Miracle-dz/UniFAR-FTUs huggingface dataset:
DeeP4R: Deep Packet Inspection in P4 using Packet Recirculation

Software-defined networks are useful for multiple tasks, including firewalling, telemetry, and flow analysis. In particular, the P4 language makes it possible to carry out some simple packet processing tasks in the data plane, i.e., on the switch itself (without real-time support from the SDN controller or a server). However, owing to the limitations of packet parsing in P4, these tasks involve only the packet headers. In this paper, we present a novel approach that allows Deep Packet Inspection (DPI) – i.e., inspection of the packet payload – in the data plane, using P4 alone. We make use of the fact that in P4, a switch can clone and recirculate packets. One copy (clone) can be recirculated, slicing off a byte in each round, and using a finite-state machine to check if a target string has yet been seen. If the target string is found, the other copy (original packet) is discarded; if not, it is passed through. Our approach allows us to build the first application-layer firewall (URL filter) in the data plane, and to achieve essentially line-rate performance while filtering thousands of URLs, on a commodity programmable switch. It may in future also be used for other DPI tasks.
Marginal Value-Based Edge Resource Pricing and Allocation for Deadline-Sensitive Tasks

In edge computing (EC), resource allocation is to allocate computing, storage and networking resources on the edge nodes (ENs) efficiently and reasonably to tasks generated by users. Due to the resource-limitation of ENs, the tasks often need to compete for the resources. Pricing mechanisms are widely used to deal with the resource allocation problem, and the valuations of tasks play a critical role in the price mechanisms. However, users naturally are not willing to expose the valuations of their tasks due to conflicts of interests. Current research works usually adopt truthful auctions to motivate the users to report honestly the valuations of their tasks. In this paper, we introduce the marginal value to estimate the valuations of tasks, and propose a marginal value-based pricing mechanism using the incentive theory, which motivates the tasks with higher marginal values to actively request more resources. The EC platform sets the resource prices using the price mechanism, and then the users determine their resource requests relying on the resource prices and the valuations of their tasks. After receiving the deadline-sensitive tasks from the users, the resource allocation can be modeled as a knapsack problem with the deadline constraints. Extensive experimental results demonstrate that our approach is computationally efficient and is promising in enhancing the utility of the EC platform and the tasks.
Open Research Online Developing a Sense of Knowing and Acquiring the Skills to Manage Pain in Children with Profound Cognitive Impairments: Mothers’ Perspectives

Children with profound cognitive impairment (PCI) are a heterogenous group who often experience frequent and persistent pain. Those people closest to the child are key to assessing their pain. This mixed method study aimed to explore how parents acquire knowledge and skills in assessing and managing their child’s pain. Eight mothers completed a weekly pain diary and were interviewed at weeks 1 and 8. Qualitative data were analysed using thematic analysis and the quantitative data using descriptive statistics. Mothers talked of learning through a system of trial and error (“learning to get on with it”); this was accomplished through “learning to know without a rule book or guide”; “learning to be a convincing advocate”; and “learning to endure and to getthingsright. ”Experientialandreflectivelearningwasevidentinthewaythemothersdevelopeda“senseofknowing”theirchild’spain.Theydrewonembodiedknowledgeofhowtheirchildusuallyexpressedandrespondedtopaintohelpmakepain-relateddecisions.Healthprofessionalsneedtosupportmothers/parentstodeveloptheirknowledgeandskillsandtogainconfidenceinpainassessmentandtheyshouldrecogniseandactonthemothers’concerns.
The Effect of Region-specific Institutions and Firm-specific Resources on Firm's Innovativeness in China

In the recent literature, the assumption that institutions are homogeneous across regions within emerging economics has been challenged. Yet little attention has been paid to the role played by subnational region-specific institutions in firm’s innovativeness. Moreover, the influences of region-level and firm-level factors have been analyzed separately without paying attention to the linkages between them. Thus, this study first tries to investigate the influences of the cross-regional institutional idiosyncrasies in intellectual property (IP) protection and economic internationalization on the local firm’s innovativeness. Furthermore, the study explores how they can interact with a firm’s internal technological competences. The issues are explored using empirical evidence from the specific context of the e-commerce industry in China. It first contributes to institutional theory by identifying the role played by region-specific institutions in innovation. The second contribution lies in developing and testing a contingency perspective combining insights from institutional theory and the resource-based view.
Dynamic Construction and Adaptation of 3D Virtual Network Topology for UAV-Assisted Data Collection

Unmanned aerial vehicles (UAV) assisted data collection from on ground devices and sensors is becoming more useful in many mission-critical applications. However, meeting the data collection requirements under dynamic channel conditions between the UAV and on ground devices relies on frequent information exchanges, which brings great challenges to the dynamic operation of the integrated UAV network due to its inherent complexity. To rapidly obtain a holistic view in assisting the UAV network operation, we first propose a three-dimensional (3D) virtual network topology which helps the UAV to make faster decisions by analyzing refined virtual indicators instead of measuring and processing related physical factors frequently in real time. To improve the efficiency of UAV data collection, dynamic adaptation of the 3D virtual network topology is achieved by a deep deterministic policy gradient (DDPG) based algorithm, where the UAV flying speed and direction, as well as the determination of the target group of on ground devices are optimized under the UAV energy constraint. Simulation results demonstrate that the proposed DDPG-based dynamic adaptation of the 3D virtual network topology can effectively improve the data collection efficiency compared with the benchmark solutions.
RADIOISOTOPE-ORIENTED VIEW OF NUCLEAR WASTE MANAGEMENT.

Drawing upon a 25-year background in separating and packaging kilocirie quantities of fission products for isot >pic power and radiation sources in the Oak Ridge NaUonal laboratory Radioisotope Program, a systen for the long-range management of high-level fission product waste is presented. This would involve an initial two-year storage of the liquid high-level waste to ailow decay of about 70% of the radioactivity, followed by separation into chemically related groups, especially periodic group I-A ( 1 3 7 Cs) and II-A (^Sr) (the principal remaining heat producers) and the 'crnair.de. of the fission products plus the toxic, long-lived transplutonium isotopes. The bghtly calcined, packaged residue is given an additional ten-year cooling period and is then chemically processed to remove uranium, plutonium, and transplutonium elements, which are packaged and burned in a nuclear reactor; a small final unburnable residue of toxic nuclides is canned for permanent vault storage or sent to space. Cesium-137 and strontium-90 are reprocessed after SO to 75 years, and the undecayed portion is recycled. The low-radiation, low-toxicity bulk fission product residues are sent to low-level waste disposal. High decontamination factors are needed only on the final separations. Vault storage under surveillance on power park or power island premises is suggested to avoid hot shipments. The cost appear* to be only moderately greater than for methods for unsupervised environmental storage.
Evaluation of selected educational opportunities of chimpanzees Pan troglodytes and Pan paniscus exhibitions in zoological garden

Evaluation of Selected Educational Opportunities of Chimpanzees Pan troglodytes and Pan paniscus Exhibitions in Zoological Gardens. The aim of the hereby research was to identify the criteria influencing the educational value of animal enclosures. Next, the criteria were used to evaluate 11 selected exhibitions of chimpanzee Pan troglodytes and Pan paniscus in different zoological gardens. The criteria were identified during two-stage cameral analysis, which included project, behavioural and organisational assumptions. The analysis provided 52 criteria divided into 9 groups: area size, vegetation, water, ground (relief), landscape (shaping of visibility), arrangement, equipment components, microclimate, animals. The criteria were then used to evaluate the exhibition using an ordinal scale (-1, 0, +1). The total result indicated the best designed and arranged exhibitions with highest educational value in Münster and Cologne. Criteria from the groups: area size, landscape (shaping of visibility) and arrangement were the key points for evaluating the educational value o fan exhibition. High notes obtained for these groups of criteria had positive influence on other criteria. Moreover, it was proved that a criteria from groups: landscape (shaping of visibility), arrangement and vegetation are decisive creation of a “natural” barrier between the visitors and animals, which harmonised with the surrounding landscape.
Is maternal pre-pregnancy Body Mass Index associated with type of Congenital Heart Disease in offspring?

HIGHLIGHTS The most common CHD was atrial septal defect for acyanotic CHD and Tetralogy of Fallot for cyanotic CHD. There was no association between maternal pre-pregnancy BMI and type of CHD in offspring. ABSTRACT Objectives: This study aimed to determine the association between maternal pre-pregnancy BMI and type of congenital heart disease (CHD) in offspring. Materials and Methods: This retrospective cross-sectional study involved all mothers of children with CHD who visited Pediatric Outpatient Unit at Dr. Soetomo General Academic Hospital, Surabaya, Indonesia, from January to December 2019. The maternal data were obtained from the KIA's (Maternal and Child Health) book or through anamnesis by telephone, while the offspring’s data were collected from medical records. The data were analyzed using the Chi-Square test. Significance was determined at a 5% level (p <0.05). Results: We studied 117 mothers of children with CHD. The most frequent maternal pre-pregnancy body mass index (BMI) was normal (BMI 17-23 kg/m2) accounting for 56.4% of the study population. The most common CHD was atrial septal defect (33.3%) among acyanotic patients and Tetralogy of Fallot (8.5%) among cyanotic patients. The Chi-Square test showed p=0.958 for the association between maternal pre-pregnancy BMI and type of CHD in offspring. Conclusion: There was no association between maternal pre-pregnancy BMI and type of CHD in offspring.
Scalable RDMA Transport with Efficient Connection Sharing

RDMA provides extremely low-latency and high- throughput data transmission as its protocol stack is entirely offloaded into the RDMA NIC. However, the increasing scale of RDMA networks requires hosts to establish a large number of connections, e.g., process-level full mesh, which easily overwhelms the limited resource on RNICs and hence significantly degrades performance. This paper presents SRM, a scalable transport mode for RDMA that remarkably alleviates resource exhaustion on RNICs. SRM proposes a kernel-based solution to multiplex workloads from different applications over the same connection. Meanwhile, to preserve RDMA’s performance benefits, SRM 1) avoids syscall overhead by sharing the working memory between user-space and kernel; 2) maintains high resource utilization through lock-free approach to avoid contention; 3) adopts multiple optimizations to mitigate the head-of-line blocking issue; 4) implements a rapid recovery mechanism to provide high system robustness. We evaluate SRM using extensive experiments and simulations. Testbed experiments reveal that SRM outperforms existing transports, including DCT, RC, and XRC, by 4x to 20x in latency for all-to-all communication pattern. Simulations of large-scale networks show that, compared with DCT, RC, and XRC, SRM achieves up to 4.42x/4.0x/3.7x speedups respectively in flow completion time while consuming the least memory.
Age-Dependent Growth-Related QTL Variations in Pacific Abalone, Haliotis discus hannai

Pacific abalone is a high-value, commercially important marine invertebrate. It shows low growth as well as individual and yearly growth variation in aquaculture. Marker-assisted selection breeding could potentially resolve the problem of low and variable growth and increase genetic gain. Expression of quantitative trait loci (QTLs) for growth-related traits, viz., body weight, shell length, and shell width were analyzed at the first, second, and third year of age using an F1 cross population. A total of 37 chromosome-wide QTLs were identified in linkage groups 01, 02, 03, 04, 06, 07, 08, 10, 11, 12, and 13 at different ages. None of the QTLs detected at any one age were expressed in all three age groups. This result suggests that growth-related traits at different ages are influenced by different QTLs in each year. However, multiple-trait QTLs (where one QTL affects all three traits) were detected each year that are also age-specific. Eleven multiple-trait QTLs were detected at different ages: two QTLs in the first year; two QTLs in the second year; and seven QTLs in the third year. As abalone hatcheries use three-year-old abalone for breeding, QTL-linked markers that were detected at the third year of age could potentially be used in marker-assisted selection breeding programs.
Support Vector Machine Application for Classification of Tempe Fermentation Maturity with Information Gain Selection Feature

Tempe is one of the ingredients of traditional Indonesian cuisine. In making tempe, a soybean fermentation process is needed which is generally still carried out in an open environment so that the maturity time becomes slow and erratic. Therefore, in the tempe fermentation process, a detector is needed to find out optimal maturity in tempe. This detection effort makes it possible to use image processing by utilizing various feature extractions through the classification process. This research utilizes a variety of image features, namely texture features using the GLCM method and various color features, namely RGB, HSV, LAB, CMYK, YUV, HIS, HCL, LCH. However, with so many features, it causes a high computational load, so that in this study the Information Gain approach was used to select features. Furthermore, the classification process is carried out using the Support Vector Machine (SVM) method with variations of linear, polynomial, gaussian and sigmoid kernels. Tempe objects in the fermentation process are divided into unripe, ripe and rotten classes with a total of 410 images as a dataset. The test results (SVM+IG) on the Sigmoid kernel produce the fastest time accuracy with a computational result of 2.18 seconds on a 30:70 split ratio, the longest split ratio is 80:20 which is 2.50 seconds on a Linear kernel and produces the highest accuracy of 96 ,74%. Furthermore, in the SVM test without using Information Gain on the gaussian kernel, it produced the fastest time accuracy of 2.28 seconds, and the longest at a split ratio of 40:60, namely 3.00 seconds in the polynomial kernel. Thus the result of using SVM+IG is that the average level of accuracy when using (SVM+IG) is faster than the SVM process without IG which obtains slower computation time. Based on the description above, this study aims to apply the SVM method to classify tempe fermented images with feature selection using Information Gain.
Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data

The recognition of human activities (HAR) using wearable device data, such as smartwatches, has gained significant attention in the field of computer science due to its potential to provide insights into individuals’ daily activities. This article aims to conduct a comparative study of deep learning techniques for recognizing activities of daily living (ADL). A mapping of HAR techniques was performed, and three techniques were selected for evaluation, along with a dataset. Experiments were conducted using the selected techniques to assess their performance in ADL recognition, employing standardized evaluation metrics, such as accuracy, precision, recall, and F1-score. Among the evaluated techniques, the DeepConvLSTM architecture, consisting of recurrent convolutional layers and a single LSTM layer, achieved the most promising results. These findings suggest that software applications utilizing this architecture can assist smartwatch users in understanding their movement routines more quickly and accurately.
Blockchain and DQN Enabled Co-Evolutionary Routing Scheme in UAV Networks

In unmanned aerial vehicle (UAV) networks, the route efficiency is crucial for effective cooperation among the UAVs. However, due to the highly dynamic topology and environment of UAV networks, current routing schemes usually suffer from performance degradation. To address this issue, in this paper we propose a blockchain and deep Q-network (DQN) enabled co-evolutionary routing (BDCoER) scheme for UAV networks. BDCoER includes two phases in real-time routing optimization, namely self-evolution phase and co-evolution phase. In the first phase, each UAV trains its own DQN model to make optimized routing decision adaptive to dynamic network environment. Then, based on blockchain, all UAVs cooperatively select the best evolved DQN model and integrate it with their own models, which realizes route co-evolution of the whole network in the second phase. Simulations are conducted to evaluate the performance of BDCoER. The simulation results show that BDCoER considerably outperforms the benchmark schemes in terms of both the end-to-end delay and the packet delivery ratio.
Quantitative Anatomical Studies in Neurosurgery: A Systematic and Critical Review of Research Methods

Background: The anatomy laboratory can provide the ideal setting for the preclinical phase of neurosurgical research. Our purpose is to comprehensively and critically review the preclinical anatomical quantification methods used in cranial neurosurgery. Methods: A systematic review was conducted following the PRISMA guidelines. The PubMed, Ovid MEDLINE, and Ovid EMBASE databases were searched, yielding 1667 papers. A statistical analysis was performed using R. Results: The included studies were published from 1996 to 2023. The risk of bias assessment indicated high-quality studies. Target exposure was the most studied feature (81.7%), mainly with area quantification (64.9%). The surgical corridor was quantified in 60.9% of studies, more commonly with the quantification of the angle of view (60%). Neuronavigation-based methods benefit from quantifying the surgical pyramid features that define a cranial neurosurgical approach and allowing post-dissection data analyses. Direct measurements might diminish the error that is inherent to navigation methods and are useful to collect a small amount of data. Conclusion: Quantifying neurosurgical approaches in the anatomy laboratory provides an objective assessment of the surgical corridor and target exposure. There is currently limited comparability among quantitative neurosurgical anatomy studies; sharing common research methods will provide comparable data that might also be investigated with artificial intelligence methods.
Large Sample Basin Experiments for Hydrological Model Parameterization: Results of the Model Parameter Experiment — MOPEX

Review(s) of: Large Sample Basin Experiments for Hydrological Model Parameterization: Results of the Model Parameter Experiment - MOPEX, edited by Vazken Andreassian, Alan Hall, Nanee Chahinian and John Schaake, IAHS Publication 307 (2006) 348 + iv pp, plus a DVD with data.
Application of a Multiphase Interleaved DC-DC Converter for Power-to-Hydrogen Systems

Power electronics plays a crucial role in the implementation of a clean hydrogen production system, whose last stage consists of a water electrolyzer requiring a DC power supply to be in operation. The most recent architectural solutions imply the use of an isolated DC-DC converter, collecting energy from medium voltage (MV) and delivering it to the electrolyzer. An equivalent electrical model of the electrolyzer is therefore needed, as well as an accurate design of the power converter stage, aiming at a high-efficiency operation of the electrolyzer’s cells and at a low-ripple supply current, to avoid premature degradation. This work investigates a full-bridge step-down isolated DC-DC converter, focusing on the opportunity of a multiphase interleaved configuration, particularly convenient for the proposed application. The considered maximum power level is 400 kW, representing a small-scale example of an industrial water electrolyzer supplied by a maximum DC voltage of 700 V. Input DC voltage is 7 kV. Power electronics’ simulation have been carried out, as well as model analysis of the proposed converter.
PHENOLIC COMPOUNDS CONTENT, ANTIOXIDANT, ANTIBACTERIAL AND ANTIFUNGAL ACTIVITIES OF RED ONIONS SKIN

This study was aimed to search and identify potentially active antibacterial, anti-oxidants, and antifungal compounds in red onion skin. Phenolic-rich onion extract was obtained from red onion skin extracted with methanol 70% for 2 h. The extract's antioxidant, antibacterial and antifungal activities were determined. The extract demonstrated the maximum DPPH radical scavenging activity (96% ±3.8) at 2000 g / mL, according to the findings of this study. Anthocyanin concentration, total soluble solids (TSS), total phenolics (TPCs), and, total flavonoids (TFs) in tested sample can all be linked to antioxidant ability. For all microorganisms tested, the minimum inhibitory concentration (MIC) of the tested sample was 200 g/mL. A red onion skin methanolic extract obtained ultimate suppression at 1000 g/mL after 7 days of incubation at 25 °C, equating to a 77% linear growth reduction of Fusarium graminearum. As a result, the outer skin of the red onion could be employed in nutritional fortification or nutraceutical preparations to improve health outcomes.
Studies on wound healing activity of red and block coloured seed, white coloured seed extracts of abrus precatorius l.

In the present study deals with the wound healing activity of Red and Black coloured seed, White colour seed of Abrus precatorius L. (Fabaceae). The seed crude extract and methanol insoluble fractions of white form resulted in early wound healing activity in with and without infection. This wound healing activity due to the presence of gums, mucilages, tannins or phenolic compounds in the seeds. The results of the antimicrobial studies are promising and further support the effectiveness of the seed extracts and fraction in controlling the infection in vivo.
A Reusable Interposer for Multi-Chiplet Microsystems Reconfiguration

The advantages of Chiplet cannot be fully exploited in the microsystem for the lack of reconfiguration technology. A reusable interposer is proposed for the functional reconfiguration of multi-Chiplet microsystems, which is integrated with a signal routing Chiplet for dynamically adjusting the path between Chiplets and 3D heterogeneous interconnection (TSV and RDL) for signal transmission. The signal routing Chiplet is analyzed with an ABCD matrix. The insertion loss is less than 5 dB and isolation is greater than 45 dB from DC to 8 GHz. The interposer has huge potential for microsystem reconfiguration and miniaturization.