Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymmetric Wake-up Scheduling based on Block Designs in Wireless Sensor Networks

In wireless sensor networks (WSNs) with symmetric duty cycles, a block design technique produces an optimal solution for neighbor discovery in terms of the worst-case discovery latency. However, block design-based neighbor discovery methods may not be applicable to WSNs with asymmetric duty operations. Thus, to address this lack of support of asymmetric WSNs, we propose a new neighbor discovery protocol (NDP) that combines two block designs for generating a set of discovery schedules. We prove that the discovery schedule generated by the proposed NDP includes at least one common active slot with any neighboring nodes within a single cycle. We also conduct a simulation study and show that the proposed NDP is better than representative NDPs such as U-Connect, Disco, SearchLight, Hedis, and Todis in terms of discovery latency and energy efficiency.
Screening of maize rhizobacteria against aflatoxigenic Aspergillus flavus strains in relation to siderophore and HCN production.

Fifty nine isolates of Aspergillus flavus and thirty nine bacterial isolates were collected from the rhizosphere soil of standing maize crop. Among the bacterial isolates, Bacillus circulans, B. pumilus, B. subtilis, Pseudomonas aeruginosa and P. putida showed antagonistic behaviour against toxigenic Aspergillus flavus in terms of growth and aflatoxin production. On further screening B. pumilus & P. aeruginosa exhibited strong (2+ ) siderophore activity whereas B. circulans and P. putida showed poor (+) and no activity (-) was recorded in case of B. subtilis. Similarly B. pumilus, P. aeruginosa and P. putida were found to have hydrogen cyanide (HCN) production ability whereas B. subtilis and B. circulans failed to produce it. Siderophore and HCN producing bacteria were found to respond against aflatoxin production.
Cursive Character Recognition in Natural Scene Images Using a Multilevel Convolutional Neural Network Fusion

The accuracy of current natural scene text recognition algorithms is limited by the poor performance of character recognition methods for these images. The complex backgrounds, variations in the writing, text size, orientations, low resolution and multi-language text make recognition of text in natural images a complex and challenging task. Conventional machine learning and deep learning-based methods have been developed that have achieved satisfactory results, but character recognition for cursive text such as Arabic and Urdu scripts in natural images is still an open research problem. The characters in the cursive text are connected and are difficult to segment for recognition. Variations in the shape of a character due to its different positions within a word make the recognition task more challenging than non-cursive text. Optical character recognition (OCR) techniques proposed for Arabic and Urdu scanned documents perform very poorly when applied to character recognition in natural images. In this paper, we propose a multi-scale feature aggregation (MSFA) and a multi-level feature fusion (MLFF) network architecture to recognize isolated Urdu characters in natural images. The network first aggregates multi-scale features of the convolutional layers by up-sampling and addition operations and then combines them with the high-level features. Finally, the outputs of the MSFA and MLFF networks are fused together to create more robust and powerful features. A comprehensive dataset of segmented Urdu characters is developed for the evaluation of the proposed network models. Synthetic text on the patches of images with real natural scene backgrounds is generated to increase the samples of infrequently used characters. The proposed model is evaluated on the Chars74K and ICDAR03 datasets. To validate the proposed model on the new Urdu character image dataset, we compare its performance with the histogram of oriented gradients (HoG) method. The experimental results show that the aggregation of multi-scale and multilevel features and their fusion is more effective, and outperforms other methods on the Urdu character image and Chars74K datasets.
Detection of the Diplodia shoot blight and canker pathogens from red and jack pine seeds using cultural methods

Abstract The shoot blight and canker pathogens Diplodia pinea and D. scrobiculata commonly and abundantly sporulate on seed cones of red pine (Pinus resinosa) and jack pine (P. banksiana) collected from Wisconsin and Minnesota forests. Cultural methods were used to investigate the incidence of these fungi in seed lots obtained from government nurseries in these states. In each of three replicate trials, seeds of each lot were assigned to four treatments before incubation on semi-selective medium: (1) not surface-disinfested; (2) surface-disinfested; (3) surface-disinfested, then inoculated with D. pinea conidia; or (4) not surface-disinfested, then inoculated with D. pinea conidia. For red pine seeds, the mean percentage positive was 2.7% for treatment 1 and 1.3% for treatment 2. Jack pine seeds were less frequently positive than red pine seeds for both treatments 1 and 2. The Diplodia species cultured was identified as D. pinea in almost every case. Diplodia pinea was much less frequently recovered from seeds that were not surface-disinfested and then inoculated (treatment 4), when compared with seeds that were inoculated with D. pinea after surface-disinfestation (treatment 3). Results confirm the potential for dissemination of D. pinea on red pine and jack pine seeds, and caution is warranted before concluding absence of the pathogen based on results using cultural methods with relatively small numbers of seeds. Although the frequency of pathogen-positive seeds was low, the large numbers of seeds planted in nurseries suggest that seeds may be a potentially important route of entry of D. pinea into nursery beds.
Joint Reflection Coefficient Selection and Subcarrier Allocation for Backscatter Systems with NOMA

Non-orthogonal multiple access (NOMA) and backscatter communication are two emerging technologies that enable low power communication for the Internet of Things (IoT) devices. In this paper, we consider a multicarrier NOMA (MC-NOMA) backscatter communication system. The objective is to maximize the aggregate data rate of the system by jointly optimizing the reflection coefficients and subcarrier allocation. The formulated problem is nonconvex and exhibits hidden monotonicity structure. To obtain the optimal solution, we propose an algorithm based on discrete monotonic optimization. The proposed algorithm can be considered as a performance benchmark. We also transform the nonconvex problem to another problem by using difference of convex functions and successive convex approximation and propose an algorithm to obtain a suboptimal solution in polynomial time. Simulation results show that the suboptimal scheme achieves an aggregate data rate close to the proposed optimal scheme. Results also show that our proposed schemes provide a higher aggregate data rate than the orthogonal multiple access (OMA) scheme.
Sentiment Analysis of Tweets- A Comparison of Classifiers on Live Stream of Twitter

Social media is gaining huge attention in today’s era due to its increased accessibility by individuals. Wherein, a mere tweet can reflect upon an individual’s right to say and express any sort of political, emotional, intellectual views disregarding the fact of how impactful of an impression it leads on the minds of society. Social media nowadays has become a solid platform for young minds to make an interaction with the outside world without having to understand the consequences. Therefore, it can have its pros and cons. Twitter is one such social media site used by people to ventilate their sentiments by posting messages popularly called "Tweets". The sentiments expressed over tweets can be categorized into positive, negative, and neutral. Thus, our model reflects on the sentiment analysis using NLP and various Machine Learning classifiers. Herein, we have compared various ML classifiers using the dynamic dataset which brings novelty in our findings.
Development and reproductive potential of diamondback moth Plutella xylostella (Lepidoptera: Plutellidae) on cultivated and wild crucifer species in Kenya

The development, survival and reproductive potential of diamondback moth Plutella xylostella (Linnaeus) were studied at 25 ± 1 °C in the laboratory in response to two cultivated Brassica oleracea cultivars (cabbage B. oleracea var. capitata and kale B. oleracea var. acephala) and four wild crucifer species Erucastrum arabicum, Raphanus raphanistrum, Rorippa nudiuscula and Rorippa micrantha. Rorippa micrantha was the most preferred species in oviposition choice tests, while cabbage and kale were least preferred. First instar larval mining period differed significantly between plant species with the longest period recorded on cabbage (3.0 days) and the shortest on R. micrantha (0.4 days). Pupal weight was significantly lower for larvae reared on R. nudiuscula, while those of the others were similar. The developmental period from first instar to adult was the shortest on R. micrantha (14.1 days) and the longest on R. raphanistrum (15.6 days). Survival to adult was not statistically affected by the host plant species. Adult longevity ranged between 18.2 days on R. raphanistrum and 24.7 days on R. nudiuscula. The females were significantly heavier than the males on all plant species. However, males lived longer than females. Moths reared on R. nudiuscula recorded the highest fecundity (326 eggs), while moths reared on cabbage had the lowest fecundity (262 eggs). Kale and R. nudiuscula recorded the longest generation time of 31.7 days, while E. arabicum had the highest net reproductive rate (126.4 eggs per day). The highest intrinsic rate of increase was calculated for R. micrantha (0.179) and the lowest for kale (0.147). This study shows the suitability of wild crucifers as hosts for P. xylostella and indicates that they may play a major role as reservoir for the pest during the absence of cultivated host plants.
FMUCR: Fuzzy-based Multi-hop Unequal Cluster Routing for WSN

Recently, wireless sensor networks play an important role in our life. Cluster routing has gained more attention in wireless sensor networks. However, hotpot problem always exists. One way to solve this problem is the unequal cluster routing. In most of the unequal routing protocols, nodes which closer to base station have the smaller cluster size than others. It will reduce the relay pressure of the node which near to the base station. In this paper, we propose the fuzzy-based multi-hop unequal cluster routing. In the cluster head election phase, relative inter cluster cost and relative intra cluster cost are proposed innovatively. Fuzzy system is used for unequal clustering which reduces the energy consumption for cluster members. In the cluster formation phase, a novel probability mechanism is proposed to let the cluster members decide which cluster to join. In the multi-hop routing phase, relative relay cluster cost is proposed innovatively for inter cluster routing. Two factors are considered for multi-hop routing which reduces the energy consumption for cluster heads. Self-adaptive rotation mechanism is proposed in the data transmission phase. It reduces the frequency for re-clustering self-adaptively which reduces the control overhead of the entire network. According to the simulation results, the proposed protocol balances and reduces the energy consumption as well as extends the network lifetime of the whole network.
Significance of TGF-β Signal Transduction Pathway in Epithelial Formation: An Area in Tissue Regeneration

Transforming growth factor beta-1 (TGFβ-1) plays a major role in cell proliferation, lineage determination, differentiation, motility, adhesion and cell death. TGFβ-1 has been related with epithelial cells involved in wound healing process. Mesenchymal stem cells (MSCs) have been found to accelerate wound healing process and repair epithelium in vitro through differentiation. Epithelial-to-mesenchymal transition (EMT) and mesenchymal-to-epithelial transition (MET) are thought to be involved in the process of wound healing as TGFβ is known as a chief inducer during EMT. During inflammation, the EMT programme is altered where the MSCs are mobilized to the injury site. Recently, dental pulp stem cells and stem cells from human exfoliated deciduous teeth have gained popularity in tissue regeneration research. This review discusses the involvement of TGFβ-1 signalling pathway during epithelial formation, wound healing and differentiation of MSCs to epithelial-like cells and how dental pulp stem cells could be related and applied in tissue regeneration.
Biology, Ecology, and Management of the Diamondback Moth

In recent years, the diamondback moth, Plutella xylostella (Lepidoptera: Yponomeutidae), has become the most destructive insect of cruciferous plants throughout the world, and the annual cost for managing it is estimated to be U.S. $I billion (168). Members of the plant family Cruciferae occur temperate and tropical climates and represent a diverse, widespread, and important plant group that includes cabbage, broccoli, cauliflower, collards, rapeseed, mustard, and Chinese cabbage, the most important vegetable crop grown in China (90), the most populous country in the world. Although the diamondback moth is believed to have originated in the Mediterranean area (64), the source of some of our most important crucifers (185), diamondback moths now occur wherever cmcifers are grown, and this insect is believed to be the most universally distributed of all Lepidoptera (107). Absence of effective natural enemies, especially parasitoids, is believed to be a major cause of the diamondback moth’s pest status in most parts of the world (92). Lack of parasitoids in a particular area may have occurred because the diamondback moth is better able than its natural-enemy complex to become established in newly planted cmcifers. Reports on the ability of diamondback moths to migrate long distances are numerous (19, 40, 54, 58, 108, 120,
Detection of Suspicious Objects Concealed by Walking Pedestrians Using WiFi

Security is of vital importance in public places. Detection of suspicious objects such as metal and liquid often requires dedicated and expensive equipment, preventing its wide deployment. This paper proposes a pervasive device-free method to detect suspicious objects concealed by walking pedestrians using WiFi Channel State Information (CSI). By analyzing the different variations of subcarrier amplitude caused by different materials, the proposed method is able to detect suspicious objects such as metal and liquid concealed by pedestrians, when they walk through the transmission link of the WiFi transmitter and receiver. The proposed method employs Convolutional Neural Network (CNN) to classify suspicious objects, on which majority voting is applied to vote for the final result, in order to improve the detection accuracy for walking pedestrians. Evaluations show that the proposed method with majority voting achieve the detection accuracy of 93.3% for metal and liquid concealed by walking pedestrians, 95.6% for exposed metal and liquid carried by walking pedestrians, and 100% for metal and liquid carried by standing pedestrians.
Simultaneous Transmit-Receive Multi-Channel Operation in Next Generation WLANs

The next-generation IEEE 802.11 standard project, IEEE 802.11be, is focused to meet the growing demands of applications including high throughput, low latency and high reliability. With the emergence of dual-radio end user devices (STAs) and tri-band Access Points (APs), efficient operation over multiple channels distributed over multiple bands is a key technology being discussed in IEEE 802.11be task group to achieve the desired objectives. Due to insufficient channel separation in frequency, STAs might be unable to perform simultaneous transmit and receive operations over the multiple channels in an asynchronous manner. To maximize the medium utilization of such constrained STAs participating in asynchronous multi-channel operation, we design and analyze C onstraint A ware Async hronous multi-channel operation (CA-ASYNC) protocol that includes an opportunistic 802.11 backoff resumption technique applied by the constrained STAs and multichannel busy state indication by the AP to the constrained STAs. Our results show that (a) CA-ASYNC’s opportunistic backoff resumption technique significantly improves the medium utilization for constrained STAs compared to alternative strategies and (b) the multi-channel busy status indication significantly decreases the collisions due to constrained STAs and improves access delay performance.
Electromagnetic Radiation Safety Verification of Electric Vehicle's Wireless Power Transmission based on Magnetic Resonance

Wireless power transmission (WPT) technology based on magnetic resonance utilizes two coils with the same frequency and Q-factor. Because of the advantages of long-distance, higher spatial freedom, and higher efficiency, it becomes an innovative method of wireless energy supply for electric vehicles. However, the electromagnetic radiation safety of the human body must be verified before large-scale application of the technology. In order to verify the safety of the human body in the wireless power transmission system of an electric vehicle, a simulation model of an electric vehicle wireless power transmission system and a three-layer human body model are established in this paper. The specific absorption rate (SAR) of the different parts of passengers is simulated by the method of the finite-difference time-domain (FDTD) with FEM software. The results of simulation show that the maximum electromagnetic radiation dose SAR under the magnetic resonance working state is 2.1693*10-13W/Kg, which is lower than the safety limit of ICNIRP guidelines and the IEEE C95.1 standard, which verifies the large-scale application of electric vehicle wireless power transmission (WPT) technology based on magnetic resonance.
VCG-QCP: A Reverse Pricing Mechanism Based on VCG and Quality All-pay for Collaborative Crowdsourcing

With the rapid development of the Internet and combined with outsourcing, a new paradigm – crowdsourcing which shines brilliantly as a new labor mode. However, the existing pricing strategies for crowdsourcing tasks have several undesirable problems, e.g., no universal pricing model, not meeting the multiple requirements of users, pricing rely too much on decision makers, etc., which bring an unreasonable allocation of task rewards so as to make the pricing results subjective and uncontrollable. Therefore, this paper proposes a reverse pricing mechanism based on VCG and quality all-pay for collaborative crowdsourcing (VCG-QCP). The actual crowdsourcing scenario is considered with VCG mechanism, and the concept of quality all-pay is introduced to evaluate the work quality of workers who might perform the task. Then a general reverse pricing model is established by mathematical modeling, and the pricing algorithm is designed based on this model. Simulations show that the proposed method can achieve higher algorithm efficiency, higher task completion quality, a reasonable balance of benefits between employers and workers, and ensuring the truthfulness of workers’ bidding.
Distributed Energy Trading Model based on Graphene Blockchain

With the increasing demand of distributed energy in the energy system, the existing centralized energy trading model can no longer meet the effective interaction of a large number of distributed energy entities in the case of non-trust. Considering the advantages of low energy consumption and high efficiency of graphene blockchain, a distributed energy trading architecture and process based on graphene blockchain is designed. The trading system consists of five levels: user layer, application layer, contract layer, network layer and data layer, with clear overall architecture, self-discipline node and strong scalability and optimization. The entire transaction process is automatically completed on time through smart contracts, effectively improving the automation and intelligence of the transaction under the premise of ensuring the rights of both parties to the transaction. Through the comparative analysis of different distributed energy trading modes, the distributed energy trading model based on graphene blockchain has high feasibility, advancement and sustainability.
Extent to Which Crude Protein May Be Reduced in Corn-soybean Meal Broiler Diets Through Amino Acid Supplementation 1

This study was conducted to explore the extent to which CP can be reduced in corn-soybean meal broiler starter diets by amino acid supplementation while maintaining adequate performance. Using corn and soybean meal of known composition, diets were formulated by linear programming using a minimum of 107.5% of NRC (1994) amino acid recommendations. No minimum CP was required; as each amino acid became limiting crystalline sources were provided to meet the minimum specification. In sequence these were Met, Thr, Lys, Val, Ile, Arg, Phe, and Trp, resulting in eight treatments ranging from 16.61 to 22.48% CP. All diets contained 0.3% sodium bicarbonate and 0.2% aluminum hydroxide as a buffer and antacid. Three additional treatments utilized potassium sulfate to maintain a minimum dietary electrolyte balance (Na + K - Cl) of 250 meq/kg. Each treatment was fed to twelve replicate groups of six male broiler chicks from 1 to 21 d. Reducing CP below 20% while providing indispensable amino acids resulted in a significant reduction in body weight (BW) and feed conversion ratio (FCR). Crude protein content of freeze-dried carcasses declined and fat content increased as diet CP decreased. Feather content (actual weight or % of BW) was not affected until the CP was reduced to less than 18%. Maintaining dietary electrolyte balance at 250 meq/kg in reduced CP diets had no significant effect on any parameter.
An Enhanced Direction Calibration Based on Reinforcement Learning for Indoor Localization System

In this paper, we propose an advanced direction calibration method for the smartphone-based indoor localization system on the basis of map information and reinforcement learning (RL). Currently, the direction estimated by pedestrian dead reckoning (PDR) is biased due to the low-precision sensor in smartphone and magnetic field distortion in indoor environment. Thus, the direction calibration methods draw increasing attention. Since the movement of pedestrian is restricted by the indoor environment, the map information could be used to correct the heading of pedestrian and then improve the localization performance. Furthermore, since the tracking of pedestrian can be modeled as a Markov decision process. we propose a novel direction calibration algorithm based on deep Q-network (DQN). Different from the traditional direction calibration algorithms that usually rely on image processing, the proposed method use DQN to find an optimal policy to determine the moving direction. We conduct experiments in a realistic representative office environment to reveal the validity of the proposed direction calibration algorithm. The experiment results indicate that the proposed algorithm can remarkably alleviate the cumulative error, and improve the accuracy, stability and robustness of the indoor positioning system.