Miracle-dz/UniFAR-FTUs huggingface dataset:
Studies on ecology and epidemiological importance of glossina swynnertoni austen, near Aitong, Maasai Mara, South Western Kenya

Different trap designs and colours were tested for G. swynnertoni. A turquoise blue colour variation of the Siamese trap was adopted as the standard for sampling. Further trap tests showed the turquoise blue Triangular trap to be better. G. swynnertoni occupied three core habitats near Aitong: namely, thicket, wooded grassland and Acacia community. Except in the Acacia community habitat, it occurred in association with G. paliidipes. The density of G. swynnertoni varied among habitats being highest in the Acacia community, intermediate in the wooded grassland and lowest in the thicket. In addition, density varied seasonally. Studies on activity patterns of G. swynnertoni using coloured electric screens showed a unimodal pattern for both males and females. The activity of males peaked between 1100-1200 h then dropped gradually. Flies remained relatively active throughout the afternoon but activity decreased rapidly towards sunset (17001800 h). Activity of females was very low in early morning but rose gradually to peak between 1400-1500 h; females remained active until sunset. Analysis of bloodmeals by Enzyme-linked Immunosorbent Assay (ELISA) Indicated that G. swynnertoni fed mainly on Suidae (64.2%). Other less-favoured hosts included ruminants (4.5%), hippopotamus (3.0%), Felidae (1.5%) and man (3.0%). Out of 1,612 G. swynnertoni dissected, 11.0% were infected with trypanosomes. Of these, 8.7% had T. wVax-type infection, 1.7% Nannomonas-type infection and 0.4% immature gut infection. No salivary gland infection was encountered although hypertrophied glands (virus-infected) were observed frequently. However, batch-feeding of tsetse on rats prior to dissection or passage of gut procyclic forms after dissection into G. m. centralis revealed the presence of T. brucei-type infection. In total, 20.9% of the resident cattle were infected with trypanosomes. Characterisation of trypanosome isolates by Polymerase Chain Reaction (PCR) and hybridisation of the amplified products with specific DNA probes revealed the presence of Trypanozoon parasites and T. congolense savannah-type in both cattle and wild G. swynnertoni. Amplification of genomic DNA of T. congolense savannahtype isolates from around Aitong, Maasai Mara and other geographical areas in arbitrarily-primed PCR revealed intra-specific variation.
MAPPO-based Energy Trading in Intelligent Community with Double Auction

With the development of electronic information technology, trading between different homes has been possible. In this paper, we investigate the energy optimization problem among multiple homes in a intelligent community, and introduce a double auction method for guiding them to trade energy. Each home is equipped with an energy storage system (ESS), an electric vehicle (EV) and load, and connected to the renewable energy and the power grid. The EV operates as an energy producer and consumer to help home energy management system (HEMS) flexibly regulate the power balance. To facilitate trading between homes and reduce the total cost of the system, we introduce a double auction (DA) market among homes, which can also protect their privacy. We formulate this problem as a partially observable markov decision process (POMDP) and propose a DA-based multi-agent proximal policy optimization (DA-MAPPO) algorithm, which solves the problem of sequential decisions and dimensional disasters. The simulations show that the model proposed can reduce the total system cost by 14.8% compared with the model without DA market. The proposed algorithm can improve the motivation of homes to participate in the DA market and protect their privacy compared with other algorithms.
Optimization of a Novel Tyrosinase Inhibitory Peptide from Atrina pectinata Mantle and Its Molecular Inhibitory Mechanism

In order to realize the multi-level utilization of marine shellfish resources and to develop the potential biological activity of processing by-products of Atrina pectinata, gelatin was extracted from the mantle and the potential whitening effect of its enzymatic peptides was explored. Taking tyrosinase inhibitory activity as the evaluation index, the enzyme hydrolysate process was optimized by response-surface methodology, and the optimal enzyme hydrolysate conditions were as follows: pH 5.82, 238 min enzyme hydrolysate time, and temperature of 54.5 °C. Under these conditions, the tyrosinase inhibition activity of Atrina pectinata mantle gelatin peptide (APGP) was 88.6% (IC50 of 3.268 ± 0.048 mg/mL). The peptides obtained from the identification were separated by ultrafiltration and LC–MS/MS, and then four new peptides were screened by molecular docking, among which the peptide Tyr-Tyr-Pro (YYP) had the strongest inhibitory effect on tyrosinase with an IC50 value of 1.764 ± 0.025 mM. The molecular-docking results indicated that hydrogen bonding is the main driving force for the interaction of the peptide YYP with tyrosinase. From the Lineweaver–Burk analysis, it could be concluded that YYP is inhibitory to tyrosinase and exhibits a mixed mechanism of inhibition. These results suggest that YYP could be widely used as a tyrosinase inhibitor in whitening foods and pharmaceuticals.
High-Gain Tapered Long Slot Array for Satcom Applications in PCB Technology With Folded Corporate Feed Network

This article presents an antenna covering the full frequency band from 17 to 31 GHz for K-/Ka-band satellite communication (Satcom) applications. A long slot array with 64 slots is designed for the radiating aperture. It is fed by a tapered corporate feed network (CFN) to reduce the sidelobe level (SLL) in the radiating plane orthogonal to the slots. A new technique to fold the CFN by a factor up to 65% is introduced without affecting the array performance. This technique can be applied to any CFN in a multilayer system. Both parts are fabricated in printed circuit board (PCB) technology. A pillbox beamformer is used to illuminate the CFN. This beamformer consists of an H-plane horn located in the focal plane of an integrated parabolic reflector. The main beam of the antenna can be steered along the slot plane to provide a ±42° coverage in elevation by changing the position of the horn in the focal plane. The pillbox beamformer is made of aluminum to minimize losses and is fabricated by computer numerically controlled (CNC) milling. The total size of the antenna is <inline-formula> <tex-math notation="LaTeX">$44.5 \times 33.1 \times 4.3\mathbf {\lambda _{0}}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\mathbf {\lambda _{0}}$ </tex-math></inline-formula> is the wavelength at the maximum frequency of operation (31 GHz).
Reading Supreme Courts from afar: Topic modelling judgements of the Supreme Courts of Sri Lanka and the United Kingdom

In legal scholarship, court judgments are pivotal in shaping jurisprudence. Situated within the field of digital humanities as it applies to legal texts, this article takes a closer look at the underlying themes in Supreme Court judgments by applying topic modelling to the judgments delivered by the Supreme Court of Sri Lanka (LKSC) and the United Kingdom Supreme Court (UKSC). Using two custom datasets curated by (web) scraping the respective LKSC and UKSC websites, this article employs Latent Dirichlet Allocation (LDA) with the Machine Learning for Language Toolkit (MALLET), a commonly used tool for topic modelling by digital humanists, to identify topics (themes) that represent the main areas of law in each jurisdiction primarily dealt with by the respective courts. 25 was selected as the number of topics after experimentation, and the topics identified in each jurisdiction were manually labelled. The results reveal the composition and evolution of judicial workloads, the shifting socio-political priorities in each jurisdiction, as well as the similarities and differences between the two courts. The findings have several implications for legal research and practice. These suggest that topic modelling can be used as a tool to organize and categorize judgments based on their themes, which can facilitate access and retrieval of relevant cases, and identify priority areas for judicial and legal training. They also challenge conventional legal taxonomies and classifications, and demonstrate the potential of computational methods for enhancing the understanding and analysis of law.
Analysis of Skin Regeneration and Barrier-Improvement Efficacy of Polydeoxyribonucleotide Isolated from Panax Ginseng (C.A. Mey.) Adventitious Root

Polydeoxyribonucleotide (PDRN) has the ability to regenerate skin cells and improve the skin barrier and wound healing. This study investigated the possibility of replacing animal-derived PDRN with plant-derived PDRN. To test this, the adventitious roots of Korean ginseng (Panax ginseng C.A. Meyer), which is commonly used to treat various diseases, were suspension-cultivated through tissue culture; subsequently, PDRN was purified using microfluidization, an ultra-high-pressure physical grinding method. The results showed that purified Panax PDRN was effective in healing skin wounds and enhancing the skin barrier. Panax PDRN promoted the proliferation of keratinocytes and fibroblasts by increasing the expression of fibronectin, filaggrin, Ki-67, Bcl-2, inhibin beta A, and Cyclin D1. It also acted as an agonist of the adenosine A2A receptor and induced the phosphorylation of focal adhesion kinase, adenosine triphosphate-dependent tyrosine kinase, and mitogen-activated protein kinase. This activated signal transduction, thereby regenerating skin cells and strengthening the barrier. These results were not only observed in skin cells but also in an artificial skin model (KeraSkinTM). The use of plant-derived PDRN instead of animal-derived PDRN can promote animal welfare and environmental sustainability. Furthermore, Panax PDRN can potentially be a new plant-derived PDRN (PhytoPDRN) that may be utilized in the treatment of various skin diseases.
STRATEGIC COST MANAGEMENT PRACTICES AND ORGANIZATIONAL PERFORMANCE: A STUDY OF MANUFACTURING FIRMS IN NIGERIA

There has been a lot of transformation in the manufacturing industry. The manufacturing processes are highly automated, multiple product mix strategies are employed, and the nature of production cost drivers are complex, production costs have increased and market competition has risen due to market globalization. Therefore, the use of the traditional cost management system, such as standard costing and predetermined overhead allocation system in the Nigerian manufacturing industry has proven to be ineffective in product costing, cost management, and decision making. As a result of these developments, it has become necessary to employ more refined product costing techniques of Strategic Cost Management. Some of the Strategic Cost Management methods are Activity-Based Costing, Target Costing, Life Cycle Costing, Balance Scorecard, and Total Quality Management. The implementation of the Strategic Cost Management system has led to improved product costing analysis and decision-making, production efficiency, and improved firm performance and market competitiveness. This study examines the effect of Strategic Cost Management practices on organizational performance of Nigerian manufacturing industry. A survey research design is employed to collect primary data, which are analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) method. The study reveals that Strategic Cost Management practices positively impact organizations’ performance. Therefore, it is recommended that manufacturing firms still using the traditional costing methods should consider employing the Strategic Cost Management methods to enhance their performance and competitiveness.
Accurate Global Point Cloud Registration Using GPU-Based Parallel Angular Radon Spectrum

Accurate robot localization and mapping can be improved through the adoption of globally optimal registration methods, like the Angular Radon Spectrum (ARS). In this paper, we present Cud-ARS, an efficient variant of the ARS algorithm for 2D registration designed for parallel execution of the most computationally expensive steps on Nvidia™ Graphics Processing Units (GPUs). Cud-ARS is able to compute the ARS in parallel blocks, with each associated to a subset of input points. We also propose a global branch-and-bound method for translation estimation. This novel parallel algorithm has been tested on multiple datasets. The proposed method is able to speed up the execution time by two orders of magnitude while obtaining more accurate results in rotation estimation than state-of-the-art correspondence-based algorithms. Our experiments also assess the potential of this novel approach in mapping applications, showing the contribution of GPU programming to efficient solutions of robotic tasks.
STGAT: A Spatio-Temporal Graph Attention Network for Travel Demand Prediction

Forecasting travel demand is a challenging task due to the complex spatial dependencies and dynamic temporal correlation of the traffic data. Furthermore, a limited representation of the given spatial graph structure may restrict the model's ability to effectively learn spatial-temporal dependencies. To extract latent semantic features from traffic data, this paper proposes a method to construct a traffic graph using the Markov cluster algorithm. The traffic semantic correlation matrix is constructed based on a traffic graph to obtain the deep semantic information. Specifically, this study proposes a Spatio-Temporal Graph Attention Network(STGAT) for travel demand prediction. STGAT uses a graph attention layer based on the Node2Vec graph embedding algorithm, two convolutional layers based on the Markov cluster algorithm, and a long short-term memory network to capture spatial-temporal dependencies. Experimental results on the NYC Taxi dataset and the Chengdu online car dataset demonstrate that STGAT achieved state-of-the-art performance compared to other baseline models.
Scheduling in an Emergency Department, Linear Formulation and Heuristic Approach

In this study, we investigate the real-world scheduling problem of the Medical Day Unit of the Emergency Department (MDU-ED) of Jeanne de Flandres University Hospital in Lille (France). We implemented a heuristic (PRH) based on hospital practitioners' rules we collected by observing the operation of MDU-ED. We propose an Integer Linear Programming (ILP) formulation that makes it feasible to solve small instances. We propose an Adaptive Iterative Destruction Construction Heuristic (IDCH) solution approach. The IDCH obtains better solutions than the PRH within reasonable processing times. We report on experiments performed on instances generated using real-world patient pathways of the MDU-ED.
Innovative Biosensing Approaches for Swift Identification of Candida Species, Intrusive Pathogenic Organisms

Candida is the largest genus of medically significant fungi. Although most of its members are commensals, residing harmlessly in human bodies, some are opportunistic and dangerously invasive. These have the ability to cause severe nosocomial candidiasis and candidemia that affect the viscera and bloodstream. A prompt diagnosis will lead to a successful treatment modality. The smart solution of biosensing technologies for rapid and precise detection of Candida species has made remarkable progress. The development of point-of-care (POC) biosensor devices involves sensor precision down to pico-/femtogram level, cost-effectiveness, portability, rapidity, and user-friendliness. However, futuristic diagnostics will depend on exploiting technologies such as multiplexing for high-throughput screening, CRISPR, artificial intelligence (AI), neural networks, the Internet of Things (IoT), and cloud computing of medical databases. This review gives an insight into different biosensor technologies designed for the detection of medically significant Candida species, especially Candida albicans and C. auris, and their applications in the medical setting.
Performance Analysis of Devops Practice Implementation Of CI/CD Using Jenkins

Continuous Integration and Continuous Delivery (CI/CD) are methods used in agile development to automate and speed up the process of building, testing, and validating services. To support and simplify all development and deployment processes, several methods such as containerized and CI/CD automation are needed. In this research, a DevOps Practice is carried out which includes process integration, deployment, and testing automatically using a tool called Jenkins. These tools are open source automation servers to help the Continuous Integration and Continuous Deployment process. Jenkins is equipped with various open source plugins that can be used to simplify and assist CI/CD automation and testing processes. The implementation of CI/CD in performance testing makes the testing process integrated, automated, and can be run on a regular basis.
Choriocarcinoma After Term Pregnancy With a Subsequent Successful Pregnancy: A Rare Entity

Gestational trophoblastic neoplasia (GTN) is a group of pregnancy-related disorders that arise from the cells of conception. They include gestational choriocarcinoma (CC), placental site trophoblastic tumor, and epithelioid trophoblastic tumor with these forms arising from a molar pregnancy, abortion, or a normal genetic pregnancy. Most cases of GTN are diagnosed when the serum hCG levels plateau or rise in patients being followed up after the diagnosis of hydatidiform mole but can also be suspected due to persistent vaginal bleeding after a normal pregnancy and delivery. Early diagnosis and treatment are pivotal for ensuring optimal outcomes and given the rarity of the disease, clinical management and treatment should be provided in specialized centers. Here, we present a rare case of a 31-year-old woman diagnosed with choriocarcinoma with pulmonary metastasis following an uncomplicated full-term pregnancy. After the suction evacuation and curettage, she underwent six cycles of chemotherapy with an excellent response, a fact that resulted in a subsequent pregnancy and birth without complications, occurring 18 months thereafter.
Anti-Singularity Clock Recovery and Rx/Tx IQ Skew Compensation Scheme

To deal with the failure of clock recovery and detect Rx IQ skew at the singularity in which the azimuth is 45° and different group delay (DGD) is odd multiple number of half symbol period, an anti-singularity clock recovery and receiver (Rx)/transmitter (Tx) IQ skew compensation scheme is proposed in this letter. The sensitivity of clock error detector at the singularity is restored by introducing polarization transformation. By judging the changes of Rx IQ skew, the effect of the Rx IQ skew detection error on the compensation at the singularity is eliminated. Besides, Tx IQ skew is detected by phase discriminators in frequency domain and compensated by interpolation filters in time domain. Compared with MIMO equalizers scheme, the proposed scheme has lower computational complexity. The simulation results of PM-16QAM Nyquist coherent system demonstrate that the scheme can work stably when DGD is odd multiple number of half symbol period and the azimuth changes rapidly. The performance of the scheme is also verified through the off-line experiment. The scheme can realize clock recovery plus Rx/Tx IQ skew detection and compensation, and the detection accuracy and compensation range could reach the requirement of practical application.
Condition Number Improvement of IRS-Aided Near-Field MIMO Channels

In near-field wireless propagation environment, line-of-sight (LoS) link becomes dominant, which leads to the signal energy focusing on the LoS path. In this paper, the concept of condition number is introduced to measure the spatial multiplexing gain available for the multiple-input multiple-output (MIMO) system. A criterion based on the minimized condition number of the MIMO channel is given out by deploying the emerging intelligent reconfigurable surfaces (IRS), which makes the energy of signals uniformly spread across the effective subchannels of the MIMO channel by adding reflective link and optimizing its phase shifters. Then, based on the improved MIMO channel, the optimal transmit and received beamforming matrices are obtained by using the singular value decomposition (SVD) approach to reach the upper bound of capacity for the IRS-aided MIMO channel. We demonstrate that deploying IRS and optimizing its phase can decrease the condition number of MIMO channel, leading to substantial multiplexing gains.
Joint Multi-UAV Deployment and Resource Allocation Based on Personalized Federated Deep Reinforcement Learning

Unmanned aerial vehicles (UAVs) are capable of serving as aerial base stations (BSs) for providing dynamic coverage and connectivity extension for the sixth-generation (6G) wireless networks. While flexibility is provided, the deployment of the UAV swarms and the associated resource allocation bring challenging issues due to dynamic nature of UAVs and difficulty in obtaining global user information. In this paper, we propose an adaptive and flexible joint UAV deployment and resource allocation scheme by exploiting a personalized federated deep reinforcement learning framework, called PFRL, with aim to maximize the long-term network throughput while enforcing user privacy and adapting to time-varying network states. To allow UAVs to make real-time decisions on resource allocation and position adjustment based on local observations while achieving a global optimal solution, we incorporate deep reinforcement learning (DRL) into federated learning framework. Specifically, we use DRL to train a local model and a personalized model on UAVs, and employ a two-level parameter aggregation scheme on a leading UAV to form a global model. The personalized model can adapt to specific environments, while exploiting the generalization of global model to accelerate the learning convergence. Numerical results show that the proposed PFRL scheme can achieve significant performance gain in terms of network throughput and convergence in comparison with some state-of-art solutions.
Computation Offloading in NOMA-Enabled Vehicular Fog Computing Networks

With the advent of the Internet of vehicles (IoV), the explosive growth of data from vehicular sensors places a heavy computing burden on green-enabled intelligent transportation systems. In this study, a paradigm of vehicular fog computing (VFC) is introduced, which is able to offload computation tasks to the nearby fog nodes. In addition, considering the advantage of non-orthogonal multiple access (NOMA), a NOMA-enabled VFC is proposed, in which a task vehicle, a main fog access point (F-AP), an idle vehicle, and other cooperative F-APs are involved to process the task. By jointly optimizing NOMA power allocation, task allocation ratio, bandwidth allocation and time slot allocation, the offloading data maximization problem is investigated. After simplifying the problem through mathematical analysis, the offloading data maximization problem is solved by the proposed interior-point method based on successive convex approximation (SCA). Simulation results show that the proposed offloading scheme performs better than other existing schemes in terms of offloading data.