Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep learning-based 3D target detection algorithm

Automated vehicle driving requires a heightened awareness of the surrounding environment, and detecting targets is a crucial element in reducing the risk of traffic accidents. Target detection is essential for autonomous driving. In this paper, we improve the CenterPoint 3D target detection algorithm by introducing a self-calibrating convolutional network into the 2D backbone network of the original algorithm. This enhancement improves network extraction speed and feature extraction capability. Additionally, we improve the two-stage refinement module of the original algorithm by extracting feature points from the multi-scale feature map rather than the single-scale feature map. This approach reduces the loss of small target feature information, and we build a data enhancement module to increase the number of training samples and improve the network model’s robustness. We validate the algorithm on the KITTI dataset and analyze domestic data visualizations. Our results show that the bird’s-eye view mAP detection accuracy of the algorithm when the target is a vehicle has improved by 1.68%, and the 3D target mAP detection accuracy has improved by 1.02% compared with the original algorithm.
Influencer Marketing and Online Advertising as A Digital Marketing: A Case Study Green Beauty Products

The beauty industry in Indonesia is experiencing rapid growth, driven by factors such as a large young population and increasing purchasing power. The government is also encouraging improvements in the supply chain of raw materials to increase the national added value. Additionally, advancements in technology and the rise of social media and digital platforms have provided opportunities for the promotion and dissemination of beauty products. Influencer marketing and online advertising have become popular strategies for promoting beauty products. These techniques have been found to have a positive impact on consumer purchase decisions. Green brand knowledge has also gained importance in the beauty industry, as consumers are becoming more conscious of environmental issues and seeking sustainable and eco-friendly products. Therefore, this study aims to examine the impact of influencer marketing, online advertising, and green brand knowledge on the purchase decisions of green beauty products. The study utilizes a quantitative research approach and analyses data from 100 consumers. The findings suggest that influencer marketing and online advertising, have a direct impact on purchasing decisions, while green brand awareness has an insignificant impact on purchase decisions. The study recommends that companies focus on creating high-quality influencer content and implementing effective online marketing strategies to enhance the impact of these techniques. Additionally, companies should choose influencers and platforms aligned with their target audience. The study acknowledges limitations in sample size and research method and suggests further research to validate the findings. Keywords—Influencer Marketing; Online Advertising; Green Brand Knowledge; Purchase Decision
A Deep Learning Approach for Autonomous Compression Damage Identification in Fiber-Reinforced Concrete Using Piezoelectric Lead Zirconate Titanate Transducers

Effective damage identification is paramount to evaluating safety conditions and preventing catastrophic failures of concrete structures. Although various methods have been introduced in the literature, developing robust and reliable structural health monitoring (SHM) procedures remains an open research challenge. This study proposes a new approach utilizing a 1-D convolution neural network to identify the formation of cracks from the raw electromechanical impedance (EMI) signature of externally bonded piezoelectric lead zirconate titanate (PZT) transducers. Externally bonded PZT transducers were used to determine the EMI signature of fiber-reinforced concrete specimens subjected to monotonous and repeatable compression loading. A leave-one-specimen-out cross-validation scenario was adopted for the proposed SHM approach for a stricter and more realistic validation procedure. The experimental study and the obtained results clearly demonstrate the capacity of the introduced approach to provide autonomous and reliable damage identification in a PZT-enabled SHM system, with a mean accuracy of 95.24% and a standard deviation of 5.64%.
Terrestrial flora and vegetation of the Western Australian wheatbelt

Six hundred and eightv-two quad rats were located ,. lCross the Western Australian wheatbelt and adjacent regions to cover as much of the geographical, edaphic and geomorphological variation of the terrestrial plant communities as possible. The study area covered 230,000 km' or 70% of the South West Botanical Province, one of the world's 25 biodiversity hotspots. It included all or part of the six biogeographical regions centred on the wheatbelt. The native vegetation in the study area is highly fragmented with 74'1. , having been cleared for agriculture. Clearing has not been uniform; more extensive areas of bushland remain along the southern and western margins. A total of 2609 taxa of vascular plants in 103 families was recorded from the quad rats. The frequencv p,lttern followed a lognormal distribution. At a scale of 400 m', there \vas little difference in species richness between woodland, mallee and shrubl,lIld formations but those on duplex soils had lower species richness than those on deep sand, Iaterite and granik soils. Over 60';:, of taxa were recorded in fewer than five quad rats. These uncommon taxa were not randomlv distributed but concentrated at the periphery of the study area, particularly in the west and south. At least eight previously unrecognized taxa were collected for the first time. Further collections of 15 taxa listed as Declared Rare under the Wildlife Conservation Act, and 161 taxa on the Department of C'onservation and Land Management's priority flora list were made. 'There was little congruence in phytogeographic patterning between lifeforms or taxonomic levels. This implies that analyses of only some components of the flora, or analyses at higher taxonomic levels will not elucidate the full phytogeographic pattern. Phytogeographic patterning in the quad rat classification, which included the 1022 most common species, was strongIv related to climatic gradients and edaphic factors. Twenty-six assemblages were defined from the species classification, four were later combined with the total number of assL'mblages analyzed reduced to 23. Of these 20 formed natural groups that were consistent with the known distribution and habitat preferences of the component taxa. Species richness in these assemblages was modeled as a surrogate for composition using climatic and soil parameters, Explanatory power of the models ranged from 43,2 to 84.2%. 'rill' present biogeogr,lphical regions and subregions defined for the studv area received little support from the ,malyses of either the quad rat classification or the assemblage patterning, ! \ vailable structural vegetation mapping also showed little correlation with composition. Species pattL'rning across the studv area was primarily gradational along a northe,lst-southwL'st rainfall gradiL'nt and a north-south temperature gr,ldient, as well as a widespn'ad diffuse pattern associakd with specific grimite ,md saline associ a tions.
Comparison of the Effectiveness and Performance of Student Workgroups in Online Wiki Activities with and without AI

: Collaborative learning has been widely acknowledged as a successful teaching method within the education field, with research indicating its positive impact on student outcomes. During the Covid-19 pandemic, when all courses transitioned online due to lockdown measures, many universities employed Learning Management Systems to facilitate continued group work among students. However, forming effective student groups remained challenging, particularly given the large number of enrolled students. To address this issue, the study proposes the application of Artificial Intelligence (Machine Learning) solution to automatically group students based on their behaviours and interactions within an e-learning environment. This paper explores the potential of Machine Learning (ML) algorithms in assisting educators to create heterogeneous groups, considering various student attributes, such as behaviour and performance, to optimise collaborative learning outcomes. Students' performance within a module was compared using a Wiki activity that employed group work over the course of two academic years. In the first experiment, groups were formed randomly, while in the second experiment, students with similar behaviours were firstly identified using a clustering algorithm and then sorted by an additional algorithm into heterogeneous groups. The results demonstrate the efficacy of the Machine Learning solution compared to the random approach in assisting educators with group formation for a collaborative activity such as the wiki, confirmed by a comparative analysis showing an improvement in student performance and satisfaction. The research contributes to the advancement of online education through the creation of more effective group dynamics using Machine Learning algorithms, thereby improving overall student learning.
ProgMachina: Feature Extraction and Processing Package for Prognostic Studies

: Prognostic studies of industrial systems essentially focus on health deterioration analysis that has recently been oriented toward data analytics and learning systems. In general, real degradation phenomena su ﬀ er from complex drifted data in which degradation pa tt erns are hidden and change over time. Accordingly, such a process requires a well-structured processing and extraction mechanism to reveal such pa tt erns, which facilitates the transition to other model reconstruction and investigation tasks. In this context, to provide additional simplicity of data processing in the ﬁ eld, a complete software package is designed and grouped into a single function that is fully automated and does not require human intervention. The package named ProgMachina (i.e., prognostic machine) provides a featured list of processed features from a life cycle that passed through de-noising, ﬁ ltering, outlier removal, and scaling process to ensure data signi ﬁ cance in terms of degradation. The package allows using a time window with a speci ﬁ c overlap to ensure that the scanning process of all possible degradation pa tt erns is properly done. Additionally, an exponential function is used to identify a corresponding health index of degraded signals. Besides, a set of well-known metrics is used to assess degradation of extracted features. Data visualization and many previous experiments on machines show the e ﬀ ectiveness of such a methodology in terms of obtained prediction accuracy and degradation assessment. The package is designed with Matlab software and made available online to be exploited in similar ﬁ elds.
Pencegahan Penyakit Mulut Dan Kuku Pada Kelompok Peternakan Sapi Lingkungan PTPN Afdeling Guci Putih Kabupaten Jember

Penyakit mulut dan kuku (PMK) juga dikenal sebagai Foot and Mouth Disease (FMD) Jenis penyakit ini disebabkan dari virus tipe A dari keluarga Picornaviridae, genus Apthovirus yakni Aphtaee epizootecae. Di provinsi Jawa Timur, wabah Penyakit Mulut dan Kuku (PMK) telah menginfeksi ribuan hewan ternak di 23 kabupaten/kota. Untuk mencegah penularan wabah PMK di lingkungan peternak sapi PTPN Afdeling Guci Putih, tim dosen dari Universitas Muhammadiyah Jember bekerja sama dengan Perhimpunan Peternak Sapi dan Kambing Indonesia (PPSKI) mengadakan sosialisasi pencegahan wabah PMK, pemeriksaan hewan oleh petugas kesehatan dan pemberian vitamin pada sapi-sapi yang ada di lingkungan tersebut. Dari serangkaian kegiatan PKM yang telah dilakukan di wilayah tersebut, peserta yang merupakan peternak mampu memahami ciri-ciri gejala klinis yang terjadi jika ada sapi yang terjangkit wabah PMK dan melakukan kegiatan-kegiatan preventif untuk mencegah penularan PMK. Pada Kegiatan monitoring dan evaluasi tampak adanya perubahan tata kelola kandang, salah satunya kebersihan makin terjaga, peternak rajin menyemprot desinfektan secara berkala dan berhati-hati jika ada sapi yang kurang sehat segera memanggil petugas kesehatan setempat.
The addition of discrimination inhibitors stimulations discrimination potential and N2O emissions were linked to predation among microorganisms in long term nitrogen application and straw returning systems

Introduction Ammonia oxidizing archaea (AOA) and ammonia oxidizing bacteria (AOB) have been proven to be key microorganisms driving the ammonia oxidation process. However, under different fertilization practices, there is a lack of research on the impact of interaction between predators and AOA or AOB on nitrogen cycling at the multi-trophic level. Methods In this study, a network-oriented microscopic culture experiment was established based on four different long-term fertilization practices soils. We used the nitrification inhibitors 2-phenyl-4,4,5,5-tetramethylimidazoline-1-oxide-3-oxyl (PTIO) and 3, 4-Dimethylpyrazole phosphate (DMPP) inhibited AOA and AOB, respectively, to explore the impact of interaction between protists and AOA or AOB on nitrogen transformation. Results The results showed that long-term nitrogen application promoted the potential nitrification rate (PNR) and nitrous oxide (N2O) emission, and significantly increased the gene abundance of AOB, but had no obvious effect on AOA gene abundance. DMPP significantly reduced N2O emission and PNR, while PTIO had no obvious effect on them. Accordingly, in the multi-trophic microbial network, Cercozoa and Proteobacteria were identified as keystone taxa of protists and AOB, respectively, and were significantly positively correlated with N2O, PNR and nitrate nitrogen. However, Nitrososphaerota archaeon as the keystone species of AOA, had an obvious negative linkage to these indicators. The structural equation model (SEM) showed that AOA and AOB may be competitors to each other. Protists may promote AOB diversity through direct trophic interaction with AOA. Conclusion The interaction pattern between protists and ammonia-oxidizing microorganisms significantly affects potential nitrification rate and N2O emission, which has important implications for soil nitrogen cycle.
SYNTHESIS, CHARACTERIZATION OF NEW ASYMMETRICALLY IMIDAZOLIUM SALTS WITH THEIR Ag(I) AND Pd(II)-N-HETEROCYCLIC CARBENE COMPLEXES

New substituted asymmetrically imidazolium salts with Ag(I) and Pd(II) N-heterocyclic carbene complexes were synthesized and characterized. In order to produce asymmetrically substituted salts, imidazolium salts have been produced using a variety of substituents, including aliphatic chain and phenyl acetamide chloride (5,6,7). Using the carbene site deprotonation process, silver(I)-NHC complexes (8,9,10) were produced from the reaction of asymmetrically substituted imidazolium salts with Ag2O. The appropriate Pd(II)-NHC (11,12,13) were then synthesized using the transmetallation process using Ag(I)-complexes. NHC as transfer reagents. The complexes were identified using spectroscopy methods such as 1H-, 13C-NMR and FT-IR spectroscopy. The recorded spectra of the prepared complexes and their salts agree with theoretical ones.
TNIK regulation of interferon signaling and endothelial cell response to virus infection

Background Traf2 and Nck-interacting kinase (TNIK) is known for its regulatory role in various processes within cancer cells. However, its role within endothelial cells (ECs) has remained relatively unexplored. Methods Leveraging RNA-seq data and Ingenuity Pathway Analysis (IPA), we probed the potential impact of TNIK depletion on ECs. Results Examination of RNA-seq data uncovered more than 450 Differentially Expressed Genes (DEGs) in TNIK-depleted ECs, displaying a fold change exceeding 2 with a false discovery rate (FDR) below 0.05. IPA analysis unveiled that TNIK depletion leads to the inhibition of the interferon (IFN) pathway [-log (p-value) >11], downregulation of IFN-related genes, and inhibition of Hypercytokinemia/Hyperchemokinemia [-log (p-value) >8]. The validation process encompassed qRT-PCR to evaluate mRNA expression of crucial IFN-related genes, immunoblotting to gauge STAT1 and STAT2 protein levels, and ELISA for the quantification of IFN and cytokine secretion in siTNIK-depleted ECs. These assessments consistently revealed substantial reductions upon TNIK depletion. When transducing HUVECs with replication incompetent E1-E4 deleted adenovirus expressing green fluorescent protein (Ad-GFP), it was demonstrated that TNIK depletion did not affect the uptake of Ad-GFP. Nonetheless, TNIK depletion induced cytopathic effects (CPE) in ECs transduced with wild-type human adenovirus serotype 5 (Ad-WT). Summary Our findings suggest that TNIK plays a crucial role in regulating the EC response to virus infections through modulation of the IFN pathway.
Use of human intravaginal tampon embedded with natural progesterone induces synchronous estrus in Santa Inês ewes

The production of Santa Inês sheep in Brazil is very important to farmers because they are rustic, adapted to most part of gastrointestinal parasites and have good maternal ability (Sousa et al., 2003; Amarante et al., 2004). To maintain these characteristics, the animals are selected, and reproductive biotechnologies are applied to induce estrus with progestagens throughout the year, due to their seasonality. Different strategies of estrus induction and synchronization are used worldwide (Balaro et al., 2017) but, traditionally, intravaginal progestagen devices are often applied. One of the disadvantages of hormonal treatment is the cost itself. Currently, there are three different commercial brands in Brazil and overall, the cost of those devices is relatively high. Alternatives must be proposed in order to make efficient treatments, without adversely affecting the reproductive efficiency. Therefore, the aim of this study was to evaluate the efficiency of three protocols of synchronous estrus induction during the transition period in Santa Inês ewes. 
Multi-omics revealed the effects of dietary energy levels on the rumen microbiota and metabolites in yaks under house-feeding conditions

Yak (Bos grunniens) is a unique large ruminant species in the Qinghai-Tibetan Plateau (QTP). Changing the energy levels of their rations can significantly improve their growth performance. Therefore, studying the effects of dietary energy levels on the rumen microflora and metabolites of yak is crucial for enhancing the development of the yak industry. Currently, there is a lack of understanding regarding the impact of feeding energy diets on rumen fermentation parameters, microbial functions, and metabolites. This study was designed to determine the appropriate energy level for feeding yak. Three test diets with metabolizable energy levels of 7.57 MJ/kg, 9.44 MJ/kg, and 11.9 MJ/kg were used and the concentration of volatile fatty acids (VFA) in rumen fluid was measured. The microbial communities, functions, and metabolites in yaks were studied by 16S rRNA sequencing, metagenome, and LC-MS non-targeted metabolomics to investigate the relationships among rumen fermentation parameters, microbial diversity, and metabolites. Ration energy levels significantly affect total VFA, acetate, propionate, butyrate, iso-valerate, valerate, and acetate/propionate (p < 0.05). At the phylum level, the dominant phyla in all three treatment groups were Bacteroidota, Firmicutes, and Actinobacteriota. At the genus level, the abundance of the unclassified_o__Bacteroidales, norank_f_Muribaculaceae, Lachnospiraceae_NK4A136_group, and Family _XIII_AD3011_group showed significant differences (p < 0.05) and were significantly correlated with differential metabolites screened for phosphatidylcholine [PC(16:0/0:0), PC(18:3/0:0)], uridine 3′-monophosphate, and adenosine monophosphate, etc. CAZymes family analysis showed that GHs and CEs differed significantly among the three groups. In addition, differential metabolites were mainly enriched in the pathways of lipid metabolism, nucleotide metabolism, and biosynthesis of other secondary metabolites, and the concentrations of differential metabolites were correlated with microbial abundance. In summary, this study analyzed the effects of ration energy levels on rumen microorganisms and metabolites of yaks and their relationships. The results provided a scientific basis for the selection of dietary energy for yaks in the house feeding period in the future.
Determinants of Customers' Behavior for the Adoption of Green Banking Products and Services: UTAUT Model-Based Explanation

Increasing environmental concerns worldwide have spurred individuals and firms to emphasize eco-friendly practices in every walk of life. This study investigates the implication of the UTAUT model in the adoption of green banking products and services by customers in Pakistan. This study also explores behavioral intention as a mediator between explanatory variables and customers' behavior for the adoption of green banking practices. This quantitative study used convenience sampling and structured self-administered questionnaires for data collection. Structural Equation Modelling (SEM) was employed on the data collected from 305 customers of commercial and the results revealed a significant impact of performance expectancy, effort expectancy, enabling situation, conditional values, and emotional values on customers’ intention and behavior toward green banking practices. Moreover, facilitating conditions and social influence positively affect customers’ adoption of green banking products and services. Behavioral intention also fully mediates the link between performance expectancy, conditional values, emotional values, and usage behavior. Thus, bank management should consider these factors when creating green banking products and services. The current study focused on the customer perspective only, undoubtedly, it is very pivotal to understand the behavior of customers about green banking. But for a holistic coverage of the factors influencing the adoption of green banking, future research might focus on varied perspectives including bank management, bank employees, and SBP by using both quantitative and qualitative methods.
Comparison of reliabilities and validity between AR based motion capture system and physical therapist : preliminary study

AR based motion capture system(ABMCS) is a program that provides multiple practical type of training exercises and rehabilitation programs and with an included motion capture sensor, it is an alternative method for range of motion measurement. The present study aimed to compare the difference between the augmented reality-based measurement tool using motion capture system and the therapist's measurement using goniometer for measuring the range of motion (ROM) of the joint. Three ROM motions on the upper extremity (shoulder abduction, flexion, external rotation), two on lower extremity (knee extension, hip abduction) and trunk flexion were measured. The measurement was done simultaneously to avoid errors and the results were recorded in degrees. Excepting shoulder external rotation and trunk flexion, the result was similar and not significant difference between AR based motion capture system compared to the goniometer. A measurement tool based on augmented reality using AR based motion capture system can be expected to have a good prospect as an evaluation tool if the evaluation is conducted in the way subject and the target join is inside the focus of the sensor.