Miracle-dz/UniFAR-FTUs huggingface dataset:
Revolutionizing Cancer Care: Advances in Carbon-Based Materials for Diagnosis and Treatment

Cancer involves intricate pathological mechanisms marked by complexities such as cytotoxicity, drug resistance, stem cell proliferation, and inadequate specificity in current chemotherapy approaches. Cancer therapy has embraced diverse nanomaterials renowned for their unique magnetic, electrical, and optical properties to address these challenges. Despite the expanding corpus of knowledge in this area, there has been less advancement in approving nano drugs for use in clinical settings. Nanotechnology, and more especially the development of intelligent nanomaterials, has had a profound impact on cancer research and treatment in recent years. Due to their large surface area, nanoparticles can adeptly encapsulate diverse compounds. Furthermore, the modification of nanoparticles is achievable through a broad spectrum of bio-based substrates, including DNA, aptamers, RNA, and antibodies. This functionalization substantially enhances their theranostic capabilities. Nanomaterials originating from biological sources outperform their conventionally created counterparts, offering advantages such as reduced toxicity, lower manufacturing costs, and enhanced efficiency. This review uses carbon nanomaterials, including graphene-based materials, carbon nanotubes (CNTs) based nanomaterials, and carbon quantum dots (CQDs), to give a complete overview of various methods used in cancer theranostics. We also discussed their advantages and limitations in cancer diagnosis and treatment settings. Carbon nanomaterials might significantly improve cancer theranostics and pave the way for fresh tumor diagnosis and treatment approaches. More study is needed to determine whether using nano-carriers for targeted medicine delivery may increase material utilization. More insight is required to explore the correlation between heightened cytotoxicity and retention resulting from increased permeability.
Decoding tax management: The role of corporate governance mechanisms

While tax pressure remains a major concern in corporate management, it is legitimate to ask whether tax has always been among the core factors of corporate governance (Chytis et al., 2020). This study aims to examine the influence of corporate governance on tax management practices within Moroccan firms listed on the Casablanca Stock Exchange (CSE). Focusing on three dimensions of corporate governance — board composition, audit committee structure, and external audit quality — we employed multiple regression analysis on data spanning 2014–2019 from a sample of 48 listed firms, totaling 288 firm observations. Significantly, board size, chief executive officer (CEO) duality, and external audit quality are found to be pivotal factors shaping tax management practices. Other corporate governance variables, notably the independence of the board of directors and the structure of the audit committee, do not appear to exert a significant influence on the tax management of listed Moroccan companies. Our study is one of the few to have addressed the issue of tax management in the Moroccan context. Moreover, it may also serve as a fundamental resource for researchers exploring the complex dynamics of tax practices in the organizational context.
Quantitative Evaluation of Bitterlich Sampling for Estimating Basal Area in Sparse Boreal Forests and Dense Tropical Forests

Bitterlich sampling is an extensively used technique in worldwide forest inventories. Although it has been proved that estimates of basal area from Bitterlich sampling are mathematically unbiased, its precision for individual forest stands may be fairly poor. An extension of validation efforts to different forest biomes could therefore provide more comprehensive assessment and understanding of the Bitterlich sampling technique. In this study, this technique was quantitatively evaluated by using simulated sparse boreal forests and dense tropical forests from an empirical forest structure model (EFSM). Theoretical estimation of basal areas and practical estimation influenced by the hidden-tree effect were both compared with true basal areas of the simulated forests. The evaluation results indicated that: 1) Bitterlich sampling can yield acceptable accuracy and precision when the count number (CN) of trees was set to 10 for the studied boreal and tropical forests with distinct characteristics, 2) the theoretical estimation of basal area can be improved by increasing the CN values for both forests, and 3) when the hidden-tree effect is encountered, the accuracy for tropical forests will be decreased by increasing the CN values, whereas the accuracy for boreal forests can still be improved. Accordingly, a relatively high CN, at a reasonable cost, is recommended for sparse boreal forests to improve the accuracy of basal area estimation. In contrast, for dense tropical forests, a CN of ten is appropriate to mitigate the hidden-tree effect.
Molecular and biochemical responses of sesame (Sesame indicum L.) to rhizobacteria inoculation under water deficit

Introduction Water scarcity is a challenge for sesame cultivation under rainfed conditions. In this scenario, a potential strategy to alleviate the water deficit is the application of plant growth-promoting bacteria. The objective of this study was to analyze the interaction of rhizobacteria with sesame cultivation under water deficit conditions. Methods An experiment was conducted in pots in a greenhouse using the BRS Morena sesame cultivar. The experimental design was completely randomized in a factorial scheme: 2 (irrigation regimes - daily irrigation and water deficit by suspending irrigation until 90% stomatal closure) x 6 (treatments with nitrogen or inoculants), with 5 replications. The types of fertilization were characterized by the addition of nitrogen (ammonium sulfate; 21% N), inoculants based on Bacillus spp. (pant001, ESA 13, and ESA 402), Agrobacterium sp. (ESA 441), and without nitrogen (control). On the fifth day after the suspension of irrigation, plant material was collected for gene expression analysis (DREB1 and HDZ7), activities of antioxidant enzymes (superoxide dismutase and catalase), relative proline content, and photosynthetic pigments. At the end of the crop cycle (about 85 days), production characteristics (root dry matter, aboveground dry matter, number of capsules, and thousand seed weight), as well as leaf nitrogen (N) and phosphorus (P) content, were evaluated. Results and Discussion There was a positive effect on both production and biochemical characteristics (proline, superoxide dismutase, catalase, and photosynthetic pigments). Regarding gene expression, most of the inoculated treatments exhibited increased expression of the DREB1 and HDZ7 genes. These biological indicators demonstrate the potential of rhizobacteria for application in sesame cultivation, providing nutritional supply and reducing the effects of water deficit.
Fabrication and Investigation of Deformable Rubber–Carbon Nanotube–Glue Gel-Based Impedimetric and Capacitive Tactile Sensors for Pressure and Displacement Measurements

Carbon nanotube–glue composite gel-based surface-type elastic sensors with a cylindrical shape deformable (flexible) metallic body were fabricated for tactile pressure and compressive displacement sensing. The fabrication of the sensors was performed using the rubbing-in technique. The effect of the pressure and the compressive displacement on the capacitance and the impedance of the sensors were investigated at various frequencies (in the range of 1 kHz to 200 kHz). It was found that under the effect of pressure from 0 to 9 g/cm2, the capacitance increased by 1.86 and 1.78 times, while the impedance decreased by 1.84 and 1.71 times at the frequencies of 1 kHz to 200 kHz, respectively. The effect of displacement on the impedance and the capacitance of the device was also investigated at various frequencies from 1 kHz to 200 kHz. The results showed that under the effect of compressive displacement up to 25 µm, the impedance of the sensors decreased on average by 1.19 times, while the capacitance increased by 1.09 times, accordingly. The frequency response of the displacement sensor showed that it matched with the low-pass filter. The obtained results are explained based on changes in the shape and geometrical parameters of the cylindrical-shaped conductive body. These results have also been explained on the basis of the distance between the conductive plates of the capacitive sensors during compression, which takes place under the effect of applied pressure or displacement. Moreover, the design of the sensors is simple and easy to fabricate, and their use is also earthy. The fabricated sensors have great potential for commercialization.
Dimensionality Reduction on Fish Dataset with Different Machine Learning Algorithms Comparison

Classification of fish is a complex task in the current situation exhibited by the great diversity of available species, even though it is used in agricultural and commercial sectors. In this work, the data is classified with the Supervised Machine Learning algorithms such as K-Nearest Neighbour, Naïve Bayes, and Decision Tree, observing the results before and after applying dimensionality reduction techniques PCA, LDA, and Truncate SVD. One of the well-known dimensionality reduction techniques Linear Discriminant Analysis LDA achieves the highest accuracy for K-NN, Naïve Bayes 93.7%, and Decision Tree 96.8%.
Características da Matéria Orgânica: Estudo Comparativo de Padrões de Espectrometria de Fluorescência e UV-Vis de Rio e Lago em Bacias Urbanizadas

The main goal of this study is to analyze organic matter in two urbanized basins considering fluorescence and UV-VIS spectroscopy techniques. The case study was the Iguaçu River in the Metropolitan Area of Curitiba, with 7 monitoring points in 107 km of river length, and the Barigui lake, located in the middle part of the Upper Iguaçu Basin, considering 3 monitoring points. Thus, the results indicate that the major source of pollution in both lake and river is domestic sewage (allochthonous source) due to the high urban development associated with a low level of sanitary sewage treatment in Curitiba and the Metropolitan Region. Additionally, part of the dissolved organic carbon found in Barigui Lake has characteristics of an autochthonous source (primary productivity), due to the hydrodynamic condition of the lake. Therefore, this paper highlights how the use of complementary essays like fluorescence and UV-vis spectroscopy can help improve the assessment of organic matter dynamics in different aquatic environments. Key-words: Upper Iguaçu Basin, organic matter monitoring, water resources management.
Real-Time Automated Detection of Errors in the Product Labels Using Image Processing and OCR

In the realm of product manufacturing and distribution, ensuring accurate and compliant product labeling is critical for both regulatory compliance and consumer safety. This research introduces a novel methodology for the automated identification of inaccuracies in product labels through the integration of image processing and Optical Character Recognition (OCR). Image processing techniques are used to recognize and analyze visual components including barcodes, logos, and product images, while OCR accurately transcribes text from the label images. The system uses a rule-based framework to compare the retrieved data to reference template in order to improve error detection. Erroneous or missing information, irregular formatting, or disparities in the visual representation of the label all constitute deviations from the intended content and should set up alarms for further investigation. The methodology has been tested and validated on a custom dataset of product labels from a variety of sectors. The proposed model is implemented in real-time, and its recognition accuracy is found to be 78%. The system's error-detection performance is impressive, making it a viable option for producers and shippers looking to standardize their quality assurance procedures and reduce their exposure to legal liability. The proposed approach not only improves accuracy but also greatly decreases the manual work typically involved with label verification, hence boosting overall operational efficiency in consumer goods production and delivery.
Optimization of Well Locations and Pumping Rates in Coastal Aquifers

A multi-objective optimization approach is used to determine pumping rates and well locations to prevent saltwater intrusion, while satisfying desired extraction rates in coastal aquifers. The problem is formulated to maximize pumping rates while minimizing the distance between stagnation points of extraction wells and the reference coastline location, such that the wells are placed as closely as possible to the coastline. The efficiency of the optimization process is improved by solving the problem through a sub-domain perturbation approach. The sequential use of multi-objective criteria, with pre-selected weights, gives the model the capability to achieve two objectives simultaneously (these results are not provided in this proceeding). This approach provides cost effective solutions to an important management problem in coastal aquifers.
Advances in the Condensation Water of Arid Regions

Water availability is the most important limiting factor in arid regions. Any additional source of water, such as condensation water, should be considered, especially in summer. Condensation water is the important source of moisture for the primary food chain, thus being an important ecological significance in arid and semiarid regions. The research methodology to determine the amount and duration of condensation water in arid regions at home and abroad was enumerated in the first part of this paper, and then the difference of spatio-temporal was enlarged on from two aspects as the quantitative character and temporal pattern of condensation water. Meanwhile, the main affecting factors of condensation water come into being were analyzed from meteorological factors, the type and situation of condensation surface, as well as plants which around the condensation surface. The ecological roles and significance of condensation water in arid regions were evaluated and the research trends of condensation water in arid regions are prospected in the last part. It is pointed that cross discipline collaboration, such as meteorology, ecology, should be paid more attention in formation mechanisms of condensation water in arid regions in the future,and also measurement method of condensation water should be developed and standardized,as well as physiological responses of desert plants to condensation water and effects of condensation water on extreme drought stress plants should be conducted.
Nanocellulose, the Green Biopolymer Trending in Pharmaceuticals: A Patent Review

The use of nanocellulose in pharmaceutics is a trend that has emerged in recent years. Its inherently good mechanical properties, compared to different materials, such as its high tensile strength, high elastic modulus and high porosity, as well as its renewability and biodegradability are driving nanocellulose’s industrial use and innovations. In this sense, this study aims to conduct a search of patents from 2011 to 2023, involving applications of nanocellulose in pharmaceuticals. A patent search was carried out, employing three different patent databases: Patentscope from World Intellectual Property Organization (WIPO); Espacenet; and LENS.ORG. Patents were separated into two main groups, (i) nanocellulose (NC) comprising all its variations and (ii) bacterial nanocellulose (BNC), and classified into five major areas, according to their application. A total of 215 documents was retrieved, of which 179 were referred to the NC group and 36 to the BNC group. The NC group depicted 49.7%, 15.6%, 16.2%, 8.9% and 9.5% of patents as belonging to design and manufacturing, cell culture systems, drug delivery, wound healing and tissue engineering clusters, respectively. The BNC group classified 44.5% of patents as design and manufacturing and 30.6% as drug delivery, as well as 5.6% and 19.4% of patents as wound healing and tissue engineering, respectively. In conclusion, this work compiled and classified patents addressing exclusively the use of nanocellulose in pharmaceuticals, providing information on its current status and trending advancements, considering environmental responsibility and sustainability in materials and products development for a greener upcoming future.
An Fc-modified monoclonal antibody as novel treatment option for pancreatic cancer

Pancreatic cancer is a highly lethal disease with limited treatment options. Hence, there is a considerable medical need for novel treatment strategies. Monoclonal antibodies (mAbs) have significantly improved cancer therapy, primarily due to their ability to stimulate antibody-dependent cellular cytotoxicity (ADCC), which plays a crucial role in their therapeutic efficacy. As a result, significant effort has been focused on improving this critical function by engineering mAbs with Fc regions that have increased affinity for the Fc receptor CD16 expressed on natural killer (NK) cells, the major cell population that mediates ADCC in humans. Here we report on the preclinical characterization of a mAb directed to the target antigen B7-H3 (CD276) containing an Fc part with the amino acid substitutions S239D/I332E to increase affinity for CD16 (B7-H3-SDIE) for the treatment of pancreatic cancer. B7-H3 (CD276) is highly expressed in many tumor entities, whereas expression on healthy tissues is more limited. Our findings confirm high expression of B7-H3 on pancreatic cancer cells. Furthermore, our study shows that B7-H3-SDIE effectively activates NK cells against pancreatic cancer cells in an antigen-dependent manner, as demonstrated by the analysis of NK cell activation, degranulation and cytokine release. The activation of NK cells resulted in significant tumor cell lysis in both short-term and long-term cytotoxicity assays. In conclusion, B7-H3-SDIE constitutes a promising agent for the treatment of pancreatic cancer.
Automated detection of buildings and roads in urban areas from VHR satellite images

In this paper, we present an unsupervised approach to detect regions belonging to buildings and roads in urban areas from very high resolution (VHR) satellite images. The proposed approach consists of three main stages. In the first stage, we extract information that is only related to building regions using shadow evidence and probabilistic fuzzy landscapes. First, the shadow areas cast by building objects are detected, and the directional spatial relationship between buildings and their shadows is modeled with the knowledge of the illumination direction. Thereafter, each shadow region is handled separately and the initial building regions are identified by iterative graph-cuts designed in two-label partitioning. The second stage of the framework automatically classifies the image into four classes: building, shadow, vegetation, and others. In this step, the previously labeled building regions as well as the shadow and vegetation areas are involved in a four-label graph optimization performed on the entire image domain to achieve the unsupervised classification result. The final stage aims to extend this classification to five classes, including the road class. For that purpose, we extract the regions that might belong to road segments and utilize that information in a final graph optimization. This final stage eventually characterizes the regions belonging to buildings and roads. Experiments performed on twelve test images selected from GeoEye-1 VHR datasets show that the presented approach has the ability to extract the regions belonging to buildings and roads in a single graph theory framework.
Research on The Advanced Block Neighborhood Relevance Algorithm in Face Recognition

Concerning the face image recognition, this paper comes up with a block neighborhood relevance algorithm. Firstly, it elaborates the computational method for face image block neighborhood relevance so as to find out the smooth region and then extract features and carry out dimension reduction. Finally, the maximum margin criterion is introduced in face image improve the classification performance. Simulation experiment proves that the algorithm adopted in this paper can effectively improve the face image recognition effect.
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column

Abstract. Research on mechanisms of organic matter degradation, bacterial activities, phytoplankton dynamics, and other processes has led to the development of numerous sophisticated water quality models. The earliest model, dating back to 1925, was based on first-order kinetics for organic matter degradation. The community-centered freshwater biogeochemistry model RIVE was initially developed in 1994 and has subsequently been integrated into several software programs such as Seneque-Riverstrahler, pyNuts-Riverstrahler, ProSe/ProSe-PA, and Barman. After 30 years of research, the use of different programming languages including QBasic, Visual Basic, Fortran, ANSI C, and Python, as well as parallel evolution and the addition of new formalisms, raises questions about their comparability. This paper presents a unified version of the RIVE model for the water column, including formalisms for bacterial communities (heterotrophic and nitrifying), primary producers, zooplankton, nutrients, inorganic carbon, and dissolved oxygen cycles. The unified RIVE model is open-source and implemented in Python 3 to create pyRIVE 1.0 and in ANSI C to create C-RIVE 0.32. The organic matter degradation module is validated by simulating batch experiments. The comparability of the pyRIVE 1.0 and C-RIVE 0.32 software is verified by modeling a river stretch case study. The case study considers the full biogeochemical cycles (microorganisms, nutrients, carbon, and oxygen) in the water column, as well as the effects of light and water temperature. The results show that the simulated concentrations of all state variables, including microorganisms and chemical species, are very similar for pyRIVE 1.0 and C-RIVE 0.32. This open-source project highly encourages contributions from the freshwater biogeochemistry community to further advance the project and achieve common objectives.
Post-adoption model of mobile payment in Indonesia: Integration of UTAUT2 and the dedication-constraint perspective

Heading towards a cashless society, consumers have undergone a significant shift toward mobile payment services after COVID-19. The proliferation of various mobile payment applications has resulted in low consumer loyalty to mobile payment providers. Thus, continuance intention of mobile payments becomes crucial for mobile payment providers. The integration of UTAUT2 and the dedication-constraint-based mechanism are adopted to elaborate approach in retaining customers. The dedication mechanism is built by examining antecedents of satisfaction. Meanwhile, the constraint mechanism is driven by switching costs, preceded by habit and economic incentives. A total of 297 mobile payment users participated by filling out questionnaires in a field survey. The results show that the dedication mechanism dominates in creating satisfaction by increasing perceived usefulness, while the constraint mechanism is more influenced by habit than economic incentives. This research provides insights for mobile payment providers to enhance satisfaction by understanding consumers' needs in using mobile payments and to increase switching costs by fostering habit, thereby encouraging continuance intention of mobile payments in the future.