Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Objective Optimal Allocation of Hybrid Electric Vehicles Charging Stations and Renewable Distributed Generators into the Distribution System

Electric vehicle charging stations (EVCS) and renewable distributed generators (RDG) units are very exciting topics that have been implemented and used to enhance the efficiency of the energy of the electrical distribution system (EDS) when it comes to their optimal integration. This paper proposed the use of the marine predators algorithm (MPA) for multi-objective optimal allocation of hybrid EVCS and RDG based on wind and solar power generation in EDS. The novel multi-objective functions proposed for minimizing the active power loss (APL), voltage stability index (VSI), fast voltage stability index (FVSI), line stability factor (LPQ), and power-voltage stability index (PVSI). The capability of the MPA approach is tested for the IEEE 69-bus and compared with other competitive optimization approaches. The simulation results obviously showed the efficiency, feasibility and superiority of the applied MPA algorithm, while respecting the other relevant techniques for optimum solutions for simultaneous hybrid EVCS and RDG unit allocation.
A Dual Attention-based Modality-Collaborative Fusion Network for Emotion Recognition

Multi-modal emotion recognition (MER) is an emerging research field in human-computer interactions. However, previous studies have explored several fusion methods to deal with the asynchronism and the heterogeneity of multimodal data but they mostly neglect the importance of discriminative unimodal information resulting in the ignorance of independence of uni-modality. Furthermore, the complementarity among different fusion strategies is seldom taken in consideration. To address these limitations, we propose a modality-collaborative fusion network (MCFN) consisting of three main components: a dual attention-based intra-modal learning module which is devoted to build the initial embedding spaces, a modality-collaborative learning approach is to reconcile the emotional information across modalities, and a two-stage fusion strategy to integrate multimodal features which are improved by a mutual adjustment approach. The proposed framework outperforms the state-of-the-art methods in overall experiments on two well-known public datasets. Our model will be available at https://github.com/zxiaohen/ Speech-emotion-recognition-MCFN
Conceptual uncertainty assessment for a catchment scale model.

A groundwater flow model of the Neogene aquifer system, representing an important water supply resource, has been developed within the performance assessments studies of a geological disposal of radioactive waste in Belgium. In order to evaluate the discrepancy between an estimated parameter and a measured value of the Kasterlee Clay aquitard, uncertainty analyses were applied on three aspects of its conceptual model. These analyses included the parameterisation, geometry (thickness) of all units and the geometrical representation of the Kasterlee Clay. The results of the analyses showed that the conceptual model of the Kasterlee Clay should be reconsidered, allowing less resistance of the unit locally. To identify such places, further site investigation is recommended.
Using IRS to Improve the Secrecy Rate of Millimeter Wave Communication System

With the development of 6G, millimeter wave communication has received extensive attention. Due to the characteristics of wireless transmission, information secrecy transmission is facing significant challenges. This paper uses the physical layer security (PLS) to explore the information secrecy transmission. Specifically, we use Intelligent Reflecting Surface (IRS) to control the wireless propagation environment and improve the secrecy rate of millimeter wave communication. The active beamforming matrix of the base station and the passive beamforming matrix of the IRS are optimized to achieve the maximum secrecy rate. We deduce the closed form solution of active beamforming and the approximate optimal solution of passive beamforming. An alternating optimization (AO) algorithm is applied to solve the non-convex optimization problem. Simulation results verify the convergence and effectiveness of the algorithm, which can obtain nearly twice the secrecy rate gain of the benchmark algorithm.
An Asymptotically Efficient Hyperbolic Localization of a Moving Target by a Distributed Radar Network With Sensors Uncertainties

This article addresses the moving target localization in a distributed radar network (DRN) with sensor uncertainties. It is assumed that the positions and velocities of the sensors are independent normal random variables with known means and variances. Assuming reference sensors on both sides of transmitters (Txs) and receivers (Rxs), we convert the elliptical localization to a hyperbolic localization problem. We introduce nuisance variables (NVs), remove dependent hyperbolic equations, establish a pseudo-linear system, and solve it by a two-stage weighted least squares (TSWLSs) estimator. Subsequently, we propose two alternative estimators to reduce the computational complexity (CC) while preserving the accuracy by selecting hyperbolas either at Txs or at Rxs. Moreover, we investigate the impact of the number and type of NVs on localization performance. Numerical simulations confirm our theoretical results and indicate that utilizing all NVs and applying averaging techniques to eliminate dependent hyperbolas produce the most accurate estimate. In addition, our proposed method has several advantages over state-of-the-art competitive methods: 1) its performance is maintained and remains closer to the Cramér–Rao lower bound (CRLB) in different random configurations of sensors and relative motions of the sensors and the target; 2) it is asymptotically efficient, achieving the CRLB up to relatively high noise levels (around 32 dB) and uncertainty levels (around 20 dB); 3) it has similar order of complexity as other methods and performs satisfactorily under practical real-time scenarios; and 4) it involves more independent equations and leads to a significant improvement in accuracy, particularly when the number of Txs or Rxs dominates the other.
Insights into the Mechanism Underlying the Influence of Glycation with Different Saccharides and Temperatures on the IgG/IgE Binding Ability, Immunodetection, In Vitro Digestibility of Shrimp (Litopenaeus vannamei) Tropomyosin

Tropomyosin (TM) is a heat-stable protein that plays a crucial role as a major pan-allergen in crustacean shellfish. Despite the high thermal stability of the TM structure, its IgG/IgE binding ability, immunodetection, and in vitro digestibility can be negatively influenced by glycation during food processing, and the underlying mechanism remains unclear. In this study, TM was subjected to glycosylation using various sugars and temperatures. The resulting effects on IgG/IgE-binding capacity, immunodetection, and in vitro digestibility were analyzed, meanwhile, the structural alterations and modifications using spectroscopic and LC-MS/MS analysis were determined. Obtained results suggested that the IgG/IgE binding capacity of glycosylated TM, immunodetection recovery, and in vitro digestibility were significantly reduced depending on the degree of glycosylation, with the greatest reduction occurring in Rib-TM. These changes may be attributable to structural alterations and modifications that occur during glycosylation processing, which could mask or shield antigenic epitopes of TM (E3: 61–81, E5b: 142–162, and E5c: 157–183), subsequently reducing the immunodetection recognition and digestive enzyme degradation. Overall, these findings shed light on the detrimental impact of glycation on TMs potential allergenicity and digestibility immunodetection and provide insights into the structural changes and modifications induced by thermal processing.
New Three Wave and Periodic Solutions for the Nonlinear (2+1)-Dimensional Burgers Equations

This research paper is about the new three wave, periodic wave and other analytical wave solutions of (2+1)-Dimensional Burgers equations by utilizing Hirota bilinear and extended sinh-Gordon equation expansion (EShGEE) schemes. Achieved solutions are verified and demonstrated by different plots with the use of Mathematica 11.01 software. Some of the achieved solutions are also described graphically by two-dimensional, three-dimensional and contour plots. The gained solutions are helpful for the future study of concerned models. Finally, these two schemes are simple, fruitful and reliable to handle the nonlinear PDEs.
Effects of Climate Changes on Net Ecosystem Exchange of CO2 and Evapotranspiration of a Tropical Rain Forest

The possible changes of Net Ecosystem Exchange (NEE) of CO2 and evapotranspiration (E) of a tropical rain forest due to climate change are described using a process-based CO2/H2O exchange model (Mixfor-SVAT). Projections of future climate conditions are provided by a global ECHAM5 model for the Special Report on Emission Scenarios (SRES) A1B. As a key region for this study, the tropical rain forest situated in Lore-Lindu National park in Central Sulawesi in Indonesia was selected. Results of the modeling experiments showed a very high sensitivity of NEE and E of this tropical rain forest to both the projected climate changes and the possible reduction of nutrients in plants due to the impact of gradually elevated CO2 in the air and depletion of soil nutrient resources. Under optimal conditions for carbon assimilation and biomass production, i.e. when the CO2 increase in the air is strictly balanced with nutrient supply, the projected climate changes will lead to increase of Gross Primary Production (GPP) by 21.3%, insignificant change of annual NEE and decrease of E by 18%. The ratio of E and precipitation, which determines the surface moisture conditions, decreases by 29.9% (from 0.78 to 0.55). It can lead to increases of soil wetness, ground water level and surface runoff in the study area in future. Limited nutrient supply can significantly reduce GPP, NEE and E. In particular the decrease of nutrient supply of about 20% can completely offset the possible GPP increase due to projected increases of temperature and CO2 concentration in the air.
Association between sulfur microbial diet and the risk of colorectal cancer precursors in older adults

Background Sulfur microbial diet (SMD), related to the enrichment of sulfur-metabolizing gut bacteria, has been confirmed to be linked to an elevated risk of early-onset colorectal adenoma in young females. However, it remains unclear whether SMD is associated with the risk of colorectal adenoma in older people, who are at greater risk for colorectal cancer. Methods All data on participants in this study were retrieved from the intervention arm of the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening test. Participants’ adherence to this dietary pattern was assessed using SMD score. Hazard ratios (HR) and 95% confidence intervals (CI) were adopted in Cox proportional hazards regression models to assess the link between SMD score and the incidence of colorectal adenoma in participants included in the study. Specific stratified analyses were constructed to assess whether this association changed in different conditions, whereas the robustness of the association was examined through sensitivity analyses. Results The mean baseline age of participants was 62.1 (SD 5.2) years (range 54.0–75.0 years). During 19,468,589 person-years of follow-up, 992 colorectal adenoma cases were documented in a total of 17,627 included participants. In a fully adjusted model, an increased risk of colorectal adenoma was determined in participants in the highest quartile of SMD score in comparison with those in the lowest quartile (HRquartile4 vs. HRquartile1 = 1.23; 95% CI: 1.02, 1.47; p = 0.017 for trend). This positive association between SMD score and adenoma risk was more evident in participants who were current or former smokers (p = 0.029 for interaction). Conclusion In this study, our results support a role for the SMD in the carcinogenicity of colorectal cancer precursors among older adults. Nevertheless, these results require validation through more research.
miniStreamer: Enhancing Small Conformer with Chunked-Context Masking for Streaming ASR Applications on the Edge

Real-time applications of Automatic Speech Recognition (ASR) on user devices on the edge require streaming processing. Conformer model has achieved state-of-the-art performance in ASR for the non-streaming task. Conventional approaches have tried to achieve streaming ASR with Conformer using causal operations, but it leads to quadratic increase in the computational cost as the utterance length increases. In this work, we propose a chunked-context masking approach to perform streaming ASR with Conformer, which limits the computational cost from quadratic to a constant value. Our approach allows self-attention in Conformer encoder to attend the limited past information in form of chunked context. It achieves close to the full context causal performance for Conformer-Transducer, while significantly reducing the computational cost and maintains a low Real Time Factor (RTF) which is highly desirable trait for resource-constrained low-power edge devices.
Hyperthermia Influences the Secretion Signature of Tumor Cells and Affects Endothelial Cell Sprouting

Tumors are a highly heterogeneous mass of tissue showing distinct therapy responses. In particular, the therapeutic outcome of tumor hyperthermia treatments has been inconsistent, presumably due to tumor versus endothelial cell cross-talks related to the treatment temperature and the tumor tissue environment. Here, we investigated the impact of the average or strong hyperthermic treatment (43 °C or 47 °C for 1 h) of the human pancreatic adenocarcinoma cell line (PANC-1 and BxPC-3) on endothelial cells (HUVECs) under post-treatment normoxic or hypoxic conditions. Immediately after the hyperthermia treatment, the distinct repression of secreted pro-angiogenic factors (e.g., VEGF, PDGF-AA, PDGF-BB, M-CSF), intracellular HIF-1α and the enhanced phosphorylation of ERK1/2 in tumor cells were detectable (particularly for strong hyperthermia, 2D cell monolayers). Notably, there was a significant increase in endothelial sprouting when 3D self-organized pancreatic cancer cells were treated with strong hyperthermia and the post-treatment conditions were hypoxic. Interestingly, for the used treatment temperatures, the intracellular HIF-1α accumulation in tumor cells seems to play a role in MAPK/ERK activation and mediator secretion (e.g., VEGF, PDGF-AA, Angiopoietin-2), as shown by inhibition experiments. Taken together, the hyperthermia of pancreatic adenocarcinoma cells in vitro impacts endothelial cells under defined environmental conditions (cell-to-cell contact, oxygen status, treatment temperature), whereby HIF-1α and VEGF secretion play a role in a complex context. Our observations could be exploited for the hyperthermic treatment of pancreatic cancer in the future.
Visual Gyroscope: Combination of Deep Learning Features and Direct Alignment for Panoramic Stabilization

In this article we present a visual gyroscope based on equirectangular panoramas. We propose a new pipeline where we take advantage of combining three different methods to obtain a robust and accurate estimation of the attitude of the camera. We quantitatively and qualitatively validate our method on two image sequences taken with a 360◦ dual-fisheye camera mounted on different aerial vehicles.
THz-over-fiber system with orthogonal chirp division multiplexing for integrated sensing and communication

We experimentally demonstrate an orthogonal chirp division multiplexing (OCDM) waveform-based terahertz-over-fiber (ToF) system for integrated sensing and communication (ISAC) applications. 32 Gbit/s data rate and 1.875 cm range resolution are simultaneously achieved at 0.14 THz.
Location-Aware Measurement for Cyber Mimic Defense: You Cannot Improve What You Cannot Measure

Cyber mimic defense is designed to ensure endogenous security, effectively countering unknown vulnerabilities and backdoors, thereby addressing a significant challenge in cyberspace. However, the immense scale of real-world networks and their intricate topology pose challenges for measuring the efficacy of cyber mimic defense. To capture and quantify defense performance within specific segments of these expansive networks, we embrace a partitioning approach that subdivides large networks into smaller regions. Metrics are then established within an objective space constructed on these smaller regions. This approach enables the establishment of several fine-grained metrics that offer a more nuanced measurement of cyber mimic defense deployed in complex networks. For example, the common-mode index is introduced to highlight shared vulnerabilities among diverse nodes, the transfer probability computes the likelihood of risk propagation among nodes, and the failure risk assesses the likelihood of cyber mimic defense technology failure within individual nodes or entire communities. Furthermore, we provide proof of the convergence of the transfer probability. A multitude of simulations are conducted to validate the reliability and applicability of the proposed metrics.
"From Gamers into Environmental Citizens": A Systematic Literature Review of Empirical Research on Behavior Change Games for Environmental Citizenship

As the global environmental crisis intensifies, there has been a significant interest in behavior change games (BCGs), as a viable venue to empower players’ pro-environmentalism. This pro-environmental empowerment is well-aligned with the notion of environmental citizenship (EC), which aims at transforming citizens into “environmental agents of change”, seeking to achieve more sustainable lifestyles. Despite these arguments, studies in this area are thinly spread and fragmented across various research domains. This article is grounded on a systematic review of empirical articles on BCGs for EC covering a time span of fifteen years and published in peer-reviewed journals and conference proceedings, in order to provide an understanding of the scope of empirical research in the field. In total, 44 articles were reviewed to shed light on their methodological underpinnings, the gaming elements and the persuasive strategies of the deployed BCGs, the EC actions facilitated by the BCGs, and the impact of BCGs on players’ EC competences. Our findings indicate that while BCGs seem to promote pro-environmental knowledge and attitudes, such an assertion is not fully warranted for pro-environmental behaviors. We reflect on our findings and provide future research directions to push forward the field of BCGs for EC.
Attention Gate Between Capsules in Fully Capsule-Network Speech Recognition

We present a novel capsule network-based speech recognition model that effectively utilizes the full context of past time capsules. The input capsule sequences are recurrently used by filtering unnecessary contextual information using multi-head attention, which uses previous time output vectors as keys and values, and current time output vectors as queries. We applied the attention gate to the sequential dynamic routing (SDR), an all-capsule speech recognition model. The proposed method attained higher accuracy than the existing SDR with two attention heads on all test sets of the TIMIT and Wall Street Journal (WSJ) corpora while maintaining the same algorithmic delay. For the WSJ corpus, 10.75% of a relative word error rate (WER) reduction was achieved when the required delay was set to 525 ms. In addition, the model showed a 1.76 × reduction in delay while maintaining the WERs. The proposed method results in an increase of approximately 0.1% in the number of parameters.
Regulation of Grain Chalkiness and Starch Metabolism by FLO2 Interaction Factor 3, a bHLH Transcription Factor in Oryza sativa

Chalkiness is a key determinant that directly affects the appearance and cooking quality of rice grains. Previously, Floury endosperm 2 (FLO2) was reported to be involved in the formation of rice chalkiness; however, its regulation mechanism is still unclear. Here, FLO2 interaction factor 3 (OsFIF3), a bHLH transcription factor, was identified and analyzed in Oryza sativa. A significant increase in chalkiness was observed in OsFIF3-overexpressed grains, coupled with a round, hollow filling of starch granules and reduced grain weight. OsFIF3 is evolutionarily conserved in monocotyledons, but variable in dicotyledons. Subcellular localization revealed the predominant localization of OsFIF3 in the nucleus. The DAP-seq (DNA affinity purification sequencing) results showed that OsFIF3 could affect the transcriptional accumulation of β-amylase 1, α-amylase isozyme 2A-like, pectinesterase 11, β-glucosidase 28 like, pectinesterase, sucrose transport protein 1 (SUT1), and FLO2 through the binding of the CACGTG motif on their promoters. Moreover, FLO2 and SUT1 with abundant OsFIF3 binding signals showed significant expression reduction in OsFIF3 overexpression lines, further confirming OsFIF3’s role in starch metabolism regulation and energy material allocation. Taken together, these findings show that the overexpression of OsFIF3 inhibits the expression of FLO2 and SUT1, thereby increasing grain chalkiness and affecting grain weight.
Comparative Analysis of Logic Reasoning and Graph Neural Networks for Ontology-Mediated Query Answering With a Covering Axiom

The problem of query answering over incomplete attributed graph data is a challenging field of database management systems and artificial intelligence. When there are rules on data structure expressed in the form of the ontology, the theoretical complexity of finding exact solution satisfying ontology constraints increases. Logic-based methods use theoretical constructions to obtain efficient rewritings of the original queries with respect to ontology and find an answer to the rewriting query over incomplete data. However, there is an opportunity to use faster machine learning methods to label all the data and query over the “most probable” data model without taking into account the ontology. This research paper investigates the effectiveness and trustworthiness of both mentioned approaches for answering ontology-mediated queries on graph databases that integrate an ontology with a covering axiom, which states that every node belongs to either of two classes. The first approach involves finding precise answers through logical reasoning and rewriting the problem into a datalog program, while the second approach employs a trained graph neural network to label data in a binary classification problem and leverages SQL for query answering. We conduct an in-depth analysis of the time performance of these approaches and evaluate the impact of training set selection on their ability of correct query answering. By comparing these approaches across various experiments, we provide insights into their strengths and limitations for answering ontology-mediated queries containing a Boolean conjunctive query. In particular, we showed the importance of logic-based approaches for ontology with a covering axiom and the inability of machine learning methods to find answers for ontology-mediated queries in large networks.
Background-Sound Controllable Voice Source Separation

There have been various approaches to separate mixed voices. In the real world, input voices contain many different kinds of background sounds but existing methods have not considered the background sounds in model architectures. These approaches are difficult to control the background sounds directly and the voice separation results include the background sounds randomly. In this paper, we propose an extended voice separation framework, background-sound controllable voice source separation that can control the degrees of background sounds of voice separation outputs using a control parameter that ranges from 0 to 1 without additional mixing procedures. Several experiments show the controllability of background sounds on various real world datasets with preserving voice separation performances.