Miracle-dz/UniFAR-FTUs huggingface dataset:
How to Mislead AI-Assisted Network Automation in SD-IPoEONs: A Comparison Study of DRL- and GAN-Based Approaches

Recently, the combination of artificial intelligence (AI) and software-defined networking (SDN) has attracted intensive research interests because it realizes and promotes AI-assisted network automation (AIaNA). Despite the initial successes of AIaNA, its vulnerabilities, i.e., the downside of the reduction of human involvement achieved by it, have not been carefully explored. In this work, we use software-defined IP over elastic optical networks (SD-IPoEONs) as the background, and study how to mislead the AIaNA system in them. Specifically, we target our attack on the deep neural network (DNN) based traffic predictor in the AIaNA system, and design an adversarial module (ADVM) that can craft and inject adversarial traffic samples adaptively to disturb its operation. We consider two approaches to design the ADVM, i.e., the deep reinforcement learning (DRL) based on deep deterministic policy gradient (DDPG), and the generative adversarial network (GAN) model. Our proposed ADVM can monitor and interact with a dynamic SD-IPoEON to train itself on-the-fly. This enables it to generate and inject adversarial samples in the most disturbing and hard-to-detect way and to severely affect the AIaNA's performance on multilayer service provisioning. Specifically, IP flows will be served incorrectly to result in unnecessary congestions/under-utilizations on lightpaths, and erroneous network reconfigurations will be invoked frequently. Simulation results confirm the effectiveness of our ADVM designs, and show that the GAN-based ADVM achieves better attack effects with smaller perturbation strength.
Experimental Study on Short-Channel Effects in Double-Gate Silicon Carbide JFETs

Short-channel effects (SCEs) in double-gate silicon carbide junction field-effect transistors (JFETs) fully fabricated by ion implantation are experimentally investigated. The threshold voltage shift, drain-induced barrier lowering, and subthreshold slope degradation are clearly observed in the fabricated p- and n-JFETs. The SCEs are quantitatively evaluated by comparing with the theoretical values obtained by solving a 2-D Poisson equation, which shows good agreement with experiments. The dominant parameter for the SCEs in JFETs is the ratio of the channel length (<inline-formula> <tex-math notation="LaTeX">${L}$ </tex-math></inline-formula>) to the channel thickness (<inline-formula> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula>), and the device scaling rule to avoid the SCEs is estimated to be <inline-formula> <tex-math notation="LaTeX">${L}/{a} > {3}$ </tex-math></inline-formula>.
Association between obesity after liver transplantation and steatosis, inflammation, and fibrosis of the graft

Nonalcoholic steatohepatitis has become one of the leading causes of liver transplantation. The development of steatosis, as well as the link to inflammation and fibrosis, after transplantation remain poorly understood. The aim of this analysis was to evaluate the influence of obesity on histopathological changes of the graft during long‐term follow‐up.
Matrix Fuzzy-Probabilistic Neural Network in Image Recognition Task

The fuzzy matrix probabilistic neural network (FMPNN) designed to solving image classification tasks where images are represented in matrix form is proposed. This neuro-fuzzy system has four layers of data processing which are fed into the system in data stream form on the "sliding window". It is supposed that formed classes of data-images can be arbitrarily overlapped in feature space creating a situation of fuzziness. The tuning of FMPNN is the combination of learning procedures such as "Lazy learning", "Winner takes all", "Learning vector quantization", "Fuzzy C-means clustering". The feature of the proposed fuzzy classification - image recognition system is the high speed of data processing allowing to process data within the concept of Data Stream Mining.
Course Selection Optimization: Case study - Faculty of Science, University of Peradeniya, Sri Lanka

Elective course selection at universities is a complex decision process which is subjective to each individual student’s personality and skill set. The aim of this research is to use machine learning techniques and expert knowledge to suggest optimal course selections by considering the student skills (profile of the student) and the profiles of the courses offered at the university. It takes into consideration the fact that if a student is performing well in a particular course, he/she can select another course of the same nature to improve the student’s results and give a solution to the daunting task of selecting elective courses. The K-Nearest Neighbour algorithm resulted in ten course clusters for the dataset and accordingly students were grouped using the highest average course cluster GPA. Application of the expert knowledge method resulted in course clusters which can be split into clusters as stipulated by the Faculty. The approach was validated for computer science courses offered at the Faculty of Science, University of Peradeniya, Sri Lanka, as a case study from 2005 to 2012.
Population ecology of loblolly pine Pinus taeda in an old field community

Density, height, diameter, and age structure of Pinus taeda L. were examined in a 21-year-old abandoned field. A density estimate of 924 trees ha-1 was obtained, a factor of 17 over that found for the area 14 years earlier. Age frequencies resembled an inverse J-shaped depletion model, with 4.3 % of all trees examined being in the 1-year age class. Median ecological longevity was estimated to be 3.2 years. Linear correlations between age, height, and dbh data were also determined.
Computational Intelligence Enabled Cybersecurity for the Internet of Things

The computational intelligence (CI) based technologies play key roles in campaigning cybersecurity challenges in complex systems such as the Internet of Things (IoT), cyber-physical-systems (CPS), etc. The current IoT is facing increasingly security issues, such as vulnerabilities of IoT systems, malware detection, data security concerns, personal and public physical safety risk, privacy issues, data storage management following the exponential growth of IoT devices. This work aims at investigating the applicability of computational intelligence techniques in cybersecurity for IoT, including CI-enabled cybersecurity and privacy solutions, cyber defense technologies, intrusion detection techniques, and data security in IoT. This paper also attempts to provide new research directions and trends for the increasingly IoT security issues using computational intelligence technologies.
Management of powdery mildew in mulberry using coccinellid beetles, Illeis cincta (Fabricius) and Illeis bistigmosa (Mulsant)

Two species of coccinellid beetles, Illeis cincta (Fabricius) and Illeis bistigmosa (Mulsant) feeding on the fungus, Phyllactinia corylea (Pess.) causing powdery mildew of mulberry were recorded in the mulberry garden of College of Agriculture, Vellayani. Studies on the efficiency of these coccinellids revealed that the treatment where the plants in which 5 pairs of adults were released showed less infection by powdery mildew and was at par with those plants on which 4 pairs, 3 pairs and 2 pairs of coccinellids were released. The percentage disease control was also highest with coccinellids than fungicides and neem oil spray.
Reconfigurable Radiation Pattern Antenna with eight Switchable Beams in Azimuth Plane for WLAN Wireless System

This work presents a reconfigurable radiation pattern antenna for WLAN wireless system. The reconfigurable antenna is fed by a coaxial cable, and consists of a star patch, eight hexagon-shaped radiation cells and a circular planar ground. The pattern-reconfigurable antenna proposed achieves eight reconfigurable states by controlling the ON and OFF of eight PIN diodes to change the direction. The state change is performed by PIN diodes connected in the gap between the central star patch and the radiation cells. The antenna has a bandwidth of 360 MHz and can steer the beam in the direction 20°, 340° in azimuth plane. The antenna main beam can be switched to eight directions in elevation plane. The simulated results are also presented and investigated.
3D Tumor Detection in Automated Breast Ultrasound Using Deep Convolutional Neural Network.

PURPOSE Automated breast ultrasound (ABUS) has drawn attention in breast disease detection and diagnosis applications. Reviewing hundreds of slices produced by ABUS is time consuming. In this paper, a tumor detection method for ABUS image based on convolutional neural network is proposed. METHODS First, integrating multi-task learning with YOLOv3, an improved YOLOv3 detection network is designed to detect tumor candidate in 2D slices. 2D detection separately treats each slice, leading to larger differences of position and score for tumor candidate in adjacent slices. Due to the influence of artifact, noise and mammary tissues, 2D detection may include many false positive regions. To alleviate these problems, a re-scoring processing algorithm is first designed. Then 3D volume forming and FP reduction scheme are built. RESULTS This method was tested on 340 volumes (124 patients, 181 tumors) with 5-fold cross validation. It achieved sensitivities of 90%, 85%, 80%, 75% and 70% at 7.42, 3.31, 1.62, 1.23 and 0.88 false positives per volume. CONCLUSION Compared with existing ABUS tumor detection methods, our method gets a promising result.
Treatment of transplant kidneys during machine perfusion

The increasing use of donation after circulatory death (DCD) and extended criteria donor (ECD) organs has raised awareness of the need to improve the quality of kidneys for transplantation. Treating kidneys during the preservation interval could improve early and long‐term graft function and survival. Dynamic modes of preservation including hypothermic machine perfusion (HMP) and normothermic machine perfusion (NMP) may provide the functional platforms to treat these kidneys. Therapies in the field of regenerative medicine including cellular therapies and genetic modification and the application of biological agents targeting ischaemia reperfusion injury (IRI) and acute rejection are a growing area of research. This review reports on the application of cellular and gene manipulating therapies, nanoparticles, anti‐inflammatory agents, anti‐thrombolytic agents and monoclonal antibodies administered during HMP and NMP in experimental models. The review also reports on the clinical effectiveness of several biological agents administered during HMP. All of the experimental studies provide proof of principle that therapies can be successfully delivered during HMP and NMP. However, few have examined the effects after transplantation. Evidence for clinical application during HMP is sparse and only one study has demonstrated a beneficial effect on graft function. More investigation is needed to develop perfusion strategies and investigate the different experimental approaches.
Can Brain Health Be Supported by Vitamin D-Based Supplements? A Critical Review

This review presents recent knowledge on the neuroprotective effects of vitamin D and their usefulness as oral supplementation when combined with other molecules, such as curcumin. A critical look at the effectiveness of vitamin D in this field is also provided. Vitamin D plays a crucial role in neuroprotection and in the cognitive decline associated with aging, where vitamin D’s levels are related to the levels of several neurotrophic factors. An important role of vitamin D has also been observed in the mechanism of neuroinflammation, which is the basis of several aging conditions, including cognitive decline and neurodegeration; furthermore, the neuroprotective effect of vitamin D in the cognitive decline of aging has recently been reported. For this reason, many food supplements created for humans contain vitamin D alone or combined with other molecules with antioxidant properties. However, recent studies also explored negative consequences of the use at a high dosage of vitamin D. Vitamin D in tissues or brain cells can also modulate calbindin-D28K, parvalbumin, and calretinin, and is involved in immune function, thanks also to the combination with curcumin. Curcumin acts as a free radical scavenger and antioxidant, inhibiting lipid peroxidation and oxidative DNA damage. In particular, curcumin is a potent immune-regulatory agent and its administration has been reported to attenuate cognitive impairments. These effects could be exploited in the future to control the mechanisms that lead to the brain decay typical of neurodegenerative diseases.
Subclinical hypothyroidism in pregnancy: controversies on diagnosis and treatment.

The negative impact of even subtle maternal thyroid hormone deficiency on pregnancy outcome and intellectual development of progeny has been known for many years but unfortunately the diagnosis and treatment of subclinical hypothyroidism in pregnant women still evokes controversies. Due to physiological changes in thyroid function and thyroid hormones metabolism occuring during pregnancy the trimester-specific reference ranges for thyroid-stimulating hormone (TSH) and free thyroid hormones should be established, but because of inter-assay differences and other confounders including ethnicity and iodine intake such norms are reliable only for local populations and unique laboratory method. In turn, the fixed reference ranges suggested by endocrine societies may carry a risk of misclassification some healthy pregnant women to be hypothyroid. The value of levothyroxine treatment on pregnancy and children cognitive outcomes is not clear. Therapeutic benefits in decreasing miscarriage and preterm delivery rates were observed when intervention was held in the first trimester in women with TSH level 2.5-10 mU/l, mainly ≥4.0 mU/l. The possible harmful effect of treatment includes preterm delivery, gestational diabetes, hypertension and pre-eclampsia. The only three prospective, randomized, placebo controlled trials evaluating efficacy of levothyroxine therapy on children intelligence quotient were started in the second trimester, may be too late to demonstrate differences between treatment and placebo groups. Awaiting for the results of future trials clinician should be aware that low-dose levothyroxine 25-50 µg daily is probably not harmful and may be beneficial but mechanistic implementation of therapy in each pregnant women with TSH >2.5mU/l seems too simplistic.
Stiffness of the interface between a colloidal body-centered cubic crystal and its liquid

Significance This paper reports the experimental determination of the stiffness of the interface between a body-centered cubic crystal and its liquid using colloids controlled by dielectrophoresis in an electric bottle. That the measured value is lower than that of alternative interfaces supports the phenomenological explanation invoked for a long-standing observation in phase transformations: the preferential nucleation of body-centered cubic crystals in the solidification of metal alloys. Equilibrium interfaces were established between body-centered cubic (BCC) crystals and their liquid using charged colloidal particles in an electric bottle. By measuring a time series of interfacial positions and computing the average power spectrum, their interfacial stiffness was determined according to the capillary fluctuation method. For the (100) and the (114) interfaces, the stiffnesses were 0.15 and 0.18 kBT/σ2 (σ: particle diameter), respectively, and were isotropic in the plane of the interface. For comparison, similar charged colloids were used to create an interface between a face-centered cubic (FCC) crystal and its liquid. Its stiffness was significantly larger: 0.26 kBT/σ2. This result gives experimental support to the explanations offered for the preferential nucleation of BCC over FCC in metallic alloys.
Physiological ecology of the low temperature range limit of European deciduous tree species

Low temperature is one of the most important factors that can exert the overarching pattern in species distribution of major European deciduous tree taxa along latitudinal and elevational gradients. Apart from low temperature, time could be an important constraint for tree species to complete growth and fully harden tissues in late summer/autumn before the occurrence of freezing temperatures. As part of the ERC-project ‘TREELIM’ the present PhD thesis aimed to assess the effect of (1) low temperature extremes, (2) mean temperatures during the growing season and (3) length of the growing season on climatic boundaries of eight major European deciduous tree species. In detail, we investigated the freezing resistance of buds and leaves before, during, and after the period of leaf emergence at the upper elevational limits of these tree species in the Swiss Alps. Further, we assessed the secondary growth of these taxa over the last 30 years using tree cores collected along elevational gradients. We aimed at disentangling the effects of temperature and time on tree growth and survival, by combining freezing resistance and tree growth measurements with spring phenology. In all investigated species, freezing resistance of buds was tightly correlated with the advancement of spring phenology (bud development). Interestingly, species’ spring phenology is perfectly adapted to escape damaging freezing temperatures to leaves, irrespective of elevation. Thus, more freezing resistant tree species can flush earlier and extend their growing season in spring. The elevational time-shift of bud burst allows species to minimise the risk of freezing damage in spring at the cost of a shorter duration of the growing season at higher elevations. Tree-ring data revealed no change in growth along a large part of the studied elevational gradient, with a rather abrupt growth decline towards species’ elevational limits. Interestingly, mean temperatures during the growing season have a strong effect on tree growth independent of the length of the growing season, while the length of the growing season becomes only effective at warmer temperature. This inconsistency might be explained by the over-proportional increase of tissue formation with warmer temperature. An increasing length of time enables many cell divisions at warm temperature, whereas only few additional cell divisions are possible at cold temperature. Interestingly, the physiological low temperature limit for any tissue formation correlates with species’ elevational limits. Nevertheless, tree species are not growth limited at their elevational limit. We rather suggest that freezing resistance, the associated timing of spring phenology, and the resulting length of the growing season jointly reflect species-specific life history requirements that control tree species cold range limits.
Effects of patient orientations, landmark positions, and device positions on the MRI RF‐induced heating for modular external fixation devices

This paper studies the RF‐induced heating for modular external fixation devices applied on the leg regions of the human bodies. Through numerical investigations of RF‐induced heating related to different patient orientations, landmark positions, and device positions under 1.5T and 3T MRI systems, simple and practical methods to reduce RF‐induced heating are recommended.
A Review of Maternal Nutrition during Pregnancy and Impact on the Offspring through Development: Evidence from Animal Models of Over- and Undernutrition

Similarities in offspring phenotype due to maternal under- or over-nutrition during gestation have been observed in studies conducted at University of Wyoming. In these studies, ewes were either nutrient-restricted (NR) from early to mid-gestation, or fed an obesogenic diet (MO) from preconception through term. Offspring necropsies occurred at mid-gestation, late-gestation, and after parturition. At mid gestation, body weights of NR fetuses were ~30% lighter than controls, whereas MO fetuses were ~30% heavier than those of controls. At birth, lambs born to NR, MO, and control ewes exhibited similar weights. This was a consequence of accelerated fetal growth rates in NR ewes, and reduced fetal growth rates in MO ewes in late gestation, when compared to their respective controls. These fetal growth patterns resulted in remarkably similar effects of increased susceptibility to obesity, cardiovascular disease, and glucose intolerance in offspring programmed mostly during fetal stages of development. These data provide evidence that maternal under- and over-nutrition similarly induce the development of the same cadre of physical and metabolic problems in postnatal life.
MiRNA Profiles of Extracellular Vesicles Secreted by Mesenchymal Stromal Cells—Can They Predict Potential Off-Target Effects?

The cardioprotective properties of extracellular vesicles (EVs) derived from mesenchymal stromal cells (MSCs) are currently being investigated in preclinical studies. Although microRNAs (miRNAs) encapsulated in EVs have been identified as one component responsible for the cardioprotective effect of MSCs, their potential off-target effects have not been sufficiently characterized. In the present study, we aimed to investigate the miRNA profile of EVs isolated from MSCs that were derived from cord blood (CB) and adipose tissue (AT). The identified miRNAs were then compared to known targets from the literature to discover possible adverse effects prior to clinical use. Our data show that while many cardioprotective miRNAs such as miR-22-3p, miR-26a-5p, miR-29c-3p, and miR-125b-5p were present in CB- and AT-MSC-derived EVs, a large number of known oncogenic and tumor suppressor miRNAs such as miR-16-5p, miR-23a-3p, and miR-191-5p were also detected. These findings highlight the importance of quality assessment for therapeutically applied EV preparations.
A Real-time QoS-Demand-Aware Computational Resource Sharing Approach in C-RAN

This paper presents a dynamic resource sharing approach aiming at optimizing computational resource performance of a baseband unit (BBU) pool in a cloud radio access network. Based on the bargaining concept in game theory, resource sharing is formulated as an optimization problem considering quality of service, real-time demand and the minimum resources that are required to prevent BBU crashes. The performance of the proposed model is evaluated in terms of BBU fulfilment level, resource usage and efficiency over time. Simulation results, for heterogeneous services in a tidal traffic environment, demonstrate that the proposed model allocates computational resources in proportion to the instantaneous demand of BBUs and the priority of the ongoing services. Results also show a minimum 97% enhancement in the efficiency of resource allocation in off-peak hours, compared to fixed allocation strategies based on peak-hour traffic demand.
Research on Electrical Characteristics of Multi-chip Itegration for the Fifth-Generation Communication

With the advent of the fifth-generation (5G) wireless communication era, the demand for low cost, lightweight, low profile and satisfied transmission performance packages is urgent. Microsystem integration technology based on silicon-based process can meet the miniaturization requirements of heterogeneous integration in 5G front-end applications. This paper focuses on the impedance matching for the impedance discontinuities mainly caused by bonding wires and through silicon vias. The matching structures are rationally designed based on the microwave transmission line theory and the structure parameters are reasonably optimized. Finally, the whole interconnection link with the use of designed matching models obtains a good transmission performance at millimeter wave frequencies.