bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sniffer dogs performance is stable over time in detecting COVID-19 positive samples and agrees with the rapid antigen test in the field
Authors: Pirrone Federica , Piotti Patrizia , Galli Massimo , Gasparri Roberto , La Spina Aldo , Spaggiari Lorenzo , Albertini Mariangela 
Published: March 2023

AbstractRapid antigen diagnostic (RAD) tests have been developed for the identification of the SARS-CoV-2 infection. However, they require nasopharyngeal or nasal swab, which is invasive, uncomfortable, and aerosolising. The use of saliva test was also proposed but has not yet been validated. Trained dogs may efficiently smell the presence of SARS-CoV-2 in biological samples of infected people, but further validation is needed both in laboratory and in field. The present study aimed to (1) assess and validate the stability over a specific time period of COVID-19 detection in humans’ armpit sweat by trained dogs thanks to a double-blind laboratory test–retest design, and (2) assess this ability when sniffing people directly. Dogs were not trained to discriminate against other infections. For all dogs (n. 3), the laboratory test on 360 samples yielded 93% sensitivity and 99% specificity, an 88% agreement with the Rt-PCR, and a moderate to strong test–retest correlation. When sniffing people directly (n. 97), dogs’ (n. 5) overall sensitivity (89%) and specificity (95%) were significantly above chance level. An almost perfect agreement with RAD results was found (kappa 0.83, SE 0.05, p = 0.001). Therefore, sniffer dogs met appropriate criteria (e.g., repeatability) and WHO's target product profiles for COVID-19 diagnostics and produced very promising results in laboratory and field settings, respectively. These findings support the idea that biodetection dogs could help reduce the spread of the virus in high-risk environments, including airports, schools, and public transport.
Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition
Authors: Ning Hailong , Lei Tao , An Mengyuan , Sun Hao , Hu Zhanxuan , Nandi Asoke K. 
Published: March 2023

AbstractAerial scene recognition (ASR) has attracted great attention due to its increasingly essential applications. Most of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ASR. However, the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features, leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene images. In addition, existing methods may suffer from poor generalisations due to millions of to‐be‐learnt parameters and inconsistent predictions between global and local features. To tackle these problems, this study proposes a scale‐wise interaction fusion and knowledge distillation (SIF‐KD) network for learning robust and discriminative features with scale‐invariance and background‐independent information. The main highlights of this study include two aspects. On the one hand, a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene images. Specifically, a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local features. On the other hand, a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during training. Comprehensive experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%, 98.74% and 95.47% on the UCM, AID and NWPU‐RESISC45 datasets, respectively, compared with state of the arts.
Transformer vibration and noise monitoring system using internet of things
Authors: Thinh Tran Ngoc Huy , Lam Pham Duc , Tran Huy Q. , Tien Lam Hoang Cat , Thai Pham Huu 
Published: March 2023

AbstractDuring continuous operation, transformer problems can occur due to various reasons. In reality, the operating parameters of the transformer have been collected and monitored through the supervisory control and data acquisition (SCADA) systems. However, these systems face many challenges when applied to no‐human substations. Currently, noise signals have been used to detect transformer errors. Abnormal noise recognition and vibration monitoring can recognize the transformer's potential defects and errors. In this study, the authors built an internet of things (IoT) system that allows remote control centres to monitor the condition of transformers through noise and vibration at non‐human substations. The proposed model was equipped with a wireless sensor network node consisting of vibration sensors, audio collectors, Arduino modules, and Lora modules. The authors set up two schemes for the IoT network: one sensor node for a 220‐kV transformer and three sensor nodes for all three phases of the 500‐kV transformer. The data obtained from the sensor node were sent to LoRa Gateway and displayed on the computer through LabVIEW. The study also enabled monitoring of parameters through IoT devices such as Desktops, Laptops, Smartphones from LabVIEW NXG Web VI platform, ThingSpeak, and Amazon S3 storage cloud. In addition, a Model Predictive Control (MPC) algorithm was applied to predict the deterioration of transformer health to maintain the system stability and, hence, prolong the transformer life and operability.
Blind matching algorithm based proxy distribution against internet censorship
Authors: Shi Xin , Guo Yunfei , Hu Hongchao , Wang Yawen , Gao Zhen , Sun Xiaoli 
Published: March 2023

AbstractProxy based censorship circumvention technique is treated as one of the most promising approaches to fighting against Internet censorship, and its practical applications like the onion router and Lantern have been widely utilized. However, the proxy distribution, as a basic and core issue, still remains unsolved due to (i) the finiteness and non‐renewable nature of proxy resources, (ii) the heterogeneity of clients and proxies requirements, and (iii) the existence of secondary censorship. In this paper, a win‐win driven proxy distribution mechanism for proxy based censorship circumvention systems is designed by leveraging the blind matching theory. Specifically, a combinational manner is developed to measure the quality of service (QoS) requirements of clients in terms of normalized bandwidth and reliability, and security requirements of proxies in terms of credits and suspicion. Then, by introducing the concept of virtual currency, the proxy distribution issue is formulated as two‐sided combinatorial problems from the perspective of clients and proxies, respectively. To solve the complex NP‐hard combinatorial problems in a win‐win manner without unilateral dominance, a two‐sided many‐to‐many context‐free matching game is constructed, in which the aspiration levels and agreement functions are defined to capture the security and QoS concerns of clients and proxies. Further, a modified notion of pairwise stability is exploited as the solution concept, and a client‐proxy blind matching algorithm with market and information decentralization is proposed to obtain the ε‐pairwise stable solution, where clients and proxies make encounters randomly and update their aspiration levels through a stochastic learning process. Finally, theoretical analysis and numerical results are carried out to validate the properties and performance of the proposed algorithm including stability, complexity, and convergence.
Model‐free adaptive integral sliding mode constrained control with modified prescribed performance
Authors: Huang Xiuwei , Dong Zhiyan , Zhang Feng , Zhang Lihua 
Published: March 2023

AbstractIn this work, a novel model‐free adaptive integral sliding model constrained control strategy with modified prescribed performance is proposed for nonlinear nonaffine systems via full‐form dynamic linearization (FFDL). Firstly, a generalized nonlinear nonaffine system with external disturbance is transformed into an affine system via the FFDL method, which contains a linearly parametric term affine to the control input and preceding output data, and an unknown nonlinear time‐varying term. Then, an adaptive estimation method and a discrete‐time extended state observer (DESO) are used to estimate the pseudo gradient (PG) vector and lumped uncertainties, respectively. Furthermore, an integral sliding mode control scheme containing a modified prescribed performance function and an anti‐windup compensator is designed to keep the output tracking error in the prescribed bound without causing any asymmetric offset error in the steady‐state and to suppress the influence of input saturation. Simulation results demonstrate the superiority of the proposed control scheme.
Multi‐level wireless transmission using resonant tunneling diodes in 300‐GHz band
Authors: Webber Julian , Yamamoto Takumi , Nishigami Naoki , Nishida Yosuke , Fujita Masayuki , Nagatsuma Tadao 
Published: March 2023

AbstractThe authors report on a terahertz band wireless communication experiment employing a resonant tunneling diode receiver (Rx) and uni‐travelling carrier photodiode transmitter. An intermediate‐frequency based transmission facilitates a low‐complexity envelope‐detection type Rx architecture for complex‐modulated signals. Forward error correction recoverable data‐rates using quadrature phase shift keying modulation and 16‐level quadrature amplitude modulation are 60 and 68 Gbit/s, respectively.
A novel self‐adaptive rectifier with wide input range
Authors: Lai Shimiao , Zhang Zihao , Liu Zhijun , Wang Ge , Zhou Yongjie , Zhu Huacheng , Yang Yang 
Published: March 2023

AbstractA 2.45‐GHz self‐adaptive rectifier based on a two‐way Gysel power divider (PD) with switch networks (SN), an impedance transform network (ITN), a feedback network (FN) and two rectifiers is developed in this paper. The SNs and ITN are controlled by FN according to output voltages. The rectifier is in single‐path mode at a low power level and switches to two‐path mode at a high power level. It has been simulated, fabricated and tested. The original input power range when efficiency is above 50% is from −3 to 11 dBm. After being connected to the PD, the input power range is expanded to −6 to 15 dBm, with a maximum efficiency of 71.1% at 1400 Ω. The proposed design only leverages one type of rectifier. The outputs are symmetric.
Coherence‐adjustable, high‐directional, electrically pumped semiconductor lasers around 650 nm
Authors: Liu Lingjuan , Wang Yufei , Ji Haiyang , Fu Ting , Zheng Wanhua 
Published: March 2023

AbstractBy fabricating patterned electrodes on a dumbbell‐shaped cavity semiconductor laser, the coherence of the laser can be switched by only changing the injection current. The threshold current and the peak power of the laser are 0.75 A and 1.41 W @ 3 A. Under the injection of small and large currents, the measured speckle contrasts are 40.0% @ 0.8 A and 7.0% @ 3 A. And the corresponding horizontal far‐field divergence angles are 4.05° and 8.7°, respectively, showing a high directionality. Such a laser with a wide range of adjustable coherence and a high directionality has a great potential in the field of bioimaging.
Structural analysis attack on finite state machine obfuscated circuits
Authors: Jin Linli , Wang Ke , Li Peng , Xi Wei , Huang Kai 
Published: March 2023

AbstractIn this letter, a structural analysis attack is explored as the first kind of attack to disclose the secret key of the circuit encrypted by the latest obfuscation technique JANUS‐HD. By extracting the finite state machine (FSM) diagram from the circuit and converting structural traces into constraints, the secret key can be deduced by the CP‐SAT solver within a few minutes or even seconds in most cases. Incorrect keys can be further pruned by sequential equivalence checking with several oracles in case more than one key is obtained. The entire attack procedure lasts no longer than a few hours even for an FSM with 4096 states.
Low‐loss and broadband InP mirror‐based out‐of‐plane optical coupler
Authors: Saito Yusuke , Ueda Yuta , Shindo Takahiko , Kurata Yu , Kanazawa Shigeru , Kobayashi Wataru , Ishikawa Mitsuteru 
Published: March 2023

AbstractThe authors developed a mirror‐based out‐of‐plane optical coupler (MOOC) for InP photonic integrated circuits (PICs). The coupler has the coupling loss and alignment tolerance comparable to those of a conventional edge coupler. An asymmetric Mach–Zehnder interferometer (a‐MZI) with the MOOC exhibits little wavelength‐dependent loss (WDL) and a high extinction ratio (ER) of >20 dB over 100‐nm wavelength bandwidth. In addition, the temperature‐independent transmittance spectra were obtained. Finally, the authors fabricated a 4‐ch MOOC array with the pitch of 40 μm, which corresponds to the core pitch of practical multi‐core fibre (MCF).
Transformer‐less unified power flow controller in medium voltage distribution networks
Authors: Abdelrahman Mohamed A. , Wang Sheng , Ming Wenlong , Wu Jianzhong , Jenkins Nick 
Published: March 2023

AbstractA transformer‐less unified power flow controller (UPFC) is a power electronic device consisting of series and shunt voltage source converters (VSCs) that are not connected to a common DC bus. It can control power flow in medium voltage (MV) distribution networks without the need for interfacing transformers. Hence there is a significant reduction in size and cost compared to a conventional UPFC. This paper investigates the operating range of a transformer‐less UPFC in an MV distribution network and the required power ratings of the series and shunt converters. The results showed that a transformer‐less UPFC is able to provide active power control using partially rated converters. However, its ability to control reactive power is limited by the current ratings of the converters. The analysis was verified using software simulation and hardware experiment.
A novel distributed privacy‐preserving control and data collection method for IoT‐centric microgrids
Authors: Alavi Seyed Amir , Javadipour Mehrnaz , Rahimian Ardavan , Mehran Kamyar 
Published: March 2023

AbstractThe privacy of electricity consumers has become one of the most critical subjects in designing smart meters and their proliferation. In this work, a multilayer architecture has been proposed for anonymous data collection from smart meters, which provides: (1) The anonymity of information for third‐party data consumers; (2) Secure communication to utility provider network for billing purposes; (3) Online control of data sharing for end‐users; (4) Low communication costs based on available Internet of things (IoT) communication protocols. The core elements of this architecture are, first, the digital twin equivalent of the cyber‐physical system and, second, the Tangle distributed ledger network with IOTA cryptocurrency. In this architecture, digital twin models are updated in real‐time by information received from trusted nodes of the Tangle distributed network anonymously. A small‐scale laboratory prototype based on this architecture has been developed using the dSPACE SCALEXIO real‐time simulator and open‐source software tools to prove the feasibility of the proposed solution. The numerical results confirm that after a few seconds of anomaly detection, the microgrid was fully stabilized around its operating point with less than 5% deviation during the transition time.
Improved LSB image steganography with high imperceptibility based on cover‐stego matching
Authors: Al‐Faydi Sama N. M. , Ahmed Sahar Khalid , Al‐Talb Heba N. Y. 
Published: March 2023

AbstractInformation security is an important factor when critical information is transferred. This paper concentrates on finding a method that can hide a message in an image without making any changes to it. The proposed method is novel and based on the Least Significant Bit (LSB) method with a high imperceptibility and capacity. Initially, the secret message bits are compressed using the LZW technique, then the compressed message bits are embedded into the LSB of the edge pixels in the cover image which have the same bit values. As a result, the cover image pixels remain unchanged and the stego image matches the cover image. The addresses of pixels that contain the compressed secret bits are maintained in a file called “location addresses” which the receiver uses to retrieve the compressed secret message. It is possible that the stego image will not be transmitted if it was previously specified by the sender and the receiver. Image quality metrics are applied to several standard images. The obtained PSNR for the stego image is infinity, SSIM and NCC are one, MSE and AD are zero for different embedding capacities in all images, which ensures the stego image's excellent imperceptibility.
Fuzzy optimisation model of an incremental capacity auction formulation with greenhouse gas consideration
Authors: Bhachu Karanveer , Elkasrawy Ayman , Venkatesh Bala 
Published: March 2023

AbstractAn incremental capacity auction (ICA) is a mechanism to procure future generation capacity in a power system. Greenhouse gas (GHG) emissions from generators negatively affect our climate and there is a real need to reduce them. Thus, it is critically important for ICA models to procure future generation capacity that reduces GHG emissions. In this paper, we propose two ICA models incorporating energy‐limited generation (renewables and storage) and a GHG emission constraint. All offers are converted into unforced capacity, negating any effect of energy limitations of generation offers. The first ICA model uses classical optimisation and considers GHG emission limits and maximises social welfare (SW). The second ICA model uses a fuzzy optimisation technique to simultaneously optimise the objectives of SW maximisation and GHG emission minimisation. Both ICA models are tested on two datasets with 10 and 338 capacity supply offers constructed using Ontario data. While both models control GHG emissions as desired, the ICA model with fuzzy optimisation is shown to find a better balance between maximising net SW and minimising GHG emissions, with superior reductions in GHG for minor decreases in SW. Results demonstrate how GHG emission reduction results in increased selection of low carbon generation.
A monocular vision positioning and tracking system based on deep neural network
Authors: Li Huijun , Zhang Yu , Ye Bin , Zhao Hailong 
Published: March 2023

AbstractIn order to locate the mobile robots in three‐dimensional indoor environment, mostly global navigation satellite system‐denied space, a monocular visual space positioning algorithm based on a deep neural network is proposed. First, the authors employ the lightweight YOLOv5 algorithm for target detection, and the LibTorch deep learning framework is used for model deployment to improve the inference speed. Moreover, a multi‐layer perceptron (MLP) neural network with four inputs and two outputs is constructed, which regress the site coordinates of the robot to complete the target localization, and this method is compared with the mathematical model solving algorithm to reflect the accuracy and superiority of positioning algorithm based on the deep neural network. The proposed positioning and tracking system has been successfully applied to international conference on robotics and automation (ICRA) robot competition, and the results show that the positioning mean error estimated by the authors’ method is within 10 cm whilst having good real‐time performance.
Cognitive flexibility moderates the association between theory of mind and children’s offer in the Ultimatum Game (La flexibilidad cognitiva modera la relación entre la teoría de la mente y las ofertas de los niños en el Juego del Ultimátum)
Authors: Wang Zhenlin , Wang Lamei , Gao Xiaozi , Zhu Liqi 
Published: March 2023

This study examined the cognitive predictors and constraints of children’s strategic offers in the Ultimatum Game. The Ultimatum Game is a one-shot negotiation on the division of a given sum of rewards between two people, where the receiver has the right to punish the proposer by rejecting unfair offers, which results in nil reward for either party. Ninety-four Hong Kong children ranged between three and six years of age (48 girls, M = 4.94, SD = 0.90) participated in an Ultimatum Game, and tasks measuring theory of mind, cognitive flexibility, inhibitory control and verbal ability. Children’s theory of mind, measured with false belief understanding tasks, was positively associated with their Ultimatum Game offers. Regression analysis further demonstrated that cognitive flexibility moderated the association between false belief understanding and children’s offers in the Ultimatum Game. Being able to take the opponent’s perspective prompted children to offer more in the Ultimatum Game, but only when their cognitive flexibility was low.
Did Late Quaternary climate change trigger shifts in mating system in temperate plant species of the Sino-Japanese Floristic Region? A commentary on ‘Genetic and demographic signatures accompanying the evolution of the selfing syndrome in Daphne kiusiana, an evergreen shrub’
Authors: Zhang Xinyi , Comes Hans Peter , Qiu Yingxiong 
Published: March 2023

This article comments on:
Eun-Kyeong Han, Ichiro Tamaki, Sang-Hun Oh, Jong-Soo Park, Won-Bum Cho, Dong-Pil Jin, Bo-Yun Kim, Sungyu Yang, Dong Chan Son, Hyeok-Jae Choi, Amarsanaa Gantsetseg, Yuji Isagi, and Jung-Hyun Lee. Genetic and demographic signatures accompanying the evolution of the selfing syndrome in Daphne kiusiana, an evergreen shrub, Annals of Botany, Volume 131, Issue 5, 11 April 2023, Pages 751–767, https://doi.org/10.1093/aob/mcac142
Divergent foraging strategies between populations of sympatric matrilineal killer whales
Authors: Tennessen Jennifer B , Holt Marla M , Wright Brianna M , Hanson M Bradley , Emmons Candice K , Giles Deborah A , Hogan Jeffrey T , Thornton Sheila J , Deecke Volker B 
Published: March 2023

AbstractIn cooperative species, human-induced rapid environmental change may threaten cost–benefit tradeoffs of group behavioral strategies that evolved in past environments. Capacity for behavioral flexibility can increase population viability in novel environments. Whether the partitioning of individual responsibilities within social groups is fixed or flexible across populations is poorly understood, despite its relevance for predicting responses to global change at the population and species levels and designing successful conservation programs. We leveraged bio-logging data from two populations of fish-eating killer whales (Orcinus orca) to quantify patterns of fine-scale foraging movements and their relationships with demography. We reveal striking interpopulation differences in patterns of individual foraging behavior. Females from the endangered Southern Resident (SRKW) population captured less prey and spent less time pursuing prey than SRKW males or Northern Resident (NRKW) females, whereas NRKW females captured more prey than NRKW males. The presence of a calf (≤3 years) reduced the number of prey captured by adult females from both populations, but disproportionately so for SRKW. SRKW adult males with a living mother captured more prey than those whose mother had died, whereas the opposite was true for NRKW adult males. Across populations, males foraged in deeper areas than females, and SRKW captured prey deeper than NRKW. These population-level differences in patterns of individual foraging behavior challenge the existing paradigm that females are the disproportionate foragers in gregarious resident killer whales, and demonstrate considerable variation in the foraging strategies across populations of an apex marine predator experiencing different environmental stressors.