Miracle-dz/UniFAR-FTUs huggingface dataset:
Inactivation of food-borne enteropathogenic bacteria and spoilage fungi using pulsed-light

The lethality of high-intensity pulsed-light emissions from low and high ultraviolet (UV) light sources on predetermined microbial populations has been investigated. Prior to treatment, the bacterial enteropathogens Bacillus cereus, Escherichia coli, and Salmonella enteritidis and the food-spoilage fungi Aspergillus niger and Fusarium culmorum were seeded separately onto the surface of either tryptone soya yeast extract or malt extract agar plates. Prescribed microbial population densities were applied to the test media and these samples were exposed to one of two light sources. These were low-pressure, xenon filled, flash lamps that produced either high or low UV intensities. They were operated in pulsed mode, being driven by a stacked Blurnlein table generator. Microbial samples were treated by exposure to different numbers of light pulses. The treated bacterial populations were reduced by /spl sim/8 log orders after 1000 light-pulses of the higher UV intensity light and the fungal counts had a corresponding reduction of 4.5 log orders. The fungus, Aspergillus niger, was shown to be significantly more resistant in spore form to the intense UV light compared with Fusarium culmorum. This resistance has been attributed to the high level of UV absorbance associated with the dark pigment present in A. niger. The pulsed light source of lower UV intensity was shown to be significantly less effective in reducing microbial populations.
On gravity–capillary lumps. Part 2. Two-dimensional Benjamin equation

A theoretical study is made of fully localized solitary waves, commonly referred to as ‘lumps’, on the interface of a two-layer fluid system in the case that the upper layer is bounded by a rigid lid and lies on top of an infinitely deep fluid. The analysis is based on an extension, that allows for weak transverse variations, of the equation derived by Benjamin (J. Fluid Mech. vol. 245, 1992, p. 401) for the evolution in one spatial dimension of weakly nonlinear long waves in this flow configuration, assuming that interfacial tension is large and the two fluid densities are nearly equal. The phase speed of the Benjamin equation features a minimum at a finite wavenumber where plane solitary waves are known to bifurcate from infinitesimal sinusoidal wavetrains. Using small-amplitude expansions, it is shown that this minimum is also the bifurcation point of lumps akin to the free-surface gravity–capillary lumps recently found on water of finite depth. Numerical continuation of the two symmetric lump-solution branches that bifurcate there reveals that the elevation-wave branch is directly connected to the familiar lump solutions of the Kadomtsev–Petviashvili equation, while the depression-wave branch apparently features a sequence of limit points associated with multi-modal lumps. Plane solitary waves of elevation, although stable in one dimension, are unstable to transverse perturbations, and there is evidence from unsteady numerical simulations that this instability results in the formation of elevation lumps.
Long wavelength (15 and 23 μm) GaAs/AlGaAs quantum cascade lasers

Quantum cascade lasers based on GaAs/AlGaAs chirped superlattice active regions have been achieved at wavelengths of 15 and 23 μm. In pulsed mode they operate up to temperatures of 220 and 100 K, respectively. While the 15 μm-laser employs a standard waveguide with n+-doped cladding, the 23 μm-laser was made with a metallic surface plasmon waveguide. The threshold current densities at cryogenic temperatures of 2.2 kA/cm2 (15 μm) and 10.2 kA/cm2 (23 μm) reflect the differences in intersubband lifetimes and waveguide losses close to the reststrahlenband.
Reconfigurable Intelligent Surface: an angular spectrum representation approach

The design of Reconfigurable Intelligent Surfaces (RISs) is largely based on treating the RIS as an infinitely large, continuous surface that steers an incident plane wave towards the desired direction. In practical implementations, however, the RIS has finite size and the incident wave is a beam of finite extent, rather than a delocalized plane wave. To understand the implications of the finite extent of both the RIS and the incident beam, here we treat the RIS in the spectral domain, where the RIS acts as a spatial filter to transform the k-content of the incident wave. We extract analytical expressions that provide the necessary insight into the dependencies and trade-offs between crucial parameters such as the beam shape, the RIS response, and the link topology. We demonstrate how our approach provides an intuitive perspective to understand the underlying mechanisms that characterize and determine the performance of RIS-aided links and to design several complex functionalities that are crucial for future wireless communications.
Cloud Native Applications Profiling using a Graph Neural Networks Approach

The convergence of Telecommunication and industry operational networks towards cloud native applications has enabled the idea to integrate protection layers to harden security posture and management of cloud native based deployments. In this paper, we propose a data-driven approach to support detection of anomalies in cloud native application based on a graph neural network. The essence of the profiling relies on capturing interactions between different perspectives in cloud native applications through a network dependency graph and transforming it to a computational graph neural network. The latter is used to profile different deployed assets like micro-service types, workloads' namespaces, worker machines, management and orchestration machines as well as clusters. As a first phase of the profiling, we consider a fine-grained profiling on microservice types with an emphasis on network traffic indicators. These indicators are collected on distributed Kubernetes (K8S) deployment premises. Experimental results shows good trade-off in terms of accuracy and recall with respect to micro-service types profiling (around 96%). In addition, we used predictions entropy scores to infer anomalies in testing data. These scores allow to segregate between benign and anomalous graphs, where we identified 19 out of 23 anomalies. Moreover, by using entropy scores, we can conduct a root cause analysis to infer problematic micro-services.
Facial Expression in Tourism Destinations Using A Deep Learning Approach

The development of the world of tourism is currently supported by information technology. New tourist attractions are introduced through social media. Apart from being easy and cheap to reach for visitors, tourist attractions must also be responsive to improved facilities and services. The step is through regular visitor surveys. One way that can be done is through visitors' facial expressions with the help of deep learning. In this research, the proposed contribution is using a convolutional neural network with 34 layers. The goal is to get good accuracy, but the computational burden of the training process is light. The image data used comes from secondary datasets to identify angry, disgusted, scared, happy, neutral, sad. , and surprised classes with a total of 5,250 images. Data Augmentation technique is used to overcome the class imbalance. The results showed that the system could recognize facial expressions with an average accuracy of 99.38%. The average computational time for the training process to get the model is 25 minutes 23 seconds, with a testing time of 1–2 seconds. Experimental data testing results above 98%: error MSE 0.0445, RMSE 0.2110, and MAE 0.0150.
Design and Simulation of Microstrip Patch Antenna for Wireless Communication Systems

In this age of information technology, wireless communication is becoming more popular every day. And these wireless applications have some effect on the antenna. In this paper, a microstrip patch antenna design, simulation, and analysis are done. And to make this design, Rogger RT/Duroid 5880, whose dielectric permittivity is 4.3, has been used as the substrate material. Besides, antenna thickness and tangent loss are 0.1 mm and 0.035, respectively. After simulation, the antenna has a return loss VSWR, directivity gain, and bandwidth are -19.89 dB, 1.22, 6.73 dBi, and 128.9 MHz, respectively. The motive of this antenna is to reduce the S-parameter and get a standard VSWR, which will have a value of less than two or close to one, as well as to improve the antenna's directivity gain and give it a good bandwidth. This antenna can be used in radars, mobile phones, wireless LANs, and other wireless applications.
Visualization of the Relationship Between Lectures and Laboratories Using SSNMF

This study aims to visualize the relationship between lectures and fields of specialization (laboratories) so that students can choose lectures with a future direction. Since the university curriculum is highly flexible; students choose their own lectures. Taking into account their own objectives, students select the basic knowledge necessary for their purposes. However, it is difficult for students without sufficient knowledge to understand their relevance from the syllabus. The purpose of this study is to propose a method for estimating the relevance between lectures and laboratories in an undergraduate school as a help to provide students with an objective analysis of lectures, i.e., not only knowledge but also examples of its use. The proposed method applies a semi-supervised non-negative matrix factorization to identify common factors of knowledge in each combination of lecture and laboratory. It is suggested that the proposed method calculates reasonable results for the relationship between lectures and laboratories.
Johannesburg Lightning Nowcasting From Meteorological Data and Electric Field Using Machine Learning

This paper presents the work and findings of using machine learning algorithms namely, logistic regression (LR), random forest (RF), and long short-term memory (LSTM) network, to nowcast Johannesburg's cloud-to-ground lightning events within a 30 km radius of the city center between the period of 1 November 2021 to 27 February 2022. The investigation evaluated each model's ability to nowcast lightning strokes over the city from recorded historical values of the electric field, air temperature, dew point, and relative humidity for a forecast horizon of 15 minutes. Performance metrics indicate that the recall score for the LSTM is the lowest at 53% while that of the RF and LR are 80% and 93% respectively. The RF and LSTM models achieved lower recall scores but demonstrated less sensitivity to making false predictions, while the logistic regression made a relatively higher number of false positive misclassifications. A precision score of 41% for the LSTM indicates that the model is able to predict non-lightning occurrence more precisely than the LR and RF models which reported precision scores of 9% and 11% respectively. The models' prediction performance over the Sentech and Hillbrow towers has also been assessed and analyzed, and results indicate that a model's predictive ability is heavily influenced by cloud-to-cloud lightning.
Out-of-plane Photons In SPECT

Photons originating from planes adjacent to the slice of interest introduce considerable qualitative and quantitative error in single photon emission computerized tomography (SPECT) images. The extent of out-of-plane contributions was studied, and a method to compensate for these photons was examined. Simulations using accurate Monte Carlo models were performed to acquire projection data. Analysis of these projections showed that as much as 12% of the total counts corresponding to a 1.8-cm-thick slice of interest were due to out-of-plane nonscatter and that out-of-plane scatter could compromise up to 16% of a projection pixel's total count. Reconstructions were obtained by maximum-likelihood expectation-maximization methods, that accounted for the out-of-plane phenomena. These reconstructions gave an improvement in count quantification, noise, and contrast as compared to performing in-plane compensation alone. These findings required 3-D reconstructions with out-of-plane compensation in order to obtain reliable quantitative SPECT images. >
Interactive Path-Following Method of Snake-like Robot

Snake-like robots are widely studied for their high degree of flexibility. An important way to avoid collisions during a journey for a snake is that the whole body moves along a given path. In order to achieve safe and efficient robot movement in the working environment, this paper proposes a path-following method that takes into account the position accuracy and computational speed. The constraints on the path curve and joint angle are clarified by the analysis of the geometric relations. A new forward kinematics model of the articulated snake-like robot is established as a control basis. Even the snake-like robots with different lengths can move along any complex 3D path using this method. Furthermore, the desired path can be artificially adjusted in real time so that the robot can avoid the obstacle through the manual control of the operator in an unknown environment. Finally, the effectiveness of the method is verified by simulations and experiments, and its efficiency performance is demonstrated. The six-section snake-like robot used in the experiment can accurately track the desired path, and the average single-step calculation takes less than 1ms. The accuracy and real-time performance of the method fully fulfill the requirement of engineering applications.
Imaging Physiological Signals

By creating two-dimensional images from one-dimensional signals, time series-to-image conversion methods can enable the use of image classification deep learning networks for the processing of physiological signals. However, how the created images are related to specific features of the physiological signal, learnable by the model, are not thoroughly investigated. Focusing on the photoplethysmogram (PPG) signal, in this work, by training deep learning models, we investigate how manually-defined features of the PPG signal are related to images created via various time series-to-image conversion methods. The results from this study could offer guidelines for selecting proper image creation methods for the classification problems involving physiological signals.
Centralized Reliability and Security Models for Home-based and Consumer Internet of Things

Consumer IOT is one of the fastest growing market in ICT (Information and Communication Technology) space which needs constant attention on device protection to safeguard the consumers data from cyber security attacks and threat. We elucidated the home IOT security management, framework and different topologies are required to deploy at home. The AI based models are required to predict the Malware (Botnet) attacks with limited consumer IOT resources and this AI based model could deployed along with home automation software or hardware or separately using microcontroller like Raspberry Pi. The local government IOT regulation and privacy act are playing vital role on data collection and data sharing between home IOT and cloud server.
Automate Descriptive Answer Grading using Reference based Models

Global universities are establishing institutional setups that offer a hybrid format of education. The next step of education is to maintain quality and flexibility, such as providing the option to convert online courses such as Massive Open Online Courses (MOOCS) to course credits. However, several universities are reluctant to completely transition to online-based education due to poor digital experience in educational tools. The available evaluation tools such as Multiple-choice answers (MCQ) aren't able to evaluate students holistically. In this study, research work aims for an improvised reference-based approach (utilizing student and reference answers) that evaluates descriptive answers with the Siamese architecture- Roberta bi-encoder based transformer models for Automated Short Answer Grading (ASAG). The architecture was designed considering ASAG tasks constrained to feasible compute resources. The research work presents the competitive performance of the models, further improvised with finetuning and hyperparameter optimization process on the benchmark SemEval-2013 2way task dataset.
Pre-hospital Triage Improvement for Ambulances via 5G Video and Vital Data Communication

The use of real-time video-audio and vital patient data significantly improves the effectiveness of remote assessment in emergencies, compared with the current audio-only pre-hospital communications. This was found during recently completed 5G trials, involving ambulance professionals. The aim of the trials was to investigate how 5G-enabled video-audio and vital patient data monitoring can benefit remote patient assessment and what are the potential implementation constraints. An indoor 5G standalone network was used for the trials, with measured round-trip time as low as 7 ms. With the trials, it was verified that the decision making was quicker and more accurate when the remotely based Chief Medical Officer of an ambulance service received real-time video-audio complimented with vital patient data (for example electrocardiogram) from the paramedic. This meant that the pre-hospital triage significantly improved accelerating patient treatment and avoiding unnecessary conveyance to the hospital. Recommendations are also given addressing the observed implementation constraints.
Coverage of Targets in Mobile Sensor Networks With Limited Mobility

In Mobile Sensor Networks (MSN), covering targets with minimum movement is an important issue. We consider two related problems but with a limited mobility model where no sensor can move beyond a certain distance. In the first problem, we minimize the sum of the movements of all sensors. And in the other, we minimize their maximum. We solve the first problem by relaxing the equivalent Integer Linear Program (ILP) where the maximum allowable distance is a parameter. Experimental results show that our algorithm gives the solution very close to the optimal. For the second problem, we apply binary search and repeatedly execute the relaxed LP until we find the smallest value of the maximum distance that gives a feasible solution. We could find movements of sensors that satisfy the above limit in all our experiments with different random placements of sensors and targets.
Robust Phase Retrieval with Non-Convex Penalties

This paper proposes an alternating direction method of multiplier (ADMM) based algorithm for solving the sparse robust phase retrieval with non-convex and non-smooth sparse penalties, such as minimax concave penalty (MCP). The accuracy of the robust phase retrieval, which employs an l1 based estimator to handle outliers, can be improved in a sparse situation by adding a non-convex and non-smooth penalty function, such as MCP, which can provide sparsity with a low bias effect. This problem can be effectively solved using a novel proximal ADMM algorithm, and under mild conditions, the algorithm is shown to converge to a stationary point. Several simulation results are presented to verify the accuracy and efficiency of the proposed approach compared to existing methods.
Combining Conditional Generative Adversarial Networks and YOLOv4 for Mango Classification

Mango is one of the most important exporting products in Taiwan and it has provided Taiwan with numerous economic benefits. To maximize the value, mangos are usually classified into grade A, grade B, and grade C before they are sent to exporting process, domestic sale, or fruit punching factory, respectively. Since the freshness of mangos is limited by time, it is important to accelerate the classification process. To improve the classification performance, this research proposes the Irwin mango classification system (IMCS) by combining Conditional Generative Adversarial Network (CGAN) and YOLOv4. CGAN is applied to help data expansion when the amount of data is insufficient. YOLOv4 is applied to classify the grade of mango and to detect the location of mango. To show our approach is better than other methods, we compare our model with YOLOv3 SPP, YOLOv4, ResNet, AlexNet, VGG 16, DenseNet, ShuffleNet, Fusion model, and the best outcome in AI CUP. The experiments show that our CGAN plus YOLOv4 (CGAN+YOLOv4) model has the best outcome with 85% precision for training, 82% precision for testing, and 90.07% for mAP. The weighted average recall (WAR) is 84.83% which is better than the best outcome of AI CUP 2020.
Greedy-Based Precise Expansion Algorithm for Customized Group Team Formation Problem

The team formation problem is a social network research issue. Studies on this topic have recently proposed various team structures. However, these structures have been inflexible and imperfect. Moreover, in order to build teams with the lowest communication cost, proposed structures have always contained some communicators assigned to the team who lack expert skills. To improve upon defective structures, we propose the Customized Group Team Formation. Teams can be constructed automatically without any predefined structure. This allows project leaders to choose exemplary teammates based on skill requirements. Additionally, the proposed greedy-based precise expansion algorithm, the method of expertise query closest to the real world, can also save preprocessing time and reduce the amount of non-professional people on the team. Experiments on real datasets show effective performance on communication cost and cardinality.
The Application of Artificial Neural Network Model in Estimation of Single Tree Volume Growth

This paper takes Pinus tabulaeformis plantation in Beijing mountainous areas for example, and establishes BP artificial neural network model which can be applied to the estimation of single tree volume growth. By using qualitative analysis and Pearson correlation analysis, the standard tree height (SH), standard diameter at breast height (SD), standard tree age (A) and storage per acre (CA) in the sample plots are selected as the input variables of the model. With the volume increment (Z) of standard trees in the last growing season in each sample plot as the output variable, the BP artificial neural network model of three-layer structure is established, and training and simulation towards the model have been carried out by using the measured comprehensive survey data of Pinus tabulaeformis plantation plot in Beijing mountainous areas. By applying error percentage method and linear regression method, the simulation effects of various models have been verified and compared. The results show that the simulation of artificial neural network model towards the single tree volume growth of Pinus tabulaeformis in Beijing mountainous areas is of high accuracy. This model can be applied to the effective prediction and simulation of tree growing process in this area after determining the reasonable input variables and network structures.
Identifying Dependent Annotators in Crowdsourcing

Crowdsourcing is the learning paradigm that aims to combine noisy labels provided by a crowd of human annotators. To facilitate this label fusion, most contemporary crowdsourcing methods assume conditional independence between different annotators. Nevertheless, in many cases this assumption may not hold. This work investigates the effects of groups of correlated annotators in multiclass crowdsourced classification. To deal with this setup, a novel approach is developed to identify groups of dependent annotators via second-order moments of annotator responses. This in turn, enables appropriate dependence aware aggregation of annotator responses. Preliminary tests on synthetic and real data showcase the potential of the proposed approach.