Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Antibacterial Potential of Green-Synthesized MgO and ZnO Nanoparticles from Two Plant Root Extracts

The green approach-based nanoparticle synthesis is considered a more cost-effective and ecologically responsible method of producing nanoparticles than other standard techniques. A major accomplishment in resolving these issues is the use of nanoparticles for environmental pollution remediation. This article describes a simple method for producing MgO and ZnO nanoparticles (NPs) using aqueous extracts of Zingiber officinale and Glycyrrhiza roots as the stabilizing and reducing agents, respectively. Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersed X-ray (EDX) spectroscopy methods were used to characterize the biologically synthesized metal oxide nanoparticles (MO NPs). The XRD results showed that the mean crystallite sizes of synthesized ZnO and MgO NPs, which have excellent purity, are 12.35 nm and 4.83 nm, respectively. The spherical or elliptical shape of the synthesized NPs was confirmed by the SEM analysis. The antibacterial activity of the synthesized NPs against both Gram-negative and Gram-positive bacteria was thoroughly investigated. With a medium zone of inhibition of 7 to 10 mm, the as-synthesized MgO NPs and ZnO NPs demonstrated moderate antibacterial activity towards various bacterial strains.
Existing Irrigation Practices and Their Improvement for Enhancing Water Productivity

3 ABSTRACT: This study describes a holistic approach to understand the limitations of current irrigation practices and to propose strategies for gaining potential benefits of crop per drop. Post Indus Water Treaty's expansion of uses in irrigation water terrifically ignored the water sustainability and unpredicted drought scenarios that would severely affect the crop production. The severity of recurring droughts and manifold increased cropping intensity, requires analysis of irrigation practices to devise adaptive strategies against highly variable climate change. In an area of intensive agriculture in the Central Punjab, irrigations are scheduled using CROPWAT model considering effective rainfall, groundwater contribution and deep percolation losses. Data on actual irrigation practices on the basis of warabandi (weekly irrigation turns) system is collected. It is found that crops were under and over irrigated for a certain period during growth stage. The results indicate that an integrated management plan is needed to regulate irrigation needs in accordance with the crop water requirements. It is noticed that there exists critical water shortage during restricted flows. Conversely, surplus water supplies and surface runoff is wasted during the forced irrigation turns and seasonal monsoon rains. This paper critically evaluates opportunities of land and water management plan for improving water productivity.
Concentrate supplementation improves cold-season environmental fitness of grazing yaks: responsive changes in the rumen microbiota and metabolome

Yak (Bos grunniens) is an important economic animal species on the Qinghai-Tibet Plateau. Yaks grazed in the cold season often suffer from nutritional stress, resulting in low production performance. This situation can be improved by properly feeding the grazing yaks in the cold season; however, there is still little information about the effect of different feeding levels on the intestinal microflora and metabolites of yaks. Therefore, this study aimed to explore the effect of feeding different doses of concentrate supplements on rumen bacterial communities and metabolites in grazing yaks during the cold season. Feed concentrate supplementation significantly improved the production performance and rumen fermentation status of grazing yaks during the cold season, and switched the type of ruminal fermentation from acetic acid fermentation to propionic acid fermentation. Ruminal fermentation parameters and ruminal bacterial abundance correlated strongly. At the phylum level, the abundance of Firmicutes increased with increasing concentrate supplementation, while the opposite was true for Bacteroidota. At the genus level, the abundance of Christensenellaceae_R-7_group, NK4A214_group, Ruminococcus, norank_f__Eubacterium_coprostanoligenes_group, norank_f__norank_o__ Clostridia_UCG-014, Lachnospiraceae_NK3A20_group, Acetitomaculum, and Family_XIII_AD3011_group increased with increasing concentrate supplementation, while the abundance of Rikenellaceae_RC9_gut_ group decreased. Dietary concentrate supplementation altered the concentration and metabolic mode of metabolites in the rumen, significantly affecting the concentration of metabolites involved in amino acid and derivative metabolism (e.g., L-aspartic acid, L-glutamate, and L-histidine), purine metabolism (e.g., guanine, guanosine, and hypoxanthine), and glycerophospholipid metabolism (e.g., phosphatidate, phosphatidylcholine, and phosphocholine), and other metabolic pathways. The strong correlation between yak rumen microorganisms and metabolites provided a more comprehensive understanding of microbial community composition and function. This study showed significant changes in the composition and abundance of bacteria and metabolites in the rumen of cool season grazing yaks fed with concentrate supplements. Changes in ruminal fermentation parameters and metabolite concentration also showed a strong correlation with ruminal bacterial communities. These findings will be helpful to formulate supplementary feeding strategies for grazing yaks in the cold season from the perspective of intestinal microorganisms.
Hyperconnected Logistic Platform for Heavy-Duty Machinery: Leveraging Physical Internet Principles to Drive the Composting Industry

The Physical Internet (PI) envisions a global logistics system that integrates physical, digital and operational connections. This study aims to develop a hyperconnected logistic platform for heavy-duty machinery (HDM) in the composting industry by utilizing a systematic methodology. The proposed architecture consists of four layers: the Domain Model, the MBSE Model, the Information Sharing Model and the Agent-based Simulation Platform. The Domain Model analyzes the current situation and investigates stakeholder viewpoints, and the MBSE Model reduces complexity and describes mutual interactions between requirements and needs. The Information Sharing Model focuses on the information exchange among the main components, and the Agent-based Simulation Platform implements the proposed platform. The feasibility of the proposed architecture is demonstrated through a use case in Styria, Austria. Three simulation-based scenarios are analyzed, starting from the semi-hyperconnected approach up to the hyperconnected approach with PI vision integration. The results indicate that the hyperconnected platform is successful in serving all composting facilities, leveraging underutilized resources and promoting high-quality compost production. Thus, the platform provides support in a local, communal setting, resulting in enhancing the circular economy within the composting sector. Our efforts aim to contribute to the realization of the Physical Internet vision and promote composting to ultimately achieve a more sustainable future.
Inductive Textile Sensing for Movement Monitoring

Textile-based wearable technologies have significant potential for monitoring movements in fitness and health applications. Inductive sensing is a promising modality due to the ease of sensors fabrication and the quality of sensors’ response. This letter initially focused on designing individual textile inductive sensors. Then, selected parameters were used to create a textile-based garment with inductive sensors capable of monitoring back movements in three directions. This letter successfully demonstrated that the developed garment can monitor back movements in three directions.
Agroclimatic analysis for mainland East Asia by a GIS approach

Climate has been long recognised as an important constraint to crop production. Many agroclimatic analyses have been developed in Mainland East Asian countries to assist their agricultural development and resource management. These analyses were all restricted to using a limited number of data points and static summations of climatic variables. The complex climatic patterns and the non-linear responses of crops to climate cannot be captured by such agroclimatic analyses. This thesis addressed the agroclimatic environment and its impact on crop production · using a different philosophy and methodology in order to overcome these shortcomings. There are four related components in this study. They are: 1). regular grid climatic data sets; 2). crop responses to the environmental elements in Mainland East Asia; 3). agroclimatic classification; and 4). crop modeling at selected representative stations from various agroclimatic zones. The regular grid climatic data sets consist of climatic surf aces and a digital elevation model (DEM). In this study they were developed at a resolution of I/20th degree. While this study focused on agroclimatic analysis these data sets can be applied to any other fields that relate to climate such as forestry, ecology and conservation. These climatic surf aces express climatic variables as functions of multi-dimensional thin plate smoothing splines in term of longitude, latitude and elevation. They were developed using the ANUSPLIN package, and are based on a network of up to 3800 stations across Mainland East Asia. Estimates for climatic variables at any location in the Mainland East Asian countries can be calculated from these surf aces with input of the appropriate independent variables. A DEM at a resolution of I/20th degree, calculated using the ANUDEM package and based on terrain data digitised from topographic maps, was used to construct data sets in this study. These data sets consist of 434,484 grid cells across the studying area. Based on such data sets, crop responses to the climatic environment were simulated using a general plant growth model GROWEST. This model transforms the non-linear
Implementation of A Deep Learning Framework for Intelligent Intrusion Detection in Internet of Things Networks

Internet of Things (IoT) has ushered in a new age that benefits humanity greatly. From automated healthcare to energy and transportation, the Internet of Things covers many subject topics. IoT devices are vulnerable to several cyberattacks due to their limited resources. As data output has expanded exponentially from a zettabyte to a trillion per year, computer and IoT network expansion has surged. Network growth has created new security issues. In such large data sets, intrusions are hard to spot. Today's networks are used for “smart” dwellings and cities, grids, devices, objects, online commerce, banking, government, etc. Due to internet data privacy and security concerns, Intrusion Detection Systems (IDS) have proliferated. Privacy, security, and resilience would suffer if IDS protection fails. Traditional defences can't handle modern attacks. Cutting-edge deep learning methods may automatically detect intrusions and network anomalies. This study's main goal is to evaluate intrusion detection using deep learning algorithms and compare the outcomes to other systems. This research work has proposed a novel deep-learning-based IoT invasion detection method. The cutting-edge Internet of Things dataset contains IoT traces and real-world attack traffic including DoS, DDoS, data harvesting, and theft attempts. Finally, all publicly available network-based IDS datasets are analyzed in this research study. The accuracy, false alarm rate, recall, precision, f-1 score, and detection rate of many deep learning IDS algorithms have been examined. Furthermore, this research study has investigated the network security and privacy issues and solutions.
Syllables and their beginnings have a special role in the mental lexicon

Significance How spoken words are stored in the mind/brain is a fascinating question for our understanding of language as well as for practical applications, e.g., in clinical contexts. Since words are built as temporal sequences of speech sounds, a relevant but not yet clearly answered question is whether individual speech sounds within these sequences contribute equally to the encoding of words. Our quantitative analysis of lexicons from 12 languages demonstrates that the distribution of lexical informativeness among speech sounds within words is organized by the syllable unit, which is a computational primitive in the production and perception of words. Specifically, the beginnings of syllables, rather than the beginnings of words, hold a privileged role in representing words in the mental lexicon.
Atlantic Continental Shelf and Slope of the United States - Ostracode zoogeography in the southern Nova Scotian and northern Virginian faunal provinces

Ostracodes were identified from 236 bottom samples taken in the region from Nova Scotia to Long Island. There i·s a distinctive difference between the assemblages in the northern and southern parts of this region. Many sublittoral cryophilic species are not present south of Cape Cod or the Northeast Channel, and several thermophilic species a.re not . found north of Cape Cod or Georges Bank. The ostracode assemblages in the southern part of the cold-temperate Nova Scotian province are a mixture of amphiatlantic cryophilic species and endemic, mainly thermophilic species; European forms make up approximately 50 percent of the species. Less than 25 percent of the species known from the mild-temperate Virginian province have been reported from European waters. More endemic species pass Cape Cod from the south than amphiatlantic cryophilic species do from the north. This is consistent with the fact that summer isotherms are more compressed than winter isotherms in the Cape Cod region, and the southern limit of distribution of most amphiatlantic species is regulated by summer high temperatures. The distribution data on which these interpretations are based are presented in a series of . figures and over 60 species distribution maps.
Concept-Aware Video Captioning: Describing Videos With Effective Prior Information

Concepts, a collective term for meaningful words that correspond to objects, actions, and attributes, can act as an intermediary for video captioning. While many efforts have been made to augment video captioning with concepts, most methods suffer from limited precision of concept detection and insufficient utilization of concepts, which could provide caption generation with inaccurate and inadequate prior information. Considering these issues, we propose a Concept-awARE video captioning framework (CARE) to facilitate plausible caption generation. Based on the encoder-decoder structure, CARE detects concepts precisely via multimodal-driven concept detection (MCD) and offers sufficient prior information to caption generation by global-local semantic guidance (G-LSG). Specifically, we implement MCD by leveraging video-to-text retrieval and the multimedia nature of videos. To achieve G-LSG, given the concept probabilities predicted by MCD, we weight and aggregate concepts to mine the video’s latent topic to affect decoding globally and devise a simple yet efficient hybrid attention module to exploit concepts and video content to impact decoding locally. Finally, to develop CARE, we emphasize on the knowledge transfer of a contrastive vision-language pre-trained model (i.e., CLIP) in terms of visual understanding and video-to-text retrieval. With the multi-role CLIP, CARE can outperform CLIP-based strong video captioning baselines with affordable extra parameter and inference latency costs. Extensive experiments on MSVD, MSR-VTT, and VATEX datasets demonstrate the versatility of our approach for different encoder-decoder networks and the superiority of CARE against state-of-the-art methods. Our code is available at https://github.com/yangbang18/CARE.
The Antifungal Fibers of Polyamide 12 Containing Silver and Metal Oxides

The textile market is a vast industry that utilizes antimicrobial polymeric materials, including various types of fabrics, for medical and personal protection applications. Therefore, this study focused on examining four types of antimicrobial fillers, namely, metal oxides (zinc, titanium, copper) and nanosilver, as fillers in Polyamide 12 fibers. These fillers can be applied in the knitting or weaving processes to obtain woven polymeric fabrics for medical applications. The production of the fibers in this study involved a two-step approach: twin-screw extrusion and melt spinning. The resulting fibers were then characterized for their thermal properties (TGA, DSC), mechanical performance (tensile test, DMA), and antifungal activity. The findings of the study indicated that all of the fibers modified with fillers kill Candida albicans. However, the fibers containing a combination of metal oxides and silver showed significantly higher antifungal activity (reduction rate % R = 86) compared to the fibers with only a mixture of metal oxides (% R = 21). Furthermore, the inclusion of metal oxides and nanosilver in the Polyamide 12 matrix hindered the formation of the crystal phase and decreased slightly the thermal stability and mechanical properties, especially for the composites with nanosilver. It was attributed to their worse dispersion and the presence of agglomerates.
Study of γ-Photon High-Resolution Fast 3-D Image Reconstruction Algorithm Based on Lossless Equivalent System Matrix

In <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-photon industrial large-space high-resolution full 3-D nondestructive testing imaging, when there is a large increase in the number of detection crystals, the number of response lines, the number of computational tasks, and the storage space of the system matrix all increase dramatically; therefore, reducing computation time and storage space becomes challenging. In this study, we propose a <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-photon high-resolution fast 3-D image reconstruction method based on a lossless equivalent system matrix (LESM), which divides the cylindrical effective field of view into multiple equivalent sector blocks, performs polar voxel discretization according to adaptive rules, and accurately calculates the system matrix corresponding to one equivalent sector block by a polar voxel stereo angle model. Furthermore, the system matrix elements corresponding to the remaining sector blocks are obtained by rotational symmetry. Meanwhile, the system matrix elements corresponding to the polar voxels in the equivalent sector block are divided into subsets according to radial and mirror symmetry to further reduce the number of system matrix elements that need to be computed, so as to realize the lossless compression and fast recovery of the 3-D system matrix. To improve the accuracy of the system matrix and effectively suppress noise, the error caused by the depth of interaction (DOI) is further reduced based on the LESM calculation, and the display of the image is completed by precomputing the mapping matrix <inline-formula> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula>. Parallel computing is used to accelerate the algorithm. Simulation and experimental results show that compared with the traditional Cartesian voxel method, the proposed method significantly reduces the computational tasks and storage space of the system matrix elements and improves the contrast and spatial resolution of industrial 3-D reconstructed images, thus meeting the demand of <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-photon industrial large-space detection imaging.
Spatial Diversity and Impact of Selected Factors on Women’s Labour Force Participation Rate in Poland during 2000–2020

The sustainable development (SD) policy is also a family-friendly policy. Its crucial element is the use of social transfers (STs) as cash benefits for children. This study aims to explain the existence of regional variation in Poland in terms of the rate of working women and to conduct an assessment of the selected factors’ impact including the impact of STs on women’s propensity to work in Poland between 2000 and 2020. The survey showed the existence of SD through a strong convergence of the study area in terms of the rate of working women (RWW). However, an increase in the RWW with a decreasing fertility rate (FR) was observed between 2017 and 2020. The study showed a negative correlation between the RWW and STs. This means that the higher the transfers, the lower the propensity of women to return to the labour market. This situation could have negative consequences in the future. Long-lasting non-working periods mean a shorter period of pension contributions. As a result, with a low fertility rate, an accelerated pauperisation of society and a growing group of people living below the poverty line can be expected, which could disrupt or halt the country’s sustainable development. Appropriate reforms are needed to enable women to return to the labour market as soon as possible.
Development of Autonomous Robots for Industrial and Domestic Applications

This research study investigates the potential design of autonomous robots for developing the domestic and industrial applications. The system underwent rigorous testing in both commercial and residential settings, demonstrating exceptional performance in navigation and achieving near-perfect scores in manipulation. Autonomous robots possess the capacity to enhance productivity, reduce expenses, and enhance safety in various scenarios, as emphasized in the literature. The report also outlines potential avenues for future research, which encompass the development of enhanced sensors and perceptual systems, collaborative systems, and advanced decision-making algorithms. In general, this study constitutes a crucial contribution to the expanding corpus of information pertaining to self-governing robots and will facilitate their prospective conception, advancement, and integration.
3DSleepNet: A Multi-Channel Bio-Signal Based Sleep Stages Classification Method Using Deep Learning

A novel multi-channel-based 3D convolutional neural network (3D-CNN) is proposed in this paper to classify sleep stages. Time domain features, frequency domain features, and time-frequency domain features are extracted from electroencephalography (EEG), electromyogram (EMG), and electrooculogram (EOG) channels and fed into the 3D-CNN model to classify sleep stages. Intrinsic connections among different bio-signals and different frequency bands in time series and time-frequency are learned by 3D convolutional layers, while the frequency relations are learned by 2D convolutional layers. Partial dot-product attention layers help this model find the most important channels and frequency bands in different sleep stages. A long short-term memory unit is added to learn the transition rules among neighboring epochs. Classification experiments were conducted using both ISRUC-S3 datasets and ISRUC-S1, sleep-disorder datasets. The experimental results showed that the overall accuracy achieved 0.832 and the F1-score and Cohen’s kappa reached 0.814 and 0.783, respectively, on ISRUC-S3, which are a competitive classification performance with the state-of-the-art baselines. The overall accuracy, F1-score, and Cohen’s kappa on ISRUC-S1 achieved 0.820, 0.797, and 0.768, respectively, which also demonstrate its generality on unhealthy subjects. Further experiments were conducted on ISRUC-S3 subset to evaluate its training time. The training time on 10 subjects from ISRUC-S3 with 8549 epochs is 4493s, which indicates its highest calculation speed compared with the existing high-performance graph convolutional networks and $\text{U}^{2 }-$ Net architecture algorithms.