Miracle-dz/UniFAR-FTUs huggingface dataset:
Do nepotism and cronyism have payoff boundaries? A cross-country investigation

In modern business, the phenomena of “nepotism” and “cronyism” are often observed, which are usually associated with corruption in the public sector and abuse of public resources. However, these phenomena are international in scale, and no country or sector of the economy is free from them. Existing research does not identify shared and contradictory interests of individuals based on nepotism or cronyism. This study aims to fill this research gap. A research hypothesis was developed and tested in order to determine a point from which nepotism and cronyism are not beneficial to their perpetrators. The research data included Poles and Albanians. According to Transparency International (2022), the obtained results showed that Polish society is currently less vulnerable to corrupt practices than Albanians. Moreover, it was substantiated that relative altruism operates as the mechanism that explains nepotism or cronyism practices. Individuals involved in nepotism or cronyism perceive these practices as a tool to reduce the risk of their operations failing. However, the phenomena lead to long-lasting reciprocity, like in the case of corruption. This study contributes to a better contextual diagnosis of organizations and helps develop preventive strategies.
Optimal Planning and Scheduling of Battery Energy Storage Systems for Isolated Microgrids

Balancing the energy demand in isolated microgrids is a critical issue especially in the presence of intermittent energy sources. Battery Energy Storage Systems (BESS) can be installed in such circumstances to supply the demand and support the reserve requirements of the isolated microgrid. However, due to the high installation costs of BESS, there is a need for proper mechanisms to select such systems and size them optimally. Furthermore, since BESS are often installed to serve multiple applications, these should be properly modeled to coordinate their different functionalities. In this thesis, a multi-year operational planning model is developed to determine the BESS optimal power rating and energy capacity along with the year of installation taking into account its coordinated operation. The model includes unit commitment formulation with renewable energy and BESS operational constraints. The optimal planning decisions are obtained for different BESS technologies under several scenarios of ownerships. The uncertain patterns of solar and wind resources and system demand are considered and several microgrid operational scenarios are created. A stochastic optimization model is developed to determine the optimal BESS size and installation year including the different states of the uncertain microgrid variables. The stochastic optimization model is solved using a decomposition based two-stage iterative approach to cope with the large computational burden of such problems.
Comparing Different Data Types for Predicting the Apple Stock

Stock markets are at the heart of market economies, and stock market prediction is a hot topic in research. There are many methods of prediction, and they often use different types of data. The two main types are technical and fundamental data. This paper explores company-specific prediction through predicting the direction of the Apple stock. Three neural networks that use different input data are compared. The Tech model uses technical data, the Comp model uses numerical fundamental data with an emphasis on company data, and the Text model uses textual news data. Several metrics, including ones useful for preventing bias when dealing with imbalanced data, were used to compare the models. The Comp and Text models showed prediction bias, and two more models, W-Comp and W-Text, were trained to attempt to mitigate that bias. The Comp model performed the best out of the original models, and W-Comp also showed good performance. W-Text performed the best out of all the models. This suggests that numerical fundamental data is useful in predicting the market, and textual data also has potential. Future research could be used to improve the performance of all the models and more thoroughly compare the types of data used.
Efficient multiscale fully convolutional UNet model for segmentation of 3D lung nodule from CT image

Abstract. Purpose: Segmentation of lung nodules in chest CT images is essential for image-driven lung cancer diagnosis and follow-up treatment planning. Manual segmentation of lung nodules is subjective because the approach depends on the knowledge and experience of the specialist. We proposed a multiscale fully convolutional three-dimensional UNet (MF-3D UNet) model for automatic segmentation of lung nodules in CT images. Approach: The proposed model employs two strategies, fusion of multiscale features with Maxout aggregation and trainable downsampling, to improve the performance of nodule segmentation in 3D CT images. The fusion of multiscale (fine and coarse) features with the Maxout function allows the model to retain the most important features while suppressing the low-contribution features. The trainable downsampling process is used instead of fixed pooling-based downsampling. Results: The performance of the proposed MF-3D UNet model is examined by evaluating the model with CT scans obtained from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset. A quantitative and visual comparative analysis of the proposed work with various customized UNet models is also presented. The comparative analysis shows that the proposed model yields reliable segmentation results compared with other methods. The experimental result of 3D MF-UNet shows encouraging results in the segmentation of different types of nodules, including juxta-pleural, solitary pulmonary, and non-solid nodules, with an average Dice similarity coefficient of 0.83 ± 0.05, and it outperforms other CNN-based segmentation models. Conclusions: The proposed model accurately segments the nodules using multiscale feature aggregation and trainable downsampling approaches. Also, 3D operations enable precise segmentation of complex nodules using inter-slice connections.
Drip Irrigation Design Equations

In a drip irrigation system, a major design is the minimization of the discharge (or emitter flow) variation along a drip irrigation line, either a lateral or a submain. When the kinetic energy is considered to be small and neglected in a drip irrigation line, the pressure variation will be simply a linear combination of the friction drop and energy gain or loss due to slopes. Five pressure profiles were presented and considered. Procedures were developed to identify pressure profiles by landslope and total friction drop at the end of the line. Equations for designing lateral length (or submain) based on a given criterion were derived for all five pressure profiles. These equations cannot be solved directly, but solutions can be obtained using a trial and error technique on a pocket calculator or by using Newton's method of approximation in a computer program. The developed mathematical equations will be useful in the future development of computerized drip irrigation system design.
Poloxamer 407/188 Binary Thermosensitive Gel as a Moxidectin Delivery System: In Vitro Release and In Vivo Evaluation

Moxidectin (MXD) is an antiparasitic drug used extensively in veterinary clinics. In this study, to develop a new formulation of MXD, a thermosensitive gel of MXD (MXD-TG) was prepared based on poloxamer 407/188. Furthermore, the gelation temperature, the stability, in vitro release kinetics and in vivo pharmacokinetics of MXD-TG were evaluated. The results showed that the gelation temperature was approximately 27 °C. MXD-TG was physically stable and can be released continuously for more than 96 h in vitro. The Korsmeyer–Peppas model provided the best fit to the release kinetics, and the release mechanism followed a diffusive erosion style. MXD-TG was released persistently for over 70 days in sheep. Part of pharmacokinetic parameters had a difference in female and male sheep (p < 0.05). It was concluded that MXD-TG had a good stability, and its release followed the characteristics of a diffusive erosion style in vitro and a sustained release pattern in vivo.
A Novel Security Architecture for WSN-Based Applications in Smart Grid

The Smart Grid (SG) aims to cope with the problems of the traditional grid, using renewable power generators. Similarly, SG benefits from the deployment of wireless sensor networks (WSNs) to enhance its aspects by monitoring the physical behavior of the power generators. However, new threats and attacks may arise due to the open nature and large scale of SG where WSNs are deployed. In this paper, we propose a new security architecture for WSNs in SG based on public key infrastructure (PKI). The key idea of the proposed architecture is to distribute the role of the certification authority (CA) among a set of sensor nodes to ensure the availability and scalability of the CA services. To elect this set of sensor nodes, we propose a novel lightweight clustering algorithm for WSNs that relies on the trust metrics of the nodes and their energy levels. The proposed architecture provides many security services such as authentication and confidentiality and mitigates many types of attacks such as Sybil and eavesdropping. Extensive simulations have been conducted using network simulator OMNET++ and Castalia framework to investigate the performance of the clustering algorithm. The results show that almost 100% of the sensors are members of clusters, and even in the presence of malicious nodes, the number of cluster heads remains static which reflects the robustness of the proposed architecture.
POI Detection of High-Rise Buildings Using Remote Sensing Images: A Semantic Segmentation Method Based on Multitask Attention Res-U-Net

A point-of-interest (POI) represents a specific point location that may be useful or interesting for people, and therefore, each and every building footprint in a topographic map can be recognized as a POI. Automatic extraction of building footprints using remote sensing images has become a challenging and important research topic, which is in demand for urban planning and development. Extensive studies have explored a variety of semantic segmentation methods using deep learning algorithms to achieve better performance in building footprint extraction; however, the existing algorithms were shown to have some limitations, which lead to poor segmentation results. Building roofs were recognized as building footprints in the previous studies. This is prone to error especially for high-rise buildings due to different sensor view angles. In this article, we propose a multitask Res-U-Net model with an attention mechanism for the extraction of the building roofs and the whole building shapes from remote sensing images and then use an offset vector method to detect the footprints of the high-rise buildings based on the boundaries of the corresponding building roofs and shapes. We also apply the online food delivery (OFD) data to parse the POI name of every building footprint. Several strategies are also developed in combination with the proposed model, including data augmentation and postprocessing. We conduct numerical experiments using real data of remote sensing images and OFD historical order data. Results demonstrate that our proposed model achieves a total F1-score of 77.05% and an intersection over union (IoU) of 63.55% in terms of the building roof segmentation, and an overall F1-score of 79.02% and an IoU of 66.05% for the whole building shape segmentation, which both achieve the best performance among all baseline models.
A Two-Layer Ensemble Method for Detecting Epileptic Seizures Using a Self-Annotation Bracelet With Motor Sensors

Using monitoring devices could help avoid injuries and even death. Currently, wearable sensors such as motion sensors and other sensors are used to detect when the patient is having a seizure and alarm their caregivers. However, the development phase of these devices requires labor-intensive work on labeling the collected data, resulting in difficulties in developing wearable monitoring devices. Thus, a more automated auxiliary method of labeling seizure data and a wearable device to detect seizures for daily monitoring use are necessary. We collected data from epileptics outside the hospital with our proposed bracelet. The subjects were asked to press the mark button after they had seizures. We also presented an automatically extraction and annotation of moving segments (EAMS) algorithm to exclude nonmoving segments. Then, we used a two-layer ensemble model (TLEM) using machine learning methods to classify seizures and non-seizure moving segments, which was designed to deal with imbalanced dataset. Then, we build two individual TLEM models separately for the overall (all day and night) seizure detection case and the night seizure detection case, owing to different imbalance of these datasets. The EAMS algorithm exclude 93.9% raw inactive data. The TLEM model achieved 76.84% sensitivity (SEN) and 97.28% accuracy (ACU) for the overall case and achieved 94.57% SEN and 91.37% ACU for the night case. These results indicate that this bracelet can capture seizures efficiently, and our proposed TLEM has higher SEN and ACU than single-layer machine learning models.
Insights into the Mechanism of Human Deiodinase 1

The three isoenzymes of iodothyronine deiodinases (DIO1-3) are membrane-anchored homo-dimeric selenoproteins which share the thioredoxin-fold structure. Several questions regarding their catalytic mechanisms still remain open. Here, we addressed the roles of several cysteines which are conserved among deiodinase isoenzymes and asked whether they may contribute to dimerization and reduction of the oxidized enzyme with physiological reductants. We also asked whether amino acids previously identified in DIO3 play the same role in DIO1. Human DIO1 and 2 were recombinantly expressed in insect cells with selenocysteine replaced with cysteine (DIO1U126C) or in COS7 cells as selenoprotein. Enzyme activities were studied by radioactive deiodination assays with physiological reducing agents and recombinant proteins were characterized by mass spectrometry. Mutation of Cys124 in DIO1 prevented reduction by glutathione, while 20 mM dithiothreitol still regenerated the enzyme. Protein thiol reductants, thioredoxin and glutaredoxin, did not reduce DIO1U126C. Mass spectrometry demonstrated the formation of an intracellular disulfide between the side-chains of Cys124 and Cys(Sec)126. We conclude that the proximal Cys124 forms a selenenyl-sulfide with the catalytic Sec126 during catalysis, which is the substrate of the physiological reductant glutathione. Mutagenesis studies support the idea of a proton-relay pathway from solvent to substrate that is shared between DIO1 and DIO3.
Stainless Steels as Sustainable Solution for Concrete Reinforcement - From Laboratory to Practice

Stainless steel reinforcing bars show excellent corrosion resistance in concrete exposed to harsh environments. In this combined electrochemical and surface analytical work, an explanation for this behavior is proposed. XPS surface analytical results (thickness, composition of the passive film and of the interface beneath the film) obtained on black steel, FeCr alloys, and a series of stainless steels after exposure to alkaline solutions simulating concrete are reported. Pitting potentials were determined in the same solutions with electrochemical experiments. It is shown that the pitting potentials of the steels can be related to the Cr (III) oxy-hydroxide and Mo (VI) content in the passive film. It is proposed to calculate a Cr and Mo oxide equivalent similar to the well-known pitting resistance equivalent number (PREN). A correlation between the critical chloride content in concrete (reported in literature for CEM II A/LL and CEM I) and the pitting potential for carbon steel, Fe12%Cr alloy, DIN 1.4301 and DIN 1.4571 stainless steels is proposed to link results of solution analysis and performance in concrete.
Influences of Long-Term Exercise and High-Fat Diet on Age-Related Telomere Shortening in Rats

(1) Obesity and exercise are believed to modify age-related telomere shortening by regulating telomerase and shelterins. Existing studies are inconsistent and limited to peripheral blood mononuclear cells (PBMCs) and selected solid tissues. (2) Female Sprague Dawley (SD) rats received either standard diet (ND) or high-fat diet (HFD). For 10 months, half of the animals from both diet groups performed 30 min running at 30 cm/s on five consecutive days followed by two days of rest (exeND, exeHFD). The remaining animals served as sedentary controls (coND, coHFD). Relative telomere length (RTL) and mRNA expression of telomerase (TERT) and the shelterins TERF-1 and TERF-2 were mapped in PBMCs and nine solid tissues. (3) At study end, coND and coHFD animals showed comparable RTL in most tissues with no systematic differences in TERT, TERF-1 and TERF-2 expression. Only visceral fat of coHFD animals showed reduced RTL and lower expression of TERT, TERF-1 and TERF-2. Exercise had heterogeneous effects on RTL in exeND and exeHFD animals with longer telomeres in aorta and large intestine, but shorter telomeres in PBMCs and liver. Telomere-regulating genes showed inconsistent expression patterns. (4) In conclusion, regular exercise or HFD cannot systematically modify RTL by regulating the expression of telomerase and shelterins.
Radiation Exposure Perturbs IL-17RA-Mediated Immunity Leading to Changes in Neutrophil Responses That Increase Susceptibility to Oropharyngeal Candidiasis

Fungal infections caused by Candida albicans are a serious problem for immunocompromised individuals, including those undergoing radiotherapy for head and neck cancers. Targeted irradiation causes inflammatory dysregulation and damage to the oral mucosa that can be exacerbated by candidiasis. Post-irradiation the cytokine interleukin-17 (IL-17) protects the oral mucosae by promoting oral epithelial regeneration and balancing the oral immune cell populations, which leads to the eventual healing of the tissue. IL-17 signaling is also critical for the antifungal response during oropharyngeal candidiasis (OPC). Yet, the benefit of IL-17 during other forms of candidiasis, such as vulvovaginal candidiasis, is not straightforward. Therefore, it was important to determine the role of IL-17 during OPC associated with radiation-induced inflammatory damage. To answer this question, we exposed Il17ra−/− and wild-type mice to head-neck irradiation (HNI) and OPC to determine if the IL-17 signaling pathway was still protective against C. albicans. HNI increased susceptibility to OPC, and in Il17ra−/− mice, the mucosal damage and fungal burden were elevated compared to control mice. Intriguingly, neutrophil influx was increased in Il17ra−/− mice, yet these cells had reduced capacity to phagocytose C. albicans and failed to clear OPC compared to immunocompetent mice. These findings suggest that radiotherapy not only causes physical damage to the oral cavity but also skews immune mediators, leading to increased susceptibility to oropharyngeal candidiasis.
Coherent Communication Over Multi Mode Fibers for Intra-Datacenter Ultra-High Speed Links

Multimode fibers (MMFs) links using vertical cavity surface-emitting lasers (VCSELs) are still the solution of choice for today’s data center shorter distances thanks to their low cost and robustness. Currently, the market is moving towards 400G-capable systems using multiple lanes at 50 Gbps per lane, based on intensity modulation (IM) and direct detection (DD). A technology switch will probably be required to overcome the 100 Gbps per lane limit that will likely be the bottleneck of future IM-DD MMF-based links. In this manuscript we propose an intra-data center system based on coherent detection and MMF, analyzing experimentally and analytically the performance of polarization multiplexed coherent communication. We show that the advantages enabled by the coherent technology can be fully exploited in an MMF-based link when central launch is ensured. In particular, we study in detail the effect of lateral offsets introduced by the connectors at the interface between two MMF facets, in terms of net optical loss and signal-to-noise ratio (SNR) degradation. A statistical analysis is performed analytically exploiting a large database of OM3 and OM4 fibers modal delays and results are presented for various combinations of fiber length, type and number of MMF-to-MMF connections along the link. We show quantitative results on the maximum acceptable lateral offsets in the MMF connectors, highlighting that in practical conditions average offsets of up to 3 $\mu$m can be tolerable.
A Novel Approach to the Authentication of Apricot Seed Cultivars Using Innovative Models Based on Image Texture Parameters

The different cultivars of apricot seeds may differ in their properties. To ensure economical and efficient seed processing, knowledge of the cultivars’ composition and physical properties may be necessary. Therefore, the correct identification of the cultivar of the apricot seeds may be very important. The objective of this study was to develop models based on selected textures of apricot seed images to distinguish different cultivars. The images of four cultivars of apricot seeds were acquired using a flatbed scanner. For each seed, approximately 1600 textures from the image, converted to the different color channels R, G, B, L, a, b, X, Y, and Z, were calculated. The models were built separately for the individual color channels; the color spaces Lab, RGB, XYZ; and all color channels combined based on selected texture parameters using different classifiers. The average accuracy of the classification of apricot seeds reached 99% (with an accuracy of 100% for the seeds of the cultivars ‘Early Orange’, ‘Bella’, and ‘Harcot’, and 96% for ‘Taja’) in the case of the set of textures selected from the color space Lab for the model built using the Multilayer Perceptron classifier. The same classifier produced high average accuracies for the color spaces RGB (90%) and XYZ (86%). For the set of textures selected from all color channels, i.e., R, G, B, L, a, b, X, Y, and Z, the average accuracy reached 96% (Multilayer Perceptron and Random Forest classifiers). In the case of individual color channels, the highest average accuracy was up to 91% for the models built based on a set of textures selected from color channel b (Multilayer Perceptron). The results proved the possibility of distinguishing apricot seed cultivars with a high probability using a non-destructive, inexpensive, and objective procedure involving image analysis.
Rapid Determination of Cephalexin in Animal-Derived Food by an Indirect Competitive Enzyme-Linked Immunosorbent Assay

Abstract Cephalexin (CFE) residues in animal-derived food and its deleterious effects on human health has attracted extensive attention. In this study, a specific anti-cephalexin monoclonal antibody (mAb) was used to develop an indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) with a half-maximal inhibitory concentration (IC50) of 1.83 µg/L that was suitable for the determination of cephalexin in animal-derived foods. The limit of detection (LOD) and limit of quantitation (LOQ) for cephalexin were from 0.44 to 0.58 µg/kg and 0.57 to 0.67 µg/kg, respectively. The recovery was from 90.1 to 103.6% with coefficients of variation (CV) less than 10.0%. In addition, the established ELISA method and high-performance liquid chromatography – mass spectrometry (HPLC-MS/MS) were used to analyze the samples with good correlation between the results. The results show that the established ic-ELISA is suitable for the determination of cephalexin in animal-derived food.