Miracle-dz/UniFAR-FTUs huggingface dataset:
Alginate-Based Edible Films and Coatings for Food Packaging Applications

Alginate is a naturally occurring polysaccharide used in the bio industry. It is mainly derived from brown algae species. Alginate-based edible coatings and films attract interest for improving/maintaining quality and extending the shelf-life of fruit, vegetable, meat, poultry, seafood, and cheese by reducing dehydration (as sacrificial moisture agent), controlling respiration, enhancing product appearance, improving mechanical properties, etc. This paper reviews the most recent essential information about alginate-based edible coatings. The categorization of alginate-based coatings/film in food packaging concept is formed gradually with the explanation of the most important titles. Emphasis will be placed on active ingredients incorporated into alginate-based formulations, edible coating/film application methods, research and development studies of coated food products and mass transfer and barrier characteristics of the alginate-based coatings/films. Future trends are also reviewed to identify research gaps and recommend new research areas. The summarized information presented in this article will enable researchers to thoroughly understand the fundamentals of the coating process and to develop alginate-based edible films and coatings more readily.
Dynamic Generation of Recommendations for EV Battery Health

Electric vehicles equipped with Lithium-ion batteries face performance loss due to battery ageing. This effect can be actively influenced through behaviour introduced by vehicle users. Therefore, this paper proposes a dynamic recommendation architecture to automatically generate dynamic recommendations in order to prolong battery lifetime.We propose dynamic recommendations as well as requirements for them. The recommendations suggest a certain user behaviour for a specific chronological scope in the future as well as a weight based on their impact on maintaining battery health.Furthermore, we present an exemplary architecture, based on the requirements. Using historical electric vehicle driving data, it can automatically derive dynamic recommendations.
Image Super-Resolution Reconstruction Based on Disparity Map and CNN

This paper presents a new method for registration of digital micro-mirror device (DMD) cameras through image reconstruction. The method called dual-channel super-resolution reconstruction (DCSR) uses a dual-channel input convolutional neural network method to iteratively obtain high resolution images. The originality of this study relies on the input color weight disparity map that enables extracting edges cues and feature cues effectively during the super-resolution. The iterative initial disparity map is obtained by a cost aggregation method based on the color weights, and the model parameters are updated during the iterative process. To validate performance, a series of the comparison experiments is developed on different network structures and parameter settings for the diverse deconv-pooling layers, convolution kernel sizes, and activation function. Experimental results show that the DCSR method can solve the sub-pixel registration problem of DMD cameras and achieve a good image reconstruction effect. The DCSR performs higher accuracy and an efficient process compared with traditional reconstruction made by single-channel input convolution neural network. The spatial resolution of the reconstructed image is significantly improved, and the detail resolution is enhanced.
Energy Cost Optimization and DER Scheduling for Unified Energy Management System of Residential Neighborhood

Consumers with distributed energy resources (DERs) and intelligent building energy management systems (BEMS) can achieve significant electricity bill reduction by participating in demand response (DR) programs. DR encourages the active consumer to alter their demand profile in response to time-varying electricity tariff or economic incentives. However, large-scale adoption of such DR schemes and BEMS in neighborhood area can lead to rebound peaks when electricity is cheaper. This paper proposes a unified approach to energy management system for a residential neighborhood that mitigates the demand peaks of the neighborhood and provides electricity bill reduction to the participating consumers. An optimization model has been developed that minimizes the energy cost of the participating consumers while reducing aggregated demand peaks in the neighborhood by scheduling the charge-discharge operation of residential storage units and energy sharing among neighbors. The effectiveness of the proposed neighborhood energy management system (NEMS) model is evaluated via a case study for a residential neighborhood in Sydney, Australia with actual electricity meter data and time of use (TOU) tariff structure for the neighborhood. Simulation results indicate the efficacy of the proposed NEMS framework and optimization model in terms of electricity bill reduction and demand peaks mitigation.
Robust Spectral Clustering via Matrix Aggregation

Spectral clustering has become one of the most popular clustering algorithms in recent years. In real-world clustering problems, the data points for clustering may have considerable noise. To the best of our knowledge, no single clustering algorithm is able to identify all different types of cluster structures. In the existing spectral clustering methods, little effort has been made to explicitly handle both the possibly considerable noise in data points and the robustness of clustering methods, which often degrades the clustering performance. In this paper, motivated by resampling and matrix aggregation, we propose a method for robust spectral clustering. In our method, we first construct multiple transition probability matrices, each is constructed by a subset of randomly selected features. Then, these matrices can be used to recover a shared low-rank similarity matrix, which is the input to the spectral clustering, and several sparse matrices, which represent the noise. The corresponding optimization problem has a low-rank constraint on the transition probability matrix. To solve the corresponding optimization problem, an optimization procedure based on the scheme of Augmented Lagrangian Method of Multipliers is designed. Experimental results on several real-world datasets show that our method has superior performance over several state-of-the-art clustering methods.
Power Allocation Schemes for DAS With OFDM Under Per-Antenna Power Constraint

In this paper, three power allocation (PA) schemes for distributed antenna (DA) systems with orthogonal frequency division multiplexing over multipath fading channels are presented. Unlike the conventional total power constraint, power constraints for individual antenna ports are considered. By maximizing the channel capacity of the system subject to per-antenna maximum power constraints, the optimization problem for the PA design is formulated. Based on the block coordinate descent method, an optimal PA scheme to the optimization problem is derived. Considering that the optimal scheme is an iterative algorithm, a suboptimal PA scheme is derived by maximizing an upper bound of the channel capacity, which provides a closed-form PA procedure. Interestingly, this suboptimal scheme can obtain almost the same performance as the optimal one for large number of subcarriers, low maximum power, and/or small number of DA ports. To reduce the performance loss, a modified suboptimal scheme with closed-form PA is developed, and it has the capacity performance very close to the optimal scheme. Computer simulations verify the effectiveness of the three proposed schemes.
Systematic Assessment of Multispectral Voxel-Based Morphometry in Previously MRI-Negative Focal Epilepsy

BACKGROUND AND PURPOSE: Voxel-based morphometry is widely used for detecting gray matter abnormalities in epilepsy. However, its performance with changing parameters, smoothing and statistical threshold, is debatable. More important, the potential yield of combining multiple MR imaging contrasts (multispectral voxel-based morphometry) is still unclear. Our aim was to objectify smoothing and statistical cutoffs and systematically compare the performance of multispectral voxel-based morphometry with existing T1 voxel-based morphometry in patients with focal epilepsy and previously negative MRI. MATERIALS AND METHODS: 3D T1-, T2-, and T2-weighted FLAIR scans were acquired for 62 healthy volunteers and 13 patients with MR imaging negative for focal epilepsy on a Magnetom Skyra 3T scanner with an isotropic resolution of 0.9 mm3. We systematically optimized the main voxel-based morphometry parameters, smoothing level and statistical cutoff, with T1 voxel-based morphometry as a reference. As a next step, the performance of multispectral voxel-based morphometry models, T1+T2, T1+FLAIR, and T1+T2+FLAIR, was compared with that of T1 voxel-based morphometry using gray matter concentration and gray matter volume analysis. RESULTS: We found the best performance of T1 at 12 mm and a T-threshold (statistical cutoff) of 3.7 for gray matter concentration analysis. When we incorporated these parameters, after expert visual interpretation of concordant and discordant findings, we identified T1+FLAIR as the best model with a concordant rate of 46.2% and a concordant rate/discordant rate of 1.20 compared with T1 with 30.8% and 0.67, respectively. Visual interpretation of voxel-based morphometry findings decreased concordant rates from 38.5%–46.2% to 15.4%–46.2% and discordant rates from 53.8%–84.6% to 30.8%–46.2% and increased specificity across models from 33.9%–40.3% to 46.8%–54.8%. CONCLUSIONS: Multispectral voxel-based morphometry, especially T1+FLAIR, can yield superior results over single-channel T1 in focal epilepsy patients with a negative conventional MR imaging.
An Improved Three-Layer Clustering Hierarchy for Wireless Sensor Networks: A Proposed Framework

Hierarchical based formation is the most efficient way and widely used approach to minimize the total energy consumed, especially in a large sensor network. Most of the previous work focused on two-layer clustering approaches whereas only very few methods focused on three-layer. In this paper, we propose an improved framework for a three-layer hierarchical based formation. In our proposed framework, we introduce a dispersed hierarchical approach for the lower level head selection and an enhanced feature of deploying grid nodes that is responsible to transmit sensed information of the sensor nodes in that particular cluster to the base station (BS) for further data analysis and generating reports. Our simulation results show that our proposed approach minimizes the total energy consumption of the sensor nodes, thereby improving the lifetime of the sensor network. Therefore, this proposed framework can be further applied to large-scale wireless sensor networks (WSN's).
Convolutional Collaborative Filter Network for Video Based Recommendation Systems

This analysis explores the temporal sequencing of objects in a movie trailer. Temporal sequencing of objects in a movie trailer (e.g., a long shot of an object vs intermittent short shots) can convey information about the type of movie, plot of the movie, role of the main characters, and the filmmakers cinematographic choices. When combined with historical customer data, sequencing analysis can be used to improve predictions of customer behavior. E.g., a customer buys tickets to a new movie and maybe the customer has seen movies in the past that contained similar sequences. To explore object sequencing in movie trailers, we propose a video convolutional network to capture actions and scenes that are predictive of customers' preferences. The model learns the specific nature of sequences for different types of objects (e.g., cars vs faces), and the role of sequences in predicting customer future behavior. We show how such a temporal-aware model outperforms simple feature pooling methods proposed in our previous works and, importantly, demonstrate the additional model explain-ability allowed by such a model.
Unobtrusive Detection of Simulated Orthostatic Hypotension and Supine Hypertension Using Ballistocardiogram and Electrocardiogram of Healthy Adults

Effective management of neurogenic orthostatic hypotension and supine hypertension (SH-OH) due autonomic failure requires a frequent and timely adjustment of medication throughout the day to maintain the blood pressure (BP) within the normal range, i.e., an accurate depiction of BP is a key prerequisite of effective management. One of the emerging technologies that provide one’s circadian and long-term physiological status with increased usability is unobtrusive zero-effort monitoring. In this paper, a zero-effort device, a floor tile, was used to develop an unobtrusive BP monitoring technique. Namely, RJ-interval, the time between the J-peak of a ballistocardiogram and the R-peak of an electrocardiogram, was used to develop a classifier that can detect changes in systolic BP (SBP) induced by the Valsalva maneuver on healthy adults (i.e., a simulated SH-OH). A t-test was used to show statistical differences between the mean RJ-intervals of decreased SBP, baseline, and increased SBP. Following the t-test, a classifier that detected a change in SBP was developed based on a naïve Bayes classifier (NBC). The t-test showed a clear statistical difference between the mean RJ-intervals of the increased SBP, baseline, and decreased SBP. The NBC-based classifier was able to detect increased SBP with 89.3% true positive rate (TPR), 100% true negative rate (TNR), and 94% accuracy and detect decreased SBP with 92.3% TPR, 100% TNR, and 95% accuracy. The analysis showed strong potential in using the developed classifier to assist monitoring of people with SH-OH; the algorithm may be used clinically to detect a long-term trend of symptoms of SH-OH.
Real-Time Data Retrieval With Multiple Availability Intervals in CPS Under Freshness Constraints

Maintaining the temporal validity of real-time data in cyber-physical systems (CPSs) is of critical importance to ensure correct decision making and appropriate system operation. Most existing work on real-time data retrieval assumes that the real-time data under study are always available, and the developed scheduling algorithms mainly focus on making real-time decisions while meeting the temporal validity (freshness) constraints. This assumption, however does not hold in many real-life CPS applications with intermittent data availability, such as in energy harvesting-based sensing systems. In this paper, we study the multi-interval availability-constrained fresh data retrieval (MAFDR) problem, which aims to retrieve all required real-time data on time for a set of decision tasks while taking both the temporal validity and data availability constraints into consideration. We present the formulation of the MAFDR problem and study its complexity under different settings. For the scenario of single decision task with unit-size data retrieval time, we propose a polynomial-time optimal data retrieval algorithm, which comprises a task finish time selection phase and an optimal retrieval schedule construction phase. For the general scenario of multiple decision tasks with nonunit-size data retrieval time, we provide an integer linear programming formulation for the MAFDR problem and propose a fast heuristic algorithm based on max flow. The effectiveness of the proposed algorithms has been validated through extensive experiments by comparing to the optimal solution and the state-of-the-art approach.
Power Quality Management in an Off-Gtrid System

The themes such as decentralized electricity production, the use of renewable electricity sources, such as photovoltaic and/or wind power plants for self-sufficient energy supply of buildings or micro grids in the so-called Off-Grid autonomous systems can be found more in the field of energy. Considering, the control technology and power quality of tradition distribution networks is facing new opportunities and challenges. This paper proposes a new approach to use the completely new Power Quality Management (hereinafter the PQM) for to avoid inappropriate or dangerous states and abnormal operation states, such as power quality (hereinafter PQ) limit excess according to the standards in an autonomous single phase Off-Grid systems. The Off-Grid system is controlled by the ADSM (Active Demand Side Management), which is a fully functional control system for Off-Grid system. Developed by the PQM which will be integrated in the ADSM in Off-Grid system, thereby adjusting the consumption plan to keep PQ parameters within the allowed limits. In addition, the proposed PQM is based on centralized control system and the real-time analysis of data acquired from conventional electronic measuring devices in common with the PQ parameters prediction.
Morphological differences between the Teleosts Couesius plumbeus (Lake chub) and Rhinichthys cataractae (Longnose dace) and their hybrids from Alberta

Marked external and osteological differences exist between the related cyprinids Couesius plumbeus and Rhinichthys cataractae. C. plumbeus differs from R. cataractae in several external features, such as eye size and mouth shape, any one of which permits identification of individuals. C. plumbeus has postcleithral and nasal bones while R. cataractae does not. Many differences in bone shape also exist. Natural hybrids between the two species from Upper and Lower Kananaskis reservoirs, southwestern Alberta, are intermediate in the average of their external and osteological characters. Some characters in the hybrids, however, are so variable that they fall within the range of both parental species. An individual hybrid may resemble one parental species in one diagnostic character and the other parental species in another. The largest individuals known of the three forms from the Kananaskis system are hybrids.
Recognizing Human Actions Using 3D Motion Trail Model

In this work, a novel descriptor is proposed for 3D motion trail model. The proposed descriptor is used to describe the motion pattern contained in the three dimensional motion trail. Key poses are selected in order to improve the performance of the proposed method. Specifically, we employ three dimensional motion trail model to represent movements. In order to speed up the computation of 3DMTM, the key poses are selected according to the kinetic energy of joint motion. Finally, Kernel extreme learning machine is used to classify the depth sequences. Experimental results show that the proposed method achieves better results on MSR Action3D dataset than the existing method.
UGA stop codon readthrough to translate intergenic region of Plautia stali intestine virus does not require RNA structures forming internal ribosomal entry site

The translation of capsid proteins of Plautia stali intestine virus (PSIV), encoded in its second open reading frame (ORF2), is directed by an internal ribosomal entry site (IRES) located in the intergenic region (IGR). Owing to the specific properties of PSIV IGR in terms of nucleotide length and frame organization, capsid proteins are also translated via stop codon readthrough in mammalian cultured cells as an extension of translation from the first ORF (ORF1) and IGR. To delineate stop codon readthrough in PSIV, we determined requirements of cis-acting elements through a molecular genetics approach applied in both cell-free translation systems and cultured cells. Mutants with deletions from the 3′ end of IGR revealed that almost none of the sequence of IGR is necessary for readthrough, apart from the 5′-terminal codon CUA. Nucleotide replacement of this CUA trinucleotide or change of the termination codon from UGA severely impaired readthrough. Chemical mapping of the IGR region of the most active 3′ deletion mutant indicated that this defined minimal element UGACUA, together with its downstream sequence, adopts a single-stranded conformation. Stimulatory activities of downstream RNA structures identified to date in gammaretrovirus, coltivirus, and alphavirus were not detected in the context of PSIV IGR, despite the presence of structures for IRES. To our knowledge, PSIV IGR is the first example of stop codon readthrough that is solely defined by the local hexamer sequence, even though the sequence is adjacent to an established region of RNA secondary/tertiary structures.
Efficient and Safe Control Flow Recovery Using a Restricted Intermediate Language

Approaches for the automatic analysis of security policies on source code level cannot trivially be applied to binaries. This is due to the lacking high-level semantics of low-level object code, and the fundamental problem that control-flow recovery from binaries is difficult. We present a novel approach to recover the control-flow of binaries that is both safe and efficient. The key idea of our approach is to use the information contained in security mechanisms to approximate the targets of computed branches. To achieve this, we first define a restricted control transition intermediate language (RCTIL), which restricts the number of possible targets for each branch to a finite number of given targets. Based on this intermediate language, we demonstrate how a safe model of the control flow can be recovered without data-flow analyses. Our evaluation shows that that makes our solution more efficient than existing solutions.
Analysis and Design of an S/SP Compensated IPT System to Minimize Output Voltage Fluctuation Versus Coupling Coefficient and Load Variation

The capability of misalignment tolerance is vital for inductive power transfer (IPT) systems. S/SP compensation topology can provide a strong capability of misalignment tolerance if it is properly designed. This paper presents a detailed deduction process to acquire the optimal coupling coefficient, tuned at which the system can achieve minimum output voltage fluctuation. The impact of primary and secondary coupling inductance on output voltage fluctuation versus load variation was also analyzed. The output voltage fluctuation is not affected by the primary coupling inductance, but it decreases when the secondary coupling inductance is increased. Theoretical analysis was validated by both simulation and experiment. Three 200-W IPT prototypes were built. An overall efficiency of 94.1% was achieved when the coupling coefficient was 0.1733. The output voltage fluctuation was as low as 1.5% when the coupling coefficient increased by 34.5%, from 0.1288 to 0.1733. With a 900% rise in load resistance (from 80 to 800 Ω), the output voltage fluctuation decreased from 5.8% to 4.0% when the secondary coupling inductance increased from 136.3 to 250.0 μH.
Data Duplicate Detection

Duplicates arise when there are multiple representations of the same real-world entities. The process of identifying these duplicates is known as duplicate detection. Duplicates may cause a lot of problems and confusion while managing the database and thus need to be eliminated. Normally for a small dataset, the process of duplicate detection does not take long and thus it is efficient. But if the dataset is larger, then the process takes a long time, sometimes as long as some days. This paper implements two methods of duplicate detection and also compares them to the traditional methods to exhibit their efficiency.