Miracle-dz/UniFAR-FTUs huggingface dataset:
Employing a Plant Probiotic Actinomycete for Growth Promotion of Lettuce (Lactuca sativa L. var. longifolia) Cultivated in a Hydroponic System under Nutrient Limitation

The consumption of lettuce is associated with an increased risk of ingesting nitrate, a naturally occurring and potentially harmful compound that can have adverse effects on human health. Hydroponic cultivation systems serve as effective tools for regulating nutrient solutions and nitrogen availability, which are essential for controlling nitrate levels. However, the techniques for reducing nutrient levels need to be appropriately calibrated based on lettuce growth responses and their interactions with the environment and growing conditions. Previous studies have demonstrated that plant probiotic actinomycetes can alleviate nutritional stress in various crops. However, there is a noticeable gap in research concerning the effects of actinomycetes on hydroponically grown lettuce, particularly under nutrient-limiting conditions. This study aimed to evaluate the effectiveness of the actinomycete Streptomyces thermocarboxydus S3 in enhancing lettuce growth in a nutrient-restricted hydroponic system. The results indicated that the detrimental effects of nutrient stress on lettuce were mitigated by the inoculation of lettuce with S. thermocarboxydus S3. This mitigation was evident in various growth parameters, including leaf count, shoot length, and the fresh and dry weights of both shoots and roots. In the presence of nutritional stress, S. thermocarboxydus S3 likely mitigated the negative effects on lettuce by reducing hydrogen peroxide levels, presumably through the synthesis of H2O2-scavenging enzymes. Furthermore, S. thermocarboxydus S3 successfully survived and colonized lettuce roots. Therefore, the inoculation of lettuce with S. thermocarboxydus S3 offers significant advantages for promoting lettuce growth in nutrient-limited hydroponic systems.
Extreme Loads on a Spar Buoy-Supported Floating Offshore Wind Turbine

An offshore wind turbine supported by a spar buoy floating platform is the subject of this study on tower and rotor extreme loads. The platform, with a 120-meter draft and assumed to be sited in 320 meters of water, supports a 5MW wind turbine. A baseline model for this turbine developed at the National Renewable Energy Laboratory (NREL) is employed in stochastic response simulations. The support platform, along with the mooring system consisting of three catenary lines, chosen for loads modeling, is based on the “Hywind” floating wind turbine concept. Our interest lies in gaining an understanding of the dynamic coupling between the support platform motion and the turbine loads. We first investigate short-term response statistics using stochastic simulation for a range of different environmental wind and wave conditions. From this study, we identify a few “controlling” environmental conditions for which long-term turbine load statistics and probability distributions are established.
Robust Jumping With an Articulated Soft Quadruped Via Trajectory Optimization and Iterative Learning

Quadrupeds deployed in real-world scenarios need to be robust to unmodelled dynamic effects. In this work, we aim to increase the robustness of quadrupedal periodic forward jumping (i.e., pronking) by unifying cutting-edge model-based trajectory optimization and iterative learning control. Using a reduced-order soft anchor model, the optimization-based motion planner generates the periodic reference trajectory. The controller then iteratively learns the feedforward control signal in a repetition process, without requiring an accurate full-body model. When enhanced by a continuous learning mechanism, the proposed controller can learn the control inputs without resetting the system at the end of each iteration. Simulations and experiments on a quadruped with parallel springs demonstrate that continuous jumping can be learned in a matter of minutes, with high robustness against various types of terrain.
Stretchable and Bendable Polydimethylsiloxane- Silver Composite Antenna on PDMS/Air Gap Substrate for 5G Wearable Applications

An engineered composite conductor is essential for developing a wearable antenna that is not only flexible but also stretchable. This paper presents the use of polydimethylsiloxane (PDMS) as the substrate and custom polydimethylsiloxane-silver conductive paste for wearable applications. The antenna is designed with an air gap PDMS substrate between the patch and sawtooth partial ground at 3.5 GHz to enhance the bandwidth and gain. Furthermore, the proposed antenna is flexible and can be bent as well as stretched up to 20%, making it suitable for use on the human body. This study investigates the antenna’s performance under bending and stretching to mimic the human body’s structure and movements. Additionally, the specific absorption rate (SAR) of the wearable antenna was analyzed for safety purposes.
Multiplex PCR targeting IS6110 and mpb64 Genes for Detecting Extra-Pulmonary Tuberculosis: A Cross-Sectional Study in a Tertiary Care Center, Kashmir, India

samples from suspected cases of EPTB. We compared the performance of mPCR with conventional methods, including Ziehl Neelsen (ZN) microscopy, culture in LJ media, and BacT/Alert system. Our study aimed to provide insight into the utility of mPCR and its different targets for diagnosing EPTB in our setting. Methods : We conducted a cross-sectional survey of 250 non-repeat clinical samples from extrapulmonary sites to detect M. tuberculosis . Both conventional diagnostic methods, including ZN microscopy, culture in LJ media, and BacT/Alert system, and molecular methods, including multiplex PCR (mPCR) using IS6110 and mpb64 as gene targets, were performed on the samples. Of the 250 samples, results for all the diagnostic methods were available for 116 samples, which were included in the final analysis. The study population comprised 83 patients with suspected EPTB and 33 controls. Results : Among the 83 samples in the EPTB group, conventional diagnostic methods, including ZN microscopy, LJ culture, and BacT/Alert system, showed low positivity rates of 6.02%, 8.43%, and 15.66%, respectively. In contrast, multiplex PCR (mPCR) using IS6110 and mpb64 as gene targets showed a significantly higher positivity rate of 79.51%. The IS6110 gene was amplified in 79.51% of the samples, while mpb64 was amplified in 49.39%. Conclusion : Our study demonstrates that multiplex PCR (mPCR) using IS6110 and mpb64 as gene targets is a more sensitive diagnostic method for extra-pulmonary tuberculosis (EPTB) than conventional methods. Both IS6110 and mpb64 showed high sensitivity of 100%, but mpb64 was more specific when compared with the gold standard. Our findings suggest that mPCR, particularly with the inclusion of mpb64 as the target gene, may be a valuable tool for the early and accurate diagnosis of EPTB.
Reducing Acquisition Costs With Random Sampling And Multidimensional Interpolation

Seismic data must be migrated, preferably before stack. Prestack migration gives best results when traces are evenly and densely sampled in inline CMP, crossline CMP, offset, and azimuth (Vermeer, 2010). Otherwise one can generate artifacts such as lateral smearing, migration smiles, and acquisition footprint. Acquisition can not economically deliver such sampling. Recently, however, 5D prestack trace interpolation (in reality, interpolation in four spatial dimensions) has become commonplace (Abma, 2010; Trad 2009; Trickett et al., 2010). Its principle goal is to provide a data set to prestack migration which is well sampled in all spatial dimensions. Figure 1: Traces acquired and processed. On the left is the traditional situation, on the right is the current situation.
Micro Watershed Modeling in India Using GIS Technologies and Agricultural Policy Environmental Extender (APEX) Model. A Case Study

Study demonstrates the application of GIS technologies and APEX model for watershed management in Salasi watershed, Himachal Pradesh India. Extensive processing of static and dynamic data for the study area were performed using GIS technologies for use in APEX model. DEM for the study area was generated using extrapolated contours taken from 1:50,000 scale Survey of India topographic map. Satellite interpreted data for the land use and digitized soil map using NBSSLUP soil map with a resolution of 1:500,000 were used to prepare the land use and soil input files for APEX model run. To know the flow direction and topographic information required to build the Subarea file of APEX, the automatic delineation of watersheds was done by using the DEM as input to AVSWAT model. The target outflow point is interactively selected. A threshold value of 25 ha has been used for generating the stream network which primarily decides the density of the stream network and consequently the number of sub basins. With this setup model was run successfully but the results could not be tested against the observed flow due to unavailability of data on observed flow for the study area. However the trend in simulated daily flow followed the trend in observed rainfall which indicate that the framework can be used to model the relative changes in flow component under different conservation practices as compared to this base run. Key Word: SWAT, APEX, GIS, Subbasin, Flow
Study of the Role of Lipoprotein Maturation Enzymes in the Pathogenesis of the Infection Caused by the Streptococcus suis Serotype 2 Sequence Type 25 North American Prototype Strain

Streptococcus suis serotype 2 is an important swine bacterial pathogen causing sudden death, septic shock, and meningitis. However, serotype 2 strains are phenotypically and genotypically heterogeneous and composed of a multitude of sequence types (STs) whose distributions greatly vary worldwide. It has been previously shown that the lipoprotein (LPP) maturation enzymes diacylglyceryl transferase (Lgt) and signal peptidase (Lsp) significantly modulate the inflammatory host response and play a differential role in virulence depending on the genetic background of the strain. Differently from Eurasian ST1/ST7 strains, the capsular polysaccharide of a North American S. suis serotype 2 ST25 representative strain only partially masks sub-capsular domains and bacterial wall components. Thus, our hypothesis is that since LPPs would be more surface exposed in ST25 strains than in their ST1 or ST7 counterparts, the maturation enzymes would play a more important role in the pathogenesis of the infection caused by the North American strain. Using isogenic Δlgt and Δlsp mutants derived from the wild-type ST25 strain, our studies suggest that these enzymes do not seem to play a role in the interaction between S. suis and epithelial and endothelial cells, regardless of the genetics background of the strain used. However, a role in the formation of biofilms (also independently of the STs) has been demonstrated. Moreover, the involvement of LPP dendritic cell activation in vitro seems to be somehow more pronounced with the ST25 strain. Finally, the Lgt enzyme seems to play a more important role in the virulence of the ST25 strain. Although some differences between STs could be observed, our original hypothesis that LPPs would be significantly more important in ST25 strains due to a better bacterial surface exposition could not be confirmed.
Association between Hospital-acquired Disability and Clinical Outcomes in Older Patients Who Underwent Cardiac Surgical

Abstract Objective: This study aimed to clarify the association between hospital-acquired disability (HAD) and prognosis in older patients who underwent cardiac surgery. Methods: This single-center, retrospective, observational study included 141 patients aged ≥65 years who underwent cardiac surgery at our hospital from November 2016 to August 2021. The primary endpoint of this study was the occurrence of major adverse cardiac and cerebrovascular events (MACCEs) within 2 years of hospital discharge. HAD was defined as a score of ≤5 on any one of the functional independence measure (FIM) subitems at discharge compared to preoperatively. Results: MACCE was observed in 16.3%, and the incidence of MACCE was significantly higher in the HAD group than that in the non-HAD group (12.1 vs. 34.5%, log-rank, p = 0.003). HAD was also significantly associated with the MACCE (hazard ratio [HD]: 2.575, 95% confidence interval [CI]: 1.001–9.655, p = 0.046). The incidence rate of HAD was 20.6%, with age (odds ratio [OR]: 1.260, 95% CI: 1.080–1.470, p = 0.004), preoperative short physical performance battery (SPPB) score (OR: 0.462, 95% CI: 0.301–0.708, p <0.001), and postoperative delirium (OR: 6.660, 95% CI: 1.480–30.000, p = 0.014) identified as significant factors. Conclusion: HAD is an independent predictor of MACCE in older patients who underwent cardiac surgery.
Genomic Island-Encoded Diguanylate Cyclase from Vibrio alginolyticus Regulates Biofilm Formation and Motility in Pseudoalteromonas

Many bacteria use the second messenger c-di-GMP to regulate exopolysaccharide production, biofilm formation, motility, virulence, and other phenotypes. The c-di-GMP level is controlled by the complex network of diguanylate cyclases (DGCs) and phosphodiesterases (PDEs) that synthesize and degrade c-di-GMP. In addition to chromosomally encoded DGCs, increasing numbers of DGCs were found to be located on mobile genetic elements. Whether these mobile genetic element-encoded DGCs can modulate the physiological phenotypes in recipient bacteria after horizontal gene transfer should be investigated. In our previous study, a genomic island encoding three DGC proteins (Dgc137, Dgc139, and Dgc140) was characterized in Vibrio alginolyticus isolated from the gastric cavity of the coral Galaxea fascicularis. Here, the effect of the three DGCs in four Pseudoalteromonas strains isolated from coral Galaxea fascicularis and other marine environments was explored. The results showed that when dgc137 is present rather than the three DGC genes, it obviously modulates biofilm formation and bacterial motility in these Pseudoalteromonas strains. Our findings implied that mobile genetic element-encoded DGC could regulate the physiological status of neighboring bacteria in a microbial community by modulating the c-di-GMP level after horizontal gene transfer.
The Effectiveness of Role-Playing Games (RPG) in Teaching Reading for Second Grade Students at Man 2 Lombok Tengah

The research entitled the effectiveness of Role Playing Game (RPG) examined in this research was how the effect of using Role playing game (RPG) to increase the second grade students reading comprehension at MAN 2 Lombok Tengah. This research was aimed to find out whether the use of Role-Playing Game (RPG) was effective or not to increase the second-grade students’ reading comprehension at MAN 2 Lombok Tengah. This research uses quantitative research methods with a pre-experimental approach in one group pre test and post test. This research was conducted at MAN 2 Lombok Tengah which consist of 112 second grade students in total and Class XI IBB was chosen as a sample with total of 17 students. The instruments used were a test of narrative that consist of 20 multiple choices. The research findings showed that the students’ difference before and after using Role Playing Game (RPG) was significantly effective. The results showed that students' pre-test scores were smaller than post test with mean score of pre test was 60.59. While the scores of post-test after treatment got better score than pre test, with mean score of 77.6. After that, 2 paired sample T-test was applied with a significance value of sig. (2 paired) was 0.001. Because the results obtained are Sig < a or 0.001 < 0.05. Then H0 is rejected and Ha is accepted. Therefore, based of the findings it can be concluded that the use of Role Playing Games (RPG) is effective to increase students' reading comprehension.
Arbeit für alle? Kosten und Verteilungswirkung einer Jobgarantie für Langzeitbeschäftigungslose in Österreich

Vor dem Hintergrund anhaltend hoher Langzeitbeschäftigungslosigkeit haben Jobgarantien seit einigen Jahren wieder vermehrt Aufmerksamkeit erregt. Als Beitrag zu der laufenden Debatte berechnet die vorliegende Studie mithilfe des Mikrosimulationsmodells EUROMOD die Nettokosten einer universellen Jobgarantie für Langzeitbeschäftigungslose in Österreich und den Effekt einer solchen Garantie auf Armut und Ungleichheit. Abhängig von der Höhe des in der Garantie gezahlten Bruttogehalts (1.500 € oder 1.700 €) und der angenommen Übergangswahrscheinlichkeit der Teilnehmenden in Beschäftigung betragen die monatlichen Nettokosten pro Person zwischen 451 und 703 €. Bei einem Bruttolohn von 1.500 € würde die Maßnahme die Zahl der armutsgefährdeten Personen um 77.100 senken und Einkommensungleichheit leicht reduzieren. Aus gesellschaftlicher Sicht würde die Garantie besonders Personengruppen, die stark von Langzeitbeschäftigungslosigkeit betroffen sind (Männer, ältere Menschen) helfen. Aus individueller Perspektive würden insbesondere Frauen, sehr junge Menschen, Familien und Mehrpersonenhaushalte ohne Kinder und tertiär Gebildete profitieren, da für diese Personen die Differenz zwischen der Höhe der Ersatzleistungen und dem Gehalt der Jobgarantie besonders groß ist. Die Ergebnisse legen nahe, dass eine universelle Jobgarantie für Langzeitbeschäftigungslose einen wichtigen und kostengünstigen Beitrag zur Förderung gesellschaftlicher Teilhabe und zur Reduzierung von Armut und Ungleichheit leisten kann.
Baby Cry Recognition by BCRNet Using Transfer Learning and Deep Feature Fusion

Deep learning theory has made remarkable advancements in the field of baby cry recognition, significantly enhancing its accuracy. Nonetheless, existing research faces two challenges. Firstly, the limited size of the database increases the risk of overfitting for a deep learning model. Secondly, the integration of multi-domain features has been neglected. To address these issues, a novel approach called BCRNet is proposed, which combines transfer learning and feature fusion. The BCRNet model takes multi-domain features as input and extracts deep features using a transfer learning model. Subsequently, a multilayer autoencoder is utilized for feature reduction, and a Support Vector Machine (SVM) is employed to select the transfer learning model with the highest classification accuracy. Then two features are concatenated to form fused features. Finally, the fused features are fed into a deep neural network for classification. Experimental results show that the proposed model is effective in mitigating the model overfitting problem due to small datasets. The fused features of the proposed method are better than the existing methods using single domain features.
Online learning condition during covid-19 pandemic among public university students

The Covid-19 pandemic has led to unexpected changes in all Institut Pengajian Tinggi Awam (IPTA) operations. In the course of the lockdown, there had been a sudden shift in the educational system from face-to-face instruction to online instruction. Studies have shown that progress in online learning and teaching will be considerably more successful if all students have access to a conducive and healthy learning environment. Thus, the present study seeks to determine the level of online learning conditions during the pandemic among IPTA students. A total of 410 students between the ages of 18 to 32 from 20 IPTAs were selected randomly to participate in the study by answering a series of survey form. Several factors, including insufficient access to appropriate ICT devices, constant internet disruption, and a lack of enthusiasm or motivation to study online, contributed to the study's findings, which indicated a need for improvement in the level of online learning condition among university students during the Covid-19 pandemic. Consequently, students who received appropriate information from their educators and perceived the enjoyment and usefulness of the learning sessions will have greater advantages in dealing with learning processes during the COVID-19 pandemic when compared to students who did not share the same feelings and treatment.
Understanding and Managing the Occupational Health Impacts on Investigators of Internet Child Exploitation

An empirical research project was carried out involving Internet child exploitation (ICE) investigators from all nine Australian police jurisdictions. The aim of the research was to examine the physical, social and psychological impacts of ICE investigation in order to inform the development of prevention and best practice guidelines. The research comprised two studies. The first study involved an online survey of 188 current, former and incoming ICE investigators and a comparison sample of 106 non-ICE police. The survey comprised a wide-ranging set of existing scales and items written specifically for this study. The second study involved a sub-sample of 32 current and former ICE investigators who agreed to take part in a semi-structured, anonymous telephone interview. Despite the disturbing nature of their role, most ICE investigators are coping well. To the extent that investigators find their role to be stressful, some of the causes of this stress relate to generic workplace issues. To the extent that exposure to ICE contributes to workplace stress, the effects do not seem to be accumulative, that is, they are not a function of the extent of exposure to ICE material. There is, however, no universal ICE investigator experience. In particular, there is a small number of investigators who experience clinically-significant adverse reactions to their exposure to ICE material.
Tree species classification on images from airborne mobile mapping using ML.NET

ABSTRACT Deep learning is a powerful tool for automating the process of recognizing and classifying objects in images. In this study, we used ML.NET, a popular open-source machine learning framework, to develop a model for identifying tree species in images obtained from airborne mobile mapping. These high-resolution images can be used to create detailed maps of the landscape. They can also be analyzed and processed to extract information about visual features, including tree species recognition. The deep learning model was trained using ML.NET to classify two tree species based on the combination of airborne mobile mapping images. Our approach yielded impressive results, with a maximum classification accuracy of 93.9%. This demonstrates the effectiveness of combining imagery sources with deep learning tools in ML.NET for efficient and accurate tree species classification. This study highlights the potential of the ML.NET framework for automating object classification and can provide valuable insights and information for forestry management and conservation efforts. The primary objective of this research was to evaluate the effectiveness of an approach for identifying tree species through a model generated using a combination of ortho and oblique images captured by a mobile mapping system.