Miracle-dz/UniFAR-FTUs huggingface dataset:
Nutritional and Metabolomic Changes of Juvenile Farmed Abalone (Haliotis iris) in New Zealand

Seasonal variations play a crucial role in the physiology, immune responses, and nutritional profile of aquatic animals. Unpredictable water temperature fluctuations, especially those caused by climate change, may negatively affect feed consumption and growth of cultured organisms, such as abalone. In addition, metabolic and nutritional changes across different seasons may have significant effects on aquaculture production. This study aimed to investigate biochemical and metabolic alterations in healthy abalone (Haliotis iris) during 1 year of grow out in a land-based farm in New Zealand. Proximate analyses were used to identify nutritional variations in whole animal tissues, and a gas chromatography–mass spectrometry-based metabolomics approach was used to identify metabolic changes in adductor muscle of abalone during different seasons in the 1-year sampling period. Results showed that protein content was higher in warmer months compared with colder months, whereas lipid, ash, and carbohydrate contents remained generally constant throughout the year. Metabolic profile fluctuations indicated higher amounts of glutamic acid, glutathione, methionine, lysine, serine, tyrosine, and glycine in January and March compared with October and July, indicating possible amino acid breakdown and collagen degradation due to warmer temperatures. Although the proximate analyses findings revealed no signs of nutritional deficiencies in abalone among seasons, the metabolic profiles suggested possible thermal stress during summer months. This study provides a foundation for further nutritional studies to optimise seasonal diets for farmed Haliotis iris and highlights the need to monitor thermal stress effects, especially during summer and/or heatwave events.
Development of a 35 GHz Network Analyzer Based Polarimetric Scatterometer.

Abstract : The Microwave Remote Sensing Laboratory (MIRSL) at the University of Massachusetts has developed a 35 GHz stepped FM-CW network analyzer based polarimetric scatterometer for the remote sensing of natural targets. Operation of the system at this frequency is achieved with up conversion and harmonic downconversion. The network analyzer facilitates real time data reduction employing different internal processing techniques. The primary function is to collect polarimetric data of stationary targets such as snow, saline ice and simulated sea ice to characterize the power returned from the target at different states (i.e. rough, smooth, dry, wet, etc.). An indoor facility at the Cold Regions Research Engineering Laboratory (CRREL) in Hanover, New Hampshire allows temperature control, so that data are collected at different growth phases. The system operates over a 2 GHz bandwidth and therefore achieves a range resolution of 7.5 cm. Using the radar range equation, the normalized radar cross section of the ice and snow was computed as a function of incidence angle at different antenna polarizations. During the past year the system was automated and now the angle of incidence, polarization and system temperature are controlled from a remote computer. This winter (1990-1991) snow data at different states were collected at CRREL, and additional saline ice experiments were performed. This time a polarimetric calibration was developed along with a polarization synthesis software implementation to determine polarimetric signatures of the ice.
The servant of god as a proactive manager: A team service solution model for meeting COVID-19 challenges in Indonesia

Abstract Hundreds of thousands of people from all walks of life have died in Indonesia from Covid−19; work practices have been disrupted and various changes have occurred, including the sphere of service in churches, foundations, schools, and universities. The study aims to understand the concept of the proactive manager as a servant of God: a representative and spokesman for God who is obliged to plan, implement, and evaluate what he/she is doing in carrying out what God wills during the challenges of the Covid−19 era in Indonesia. The research method used was content analysis from secular and biblical texts. The results showed that: (1) A proactive person identifies opportunities, takes initiatives, and perseveres in bringing about positive changes to the environment. (2) As a servant of God, a proactive manager is a representative and spokesman for God who plans, implements, and assesses his/her actions to carry out God’s purpose. Proactive leaders associated with God consistently develop team motivation, anticipate, and respond to the various challenges ahead, and make strategic decisions. Proactive leaders are transformative.
Comment on “Moist Static Energy Transport Trends in Four Global Reanalyses: Are They Downgradient?” by Clark et al. ( 2022 )

Given the key role that atmospheric heat transport plays in Earth's climate system, efforts to document its changes over the satellite era are valuable. Clark et al. (2022, https://doi.org/10.1029/2022GL098822) calculated trends in atmospheric heat transport among four reanalysis data sets and found substantial disagreements between data sets. However, after accounting for the lack of mass‐conservation in reanalysis data sets, we find much smaller magnitude trends, with much better agreement among reanalyses. This highlights the importance of mass corrections when calculating atmospheric heat transport.
Small footprint SLIDE demonstrator for 40-Hz volume rate multiphoton microscopy

The recently developed SLIDE microscope enables rapid imaging in nonlinear two-photon microscopy, where frame rates of 4 kHz are achieved. Such fast acquisition speeds coupled with the molecular specificity of fluorescence markers and the high optical resolution in the sub-μm range allow volume scan rates at 40 Hz. A commercially available Fourier Domain Mode Locked Laser system (Optores GmbH, Munich, Germany) was used as the light source emitting at 1060 nm (Bandwidth 15 nm). An electro-optical Modulator (EOM) splits the light of a single sweep duration up into 600 pulses with 30 ps pulse duration each. Each of it is then spatial separated by a diffractive grating. Only one scanner is needed for beam steering to excite the slow axis resulting to a frame rate of 4 kHz. Using a piezo driver for the objective of the microscope at a frequency of 20 Hz, a live 4D volume scan of 40 Hz with 600 x 400 x 100 voxel is possible. Until now, SLIDE systems were bulky and bound onto a fixed optical desk. The Medical Laser Centre Lübeck developed a transportable and reliable SLIDE system, so that this new and highly innovative technology can be made available to various biological laboratories in Europe. This work was conducted in the framework of the EU project “Faircharm. ”
Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods

In the context of escalating global environmental concerns, the importance of preserving water resources and upholding ecological equilibrium has become increasingly apparent. As a result, the monitoring and prediction of water quality have emerged as vital tasks in achieving these objectives. However, ensuring the accuracy and dependability of water quality prediction has proven to be a challenging endeavor. To address this issue, this study proposes a comprehensive weight-based approach that combines entropy weighting with the Pearson correlation coefficient to select crucial features in water quality prediction. This approach effectively considers both feature correlation and information content, avoiding excessive reliance on a single criterion for feature selection. Through the utilization of this comprehensive approach, a comprehensive evaluation of the contribution and importance of the features was achieved, thereby minimizing subjective bias and uncertainty. By striking a balance among various factors, features with stronger correlation and greater information content can be selected, leading to improved accuracy and robustness in the feature-selection process. Furthermore, this study explored several machine learning models for water quality prediction, including Support Vector Machines (SVMs), Multilayer Perceptron (MLP), Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM). SVM exhibited commendable performance in predicting Dissolved Oxygen (DO), showcasing excellent generalization capabilities and high prediction accuracy. MLP demonstrated its strength in nonlinear modeling and performed well in predicting multiple water quality parameters. Conversely, the RF and XGBoost models exhibited relatively inferior performance in water quality prediction. In contrast, the LSTM model, a recurrent neural network specialized in processing time series data, demonstrated exceptional abilities in water quality prediction. It effectively captured the dynamic patterns present in time series data, offering stable and accurate predictions for various water quality parameters.
High-Gain 60 GHz Slot Antenna with Symmetric Radiation Characteristics

This paper introduces a new generic design perspective to boost the gain and provide symmetric radiation characteristics of electric or magnetic current elements. The symmetric radiation characteristic is similar to a magnetoelectric dipole but with higher gain and narrower bandwidth. The antenna is realized by an excited wide slot surrounded by a rectangular dielectric ring. The antenna achieves 11.5 dBi gain and 13% relative matching bandwidth centered at 60 GHz. A planar $4 \times 4$ array is designed and found in good agreement with measured results. A measured gain of 21 dBi is achieved, which indicates a higher efficiency compared to previously published designed arrays within the 60 GHz band.
High Torque Density and Lightweight Design of Permanent Magnet In-Wheel Motor Based on Magnetic Field Modulation Effect

In this article, by incorporating the magnetic field modulation effect into the in-wheel motor design, a spoke-type permanent magnet in-wheel (SPM-IW) motor is proposed for the potential low-speed direct-drive applications. The key to utilizing the flux-modulated technology is to introduce the permeance modulation function into both the stator and rotor sides. Thereupon, the waveform of the permeance modulation function is analyzed, where the contained harmonics are further investigated. It aims at differentiating which one possesses a positive or negative influence on motor performances. By enhancing the positive harmonics or suppressing the negative harmonics, the output torque of the SPM-IW motor is improved effectively. Meanwhile, with the optimization design of the permeance modulation function in the stator and rotor, the weight of the motor is also reduced to some extent. And then, the electromagnetic performances of the SPM-IW motor are investigated in detail. Finally, a prototype motor is built for experimental validation. The study results show that the proposed SPM-IW motor realizes the performance design demands for high torque density and lightweight.
RSFNet: a method for remote sensing image semantic segmentation based on fully convolutional neural networks

The advancement of remote sensing technology has broadened the application scope of remote sensing image data across various fields. Traditional methods, when processing remote sensing images, face limitations in efficiency and generalization capabilities due to their intricate geographical features. In contrast, deep learning segmentation methods exhibit superior performance but struggle with contextual detail loss and multi-scale features. In this paper, we introduce the RSFNet model to tackle these issues. The model employs spatial paths to extract detailed information from low-level features, presents a residual ASPP incorporating an attention mechanism, and utilizes a feature map slicing module to capture small target features. Experimental results show that RSFNet attains 88.38% pixel accuracy (PA) and 81.06% mean intersection over union (mIoU) on the Potsdam dataset, proving its suitability for semantic segmentation of remote sensing images.
Influence of Virtual Anchor Characteristics on Consumers’ Consumption Willingness

Nowadays, virtual anchors that emerged on the video platform represented by Youtube are loved and favored by young consumers, and the virtual anchor industry is in rapid development. However, the research on the emerging phenomenon and industry of virtual anchors is still limited. In order to fill the research gaps in current studies on virtual anchors, this study attempts to find out the factors that influence consumers’ consumption willingness related to virtual anchors (i.e., subscription, super chat, gift-giving, events, good purchases, etc.). This study empirically proves that virtual anchor characteristics, including physical attractiveness, interactivity, and entertainment have a positive impact on the audience’s consumption willingness. Furthermore, the entertainment characteristic of virtual anchors shows the most effect of impact on the audience’s consumption willingness, and the physical attractiveness of virtual anchors shows the least effect on that. The findings of this study can help expand the research on the virtual anchor domain and bring management implications to virtual anchor companies and benefit the development of the virtual anchor industry.
Leadership Styles of the Tennis Teacher-coaches in a Selected University in China: Inputs for a Leadership Coaching Program

Objective: To use a descriptive comparative correlation study design to conduct a questionnaire test on tennis players in Hainan University, investigate and analyze the leadership style of coaches, and explore a broader understanding by allowing tennis players to evaluate the leadership style of coaches. Methods: 312 tennis players and 5 tennis teachers and coaches of Hainan University were studied with 317 people to test their related indicators and interview-related questions. To analyze the data to be gathered, SPSS 27 will be used for the data management of the responses. Outcome: Coaches' primary role is to motivate athletes to improve their performance. Coaches are also responsible for building sound, positive relationships with their athletes because they significantly impact the athletes' training processes, performance outcomes, and many aspects of their personal lives. Conclusion: Players’ sex, age, and year level can be directly influenced by their coaches’ attributes, leadership, and emotional state. The tennis coaches' leadership style in various dimensions is relatively high as assessed by the student players. The personal attributes of the players have the impact of the quality of leadership of the coaches.
Low-Complexity Scheduled Expectation Propagation Based on QRD and SIC

This letter presents novel Expectation Propagation (EP) algorithms for Single User (SU) and Multiple Users (MU) Multiple-Input Multiple-Output (MIMO) detection that address the performance loss and slow convergence of classic Scalar EP (SEP) solutions with even lower computational complexity. The proposed algorithms employ QR Decomposition (QRD), optimized EP message scheduling, and Successive Interference Cancellation (SIC) techniques to achieve superior performance and convergence speed comparable to those of Vector Expectation Propagation (VEP). Our results are illustrated through error rate curves and analysis of performance-complexity trade-offs in SU and MU scenarios.
High Isolation Compact Two Port 5G MIMO Diversity Antenna with Asymmetrical Feed and Partial Ground Structure

|This paper presents a forthcoming compact high-performance two-element multiple-input-multiple-output (MIMO) diverse antenna for wireless-LAN 5 GHz band and sub-6 GHz 5G (NR) band. The proposed antenna consists of two symmetrical antenna elements with an inverted T-shaped ground structure. The antenna attributes such as S -parameters, realized gain, current distribution, and radiation patterns are studied. Additionally, MIMO performance is also investigated in terms of envelope correlation coeﬃcient (ECC), diversity gain (DG), total active re(cid:13)ection coeﬃcient (TARC), and multiplexing eﬃciency. The antenna covers the entire 5G band for wireless communication, with an eﬀective band ( (cid:0) 10-dB) of 2.92 to 5.72 GHz (provides bandwidth of 2.8 GHz). The obtained values indicate that the measured performance is in reasonable agreement with simulated one. Additionally, eﬃciency and gain were around 95% and above 3 dB across the band of interest, respectively.
Pengaruh Strategi Marketing & Perilaku Konsumen Terhadap Keputusan Pembelian Produk Healthy Food di Indonesia: Hit and Run or Sustainable Business

Penelitian ini merupakan penelitian deskriptif kuantitatif yang bertujuan untuk mengetahui apakah bisnis makanan sehat di Indonesia berpotensi untuk dilanjutkan atau tidak. Makanan sehat sendiri merupakan komponen makanan yang berdasarkan kajian ilmiah memiliki fungsi fisiologis tertentu di luar fungsi dasarnya, terbukti tidak berbahaya dan bermanfaat bagi kesehatan. Dimana usaha ini dilatarbelakangi oleh kesadaran masyarakat Indonesia akan semakin banyaknya penyakit berbahaya dan berisiko yang menyebabkan kematian di Indonesia semakin meningkat. Hal-hal yang terkait untuk memperkuat analisis terkait dengan perilaku konsumen seperti persepsi, kepribadian, motivasi, minat dan juga kebutuhan konsumen. Penelitian ini menggunakan kuesioner yang didistribusikan ke sampel yang dianggap tepat oleh peneliti. Hasil penelitian menunjukkan bahwa strategi pemasaran dan perilaku konsumen berdasarkan kebutuhan & juga motivasi sangat berpengaruh dalam kontinuitas bisnis makanan sehat di Indonesia. Jika masyarakat Indonesia tetap memprioritaskan keduanya, maka bisnis ini akan menjadi bisnis yang berkelanjutan.
Brownian movement and thermophoretic aspects in third-grade nanofluid over oscillatory moving sheet

An enhancement in energy by using nanoparticles is a topic of broad interest in the present century due to its industrial and engineering importance. Therefore, this investigation is based on the thermophoretic and Brownian movement aspects in third-grade nanofluid flow under an applied magnetic field. The role of Joule dissipation is considered in the energy expression. The flow generation is due to the time-dependent periodic motion of the sheet. The partial differential system is converted into expressions of ordinary ones via appropriate variables. The local similarity solution for a coupled system of nonlinear ordinary differential expressions is developed by a homotopy analysis method. A theoretical based graphical analysis is comprehensively discussed for the flow parameters. The numerical data of local Nusselt and Sherwood numbers are expressed in tabular form. It is observed that the presence of non-Newtonian parameters result in an enhancement of the momentum boundary layer. Both thermophoresis and Brownian parameters play an efficient role to enhance the nanoparticles’ temperature. This study also reveals that the factor of skin friction oscillates and increases periodically by increasing the non-Newtonian parameters.
A New Model for Deriving the Priority Weights from Hesitant Triangular Fuzzy Preference Relations

Fuzzy preference relation is a common tool to express the uncertain preference information of decision maker in the process of decision making. However, the traditional fuzzy preference relation will fail under hesitant fuzzy environment as the membership has a single value. In addition, it is very difficult to obtain the precise membership values. Therefore, a new model of fuzzy preference relation is proposed in this paper. Firstly, the concept of hesitant triangular fuzzy preference relation is defined and its properties are investigated based on the concepts of hesitant fuzzy set, hesitant triangular fuzzy set, fuzzy preference relation, and hesitant fuzzy preference relation. Then, the steps of applying this novel model are offered for the case of determining the weights of failure modes. Finally, an example is used to illustrate the proposed model.
A novel statistical model for flood prediction in the Eel River watershed, New Brunswick, Canada

ABSTRACT A strong correlation between the effect of climate change and the increase in flooding frequency and magnitude has been reported in Canada. Consequently, there is a crucial need to examine the effects of future climate change scenarios on flooding conditions. The main objective of this research is to better understand the destructive effects of flood events under historical and future climate change conditions for a small watershed (Eel River watershed) in New Brunswick (NB), Eastern Canada. A practical model had been developed using the modified Artificial Neural Network (ANN) in MATLAB by the authors of this study. The architecture and data structure of ANN is characterized by a back propagation with the Levenberg–Marquardt method. The observed daily total precipitation, daily maximum and minimum air temperatures, daily discharge for the period 1967 to 1983, the simulated monthly maximum and minimum air temperatures, and monthly total precipitation for the period of 1996–2099 from the CanESM2, the second-generation Canadian Earth System Model (CGCM), were used as input of the model. The Representative Concentration Pathways (RCP 4.5 and 8.5), as suitable climate change scenarios, were selected based on the Intergovernmental Panel on Climate Change (IPCC) recommendations for flood studies. Daily values of temperatures, precipitations, and discharges were converted to monthly mean values for better prediction of the output results. In addition, two series of observed discharges were prepared using mean monthly (Qavg) and daily maximum discharges (Qd) as the Target of the model. For more accurate analysis, the time frames of 1996–2012 (for the historical) and 2022–2038, 2039–2055, 2056–2072, 2073–2089, and 2083–2099 (for the future) were considered with a duration of 16 years for each time frame. The output results of ANN were predicted daily maximum (Qd) and mean (Qavg) discharges under the impact of climate change scenarios. As a part of the developed model, Flood Frequency Analysis (FFA) was undertaken using the generalized extreme value (GEV) and the three-parameter lognormal (LN3) distributions based on the predicted and observed discharges. The performance of FFA and ANN were demonstrated using the Anderson–Darling (AD), the Chi-square (CS) tests and coefficient of correlation (R) and mean squared error (MSE), respectively. In conclusion, the three most critical time frames with the highest values of predicted discharges were 2022–2038, 2056–2072, and 2073–2089 for RCP4.5 and 2039–2055, 2073–2089, and 2083–2099 for RCP8.5. Also, based on the FFA, the magnitudes of flood recurrence for the future time period of 100 years will dramatically increase according to the most critical time frames of 2056–2072 and 2039–2055 for RCP 4.5 and 8.5, respectively. Findings indicated that the Eel River watershed will encounter severe floods, and about a 50% increase in mean discharge, especially for the critical time frames. Finally, flood occurrences show increasing trends due to climate change effects in the most critical time frames.