Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of a low-cost and low-power environmental monitoring system

IoT is a revolutionary technology that represents the future of computing and communications. The paper focuses on the characterization of a Wireless Sensor Network used for smart agriculture, which is one of the new frontiers of IoT. Smart farming is the application of information and data technologies for optimizing complex farming systems. WSNs are widely used to measure agri-related information like temperature, humidity, solar radiation, presence of pollution and so on. In this paper the design of a wireless mesh network composed by low cost and low power system-on-a-chip is presented dealing with the frequently encountered issues of wireless networks. In particular, this work proposes a framework for the characterization of customized devices designed for monitoring networks: different types of test are performed in order to analyze the behavior of the components. The work focuses on the maximum coverage distance which is a critical parameter in agriculture field since the largeness of the involved area; it also deals with the thermal characterization to improve reliability performances and ADC calibration to ensure high accuracy of the acquired data. Moreover, the paper pays great attention on the power consumption of the sensor node, in fact the minimization of the current consumption of the nodes is a mandatory requirement to ensure low maintenance costs.
Standardizing Performance Measurement of Dye-Sensitized Solar Cells for Indoor Light Harvesting

During the last five years the demand of indoor photovoltaic (IPV) technology is growing rapidly for the Internet of Things. Until now, there is no standardized measurement methodology for IPV devices. So, it is very hard to estimate device efficiency accurately in indoor illuminating condition. This is one of the main obstacles for the commercialization of IPV devices. A standardized universal measurement methodology is highly needed. Therefore in this study, a series of N719 dye based dye-sensitized photovoltaic (PV) cells have been fabricated by varying the working electrode thickness. Then, a low irradiance measurement system has been configured to develop an ideal indoor environment with diffused light by adjusting the distance (1.4 m) between the test cell and light source. Furthermore, the inner wall of the measurement system has been covered by white paper. PV cells are tested under the illumination of three different light sources such as cold cathode fluorescent lamp (CCFL), cool white LED (LED 5600 K), and warm white LED (LED 3200 K)), at the same irradiance power intensity ( $150~\mu \text{W}$ /cm2) and luminance (500 Lux) values, to standardize the measurement methodology for PV cells in indoor environment. This study shows that, the response of a PV cell to different light sources can be realized more accurately, if the PV cell is tested under the illumination of different light sources at a fixed irradiance power intensity value instead of a fixed luminance value.
Transcriptional Regulators and Human-Specific/Primate-Specific Genes in Neocortical Neurogenesis

During development, starting from a pool of pluripotent stem cells, tissue-specific genetic programs help to shape and develop functional organs. To understand the development of an organ and its disorders, it is important to understand the spatio-temporal dynamics of the gene expression profiles that occur during its development. Modifications in existing genes, the de-novo appearance of new genes, or, occasionally, even the loss of genes, can greatly affect the gene expression profile of any given tissue and contribute to the evolution of organs or of parts of organs. The neocortex is evolutionarily the most recent part of the brain, it is unique to mammals, and is the seat of our higher cognitive abilities. Progenitors that give rise to this tissue undergo sequential waves of differentiation to produce the complete sets of neurons and glial cells that make up a functional neocortex. We will review herein our understanding of the transcriptional regulators that control the neural precursor cells (NPCs) during the generation of the most abundant class of neocortical neurons, the glutametergic neurons. In addition, we will discuss the roles of recently-identified human- and primate-specific genes in promoting neurogenesis, leading to neocortical expansion.
Drug Review: Ibrutinib

Abstract Ibrutinib is an irreversible BTK inhibitor, characterized by high selectivity and potency. It has revolutionized the therapy of B-cell lymphomas, especially chronic lymphocytic leukemia (CLL) and mantle cell lymphoma. Importantly, it has expanded the armamentarium for those patients who are refractory to conventional chemoimmunotherapy. This small-molecule inhibitor has shown efficacy in this difficult-to-treat subset – those with del(17p)/TP53-mutated CLL. Its immunomodulatory properties make it an excellent choice for combining with other immunotherapeutic agents such as venetoclax. The drug is not without drawbacks. The need for indefinite therapy and the presence of adverse effects such as infection, bleeding, hypertension, and arrhythmia temper our enthusiasm for this versatile drug. But overall, ibrutinib's favorable risk profile and lack of myelosuppression make it an ideal therapy for the elderly and those with multiple comorbidities.
A CNN-LSTM Model for Traffic Speed Prediction

Increasingly serious traffic congestion requires an accurate and timely traffic speed prediction, which will significantly benefit both individual drivers and decision makers in travel planning and traffic management. However, traffic speed prediction is a long-standing and challenging topic. Due to the availability of traffic datasets and powerful computation resources, deep learning becomes a promising solution to this problem. In this paper, based on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, we propose a model named CLM, which is the first to make use of CNN to extract the features of daily and weekly periodicity of traffic speed at the target area and also extract the spatiotemporal features together with the output of CNN by LSTM layers. We conduct comprehensive simulations to assess the performance of our proposed method based on the real-world dataset of Hong Kong. The results indicate that our proposed CLM model can better predict traffic speed in different forecast time periods than the other five competing methods, including SVR, MLP, Lasso, Random forest, and LSTM.
Dietary supplements of methionine increase breast meat yield and decrease abdominal fat in growing broiler chickens

The response of male broiler chickens to graded amounts of an essential amino acid (methionine) or an amino acid analogue (methionine hydroxy analogue free acid, MHA-FA) was examined in this experiment. The effects of each supplement on food intake, food conversion and the masses of a demand tissue (breast meat) and an indicator of body fat (abdominal fat pad) were measured. Birds grew significantly faster, ate more and had a lower food conversion ratio when there was more DL-methionine or MHA-FA in the diet. After standardising the differences in food intake between treatments, there were still highly significant differences in liveweight between diets. After removing the variation due to treatment differences in body mass, the data showed that adding either DL-methionine or MHA-FA to the diet increased the mass of breast meat and reduced the size of the abdominal fat pad. Exponential equations fitted to the body mass and feed conversion ratio data indicate that, in an equimolar comparison, MHA-FA is 78 and 70% as potent as DL-methionine for growth and feed conversion ratio respectively. Similar equations fitted to the carcass data show that MHA-FA is only 71% as potent as DL-methionine in depositing breast tissue. Increasing amounts of DL-methionine reduced the variability in liveweight and carcass, breast and abdominal fat pad weights. Adding 0.05% MHA-FA reduced variability in these traits compared with the basal ration but higher amounts of MHA-FA had no additional effect.
Energy-Efficient Precoder Design for URLLC-Enabled Downlink Multi-User MISO Networks Using Finite Blocklength Codes

One of the key applications in the fifth generation (5G) communication systems is to support extremely high reliability $(\sim 99.999$%) and low latency $(\lt 1$ ms), namely ultra-reliable and low-latency communication. In this paper, we consider the problem of maximizing energy efficiency (EE) for downlink multi-user multiple-input single-output (MISO) networks under short packet transmission. An optimization problem is formulated to jointly optimize the precoders at the base station (BS) for serving multiple downlink users and the error probability with finite blocklength (FBL) codes, subject to the constraints on decoding error probability per URLLC user and on the BS transmit power. Since the formulated problem is non-convex, we convert this problem into a convex one by analyzing the structure of the EE objective. We then propose an algorithm to find a near-optimal solution for maximizing the EE. Simulation results validate the effectiveness of the proposed algorithm that supports energy-efficient URLLC.
Codebook Performance Evaluation of mmWave in Train Communications

Improved coverage, high data rates and Quality of Service (QoS) constitute important features of 5G networks. The spectrum limitations, currently faced in 4G, will be resolved by adapting millimeter-Wave (mmWave) frequencies technologies, which will also result in high throughput. High mobility scenarios will pose additional challenges, in 5G communications, due to the rapid change in the environment, i.e. channel coefficients and Doppler effect. In this paper, we introduce our research on the achievable throughput, coverage and Signal-to-Noise-Ratio (SNR), considering a High-Speed Train (HST) traveling on two different rail scenarios in the UK. Applying two beamforming (BF) techniques (maximum ray selection and codebook based) and employing a fixed beam pattern in the antennas, we compare the performance of the different schemes, considering different distances between the Access Points (APs) at the rail track, looking also at the minimum network requirements to maintain connection. We show that overall the employment of BF improves the achievable throughput and SNR, with the codebook based approach achieving constant coverage with stable throughput in a 300m radius mmWave cell.
Neural Networks for Intrusion Detection

This paper presents Intrusion Detection Systems (IDS), Intrusion Detection and Prevention Systems (IDPS) and their classification emphasizing on the use of neural networks in IDS. Contemporary IDS usually include both signature verification and anomaly detection approaches realized by rule-based expert system and statistical module correspondingly. Neural networks may be used mainly as additional module to the statistical module to better recognize the user behavior. User behavior may be represented as frequency pattern of users command history. The paper presents an example of user profile vector for Unix-based platforms.
Optimal Energy Allocation Against Denial-of-Service Attack in Cache-enabled Wireless Networks

In this paper, the security issue of cache-enabled wireless networks is considered, where data transmission from the small base station (SBS) to the user is interfered by denial-of-service (DoS) attacks. To deal with this type of DoS attacks, we firstly investigate the energy dispatch problem from the perspective of the SBS. We further establish an optimization model by using the average number of subfiles requested from the macro base station (MBS) to measure the system performance, and then present the corresponding algorithm to derive the optimal scheduler. Additionally, numerical simulations are carried out to validate the effectiveness of the proposed algorithm.
Radio Environment Maps for Dynamic Frequency Selection in V2X Communications

In this paper, we investigate the concept of database supported Vehicular Dynamic Spectrum Access (VDSA) for platooning. As various researchers show that the 5.9 GHz band, devoted for Intelligent Transportation Systems, may suffer from congestion of the channel, we propose to offload part of this traffic to white-spaces with the guidance of the active database system. In our work, we describe our measurement campaign which delivered data for population of the dedicated radio environment map. Once the map is created, it was used in three proposed algorithms for VDSA: an optimal and two pragmatic approaches.
Enhancement-Mode InAlN/GaN Power MOSHEMT on Silicon With Schottky Tri-Drain Extension

This study demonstrates an enhancement-mode (E-mode) InAlN/GaN MOSHEMT on a silicon substrate with a Schottky tri-drain extension (STDE) to yield a large breakdown voltage (<inline-formula> <tex-math notation="LaTeX">$\text{V}_{\text {BD}}$ </tex-math></inline-formula>) while maintaining a low on-resistance (<inline-formula> <tex-math notation="LaTeX">$\text{R}_{\text {on}}$ </tex-math></inline-formula>). The E-mode operation is realized by a recessed tri-gate nanowire structure. The STDE functions as a drain-connected field plate (FP) to effectively distribute the electric field (E-field) around the drain contact edge, improving the <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\text {BD}}$ </tex-math></inline-formula>. Moreover, through the metal of the STDE directly contacting the 2-D electron gas (2-DEG) from the sidewalls, a low specific <inline-formula> <tex-math notation="LaTeX">$\text{R}_{\text {on}}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$\text{R}_{\text {on, sp}}$ </tex-math></inline-formula>) is achieved. The proposed device with a gate-to-drain length (<inline-formula> <tex-math notation="LaTeX">$\text{L}_{\text {GD}}$ </tex-math></inline-formula>) of <inline-formula> <tex-math notation="LaTeX">$5~\mu \text{m}$ </tex-math></inline-formula> exhibits a threshold voltage (<inline-formula> <tex-math notation="LaTeX">$\text{V}_{\text {TH}}$ </tex-math></inline-formula>) of +0.9 V, large maximum drain current (<inline-formula> <tex-math notation="LaTeX">$\text{I}_{\text {D, max}}$ </tex-math></inline-formula>) of 815 ± 27 mA/mm, high <inline-formula> <tex-math notation="LaTeX">$\text{I}_{\text {on}}/\text{I}_{\text {off}}$ </tex-math></inline-formula> ratio of 10<sup>10</sup>, steep subthreshold swing (SS) of 67 mV/decade, superior <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\text {BD}}$ </tex-math></inline-formula> of 830 V, and low <inline-formula> <tex-math notation="LaTeX">$\text{R}_{\text {on, sp}}$ </tex-math></inline-formula> of 0.74 ± 0.04~\text{m}\Omega \cdot cm<sup>2</sup>. With <inline-formula> <tex-math notation="LaTeX">$\text{L}_{\text {GD}}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$10~\mu \text{m}$ </tex-math></inline-formula>, a <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\text {BD}}$ </tex-math></inline-formula> of 1190 V is achieved, corresponding to a <inline-formula> <tex-math notation="LaTeX">$\text{R}_{\text {on, sp}}$ </tex-math></inline-formula> of 1.39 ± <inline-formula> <tex-math notation="LaTeX">$0.07~\text{m}\Omega \cdot$ </tex-math></inline-formula> cm<sup>2</sup>. These results reveal great potential for future E-mode power device applications.
Estimation of Sea Level Variability in the China Sea and Its Vicinity Using the SARIMA and LSTM Models

With a gradually rising global average sea level, it is of great significance to predict changes in the sea level. However, sea level variations often exhibit both linear and nonlinear characteristics, complicating the prediction of sea level changes with a single model. The seasonal autoregressive integrated moving average (SARIMA) model can fully consider the linear characteristics of time series, but its nonlinear prediction ability is poor; the long short-term memory (LSTM) model can compensate for this shortcoming. To predict complex sea level changes, we propose a strategy to combine the SARIMA and LSTM models to increase the sea level prediction accuracy. In our method, sea level anomaly (SLA) time series are decomposed into the trend and seasonal term and random term; then, the SARIMA model is used to predict the trend and seasonal term of sea level variations, whereas the random term is predicted by LSTM. Sea surface height data from 1993 to 2018 are used in an experiment. Compared with other models, the performance of the SARIMA+LSTM model is superior in predicting sea level changes with a minimum root mean square error of 1.155 cm and a maximum determination coefficient (R2) of 0.89 during the testing period. Furthermore, the predicted results are in close agreement with the SLA data, which indicates that the SARIMA+LSTM model could be successfully used for the estimation of sea level variability.
Association of Phytophthora with Declining Vegetation in an Urban Forest Environment

Urban forests consist of various environments from intensely managed spaces to conservation areas and are often reservoirs of a diverse range of invasive pathogens due to their introduction through the nursery trade. Pathogens are likely to persist because the urban forest contains a mixture of native and exotic plant species, and the environmental conditions are often less than ideal for the trees. To test the impact of different land management approaches on the Phytophthora community, 236 discrete soil and root samples were collected from declining trees in 91 parks and nature reserves in Joondalup, Western Australia (WA). Sampling targeted an extensive variety of declining native trees and shrubs, from families known to be susceptible to Phytophthora. A sub-sample was set aside and DNA extracted for metabarcoding using Phytophthora-specific primers; the remaining soil and root sample was baited for the isolation of Phytophthora. We considered the effect on the Phytophthora community of park class and area, soil family, and the change in canopy cover or health as determined through sequential measurements using remote sensing. Of the 236 samples, baiting techniques detected Phytophthora species from 24 samples (18 parks), while metabarcoding detected Phytophthora from 168 samples (64 parks). Overall, forty-four Phytophthora phylotypes were detected. Considering only sampling sites where Phytophthora was detected, species richness averaged 5.82 (range 1–21) for samples and 9.23 (range 2–24) for parks. Phytophthora multivora was the most frequently found species followed by P. arenaria, P. amnicola and P. cinnamomi. While park area and canopy cover had a significant effect on Phytophthora community the R2 values were very low, indicating they have had little effect in shaping the community. Phytophthora cinnamomi and P. multivora, the two most invasive species, often co-occurring (61% of samples); however, the communities with P. multivora were more common than those with P. cinnamomi, reflecting observations over the past decade of the increasing importance of P. multivora as a pathogen in the urban environment.
Undergraduate support for university smoke-free and vape-free campus policies and student engagement: a quasi-experimental intervention

Abstract Background College campuses have policies restricting smoking/vaping on campus. Previous studies involving mostly European-American students showed smoking prevalence declines following implementation of such policies. Objective To evaluate a social media campaign promotive of stronger campus support for an existing campus no-smoking/no-vaping policy where most (∼75%) of the undergraduates were non-European-American. A demographically comparable university served as a no-intervention control. Participants Target was 200 random intercept surveys at each university during fall 2016, spring 2017. Of 800 respondents, 681 were undergraduates. Methods Baseline and post-intervention surveys assessed awareness of and support for campus-wide smoke-free/vape-free policies. Staged smoke-free/vape-free policy violations assessed students’ propensity to intervene in support of the policy. Results Respondent support for the no-smoking/no-vaping policy did not change. Conclusions The social media campaign and Policy Ambassadors program did not increase support for the campus no-smoking/no-vaping policy. Most (∼90%) respondents agreed that the campus no-smoking/no-vaping policy was important for public health.
Association between the Door-to-balloon Time and Mid-term Clinical Outcomes in Patients with ST-Segment Elevation Myocardial Infarction

Objective In primary percutaneous coronary intervention (PCI), the door-to-balloon time (DTBT) is known to be associated with in-hospital death in patients with ST-segment elevation myocardial infarction (STEMI). However, little is known regarding the association between the DTBT and the mid-term clinical outcomes in patients with STEMI. The purpose of this study was to investigate the association between the DTBT and mid-term all-cause death. Methods The study population included 309 STEMI patients, who were divided into the short DTBT (DTBT<60 minutes, n=103), intermediate DTBT (DTBT 60-120 minutes, n=174) and long DTBT (DTBT >120 minutes, n=32) groups. The median follow-up period was 287 days (interquartile range: 182-624 days). Results The incidence of all-cause death in the long DTBT group was significantly higher in comparison to the other groups (p<0.001). In the multivariate Cox regression analysis, although a short DTBT [vs. intermediate DTBT: hazard ratio (HR) 1.00, 95% confidence interval (CI) 0.39-2.55, p=0.99] was not associated with all-cause death, a long DTBT (vs. intermediate DTBT: HR 2.80, 95% CI 1.26-6.17, p=0.011) was significantly associated with all-cause death, after controlling for confounding factors such as Killip class 4, an impaired renal function, and the number of diseased vessels. Conclusion The DTBT was significantly associated with the incidence of mid-term all-cause death. Our results support the strong adherence to the DTBT in patients with STEMI.
Prevalence and risk factors associated with systemic hypertension in dogs with spontaneous hyperadrenocorticism

Abstract Background Systemic hypertension (SH) is common in dogs with hyperadrenocorticism (HAC) however there are not many studies assessing its prevalence and risk factors. Objectives To determine the prevalence and severity of SH in dogs with HAC and its association with clinical and laboratory findings to identify potential risk factors. Animals Sixty‐six client owned dogs with spontaneous HAC. Methods Retrospective cross‐sectional study. Medical records of dogs with HAC were reviewed. Systolic blood pressure (SBP) was measured using Doppler ultrasonography. Clinical signs, physical examination findings and clinicopathologic data (CBC, serum biochemistry and electrolytes, urinalysis and urinary culture, and adrenal function tests) were reviewed for analysis. Results Prevalence of SH (≥150 mm Hg) was 82% (54/66) and prevalence of severe SH (≥180 mm Hg) was 46% (30/66). All dogs with thrombocytosis had SH (P = .002), and a platelet count ≥438 × 103/μL was 100% specific and 61.1% sensitive to predict SH (AUC = .802, P = .001). Median potassium levels were lower in hypertensive dogs (4.1 mEq/L, range 3.1‐5.4 mEq/L) than in normotensive ones (4.5 mEq/L, range 4.0‐5.0 mEq/L) (P = .007). Dogs with UPC ≥ 0.5 had higher median SBP than those without proteinuria (P = .03). Dogs with concurrent diabetes mellitus seemed to have a reduced risk of SH (OR = .118, 95%CI = .022‐.626, P = .02). Conclusions and Clinical Importance Systemic hypertension is common in dogs with HAC and is frequently severe. Blood pressure should be routinely assessed in these dogs, especially if thrombocytosis, proteinuria or low potassium concentrations are present.