scheme for edge intelligence applications.

Convolutional neural networks (CNN) have attracted much attention as a commonly used method for hyperspectral image (HSI) classification in recent years, however, CNNs can only be applied to Euclidean data and have limitations in dealing with relationships due to the limitations of local feature extraction. However, each pixel of a hyperspectral image contains a set of spectral bands that are correlated and interact with each other, and the methods used to process Euclidean data cannot effectively obtain these correlations. In contrast, the graph convolutional network (GCN) can be used in non-Euclidean data, but usually leads to oversmoothing and ignoring local detail features due to the need for superpixel segmentation processing to reduce computational effort. To overcome the above problems, we constructed a network a fusion network based on GCN and CNN, which contains two branches: a graph convolutional network based on superpixel segmentation and a convolutional network with added attention mechanism. The graph convolu-tional branch can extract the structural features and capture the relationships between the nodes, and the convolutional branch can extract the detailed features in the local fine region. Owing to the fact that the features extracted from the two branches are different, the classification performance can be improved by fusing the complementary features extracted from the two branches. To vali-date the proposed algorithm, experiments were conducted on three widely used datasets, namely Indian Pines, Pavia University, and Salinas, and the overall accuracy of 98.78% was obtained in the Indian Pines dataset, and the overall accuracy of 98.99% and 98.69% was obtained in the other two datasets. The results showed that the proposed fusion network can obtain richer features and achieve high classification accuracy.

To solve two key problems in the identification of people who are infected with COVID-19: the first problem is that the identification accuracy is not high enough. The second problem is that present identification method such as nucleic acid testing is expensive in many countries. Methods: So, I decided to design a fast identification method for COVID-19 patients which is based on deep learning. After the model (CoughNet) learns more than 6,000 cough spectrograms of both COVID-19 patients and normal people, the accuracy rate of identification of COVID-19 patients and normal people is higher than 99% in the test set. Structure: This paper is mainly divided into three parts: the first part introduces the main background and research status of the research; The second part introduces the research methods; The third part introduces the specific process of the experiment.

Recently, much attention has been devoted to finding highly efficient and powerful activation functions for CNN layers. Because activation functions inject different nonlinearities between layers that affect performance, varying them is one method for building robust ensembles of CNNs. The objective of this study is to examine the performance of CNN ensembles made with different activation functions, including six new ones presented here: 2D Mexican ReLU, TanELU, MeLU+GaLU, Symmetric MeLU, Symmetric GaLU, and Flexible MeLU. The highest performing ensemble was built with CNNs having different activation layers that randomly replaced the standard ReLU. A comprehensive evaluation of the proposed approach was conducted across fifteen biomedical data sets representing various classification tasks. The proposed method was tested on two basic CNN architectures: Vgg16 and ResNet50. Results demonstrate the superiority in performance of this approach. The MATLAB source code for this study will be available at https://github.com/LorisNanni.

Topology optimization is a pioneering design method that can provide various candidates with high mechanical properties. However, the high-resolution for the optimum structures is highly desired, normally in turn leading to computationally intractable puzzle, especially for the famous Solid Isotropic Material with Penalization (SIMP) method. In this paper, an efficient and high-resolution topology optimization method is proposed based on the Super-Resolution Convolutional Neural Network (SRCNN) technique in the framework of SIMP. The SRCNN includes four processes, i.e. refining, path extraction & representation, non-linear mapping, and reconstruction. The high computational efficiency is achieved by a pooling strategy, which can balance the number of finite element analysis (FEA) and the output mesh in optimization process. To further reduce the high computational cost of 3D topology optimization problems, a combined treatment method using 2D SRCNN is built as another speeding-up strategy. A number of typical examples justify that the high-resolution topology optimization method adopting SRCNN has excellent applicability and high efficiency for 2D and 3D problems with arbitrary boundary conditions, any design domain shape, and varied load.

Use of local anesthesia at the time of ring castration and tail docking can improve lamb welfare. However, few local anesthetics are registered for sheep, and data on their duration of effect is limited. Three studies were conducted to evaluate the efficacy of procaine (P), lidocaine (L) and bupivacaine (B) in terms of observed alleviation of behavioral responses to castration and/or tail docking in 10-min blocks in the first 60 min post treatment. In each study, comparisons were made between two groups of lambs castrated and/or tail docked with rubber rings and either receiving the agent using the NUMNUTS® instrument (N) or receiving no anesthetic agent (RR). Acute pain behavior was lower in NL (n = 28) than RRL (n = 15) males in the first 10 min post procedure (P &lt; 0.05); lower in NB (n = 16) than RRB (n = 16) males in periods 10-20 min (0.05 &lt; P &lt; 0.01), 20-30 min (P &lt; 0.05) and 40-50 min (0.05 &lt; P &lt; 0.01); lower in NB (n = 16) than RRB (n = 16) females between 20 and 40 min post-procedure (0.05 &lt; P &lt; 0.01); lower in NP (n = 8) than RRP (n = 7) males in period 10-20 min (0.05 &lt; P &lt; 0.01), and lower in NP (n = 9) than RRP (n = 9) females in periods 0-10 min (0.05 &lt; P &lt; 0.01), and 10-40 min (P &lt; 0.05). Analgesic benefits were modest, and the effects of procaine appear to last longer than lidocaine, while bupivacaine is slower to take effect than either procaine or lidocaine but may provide longer lasting analgesia. The duration of action of local anesthetics is short in sheep, and detailed behavioral evaluations are required in the first hour post procedure to establish efficacy.

Popular geo-computer vision works make use of aerial imager with sizes ranging from 64 × 64 to 1024 × 1024 pixels without any overlap, although the learning process of deep learning models can be affected by the reduced semantic context or the lack of information near image boundaries. In this work, the impact of three image sizes (256 × 256, 512 × 512, and 1024 × 1024 pixels) and two image overlap levels (no overlap and 12.5% overlap) on the performance of road classification models was statistically evaluated. For this, two convolutional neural networks used in various tasks of geospatial object extraction were trained (using the same hyperparameters) on a large dataset (containing aerial image data covering 8650 km² of the Spanish territory that was labelled with binary road information) under twelve different scenarios, each scenario featuring a different combination of tile size and overlap. To assess their generalisation capacity, the performance of all resulting models was evaluated on a data from novel areas covering approximately 825 km². The performance metrics obtained were analysed using appropriate descriptive and inferential statistical techniques to evaluate the impact of the performance at distinct levels of the fixed factors (tile size, tile overlap, neural network architecture). Statistical tests were applied to study the main and interaction effects of the fixed factors on the performance. A significance level of 0.05 was applied to all the null hypothesis tests. The results were highly significant for the main effects (p-values lower than 0.001), while the two-way and three-way interaction effects among them had different levels of significance. The results indicate that the training of road classification models on images with a higher tile size (more semantic context) and tile overlap (additional border context and continuity) significantly impacts their performance. The best model was trained on a dataset featuring tiles with a size of 1024 × 1024 pixels and a 12.5% overlap and achieved a loss value of 0.0984, an F1 score of 0.8728, and an ROC-AUC score of 0.9766, together with an error rate of 3.5% on the test set.

The deaf-mute and individuals with hearing disabilities who communicate through sign language should not be overlooked or excluded. It is a social responsibility to strive for an inclusive world without inequality. Advances in technology offer various solutions to address the communication gap, one of which is the use of computer-based sign language recognition systems. This project proposes a Malaysian Sign Language recognition model utilizing MobileNetV1, a convolutional neural network-based algorithm, to interpret sign language in real-time settings through a website application using Streamlit. The outcome of the model test shows that it has achieved a recognition rate of 96 percent. Although the proposed model has demonstrated the ability to recognize sign language, it is suggested that further improvement can be achieved by enhancing the quality and diversity of the dataset. The implementation of a more comprehensive dataset would lead to improved performance and increased accuracy of the model. This project underscores the importance of considering and accommodating the needs of the deaf-mute and individuals with hearing disabilities and highlights the potential of technology to bridge communication barriers and achieve a more inclusive society.

As the world increasingly becomes more interconnected, the demand for safety and security is ever-increasing, particularly for industrial networks. This has prompted numerous researchers to investigate different methodologies and techniques suitable for intrusion detection systems (IDS) requirements. Over the years, many studies have proposed various solutions in this regard including signature-based and machine-learning (ML) based systems. More recently, researchers are considering deep learning (DL) based anomaly detection approaches. Most proposed works in this research field aimed to achieve either one or a combination of high accuracy, considerably low false alarm rates (FARs), high classification specificity and detection sensitivity, achieving lightweight DL models, or other ML and DL-related performance measurement metrics. In this study, we propose a novel method to convert a raw dataset to an image dataset to magnify patterns. Based on this we devise an anomaly detection for IDS using a lightweight convolutional neural network (CNN) that classifies denial of service and distributed denial of service. The proposed methods were evaluated using a modern dataset, CSE-CIC-IDS2018, and a legacy dataset, NSL-KDD. We have also applied a combined dataset to assess the generalization of the proposed model across various datasets. Our experimental results have demonstrated that the proposed methods achieved high accuracy and considerably low FARs with high specificity and sensitivity. The resulting loss and accuracy curves have also demonstrated the excellent generalization of the proposed lightweight CNN model, effectively avoiding overfitting. This holds for both the modern and legacy datasets, including their mixed version.