Miracle-dz/UniFAR-FTUs huggingface dataset:
An RL-Based Model for Optimized Kubernetes Scheduling

In this paper, we present RLKube, a Reinforcement Learning (RL)-based custom Kubernetes (K8s) scheduler plugin for optimized task scheduling. RLKube objectives are maximizing resource utilization and Pod throughput as well as improving energy efficiency in a K8s cluster. We used Double Deep Q-Network (DDQN) with Prioritized Experience Replay (PER) and utilized different reward functions to train the RL agent. Also, we have developed corresponding policies for each objective. We have evaluated the effectiveness of RLKube using various datasets simulating a diverse set of realistic load patterns. The results show that RLKube outperforms the default K8s scheduling policies in terms of throughput and energy usage, highlighting its potential for Improving task scheduling in K8s clusters.
Energy Flow Analysis and Optimization of Tahe No.1 Union Station

The Tahe No. 1 Union station has undergone many transformations, and the existing energy flow direction is unreasonable. In order to improve the efficiency of energy utilization within the Union Station, the energy flow diagram of Tahe No. 1 Union Station was firstly analyzed, and it was found that there were weak links in heat loss and heat energy consumption at the separator equipment. Afterwards, the existing heat exchange network was analyzed using the problem table method in the pinch technology, and it was found that there were two hot streams that violated the principle of pinch technology, and energy-saving transformation was carried out on them, the energy-saving potential could reach 54.7%. After analysis, it was found that if there is a heat exchange process in the system, it can be pinch technology to find out what is not right in the system and to modify it. The pinch point analysis technique is therefore a highly applicable energy saving technique.
Combined Wind and Photovoltaic Power Forecasting Based on Attention-BiLSTM Multitask Learning for Renewable Energy System

In the renewable energy system with renewable energy as the main body, the random fluctuation of a high proportion of renewable energy intensifies the instability of the power system after grid connection. To solve this problem, this paper proposes a combined forecasting model of wind and photovoltaic power based on Attention-BiLSTM hybrid model and Progressive Layered Extraction (PLE) multitask learning method. Meteorological and historical power data are used as input, and measured power data are used as the forecasting target. The model can reflect the spatio-temporal correlation between wind and photovoltaic power, and extract the nonlinear coupling information between them to improve the forecasting accuracy. The proposed method is validated using real power generation data. The analysis results show that the proposed model can not only improve the power forecasting accuracy, but also obtain the forecasting results of wind and photovoltaic power at the same time, which reduces the workload and has certain engineering application value.
Topology Identification of Low-Voltage Distribution Networks Considering Hidden Errors

Accurate topology information is the key to supporting these requirements of coordinated operation of source-load-storage resource in low voltage distribution network (LVDN). The comprehensive coverage of smart meters provides a data basis for low-voltage topology identification (LVTI). However, due to electricity theft, PLC crosstalk, interruption of communication, the measurement data will be distorted, which will seriously affect the performance of LVTI methods. Thus, this paper defines the hidden errors and proposes a LVTI method based on layer-by-layer stepwise regression. Firstly, a multi-linear regression model for LVTI based on the energy conservation principle is established. Secondly, the significance factor according to F-test was proposed to modify the identification results considering hidden errors. Thirdly, the regression model and significance threshold parameters were iteratively updated layer by layer to improve the recall rate of the final identification results. Finally, the test system simulation of 63 users and practical application results show that the proposed method can ensure more than 90% of the precision rate under the influence of hidden errors, which has significant engineering application value.
Determining the developer’s intentions for impactful BoP technology creation in the Indian context: an empirical approach utilizing factor analysis

The world is witnessing a paradigm shift in technology-based development. The latest trends in research are gaining traction toward exploring the previously neglected Bottom of the Pyramid (BoP) and its innovation practice for sustainability. However, there is a scarcity of literature analyzing the development aspects of BoP technologies considering the developers’ outlook. Therefore, the purpose of this research is to investigate and extract the critical factors relating to the prime intentions of technology developers essential for effective BoP technology development in emerging economies. The empirical data is extracted from associates of BoP technology interventions across different Indian organizations followed by an exploratory factor analysis (EFA) method to obtain the essential factors. The results yield distinct factors that describe the unique dimensions of technology developers’ intention while engaging in formulating a viable technology. The formative contribution of this study radiates the salient role of unique technology development in a novel discourse of BoP and emerging economies. The findings provide opportunities for the upcoming technology developers in designing their strategic formulation for technology creation that is governed by need-oriented, inclusively developed innovations catering to environmentally sound technology solutions for the long-term development of the BoP population.
Optimal Configuration Method for Dynamic Reactive Power Compensation in AC/DC Receiving-end Systems Considering Reactive Power Support of Local Synchronous Generators

In regions characterized by relatively rapid economic development, where available sites for configuring dynamic reactive power compensation devices are limited, and substantial investment costs are involved, a solution is needed. To address this challenge, this paper proposes a bi-level optimization model that accounts for the contribution of synchronous generators to dynamic reactive power compensation in AC/DC receiving-end systems. In this model, the local synchronous generators are used as a kind of dynamic reactive power source, which can offer their transient reactive power support during system faults to minimize the additional demand for dynamic reactive power compensation devices. And the Dynamic and Static Comprehensive Load Rate is proposed to identify vulnerable voltage nodes as installation candidates. On this basis, the objective function of the upper-layer optimization model is maximum the comprehensive assessment index on behalf of transient reactive power support by generators, which optimizes the initial operational state of generators to enhance their reactive power support capability for vulnerable voltage nodes. In the lower-layer optimization model, the improved Voltage-reactive Trajectory Sensitivity Index is utilized as the objective function to determine the optimal installation nodes and compensation capacity. Finally, an improved 22-node system with dual feeders is taken as a case study, which validated the effectiveness and economic viability of the proposed approach.
An Interpretable LSTM Network for Solar Flare Prediction

Deep learning models are often considered black box models as their internal workings tend to be opaque to the user. Because of this lack of transparency, it is challenging to understand the reasoning behind the model’s predictions. Here, we present an approach to making a solar flare prediction model interpretable. This model, built based on a long short-term memory (LSTM) network with an attention mechanism, aims to predict whether an active region (AR) on the Sun’s surface would produce a large flare, namely an M- or X-class flare, within 24 hours. The flare events used in this study are collected from the Geostationary Operational Environmental Satellite X-ray flare catalogs provided by the National Centers for Environmental Information. The crux of our approach is to model data samples in an AR as time series and use the LSTM network to capture the temporal dynamics of the data samples. Each data sample has 22 features including magnetic parameters and flare history parameters. To make the model’s predictions accountable and reliable, we leverage post hoc model-agnostic techniques, which help elucidate the factors contributing to the predicted output for an input sequence and provide insights into the model’s behavior across multiple sequences within an AR. To our knowledge, this is the first time that interpretability has been added to an LSTM-based flare prediction model.
Hi2LoRa: Exploring Highly Dimensional and Highly Accurate Features to Push LoRaWAN Concurrency Limits with Low Implementation Cost

LoRa Wide Area Network (LoRaWAN) has emerged as a dominant technology for Low Power Wide Area Networks (LPWAN). However, due to the ever-growing network size, packet collisions caused by concurrent transmissions have become a serious challenge in LoRa Wan.Existing studies have either ignored the issue by exploring only a few inaccurate features or addressed it using a complex receiver with up to eight antennas. To strike a better balance between implementation cost and system performance, we propose Hi2LoRa, which leverages highly dimensional and highly accurate features for LoRa concurrent decoding with only two receiving antennas. The feature dimensions are extended by exploring various types of hardware imperfections and channel state information inherent to each transceiver pair. To improve feature accuracy, low pass filters and BiLSTM networks are employed to trace and learn their temporal patterns. Additionally, an effective collision suppression strategy is introduced to combat feature corruption from other concurrent packets. Extensive evaluations on real-world testbeds show that the achievable concurrency in Hi2LoRa is either close to that of state-of-the-art approaches with much higher complexity (e.g., using eight antennas) or 2.7 x of prior work with comparable complexity (e.g., using two antennas).
Improving a multiobjective evolutionary algorithm applied to batch scheduling in pharmaceutical manufacturing

Multiobjective Evolutionary Algorithms (MOEA’s) have been developed for optimization problems involving conflicting objectives. Real-world Batch Sequencing (BS) in pharmaceutical manufacturing presents a multiobjective optimization challenge, compounded by constraints and uncertain demands. This complexity often leads to low convergence of feasible solutions. In this study, we evaluate population initialization strategies, propose an optimized mutation operator, and explore various crossover types to enhance solution quality, measured by metrics including the number of non-dominated feasible solutions (NFS), hypervolume (HV), Inverted Generational Distance Plus (IGD+), Error Rate (E), and Coverage of Two Sets (CS).
Exploration and Mitigation of the Impact of Lighting Conditions on Multi-spectral Image Classification

Imaging spectroscopy is a potent and versatile technique, offering valuable spatial and spectral data across a diverse range of applications such as agriculture, environmental monitoring, material characterization and remote sensing. Nevertheless, due to its passive nature, multi-spectral and hyperspectral sensors heavily rely on appropriate illumination sources, especially in scenarios where natural sunlight might be insufficient or unavailable. To comprehend the challenges associated with illumination source selection, an extended experimental effort has been launched to investigate the effects of different lighting conditions on multi-spectral image analysis. A dataset of images captured from both the visible (16 bands) and Near-Infrared (25 bands) spectral regions under Full-Spectrum halogen and visible-spectrum LED illumination sources, was generated. Within this dataset, four distinct types of plastic waste, namely High Density PolyEthylene (HDPE), Low Density PolyEthylene (LDPE), PolyEthylene Terephthalate (PET), as well as PolyPropylene (PP), are incorporated. A set of machine learning models aim to distinguish the different kind of plastics. The models’ accuracy, was employed as metric to quantify the impact different lighting conditions have on multi-spectral image classification. This work underlines the necessity of appropriate illumination sources on such imaging applications, while providing crucial insights to mitigate the effects of light conditions on the classification performance.
Multispectral Image Demosaicking Based on Multi-scale Dense Connections and Large-kernel Attention

Single-sensor multispectral cameras generally employ a multispectral filter array (MSFA) to rapidly acquire spatial-spectral information. However, MSFA cameras capture only one spectral band’s information at each pixel location. Thus, to fully utilize the spatial-spectral information recorded by MSFA cameras, demosaicing methods are necessary. In this paper, we propose a novel two-stage demosaicing method that combines the traditional method and the deep learning method. We first use a weighted bilinear interpolation convolution filter to preliminarily interpolate the raw MSFA image. And then a multi-scale dense connections large kernel attention method (MDLKA) is proposed to further reconstruct the full-resolution spectral images. MDLKA can effectively increase the image receptive field and capture long-range dependence. Through dense connections, attention maps of different scales are fused to enrich image features. Finally, compared with current advanced demosaicing methods on the dataset provided by NTIRE 2022, we exceed traditional interpolation methods by 11–15 dB in PSNR. Compared with the mosaic convolution-attention network (MCAN) and Res2Unet network based on deep learning methods, we exceed them by 5.4 dB and 1.95 dB in PSNR, respectively.
An Indigenous Psychology Perspective for Mental Health Services and Research in Indonesia

Due to a lack of focus and practice, mental health issues in Indonesia have received little attention. Moreover, cultural, religious, economic, and local factors determine perceptions of mental health and poverty. To examine these issues, Indigenous Psychology was carried out as a perspective and narrative review that accompanies its research methods. To reach the goals of this study, some of the most important results are described in this report, particularly the state of affairs and context of mental health studies and offerings in Indonesia, the intertwining of mental health with a way of life and poverty, the hyperlink between mental health and poverty, how indigenous psychology as a new paradigm in the ﬁeld of psychology plays a role in reading mental health issues, and the challenges confronted by indigenous psychology in contributing to mental health studies and offerings in Indonesia. In conclusion, this examination indicates that mental health must be viewed as a socio culturally built phenomenon. The praxis consequence is that in public policy making, such as studies in the area of mental health, this locality variable has to be a good consideration.
Exosomes From miRNA-126-Modified Endothelial Progenitor Cells Exosomes From miRNA-126-Modified Endothelial Progenitor Cells Alleviate Brain Injury and Promote Functional Recovery After Alleviate Brain Injury and Promote Functional Recovery After Stroke Stroke

Aims: We previously showed that the protective effects of endothelial progenitor cells (EPCs)-released exosomes (EPC-EXs) on endothelium in diabetes. However, whether EPC-EXs are protective in diabetic ischemic stroke is unknown. Here, we investigated the effects of EPC-EXs on diabetic stroke mice and tested whether miR-126 enriched EPC-EXs (EPC-EXs miR126 ) have enhanced efficacy. Methods: The db/db mice subjected to ischemic stroke were intravenously admin-istrated with EPC-EXs 2 hours after ischemic stroke. The infarct volume, cerebral microvascular density (MVD), cerebral blood flow (CBF), neurological function, angiogenesis and neurogenesis, and levels of cleaved caspase-3, miR-126, and VEGFR2 were measured on day 2 and 14. Results: We found that (a) injected EPC-EXs merged with brain endothelial cells, neurons, astrocytes, and microglia in the peri-infarct area; (b) EPC-EXs miR126 were more effective than EPC-EXs in decreasing infarct size and increasing CBF and MVD, and in promoting angiogenesis and neurogenesis as well as neurological functional recovery; (c) These effects were accompanied with downregulated cleaved caspase-3 on day 2 and vascular endothelial growth factor receptor 2 (VEGFR2) upregulation till day 14. Conclusion: Our results indicate that enrichment of miR126 enhanced the therapeutic efficacy of EPC-EXs on diabetic ischemic stroke by attenuating acute injury and promoting neurological function recovery.
Journal of Digital Forensics, Journal of Digital Forensics, Security and Law Security and Law

Viber is one of the widely used mobile chat application which has over 606 million users on its platform. Since the recent release of Viber 6.0 in March/April 2016 and its further updates, Viber provides end-to-end encryption based on Open Whisper Signal security architecture. With proprietary communication protocol scattered on distributed cluster of servers in diﬀerent countries and secure cryptographic primitives, Viber oﬀers a diﬃcult paradigm of traﬃc analysis. In this paper, we present a novel methodology of identiﬁcation of Viber traﬃc over the network and established a model which can classify its services of audio and audio/video calls, message chats including text and voice chats, group messages and ﬁle/media sharing. Absolute detection of both parties of Viber voice and video calls is also demonstrated in our work. Our ﬁndings on Viber traﬃc signatures are applicable to most recent version of Viber 6.2. 2 for android and iOS devices.
Improving (Very) Short Range Precipitation Forecasting in New Zealand

• Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person. • Authors control the copyright of their thesis. You will recognise the author's right to be identified as the author of this thesis, and due acknowledgement will be made to the author where appropriate. • You will obtain the author's permission before publishing any material from their thesis.
The Information-Processing Perspective on Categorization.

Categorization behavior can be fruitfully analyzed in terms of the trade-off between as high as possible faithfulness in the transmission of information about samples of the classes to be categorized, and as low as possible transmission costs for that same information. The kinds of categorization behaviors we associate with conceptual atoms, prototypes, and exemplars emerge naturally as a result of this trade-off, in the presence of certain natural constraints on the probabilistic distribution of samples, and the ways in which we measure faithfulness. Beyond the general structure of categorization in these circumstances, the same information-centered perspective can shed light on other, more concrete properties of human categorization performance, such as the results of certain prominent experiments on supervised categorization.
Plastic Surgery: Beware the Candle Burning at Both Ends

Background: Plastic surgery dates back to 800 BC, where forehead flaps were used to reconstruct noses in India. Today, it is one of the most romanticized fields in medicine. Due to the influence of social media, there has never been a larger spotlight. Ironically, this spotlight brings a narrowed perception of the scope of plastic surgery. This study aimed to assess the scope through the eyes of the average American to identify gaps in knowledge to better represent the field. Methods: A series of questions were developed under survey methodologists and administered by Qualtrics. Responses were gathered, and data were analyzed to assess the public’s knowledge of plastic surgery’s scope. Results: Two thousand five hundred responses were obtained, balanced across demographics similar to that of the United States. The US population has a poor understanding of the scope of plastic surgery and how to obtain board certification. Conclusions: This survey demonstrates a gap in awareness of plastic surgery as a field and the scope outside aesthetic procedures dramatized by the media. There remains no clear understanding of the qualifications of plastic surgeons or the provider makeup of the field of cosmetic surgery. Subspecialties proved to be overlooked, and knowledge of board certification was sparse. Further effort is needed to educate both the public and patients of the scope of plastic surgery, so that they might seek and gain access to appropriate treatment in the most efficient manner to optimize outcomes regarding the form and function of the body.
The eﬃcacy of ammonium as seed priming agents for promoting tomato ( Solanum lycopersicum L.) germination under salinity stress

e aim of the current study was to assess the eﬀectiveness of NH 4+ priming to enhance tomato seed germination and plant growth under saline stress. In the absence of light at 25 ± 1 °C, tomato seeds were primed with 50 and 100 mmol of NH 4 NO 3 and with 50 and 100 mmol of (NH 4 ) 2 SO 4 for 12 and 24 h, respectively. In the seed germination test, twenty primed seeds were used for each treatment, along with five replica plates and a control, and were all incubated at 25 °C with 25 mL of moistened water. In a second greenhouse experiment, primed seeds were planted in garden soil and watered with tap water, salt water, and a control. e concentration of saline water (50, 100, 150, and 200 mmol NaCl) was gradually raised aer a 10-day break. e germination percentages in T5 (50 mmol (NH 4 ) 2 SO 4 , 12 hrs) were 82 ± 3.7% and 80 ± 4.47%, respectively, followed by T1 and T3 (50 mmol (NH 4 ) 2 SO 4 , 12 h and 100 mmol NH 4 NO 3 , 12 h). When compared to the unprimed tomato seeds, the NH 4+ priming with NH 4 NO 3 and (NH 4 ) 2 SO 4 improved plant height and other growth parameters. Furthermore, the chlorophyll and total flavonoid content were improved in both saline and non-saline treatments. In terms of salinity, the NH 4+ priming increased the proline content while decreasing the total protein content. It is concluded that further research will be needed to clarify the eﬀect of NH 4 NO 3 and (NH 4 ) 2 SO 4 as NH 4+ priming in tomato plants because other factors and nutrition can play a role in seed germination and plant growth development.
Eﬃcient Multiple Imputation for Diverse Data in Python and R : MIDASpy and rMIDAS

This paper introduces software packages for eﬃciently imputing missing data using deep learning methods in Python ( MIDASpy ) and R ( rMIDAS ). The packages implement a recently developed approach to multiple imputation known as MIDAS, which involves introducing additional missing values into the dataset, attempting to reconstruct these values with a type of unsupervised neural network known as a denoising autoencoder, and using the resulting model to draw imputations of originally missing data. These steps are executed by a fast and ﬂexible algorithm that expands both the quantity and the range of data that can be analyzed with multiple imputation. To help users optimize the algorithm for their particular application, MIDASpy and rMIDAS oﬀer a host of user-friendly tools for calibrating and validating the imputation model. We provide a detailed guide to these functionalities and demonstrate their usage on a large real dataset.