Miracle-dz/UniFAR-FTUs huggingface dataset:
The use of multisource spatial data for determining the proliferation of stingless bees in Kenya

ABSTRACT Stingless/meliponine bees are eusocial insects whose polylactic nature enables interaction with a wide variety of wild plants and crops that enhance pollination and, hence, support ecosystem services. However, their true potential regarding pollination services and honey production is yet to be fully recognized. Worldwide, there are over 800 species of meliponine bees, with over 20 species documented on the African continent. Out of these, only 12 species have been well documented in Kenya. Moreover, interest on meliponine bees has increased amid climate change, agricultural intensification, and other anthropogenic effects. Generally, stingless bees are under-researched, with no previous documented evidence of their ecological niche (EN) distribution in most African countries. Hence, this study sought to establish the influence of bioclimatic, topographic, and vegetation phenology on their spatial distribution and change patterns. Stingless response variables from 490 sample points were collected and used in conjunction with 11 non-conflating features to build stingless ecological niche models. Six machine learning-based EN models were used to predict the distribution of seven stingless bees’ species combined. The results from the EN models showed that annual precipitation was the most influential variable to stingless bee distribution (contributing 43.09% logit), while potential evapotranspiration and temperature seasonality contributed 21.18% of the information needed to predict the spatial distribution of stingless bees. Vegetation phenology (21.36%) and topography (14.36%) had moderate effect on stingless bees’ distribution. On the other hand, high seasonality in precipitation and temperature indicated high stingless niche variability in the future (i.e. 2055). The performance of six EN algorithms used to predict distribution of stingless bees was found to be “excellent” for random forest (true skills statistics (TSS) = 0.91) and ranger (TSS = 0.90) and “good” for generalized additive models (TSS = 0.87), multivariate adaptive regression spline (TSS = 0.80), and boosted regression trees (TSS = 0.80), while they were “fair” for recursive portioning and regression trees (TSS = 0.79). These EN models could be utilized to inform stingless bee farming and insects pollinated crops by highlighting regions that provide highly suitable conditions for stingless bees. Additionally, the findings could be harnessed to increase both bee and agricultural productivity and forest conservation efforts through supplementary pollination services.
Community Detection Based on Node Influence and Similarity of Nodes

Community detection is a fundamental topic in network science, with a variety of applications. However, there are still fundamental questions about how to detect more realistic network community structures. To address this problem and considering the structure of a network, we propose an agglomerative community detection algorithm, which is based on node influence and the similarity of nodes. The proposed algorithm consists of three essential steps: identifying the central node based on node influence, selecting a candidate neighbor to expand the community based on the similarity of nodes, and merging the small community based on the similarity of communities. The performance and effectiveness of the proposed algorithm were tested on real and synthetic networks, and they were further evaluated through modularity and NMI anlaysis. The experimental results show that the proposed algorithm is effective in community detection and it is quite comparable to existing classic methods.
Treatment of Critical Size Femoral Bone Defects with Biomimetic Hybrid Scaffolds of 3D Plotted Calcium Phosphate Cement and Mineralized Collagen Matrix

To treat critical-size bone defects, composite materials and tissue-engineered bone grafts play important roles in bone repair materials. The purpose of this study was to investigate the bone regenerative potential of hybrid scaffolds consisting of macroporous calcium phosphate cement (CPC) and microporous mineralized collagen matrix (MCM). Hybrid scaffolds were synthetized by 3D plotting CPC and then filling with MCM (MCM-CPC group) and implanted into a 5 mm critical size femoral defect in rats. Defects left empty (control group) as well as defects treated with scaffolds made of CPC only (CPC group) and MCM only (MCM group) served as controls. Eight weeks after surgery, micro-computed tomography scans and histological analysis were performed to analyze the newly formed bone, the degree of defect healing and the activity of osteoclasts. Mechanical stability was tested by 3-point-bending of the explanted femora. Compared with the other groups, more newly formed bone was found within MCM-CPC scaffolds. The new bone tissue had a clamp-like structure which was fully connected to the hybrid scaffolds and thereby enhanced the biomechanical strength. Together, the biomimetic hybrid MCM-CPC scaffolds enhanced bone defect healing by improved osseointegration and their differentiated degradation provides spatial effects in the process of critical-bone defect healing.
Conceptualizing the Role of Target-Specific Environmental Transformational Leadership between Corporate Social Responsibility and Pro-Environmental Behaviors of Hospital Employees

The healthcare sector throughout the world is identified for its outsized carbon footprint. Despite the mounting importance of employees’ pro-environmental behavior (PEB) for decarbonization, the role of PEB in a healthcare context was less emphasized previously, especially in a developing country context. To address this knowledge gap, the current work was carried out to examine the relationship between a hospital’s corporate social responsibility (CSR) initiatives and PEB with the mediating effect of environmental-specific transformational leadership (ESTL). At the same time, the conditional indirect effect of altruistic values (AV) was also considered in the above relationship. The data were collected through a questionnaire by employing a paper-pencil method from the hospital employees (n = 293). By considering the structural equation modeling, the hypothesized relationships were validated. The results indicated that CSR directly (β1 = 0.411) and indirectly, via ESTL, (β4 = 0.194) influenced the PEB of employees. It was also realized that A.V produced a conditional indirect effect in this relationship (β5 = 0.268). This work tends to help a hospital to improve its environmental footprint through CSR and ESTL. Moreover, the current work also highlights the role of employees’ values (e.g., A.V) to guide the environment-specific behavior of employees.
The Influence of Research Follow-Up during COVID-19 Pandemic on Mental Distress and Resilience: A Multicenter Cohort Study of Treatment-Resistant Depression

Background: During the COVID-19 outbreak, patients with mental disorders have faced more negative psychological consequences than the public. For people with treatment-resistant depression (TRD), it is unclear whether research engagement would protect them from the deterioration of their symptoms. The study aimed to examine if chronic depressive patients would have improved resilience and mental distress levels after follow-up interviews during an observation period under COVID-19. Methods: The study was nested within a three-year prospective cohort study. A two-group comparison design was conducted, i.e., the follow-up group with regular research interviews every three months after baseline assessment and the control group with one assessment-only interview. The two groups were compared with demographics, psychosocial, and suicide information. Results: Baseline assessments were not significantly different in sociodemographic variables, suicide risks, mental distress, and resilience between groups. Significant differences were detected in resilient coping and mental distress levels (p < 0.05). The follow-up group (n = 46) experienced a higher level of resilient coping (37% vs. 25%) and lower level of mental distress (47.8% vs. 64.7%) than the control group (n = 68). Conclusions: Findings highlight under universal government strategy against COVID-19, TRD patients receiving regular research follow-ups exhibited better resilience and less mental distress than those without regular support from healthcare providers.
Molecular Hallmarks of Ischemia with Non-Obstructive Coronary Arteries: The “INOCA versus Obstructive CCS” Challenge

Up to 4 million patients with signs of myocardial ischemia have no obstructive coronary artery disease (CAD). The absence of precise guidelines for diagnosis and treatment in non-obstructive CAD encourages the scientific community to fill the gap knowledge, to provide non-invasive and less expensive diagnostic tools. The aim of our study was to explore the biological profile of Ischemia with Non-Obstructive Coronary Arteries (INOCA) patients with microvascular dysfunction compared to patients presenting with obstructive chronic coronary syndrome (ObCCS) in order to find specific hallmarks of each clinical condition. We performed a gene expression array from peripheral blood mononuclear cells (PBMCs) isolated from INOCA (n = 18) and ObCCS (n = 20) patients. Our results showed a significantly reduced gene expression of molecules involved in cell adhesion, signaling, vascular motion, and inflammation in INOCA as compared to the ObCCS group. In detail, we found lower expression of Platelet and Endothelial Cell Adhesion Molecule 1 (CD31, p < 0.0001), Intercellular Adhesion Molecule-1 (ICAM1, p = 0.0004), Tumor Necrosis Factor (TNF p = 0.0003), Transferrin Receptor (TFRC, p = 0.002), and Vascular Endothelial Growth Factor A (VEGFA, p = 0.0006) in the INOCA group compared with ObCCS. Meanwhile, we observed an increased expression of Hyaluronidase (HYAL2, p < 0.0001) in INOCA patients in comparison to ObCCS. The distinct expression of molecular biomarkers might allow an early and non-invasive differential diagnosis between ObCCS and INOCA, improving clinical management and treatment options, in the era of personalized medicine.
Multi-Criteria Assessment of the Economic and Environmental Sustainability Characteristics of Intermediate Wheatgrass Grown as a Dual-Purpose Grain and Forage Crop

Kernza® intermediate wheatgrass [IWG; Thinopyrum intermedium (Host) Barkworth & Dewey] is a novel perennial cool-season grass that is being bred for use as a dual-purpose grain and forage crop. The environmental benefits of perennial agriculture have motivated the development of IWG cropping systems and markets for perennial grain food products made with Kernza, but the economic viability and environmental impact of IWG remain uncertain. In this study, we compared three-year cycles of five organic grain production systems: an IWG monoculture, IWG intercropped with medium red clover, a continuous winter wheat monoculture, a wheat–red clover intercrop, and a corn–soybean–spelt rotation. Economic and environmental impacts of each cropping system were assessed using enterprise budgets, energy use, greenhouse gas (GHG) emissions, and emergy indices as indicators. Grain and biomass yields and values for production inputs used in these analyses were obtained from experimental data and management records from two separate field experiments conducted in New York State, USA. Grain yield of IWG averaged 478 kg ha−1 yr−1 over three years, equaling approximately 17% of winter wheat grain yield (2807 kg ha−1 yr−1) over the same period. In contrast, total forage harvested averaged 6438 kg ha−1 yr−1 from the IWG systems, approximately 160% that of the wheat systems (4024 kg ha−1 yr−1). Low grain yield of IWG greatly impacted economic indicators, with break-even farm gate prices for Kernza grain calculated to be 23% greater than the current price of organic winter wheat in New York. Energy use and GHG emissions from the IWG systems were similar to the annual systems when allocated per hectare of production area but were much greater when allocated per kg of grain produced and much lower when allocated per kg of biomass harvested inclusive of hay and straw. Emergy sustainability indices were favorable for the IWG systems due to lower estimated soil erosion and fewer external inputs over the three-year crop cycle. The results show that the sustainability of IWG production is highly dependent on how the hay or straw co-product is used and the extent to which external inputs can be substituted with locally available renewable resources. Integrated crop–livestock systems appear to be a viable scenario for the adoption of IWG as a dual-use perennial grain and forage crop.
The Anti-Genotoxic Activity of Wastewaters Produced after Water-Steam Distillation of Bulgarian Rosa damascena Mill. and Rosa alba L. Essential Oils

The steam distillation of valuable rose essential oil from R. damascena Mill. and R. alba L. generates large volumes of wastewaters. Although such wastewaters are bio-pollutants, they contain valuable bioactive compounds. In this study we investigated the cytotoxic/genotoxic and anti-cytotoxic/anti-genotoxic potential of these products. We used cytogenetic methods for induction of chromosome aberrations and micronuclei in two different experimental test-systems: ahigher plant and human lymphocyte cultures. Different experimental schemes of treatment with the waste products showed that the genotoxic activity of wastewater from the distillation of oils from R. alba and R. damascena was low in both test–systems. Human lymphocytes showed a higher sensitivity to the products than plant cells. Both types of waste products manifested anti-genotoxic effect against N-methyl-N′-nitro-N-nitrosoguanidine, a direct mutagen. The wastewaters obtained from steam distillation of rose essential oil have cytoprotective/genoprotective effect and could decrease DNA damage. Data are promising for further use of these products in pharmacy and other areas of human life.
Simulation and Analysis of Mie-Scattering Lidar-Measuring Atmospheric Turbulence Profile

Based on the residual turbulent scintillation theory, the Mie-scattering lidar can measure the intensity of atmospheric turbulence by detecting the light intensity scintillation index of the laser return signal. In order to evaluate and optimize the reliability of the Mie-scattering lidar system for detecting atmospheric turbulence, the appropriate parameters of the Mie-scattering lidar system are selected and optimized using the residual turbulent scintillation theory. Then, the Fourier transform method is employed to perform the numerical simulation of the phase screen of the laser light intensity transformation on the vertical transmission path of atmospheric turbulence. The phase screen simulation, low-frequency optimization, and scintillation index calculation methods are provided in detail, respectively. Based on the phase distribution of the laser beam, the scintillation index is obtained. Through the relationship between the scintillation index and the atmospheric turbulent refractive index structure constant, the atmospheric turbulence profile is inverted. The simulation results show that the atmospheric refractive index structure constant profile obtained by the iterative method is consistent with the input HV5/7 model below 6500 m, which has great guiding significance to carry out actual experiments to measure atmospheric turbulence using the Mie lidar.
College English Curriculum Requirements: A Theoretical Understanding and Application

A Multimedia English Teaching Model is raised in College English Curriculum Requirements issued by State Education Ministry. This Model has improved the traditional teacher-centered model, placing a premium on individualized teaching and independent learning. Nevertheless, in practice, it is of great importance to have an accurate understanding and application of the new model and avoid “transplanting” the new model blindly. Universities and colleges need to design appropriate teaching programs by incorporating the new model.
Barriers to Blockchain Adoption in the Circular Economy: A Fuzzy Delphi and Best-Worst Approach

Blockchain can help to fundamentally alter aspects of circular economy (CE) activities and overcome pressing sustainability issues. Nevertheless, limited studies have investigated the barriers to blockchain adoption in the CE. This study aims to close the knowledge gap by providing a comprehensive review of the barriers hampering the adoption and integration of blockchain technology in the CE. An integrated approach based on fuzzy Delphi and best-worst methods has been applied to analyze and rank the barriers. Sixteen barriers to blockchain adoption in the CE were identified from the academic literature and validated by a panel of experts. The findings from the fuzzy Delphi technique identified ten significant barriers for further analysis. Then, using the best-worst method, the optimal weights were determined based on the experts’ judgment to recognize the importance of each barrier. The findings from this method showed that a lack of knowledge and management support, reluctance to change and technological immaturity are the most significant barriers. In contrast, the least significant barriers are investment costs, security risks, and scalability issues. Theoretically, this study is the first to apply an integrated approach combining fuzzy Delphi and best-worst techniques to prioritze the barriers to blockchain adoption in the CE. It also provides valuable insights for managers and decision-makers that can be used to optimize blockchain implementations in the CE.
Intelligent Medical IoT-Enabled Automated Microscopic Image Diagnosis of Acute Blood Cancers

Blood cancer, or leukemia, has a negative impact on the blood and/or bone marrow of children and adults. Acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML) are two sub-types of acute leukemia. The Internet of Medical Things (IoMT) and artificial intelligence have allowed for the development of advanced technologies to assist in recently introduced medical procedures. Hence, in this paper, we propose a new intelligent IoMT framework for the automated classification of acute leukemias using microscopic blood images. The workflow of our proposed framework includes three main stages, as follows. First, blood samples are collected by wireless digital microscopy and sent to a cloud server. Second, the cloud server carries out automatic identification of the blood conditions—either leukemias or healthy—utilizing our developed generative adversarial network (GAN) classifier. Finally, the classification results are sent to a hematologist for medical approval. The developed GAN classifier was successfully evaluated on two public data sets: ALL-IDB and ASH image bank. It achieved the best accuracy scores of 98.67% for binary classification (ALL or healthy) and 95.5% for multi-class classification (ALL, AML, and normal blood cells), when compared with existing state-of-the-art methods. The results of this study demonstrate the feasibility of our proposed IoMT framework for automated diagnosis of acute leukemia tests. Clinical realization of this blood diagnosis system is our future work.
Partial replacement of soybean meal with soybean silage in lactating dairy cows diet: part 1, milk production, digestibility, and N balance

Abstract The high reliance of the European livestock sector on imported soybean meal (SBM), especially from South America, poses environmental problems, like greenhouse gas emissions for transportation and land-use change with the loss of carbon stock and biodiversity. Aim of the present study was to evaluate the partial substitution of SBM with whole-plant soybean silage in the diet of dairy cows. Thirty-six lactating Holstein cows were arranged according to a change-over design, with 2 weeks of adaptation and 5 days of sampling per period. A control diet (CON) was based on maize silage and SBM, representing 10.7% of total dry matter (DM). In a soybean silage diet (SBS) 35% (on DM basis) of SBM was replaced by soybean silage. The dietary treatment did not affect DM intake, milk production, and dairy efficiency while cows fed SBS resulted in lower milk crude protein (3.43 vs. 3.55%, p < .001) and higher milk urea (30.5 vs. 28.7 mg/dL, p = .002), in comparison with CON. Nutrients digestibility was lower for SBS than CON; particularly fibre digestibility was 31.5 vs. 38.8% (p < .001). The efficiency of nitrogen utilisation was higher for CON than SBS (32.7 vs. 31.3%, p = .003). Soybean silage did not penalise feed intake and milk production. However, to fully exploit this forage, digestibility, and nitrogen utilisation efficiency should be improved. Highlights Soybean silage can substitute one-third of soybean meal in dairy cow diet Soybean silage inclusion in the diet did not affect milk yield and DMI Soybean silage inclusion in the diet reduced N use efficiency
Integrative Effects of CO2 Concentration, Illumination Intensity and Air Speed on the Growth, Gas Exchange and Light Use Efficiency of Lettuce Plants Grown under Artificial Lighting

This study investigates and quantifies the integrative effects of CO2 concentration (500, 1000 and 1500 µmol mol−1), illumination intensity (100, 200 and 300 μmol m−2 s−1) and air speed (0.25, 0.50 and 0.75 m s−1) on the growth, gas exchange and light use efficiency of lettuce plants (Lactuca sativa L.) grown under artificial lighting. The results show that lettuce growth and gas exchange are closely related to CO2 concentration and illumination intensity, while air speed enhances CO2 transport during photosynthesis. The most influential two-way interactions were observed between CO2 concentration and illumination intensity on the fresh and dry weights of lettuce shoots with effect sizes of 34% and 32%, respectively, and on the photosynthesis, transpiration and light use efficiency, with effect sizes of 52%, 47% and 41%, respectively. The most significant three-way interaction was observed for the photosynthetic rate, with an effect size of 51%. In general, the fresh and dry weights of lettuce plants increased by 36.2% and 20.1%, respectively, with an increase in CO2 concentration from 500 to 1500 µmol mol−1 and by 48.9% and 58.6%, respectively, with an increase in illumination intensity from 100 to 300 μmol m−2 s−1. The photosynthetic rate was found to be positively correlated with CO2 concentration, illumination intensity and air speed. The transpiration rate and stomatal conductance increased by 34.9% and 42.1%, respectively, when the illumination intensity increased from 100 to 300 μmol m−2 s−1. However, as CO2 concentration increased from 500 to 1500 μmol mol−1 and air speed increased from 0.25 to 0.75 m s−1, the transpiration rate decreased by 17.5% and 12.8%, respectively. With the quantified data obtained, we were able to adequately determine how CO2 concentration, illumination intensity and air speed interact with their combined effects on the growth of lettuce plants grown in indoor cultivation systems with artificial lighting.