Miracle-dz/UniFAR-FTUs huggingface dataset:
The Synergistic Effect of GH13 and GH57 GBEs of Petrotoga mobilis Results in α-Glucan Molecules with a Higher Branch Density

Glycogen is a biopolymer consisting of glycosyl units, with a linear backbone connected by α-1,4-linkages and branches attached via α-1,6-linkages. In microorganisms, glycogen synthesis involves multiple enzymes, with glycogen branching enzymes (GBEs) being vital for creating α-1,6-linkages. GBEs exist in two families: glycoside hydrolase (GH) 13 and GH57. Some organisms possess either a single GH13 or GH57 GBE, while others, such as Petrotoga mobilis, have both types of GBEs. In this study, the simultaneous use of a GH13 and GH57 GBE each from Petrotoga mobilis for α-glucan modification was investigated using a linear maltodextrin substrate with a degree of polymerization of 18 (DP18). The products from modifications by one or both GBEs in various combinations were analyzed and demonstrated a synergistic effect when both enzymes were combined, leading to a higher branch density in the glycogen structure. In this cooperative process, PmGBE13 was responsible for creating longer branches, whereas PmGBE57 hydrolyzed these branches, resulting in shorter lengths. The combined action of the two enzymes significantly increased the number of branched chains compared to when they acted individually. The results of this study therefore give insight into the role of PmGBE13 and PmGBE57 in glycogen synthesis, and show the potential use of both enzymes in a two-step modification to create an α-glucan structure with short branches at a high branch density.
Automatic Bangla Image Captioning Based on Transformer Model in Deep Learning

—Indeed, Image Captioning has become a crucial aspect of contemporary artificial intelligence because it has tackled two crucial parts of the AI field: Computer Vision and Natural Language Processing. Currently, Bangla stands as the seventh most widely spoken language globally. Due to this, image captioning has gained recognition for its significant research accomplishments. Many established datasets are found in English but no standard datasets in Bangla. For our research, we have used the BAN-Cap dataset which contains 8091 images with 40455 sentences. Many effective encoder-decoder and Visual Attention approaches are used for image captioning where CNN is utilized for the encoder and RNN is used for the decoder. However, we suggested a transformer-based image captioning model in this study with different pre-train image feature extraction models like Resnet50, InceptionV3, and VGG16 using the BAN-Cap dataset and find out its effective efficiency and accuracy based on many performances measured methods like BLEU, METEOR, ROUGE, CIDEr and also find out the drawbacks of others model.
The Impact of the Dark Side of Leadership on the Psychological State of Employees after the Covid-19 Pandemic:

This study aims to review the adverse effects of the dark side of leadership on employees’ psychological states, including distress and anxiety, after the COVID-19 pandemic, through the lens of psychological theories. A literature review was used to examine the dark side of leadership, which included any form of abusive, toxic, or destructive behaviors and characteristics that negatively affect the psychological states of employees. The effect on employees’ psychological state is crucial as it can influence their attitudes and performance. The Conservation of Resources and the Psychological Contract theory were employed to explain the emergence of the dark leaders’ behaviors and their consequences on employees’ psychological states in anticipation of extreme organizational changes after the pandemic. The employees’ psychological resources could be depleted due to exposure to abusive leaders while adapting to organizational changes post the pandemic. Also, the mistreatments from the destructive leaders could breach the employees’ psychological contract. The emergence of dark leadership practices might cause psychological distress. Previous psychological studies suggest that the psychological resources of employees can be replenished by seeking social support, while the organization can contribute by implementing intervention programs.
What can be learned from underdetermined geodetic slip inversions: the Parkfield GPS network example

Often geodetic data are inverted for fault slip using less independent constraints than model parameters, and the solution is non-unique. That underdetermined geodetic slip inversions cannot provide unique slip distributions does not mean that they cannot provide other unique information regarding the slip distribution. In order to see which of the slip distribution attributes are obtainable by underdetermined inversions, we considered a synthetic GPS data set and inverted it for slip. We set the fault and network geometries to be identical to those of the Parkﬁeld segment and the 14 SCIGN GPS sites next to it. We show that while slip inversions of such data yield robust estimate of the geodetic potency and the moment centroid, neither the spreadness nor the skewness may be resolved given the SCIGN network conﬁguration. Furthermore, we show that randomly constructed networks are better conﬁgured than the Parkﬁeld network, in the sense that they better recover the macroscopic attributes of the slip distribution. Finally, we show that the moment magnitude may be recovered using individual GPS stations, provided that these stations are not located in close proximity to the fault zone.
Heavy mineral beach placers in southeastern Australia; their nature and genesis

Southeastern Australia has been one of the world's most productive heavy mineral sand provinces. Concentrations of detrital rutile, zircon, ilmenite, and minor monazite occur as beach placers and in dunes in high-stand barriers of Quaternary age along the present coast. Although individual mineral grains are of terrigeneous origin, the heavy mineral deposits themselves are not linked to specific source rocks in the hinterland. Rather, they arise under the action of waves during the building of coastal sand barriers. A number of fractionating mechanisms that concentrate heavy minerals operate during marine transgressions and under highstand conditions to produce three different types of beach placers. Type A deposits occur along erosional discontinuities in leaky embayments or between barriers of different age. Here, repeated storm reworking has produced beach face concentrations on drift-aligned sectors of coast by a process termed "littoral bypassing fractionation. " Type B heavy mineral deposits are found in back-barrier washover facies (and sometimes as condensed sections) at the rear of swash-aligned barriers in coastal embayments. These were produced by a process of transgressive barrier fractionation that reworked heavy minerals from shelves undergoing erosion during marine transgressions. Type C heavy mineral deposits occur in prograded barriers and represent episodic influxes of remobilized heavy mineral-rich sand that originated elsewhere through the two fractionation mechanisms mentioned above. Necessary environmental conditions for these fractionating mechanisms to operate include: low rates of clastic sediment supply and long periods of weathering and abrasion to create a mature heavy mineral suite, an energetic swell wave climate driving large sand fluxes onshore and alongshore, and changing sea levels (especially marine transgressions) that have the effect of moving heavy minerals from the shelf onto the present coast. Because of the last-mentioned effect, the mineral prospectivity for the southeastern Australian continental shelf is not high.
A Comparative Study on Antioxidant Activities of Selected Plants From Road Sides, Salem, Tamil Nadu, India

This study was undertaken to know the antioxidant potential of plants located at road sides. For this study, two sites were selected from Salem District, Tamil Nadu, India. The road sides of New bus stand (site 1) and Saradha college road (site 2), Salem. Commonly available plants were selected from both the study areas. The commonly available plants used for the present research are: Polyalthiya longifolia, Pongamia pinnata, Ficus religiosa, Muntinga calabura, Albizia saman, Azadirachta indica, Cocos nusifera, Tamarindus indica,Tectona grandis, Casurina equisetifolia, Psidium guajava, Delonix regia, Terminalia catappa. Aqueous leaf extract was used for all the experiments. The fresh plant leaves collected were assessed for its secondary metabolites and antioxidant activities by different methods. The secondary metabolites were found to be high with Muntinga calabura, Ficus religiosa in site 1 and Delonix regia, Terminalia catappa in site 2. Antioxidant activity was high with Polyalthiya longifolia, Casuarina equisetifolia, Azadirachta indica, Terminalia catappa and Cocos nusifera. The changes observed might be due to the variation in nutrient content of the leaf as well as on the duration of exposure of pollution.
A Probiotic Bacillus amyloliquefaciens D-1 Strain Is Responsible for Zearalenone Detoxifying in Coix Semen

Zearalenone (ZEN) is a mycotoxin produced by Fusarium spp. , which commonly and severely contaminate food/feed. ZEN severely affects food/feed safety and reduces economic losses owing to its carcinogenicity, genotoxicity, reproductive toxicity, endocrine effects, and immunotoxicity. To explore efficient methods to detoxify ZEN, we identified and characterized an efficient ZEN-detoxifying microbiota from the culturable microbiome of Pseudostellaria heterophylla rhizosphere soil, designated Bacillus amyloliquefaciens D-1. Its highest ZEN degradation rate reached 96.13% under the optimal condition. And, D-1 can almost completely remove ZEN (90 μg·g−1) from coix semen in 24 h. Then, the D-1 strain can detoxify ZEN to ZEM, which is a new structural metabolite, through hydrolyzation and decarboxylation at the ester group in the lactone ring and amino acid esterification at C2 and C4 hydroxy. Notably, ZEM has reduced the impact on viability, and the damage of cell membrane and nucleus DNA and can significantly decrease the cell apoptosis in the HepG2 cell and TM4 cell. In addition, it was found that the D-1 strain has no adverse effect on the HepG2 and TM4 cells. Our findings can provide an efficient microbial resource and a reliable reference strategy for the biological detoxification of ZEN.
Enhanced Myocardial Infarction Identification in Phonocardiogram Signals Using Segmented Feature Extraction and Transfer Learning-Based Classification

Myocardial Infarction (MI), commonly known as a heart attack, is a type of cardiovascular disease characterized by the death of heart muscle cells. This condition occurs due to the blockage of blood vessels around the heart, inhibiting blood flow and causing an insufficient oxygen supply to the body. Typically, cardiovascular disease tests involve electrocardiogram (ECG) and photoplethysmogram (PPG) signals. In recent years, researchers have explored the application of Phonocardiogram (PCG) signals for cardiovascular detection due to their non-invasive, efficient, accessible, and cost-effective nature. While deep learning has been successful in object detection in digital images, its application to PCG signals for heart attack detection is rare. This study bridges this gap by introducing an enhanced technique called the Myocardial Infarction Detection System (MIDs). In contrast to previous deep learning research, this study employs a transfer learning algorithm as a classifier for MI feature datasets. Feature extraction is performed in segments to obtain more accurate MI features. Six feature extraction methods and transfer learning models based on Convolutional Neural Networks (CNN) using the VGG-16 architecture were selected as the primary components for MI identification. Additionally, this study compares these models with other CNN transfer learning models, such as VGG-19 and Xception, to assess their performance. Two experimental scenarios were conducted to evaluate MIDs performance in MI detection: experiments without hyperparameter tuning and with hyperparameter tuning. The results indicate that MIDs with CNN (VGG-16) after tuning exhibited the highest detection performance compared to other transfer learning CNN models, both with and without tuning. The accuracy, specificity, and sensitivity of MIDS detection with this configuration were 96.7%, 96.0%, and 97.4%, respectively. This research contributes to the development of an enhanced MI detection technique based on PCG signals using a transfer learning CNN.
Intrinsic Factors Contributing to Shrimp and Blood Clamp Allergic Reaction and Allergic Like Symptoms

Factors that contribute to food allergenicity is still unknown. To determine food intrinsic properties that contribute to L. vannamei and A. granosa allergenicity or allergy like symptoms this study identify total protein and metal profiles, histamine contents, and protein heat stability. Histamine content is measured colorimetrically. Protein heat-stability is observed with sodium dodecyl sulfate – polyacrylamide gel electrophoresis (SDS-PAGE). Allergy-related metal concentration are determined with Atomic Absorption Spectroscopy (AAS). Fresh L. vannamei and A. granosa contained ~100 and ~25 ppm histamine, respectively. Storage at -20 oC for 3 days relatively did not changed histamine content of both foods. Storage at 4-8 oC for 3 days increased histamine content in L. vannamei, but not in A. granosa. Protein sized <70 kDa of both samples are relatively heat-stable. L. vannamei and A. granosa contained Zn with concentration of 85 ppm and 92 ppm, subsequently. All samples did not contain nickel (Ni), chromium (Cr), and lead (Pb). Higher histamine content and heat-stable protein number of L. vannamei could contribute to higher prevalence of its allergy or allergic like symptoms than A. granosa. High Zn content might contribute to allergic like symptoms of both foods.
Mixing Structure of High-Frequency Internal Waves in the Upper EasternEquatorial Pacific

Abstract A thermistor chain towed from 140° to 110°W along the equator revealed the presence of high-frequency internal waves in the upper 125 m having zonal wavelengths of 150–250 m. Turbulence dissipation rates, e, observed from a free-falling profiler were high when wave packets were present. Unfortunately, the frequency of the vertical profiles of e taken did not resolve the internal wave cycle, so a dynamical link between the waves and the mixing could not be directly observed with vertical profiler data. It is presumed that either wave-induced shear instability or advective instability destabilized the waves and led to increased e. The thermistor data, which were sampled at 20 Hz or approximately 12.5 cm horizontally, resolved part of the inertial subrange of turbulence and are used to determine the structure of turbulence within an internal wave cycle. A temperature gradient variance method for estimating e relies on a fully resolved Batchelor spectrum that, for this experiment, would have required.
POLICY IMPLICATIONS ON THE REDUCTION OF NITROGEN FERTILIZER USE ON NON-IRRIGATED CORN-WINTER WHEAT PRODUCTION IN NORTH ALABAMA

Environmental and economic effects of nitrogen fertilizer reduction on North Alabama small farms are evaluated using GPFARM. GPFARM incorporates climate, soil data and management practices to simulate science and economic analysis for a farm/ranch management unit. Nitrate leaching, crop yield and profitability are evaluated and presented under different policy scenarios. Key words: nitrate leaching, profitability, management unit, GPFARM
Modeling of the effect of flow depth on sediment discharged by rain‐impacted flows from sheet and interrill erosion areas: a review

Sediment, nutrients and pollutants discharged from sheet and interrill erosion areas by rain‐impacted flows may influence water quality in streams and rivers. The depth of water on the soil surface influences the capacity of raindrop impacts to detach soil material underlying rain‐impacted flows, and a number of so‐called process‐based and mechanistic models erroneously use equations on the basis of the effect of water depth on splash erosion to account for this effect. Also, a number of these models require complex mathematical solutions to make them operate and can only predict sediment composition and discharges well if many of their parameters are calibrated specifically to the situations where they are being applied. Experiments with rain‐impacted flows, where flow depth and velocity over eroding surfaces have been controlled, have been reported in the literature and provide more appropriate equations to account for the drop size – flow depth interactions that affect detachment and transport of particles in rain‐impacted flows. There is a need to develop modeling approaches that rely on relevant data obtained under well‐controlled flow conditions where flow depths and velocities are known. Copyright © 2012 John Wiley & Sons, Ltd.
The efficacy of mindfulness-based interventions on mental health among university students: a systematic review and meta-analysis

Introduction We aimed to estimate the effect of mindfulness therapy on mental health. Methods Two researchers searched 12 databases to identify relevant trials that were published from 1 January 2018 to 1 May 2023. We performed a meta-analysis to determine the effect of mindfulness therapy on depression, which was measured by the Beck Depression Inventory (BDI), Patient Health Questionnaire-9 (PHQ-9), Quick Inventory of Depressive Symptomatology (QIDS), Hamilton Depression Rating Scale (HDRS), Patient-Reported Outcomes Measurement Information System (PROMIS), Hospital Anxiety and Depression Scale (HADS), and Depression Anxiety Stress Scales (DASS); anxiety, which was measured by the Beck Anxiety Inventory (BAI), PROMIS, and DASS, Generalized Anxiety Disorder-7 (GAD-7); stress, which was measured by the Perceived Stress Scale (PSS), DASS, and GAD-7; mindfulness, which was measured by the GAD-7, Five Facet Mindfulness Questionnaire (FFMQ), Mindful Attention Awareness Scale (MAAS), Short Form-12 Mental Component Score (SF-12 MCS) and Short Form-12 Physical Component Score (SF-12 PCS); and sleep quality, which was measured by the Pittsburgh Sleep Quality Index (PSQI). After screening studies based on the inclusion and exclusion criteria, 11 randomized controlled trials (RCTs) involving 1,824 participants were ultimately included. Results All these studies demonstrated positive effects of mindfulness therapy on depression (SMD = −0.33, 95% CI: [−0.44, −0.22], p < 0.00001, I2 = 29%), anxiety (SMD = −0.35, 95% CI: [−0.46, −0.25], p < 0.00001, I2 = 40%), stress (SMD = −0.39, 95% CI: [−0.48, −0.29], p < 0.00001, I2 = 69%) and sleep quality scores (SMD = −0.81, 95% CI: [−1.54, −0.09], p = 0.03, I2 = 0%). However, there was no significant difference in mindfulness (SMD = −0.12, 95% CI: [−0.36, −0.12], p = 0.34, I2 = 34%) between the mindfulness therapy group and the control group. Discussion In future studies, it is necessary to consider the investigation on whether the strategies of improving the mindfulness therapy in adherence and fidelity can work on the improvement of the outcomes in mental health. Systematic Review Registration https://www.crd. york.ac. uk/PROSPERO/, https://www.crd. york.ac. uk/PROSPERO/, identifier [CRD42023469301].
Analysis of Current Advancement in 3D Point Cloud Semantic Segmentation

INTRODUCTION: The division of a 3D point cloud into various meaningful regions or objects is known as point cloud segmentation. OBJECTIVES: The paper discusses the challenges faced in 3D point cloud segmentation, such as the high dimensionality of point cloud data, noise, and varying point densities. METHODS: The paper compares several commonly used datasets in the field, including the ModelNet, ScanNet, S3DIS, and Semantic 3D datasets, ApploloCar3D, and provides an analysis of the strengths and weaknesses of each dataset. Also provides an overview of the papers that uses Traditional clustering techniques, deep learning-based methods, and hybrid approaches in point cloud semantic segmentation. The report also discusses the benefits and drawbacks of each approach. CONCLUSION: This study sheds light on the state of the art in semantic segmentation of 3D point clouds.
Toward Context-Aware Federated Learning Assessment: A Reality Check

Federated learning (FL) enabled creating models that are competitive to centralized machine learning models, without compromising user privacy. Participating FL clients train local models on their data and only share model weights. An FL server aggregates these weights into global weights that are pushed to clients for the next training round. Despite FL research growth, most of this work is conceived in experimental simulated environments that do not reflect its applicability to real-world scenarios. Also, existing open-source FL testbeds/frameworks have drawbacks that prohibit convenient deployment over a large spectrum of heterogeneous clients in realistic environments. These drawbacks include simulations, unrealistic data sets, not supporting heterogeneity, and not having realistic environment control in terms of network and client churn, for example. In this article, we introduce (RealFL) a novel, realistic, open-source, and extendable platform for FL that supports a large scale of heterogeneous clients. It enables a realistic assessment of FL solutions by controlling various environmental parameters, e.g., network, client churn, data distribution, training complexity, and client heterogeneity. Using these parameters, we assess RealFL performance through an extensive evaluation. Preliminary evaluation shows a performance gap of up to 72% in training time and 27% in accuracy between FL-simulated environments and RealFL. Moreover, extensive evaluation reveals that realistic environmental parameters could affect accuracy by up to 52.7%, training time by up to 77.5%, and communication overhead by up to 98%.