Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Removal of Non-Steroidal Inflammatory Drugs from Water by Quaternary Chitosan-Based Magnetic Nanosorbents

Non-steroidal anti-inflammatory drugs (NSAIDs) are among the most common pharmaceuticals used worldwide. They are widely detected in natural waters due to their persistence in wastewater treatment, and their removal is desirable in wastewater management. As a contribution to tackle this challenge, this study explores magnetic quaternary chitosan-based nanosorbents for the effective magnetically assisted removal of three NSAIDs (diclofenac, naproxen, and ketoprofen) from water. Toward this goal, silane groups were grafted onto the backbone of trimethyl chitosan through the reaction with an epoxide functionalized silane. Once silanized, the modified chitosan was employed to coat Fe3O4 nanoparticles. The prepared materials were characterized using FTIR spectroscopy and solid-state 29Si and 13C NMR spectroscopy, which confirmed the encapsulation of Fe3O4 nanoparticles with a hybrid siliceous material enriched in trimethyl chitosan. The effect of the initial NSAIDs concentration, pH, and contact time in the adsorption behavior was investigated. The kinetic data were well described by the pseudo-second-order kinetic model, indicating a chemisorption mechanism. The maximum adsorption capacities estimated from the Langmuir model were 188.5 mg/g (0.5925 mmol/g), 438.1 mg/g (1.7371 mmol/g), and 221.5 mg/g (0.8710 mmol/g) for diclofenac, naproxen, and ketoprofen, respectively. These adsorption capacities are higher than those of most reported sorbents, indicating the potential of these biosorbents to remove the selected NSAIDs using low-energy magnetically assisted separation.
Cooperative Approaches to Data Sharing and Analysis for Industrial Internet of Things Ecosystems

The collection and analysis of industrial Internet of Things (IIoT) data offer numerous opportunities for value creation, particularly in manufacturing industries. For small and medium-sized enterprises (SMEs), many of those opportunities are inaccessible without cooperation across enterprise borders and the sharing of data, personnel, finances, and IT resources. In this study, we suggest so-called data cooperatives as a novel approach to such settings. A data cooperative is understood as a legal unit owned by an ecosystem of cooperating SMEs and founded for supporting the members of the cooperative. In a series of 22 interviews, we developed a concept for cooperative IIoT ecosystems that we evaluated in four workshops, and we are currently implementing an IIoT ecosystem for the coolant management of a manufacturing environment. We discuss our findings and compare our approach with alternatives and its suitability for the manufacturing domain.
New blend nanocomposite membranes based on PBI/sulfonated poly(ether keto imide sulfone) and functionalized quantum dot with improved fuel cell performance at high temperatures

In this work, we reported the synthesis of a sulfonated poly(ether keto imide sulfone) (SPEKIS) using a novel aromatic diol containing nitrogen heterocycles and sulfonic monomer. New nanocomposite blend membranes were prepared using obtained SPEKIS and polybenzimidazole (PBI) with the incorporation of zinc sulfide (ZnS) functionalized quantum dots (FQDs) having both COOH and NH2 groups with a solution‐casting method and were used as proton exchange membranes. The SPEKIS and ZnS FQDs were used for the first time in the preparation of new nanocomposite blend membranes based on PBI. The purpose of this study is to investigate the effect of SPEKIS and ZnS FQDs on the PBI membrane performance in proton conductivity, mechanical and chemical stability, and fuel cell performance. The result displayed that the phosphoric acid doping content and proton conductivity of the PBI‐SPEKIS‐ZnS FQDs blend nanocomposite membranes enhanced which can be attributed to interactions of thiazole rings and SO3H group of SPEKIS, NH2 and COOH groups of ZnS FQDs, NH groups of PBI, and phosphoric acid with each other. The best membrane performance (the best proton conductivity) of the prepared nanocomposite blend membranes was obtained at 162 mS/cm at 7% relative humidity and 180°C. The power density of the PBI‐SPEKIS blend membranes (SPEKIS/PBI: 24 wt%) was obtained 0.55 W/cm2 while this parameter for the PBI‐SPEKIS‐ZnS FQDs blend nanocomposite membranes (SPEKIS/PBI: 24 and 3 wt% of ZnS FQDs) obtained 0.67 W/cm2 in 7% RH, 0.5 V, and 180°C. These outcomes highlighted the point that the prepared nanocomposite membranes of this research have valuable properties to be considered as a capable sample for the high‐temperature fuel cell with enhanced performance.
Multi‐spurious harmonics suppression in folded hairpin line bandpass filter by meander spur‐line

The present article exhibits the improvement in wide stopband harmonic suppression techniques of fourth‐order compact hairpin‐line Chebyshev type narrowband (4%) bandpass filter centered at 2.5 GHz by meander spur‐line. Initially, the compactness of 42% over the conventional hairpin‐line cell has been achieved by folding the open‐end arms of the hairpin line to folded hairpin‐line cell. Accordingly, the improvement of the skirt characteristics and the shifting of the harmonics to higher frequencies have been observed with the cost of harmonics attenuation levels' degradation. Subsequently, an improvement in the stopband rejection level of 40 dB up to 4f0 has been achieved by incorporating meander spur‐line to all the coupled edges of the adjacent folded cells of the compact filters along with compactness of 38%.
Breaking Entry-and Species Barriers: LentiBOOST® Plus Polybrene Enhances Transduction Efficacy of Dendritic Cells and Monocytes by Adenovirus 5

Due to their ability to trigger strong immune responses, adenoviruses (HAdVs) in general and the serotype5 (HAdV-5) in particular are amongst the most popular viral vectors in research and clinical application. However, efficient transduction using HAdV-5 is predominantly achieved in coxsackie and adenovirus receptor (CAR)-positive cells. In the present study, we used the transduction enhancer LentiBOOST® comprising the polycationic Polybrene to overcome these limitations. Using LentiBOOST®/Polybrene, we yielded transduction rates higher than 50% in murine bone marrow-derived dendritic cells (BMDCs), while maintaining their cytokine expression profile and their capability to induce T-cell proliferation. In human dendritic cells (DCs), we increased the transduction rate from 22% in immature (i)DCs or 43% in mature (m)DCs to more than 80%, without inducing cytotoxicity. While expression of specific maturation markers was slightly upregulated using LentiBOOST®/Polybrene on iDCs, no effect on mDC phenotype or function was observed. Moreover, we achieved efficient HAdV5 transduction also in human monocytes and were able to subsequently differentiate them into proper iDCs and functional mDCs. In summary, we introduce LentiBOOST® comprising Polybrene as a highly potent adenoviral transduction agent for new in-vitro applications in a set of different immune cells in both mice and humans.
Soft synthesis and characterization of goethite-based nanocomposites as promising cyclooctene oxidation catalysts

Goethite based nanocomposites with a different composition such as 6FeO(OH)·MnO(OH)·0.5H2O (Mn-composite), xFeO(OH)·M(OH)2·yH2O (Co-composite (M: Co, x = 12, y = 3), Ni-composite (M: Ni, x = 7, y = 2)) and xFeO(OH)·MO·yH2O (Cu-composite (M: Cu, x = 5.5, y = 3), Zn-composite (M: Zn, x = 6, y = 1.5)) have been prepared by a soft chemical synthesis consisting in acetate hydrolysis. The data provided by Fourier transform infrared (FTIR), ultraviolet-visible-near infrared (UV-Vis-NIR), electron paramagnetic resonance (EPR) and Mössbauer spectra account for a slight modification of all composites' physicochemical properties compared to the starting material. Powder X-ray diffraction and transmission electron microscopy (TEM) investigations revealed the secondary phase nature and presence along with that of goethite. The TEM data are also consistent with a nano rod-like morphology with a 5–10 nm width and an average length of 40 nm. The catalytic oxidation of cyclooctene with O2 using isobutyraldehyde as reductant and acetonitrile as a solvent was performed in batch conditions for 5 h at room temperature. The selectivity for the epoxide was higher than 99% for all tested solids. The conversion of cyclooctene decreased from 55% to 4% following the same order of variance as the base/acid sites ratio: Mn-composite > Fe-composite > Co-composite > Ni-composite > Zn-composite > Cu-composite. The 6FeO(OH)·MnO(OH)·0.5H2O (Mn-composite) exhibited the most promising catalytic activity in cyclooctene oxidation, which can be correlated with the redox ability of Mn(iii) combined with the increased base character of this solid. The catalytic activity of this sample decreases by 10% after several successive reaction cycles.
Extension services in Ethiopia: First adoption of chemical fertilizers in rural villages

In this paper we evaluate the efficiency of extension programmes in the adoption of chemical fertilizers in Ethiopia between 1994 and 2004 using matching techniques in a quasi-experimental setting. In addition to common factors, measures of culture, proxied by ethnicity and religion, aim to control for potential tensions between extension agents and peasants. We find considerable impact of extension services on the first fertilizer adoption. Although, there are several exercises of evaluation of extension services policies, this paper manages to disentangle the policy treatment from other confounding effects for two reasons. Firstly, we focus on the first adoption decision to isolate the effect of new information provided by the extension service. Secondly, we restrict the sample to villages characterized by late adoption, id est villages where adoption had not occurred before and its appearance can be associated with the policy action with more certitude.
Biogeographic drivers of community assembly on oceanic islands: The importance of archipelago structure and history

Accounting for geo‐environmental dynamics is crucial to understand community assembly across islands. Whittaker et al. (J Biogeogr, 35:977–994, 2008)’s General Dynamic Model (GDM) aims towards this goal. Yet, it does not explicitly consider that most islands belong to archipelagos. We examined how island biodiversity dynamics are influenced by the interaction of eco‐evolutionary processes acting at the archipelago level with each island's geo‐environmental dynamics.
Female infertility and herbal medicine: An overview of the new findings

Abstract Infertility is defined as the failure to achieve a successful pregnancy after 12 months’ sexual activity that affects 15%–17% of couples in the world and about 50% of them are related to female infertility factors. In this study, using the PRISMA checklist and MeSH keywords, 128 articles were extracted from various databases (PubMed, Cochrane library, WHO, Iranmedex, Science Direct, SID, and Google Scholar search engine) without language and time restrictions, and 128 articles were selected after eliminating duplicate studies. In this review, we present some solid evidence for role of herbal medicine in the treatment of female infertility. The results of this study showed that different parts of some plants are rich in polyphenolic compounds (isoflavones and flavonoids) and other compounds which are beneficial to in reproductive health in women. The compounds in these plants, along with regulating the female endocrine pathways, and improving symptoms of menopause, treat female reproductive disorders such as polycystic ovary syndrome (PCOS), premature ovarian failure (POF), endometriosis, hyperprolactinemia, and hypothalamic dysfunction; moreover, because of their anticancer, antioxidant, and antidepressant properties, they can be used in traditional medicine or in the pharmaceutical industry as safe compounds in women's health.
RSIAM risk profile for managing risk factors of international construction joint ventures

Abstract The international construction joint venture (ICJV) has been adopted in the global construction industry. However, it is extremely risky due to the size, complexity, and multifaceted operations of ICJV projects. Although there were many studies focusing on the risks in the ICJV, none of them examined simultaneously the chain of risk factors, risk sources, as well as the impact spectrum of risk factors, risk assessment, and risk response measures. The objective of this study is to establish a RSIAM profile, which stands for risk factors (R), risk sources (S), impact spectrum of risk factors on ICJV project objectives (I), risk assessment in different stages (A), and risk response measures for the implementation of ICJV projects (M). In this study, the risk factors were identified based on previous studies and pilot tests with 8 experienced professionals. Structured interviews with 15 experienced professionals were conducted to identify the risk sources and the impact spectrum of risk factors, to assess the likelihood of occurrence and the impact of each risk factor, as well as to compile risk response methods and measures. The results from the interviews were then used to build the RSIAM risk profile for ICJV projects. The RSIAM risk profile can assist practitioners to manage all risk factors in every stage of ICJV projects systematically and efficiently.
The Impact of Exterior Surround Detail on Daylighting Simulation Results

ABSTRACT The accuracy of daylighting simulations for the built environment strongly depends on the modeling of the exterior surround. The practice of modeling a space with a flat ground surface with little or no external architectural or landscape elements, particularly early in the design phase, is often applied. In this study, this approach and a number of different levels of detail for modeling the exterior surround were investigated by comparing real-life empirical measurements with their corresponding simulation results. A total of 13 different exterior views were studied under 15 different sky/seasonal conditions to investigate the contribution of the surrounding features to the daylight delivered inside a small office. These features were classified into one of four different groups – sky, architectural structures, standing vegetation, and horizontal ground. Five of these locations were chosen for further simulation by applying six levels of detail to the exterior surround in a simulation model. The results reveal that ignoring exterior surround objects (common practice for some) yielded an average error of 67.9%, while a high level of detail (which applied high polygon vegetation representations) resulted in an average error of only 11.3%. A detailed model of the surround with the IES LM-83 suggested method of modeling trees resulted in an average error of 22.8%.
Sunscreens’ UV Filters Risk for Coastal Marine Environment Biodiversity: A Review

Considering the rapid growth of tourism in recent years and the acknowledgement that exposure to solar UV radiation may cause skin cancer, sunscreens have been widely used by beachgoers in recent decades. UV filters contained in sunscreens, however, were recently identified as emerging pollutants in coastal waters since they accumulate in the marine environment with different adverse effects. In fact, exposure to these components was proven to be toxic to most invertebrate and vertebrate marine species. Some UV filters are linked to the production of significant amounts of reactive oxygen species (ROS), such as hydrogen peroxide, and the release of inorganic micronutrients that may alter the status of coastal habitats. Bioaccumulation and biomagnification have not yet been fully addressed. This review highlights recent progress in research and provides a comprehensive overview of the toxicological and ecotoxicological effects of the most used UV filters both on the abiotic and biotic compartments in different types of coastal areas, to gain a better understanding of the impacts on coastal biodiversity.
Pre-trained feature aggregated deep learning-based monitoring of overshooting tops using multi-spectral channels of GeoKompsat-2A advanced meteorological imagery

ABSTRACT Overshooting tops (OTs) play a crucial role in carrying tropospheric water vapor to the lower stratosphere. They are closely related to climate change as well as local severe weather conditions, such as lightning, hail, and air turbulence, which implies the importance of their detection and monitoring. While many studies have proposed threshold-based detection models using the spatial characteristics of OTs, they have shown varied performance depending on the seasonality and study areas. In this study, we propose a pre-trained feature-aggregated convolutional neural network approach for OT detection and monitoring. The proposed approach was evaluated using multi-channel data from Geo-Kompsat-2A Advanced Meteorological Imager (GK2A AMI) over East Asia. The fusion of a visible channel and multi-infrared channels enabled the proposed model to consider both physical and spatial characteristics of OTs. Six schemes were evaluated according to two types of data pre-processing methods and three types of deep learning model architectures. The best-performed scheme yielded a probability of detection (POD) of 92.1%, a false alarm ratio (FAR) of 21.5%, and a critical success index (CSI) of 0.7. The results were significantly improved when compared to those of the existing CNN-based OT detection model (POD increase by 4.8% and FAR decrease by 29.4%).
RS-Lane: A Robust Lane Detection Method Based on ResNeSt and Self-Attention Distillation for Challenging Traffic Situations

Lane detection plays an essential part in advanced driver-assistance systems and autonomous driving systems. However, lane detection is affected by many factors such as some challenging traffic situations. Multilane detection is also very important. To solve these problems, we proposed a lane detection method based on instance segmentation, named RS-Lane. This method is based on LaneNet and uses Split Attention proposed by ResNeSt to improve the feature representation on slender and sparse annotations like lane markings. We also use Self-Attention Distillation to enhance the feature representation capabilities of the network without adding inference time. RS-Lane can detect lanes without number limits. The tests on TuSimple and CULane datasets show that RS-Lane has achieved comparable results with SOTA and has improved in challenging traffic situations such as no line, dazzle light, and shadow. This research provides a reference for the application of lane detection in autonomous driving and advanced driver-assistance systems.
pH sensors based on amino-terminated carbon nanomembrane and single-layer graphene van der Waals heterostructures

The ability of graphene to transduce an adsorption event of ions into a detectable electrical signal has sparked a lot of interest for its use in sensors. However, a low concentration of the chemically active sites for binding analytes on the graphene surface has significantly prevented its applications so far. Here, we report on implementation of the van der Waals heterostructure based on a monolayer graphene and an ∼1-nm-thick molecular carbon nanomembrane (CNM) in a solution-gated field-effect transistor (FET) for pH sensing. The nondestructive functionalization of a graphene FET with the amino-terminated CNM (NH2-CNM) enables the induction of chemically active groups in the vicinity of the graphene sheet, maintaining its charge carrier transport properties. We applied complementary characterization techniques, including Raman spectroscopy, x-ray photoelectron spectroscopy, and optical and atomic force microscopy as well as field-effect and electrical impedance measurements to characterize the engineered NH2-CNM/graphene devices. We demonstrate their high pH resolution with a minimum detectable pH change of ∼0.01 at pH 2 and ∼0.04 at pH 12, with a response time in the range of seconds, and we apply an electrical double-layer model to rationalize the experimentally observed performance theoretically. The developed device concept enables the engineering of microscale pH sensors for applications in biological and environmental sciences.
Altered Spontaneous Brain Activity Patterns in Patients with Age-related Macular Degeneration: A Resting-State fMRI Study Running head: Functional Connectivity Density alterations in age-related macular degeneration patients

Background Many neuroimaging studies have confirmed that age-related macular degeneration (AMD) is closely related to the changes of brain functional areas. However, the value of spontaneous brain activity changes induced by AMD in early diagnosis of diseases has not been confirmed. Purpose To explore potential functional network brain activity in patients with AMD using the voxel-wise degree centrality (DC) method and reveal the functional changes of specific brain regions in patients with AMD. Material and Methods Eighteen patients with AMD (10 males, 8 females) and eighteen healthy controls (HCs) (10 males, 8 females) matched by gender, age, and education level were enrolled. The resting-state fMRI scanning was applied to measure changes in initiative brain activities. We displayed the differences between the AMD group and the healthy group with the receiver operating characteristic curves. Using correlation analysis, the average DC values in specific cerebrum regions were analyzed to explore changes of behavioral performance in AMD patients. The Hamilton Rating Scale for Depression and Anxiety (Ham-D/A) was used to evaluate the relationship between emotional states and DC values of the subcallosal gyrus in AMD patients. Results In AMD patients, DC values were significantly lower in the bilateral lingual gyrus but were higher in the bilateral inferior temporal gyrus, bilateral subcallosal gyrus, bilateral fusiform gyrus, bilateral middle frontal gyrus, and left orbital middle frontal gyrus/insular gyrus/inferior frontal gyrus. There was no direct relationship between the behavioral performance and the average DC values of some areas. Conclusion The DC values changed with the fluctuation of spontaneous brain activities in AMD patients, which may be beneficial for clinical diagnosis.
Perbandingan Algoritma Klasifikasi Data Mining untuk Penelusuran Minat Calon Mahasiswa Baru

During the pandemic period, AMIK HASS faces difficulties to determine new student candidates. In order to attract public interest, the Marketing Department has implemented several strategies to attract prospective students to become new students. The data mining technique used in predicting is a classification that includes Naïve Bayes, J48 Decision Tree, and K-Nearest Neighbor. This study aims to perform a comparative analysis of data mining classification algorithms using WEKA tools. The method used in this study is CRISP-DM. The dataset used by the three classifications is 5.934 records with split mode, the percentage of testing is 70% as much as 4154 as training data and 30% as much as 1780 data as test data. Based on the test results on the three classification models, the highest accuracy value is obtained in the J48 Decision Tree classification, which has a value of 90.3%. While the K-Nearest Neighbor classification has a lower accuracy of 87.52% and the Naïve Bayes classification has an accuracy of 87.24%. The comparison of the AUROC J48 Decision Tree test results has the highest value of 0.9654 while the Naïve Bayes results are 0.9461 and the K-Nearest Neighbor results are 0.9343. The three classifications with ABK scores above 0.90 are included in the excellent classification category.