Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrochemical Biosensors for the Detection of Antibiotics in Milk: Recent Trends and Future Perspectives

Antibiotics have emerged as ground-breaking medications for the treatment of infectious diseases, but due to the excessive use of antibiotics, some drugs have developed resistance to microorganisms. Because of their structural complexity, most antibiotics are excreted unchanged, polluting the water, soil, and natural resources. Additionally, food items are being polluted through the widespread use of antibiotics in animal feed. The normal concentrations of antibiotics in environmental samples typically vary from ng to g/L. Antibiotic residues in excess of these values can pose major risks the development of illnesses and infections/diseases. According to estimates, 300 million people will die prematurely in the next three decades (by 2050), and the WHO has proclaimed “antibiotic resistance” to be a severe economic and sociological hazard to public health. Several antibiotics have been recognised as possible environmental pollutants (EMA) and their detection in various matrices such as food, milk, and environmental samples is being investigated. Currently, chromatographic techniques coupled with different detectors (e.g., HPLC, LC-MS) are typically used for antibiotic analysis. Other screening methods include optical methods, ELISA, electrophoresis, biosensors, etc. To minimise the problems associated with antibiotics (i.e., the development of AMR) and the currently available analytical methods, electrochemical platforms have been investigated, and can provide a cost-effective, rapid and portable alternative. Despite the significant progress in this field, further developments are necessary to advance electrochemical sensors, e.g., through the use of multi-functional nanomaterials and advanced (bio)materials to ensure efficient detection, sensitivity, portability, and reliability. This review summarises the use of electrochemical biosensors for the detection of antibiotics in milk/milk products and presents a brief introduction to antibiotics and AMR followed by developments in the field of electrochemical biosensors based on (i) immunosensor, (ii) aptamer (iii) MIP, (iv) enzyme, (v) whole-cell and (vi) direct electrochemical approaches. The role of nanomaterials and sensor fabrication is discussed wherever necessary. Finally, the review discusses the challenges encountered and future perspectives. This review can serve as an insightful source of information, enhancing the awareness of the role of electrochemical biosensors in providing information for the preservation of the health of the public, of animals, and of our environment, globally.
Power Quality Disturbance Identification Algorithm Based on Empirical Wavelet Transform And Time-Domain Kurtosis Feature Analysis

In the process of power quality (PQ) disturbance recognition, there is redundancy in feature extraction of the disturbance signal, so it will lead to complex structure of recognition model, difficult training and low recognition accuracy. To address the above problems, a new method of recognition and classification based on empirical wavelet transform (EWT) with time-domain kurtosis feature analysis and LSTM neural network is proposed. Firstly, EWT is used to obtain the multi-resolution analysis (MRA) components; secondly, the MRA segments are filtered to reduce redundancy by adding windows to the decomposed time-domain signals and calculating the kurtosis so as to determine the starting and ending positions of the effective feature information; to facilitate the subsequent neural network training, the energy entropy is calculated for the filtered MRA segments to obtain the energy entropy sequence. Finally, the LSTM neural network is used for recognition analysis to identify the types of disturbance signals. The simulation results show that the proposed optimized screening method has lower feature vector redundancy, lower complexity of the LSTM recognition model, shorter training time, and an average recognition accuracy of 98.22%, which is 8.04% better than the recognition results of traditional neural networks and 11.04% better than the recognition results of vector machines, compared with the traditional power quality disturbance recognition method.
An Interoperability Middleware for IIoT

Concepts of Industry 4.0 and the Industrial Internet of Things are increasingly common nowadays due to the latest communication technology and microelectronics innovations. However, with the advancement of these technologies, various companies have developed a significant number of new devices, IoT platforms, and communication protocols, causing interoperability issues. In an industry, interoperability relates to different aspects, such as physical communication, divergent device communication protocols, and syntactical problems, referring to distinct data structures. So, communication problems can make it unfeasible to use heterogeneous devices, increasing the project’s financial cost and development time. Developing a new standard for solving these matters may bring interoperability-related drawbacks rather than effectively solving these issues. For this reason, this paper proposes developing middleware for IIoT gateways to mitigate interoperability issues by combining standard ontologies and standardized digital representation. The middleware was evaluated in a use case application scenario that consists of three devices equipped with at least five actuators and sensors, using different communication protocols and data structures.
Development of Automatically Controlled Facial Osteotomy Robot using Force Information - For Safe Le Fort I Osteotomy -

The development of automatically controlled medical robots (ACMR) has certain limitations. Due to their rigidity, if they are used in surgery, it would be extremely difficult to protect the patient’s soft blood vessels and nerves. Hence, ACMR have not been put into practical use. The main purpose of this research is to address the limitations in order to develop the world’s first ACMR that has a successful practical application within surgery. The first surgical procedure we aim to apply the ACMR to is Le Fort I osteotomy in maxillofacial surgery. While performing Le Fort I osteotomy, the robots must avoid damage to the descending palatine artery, which can cause fatal bleeding. We succeeded in automatically stopping the osteotomy immediately before the artery without the need to acquire model numerical information in advance, that is, with a model-free detection. The proposed method for auto-stopping is to identify the osteotomy reaction force obtained by the reaction force observer using the concept of the Z-score.
Vessel Centripetality: A New Indicator for Morphological Analysis of Arteries and Veins

It is well known that arteries and veins play different roles in retina, and the retinal arteriolar-to-venular ratio may hint at some retinal diseases. Tortuosity of retinal vessels has been identified as one of earliest indicators to a number of vascular and nonvascular diseases. Therefore, it is essential for quantitative analysis of morphological differences between arteries and veins. Retinal arteries and veins have a clear tendency toward the center of foveal avascular zone (FAZ) during the extension process. In order to quantitatively describe this morphological feature, we propose a new indicator, vessel centripetality (VC), to distinguish retinal arteries and veins in optical coherence tomography angiography (OCTA) images. In order to reflect the centripetality, the arteries and veins are graded separately, mainly divided into first-level and second-level vessels. The experimental results on a manually annotated artery-to-vein (AV) dataset with 100 OCTA cubes demonstrate that VC is an effective indicator to describe the morphological differences of arteries and veins, and the utilization of global retinal vessel position information makes VC more stable than existed morphological indicators that only consider the local position information.
A Novel Wavy Channel Gate-All-Around FETs for Next-Generation CMOS Applications

The work presents a novel wavy channel nanosheet field effect transistor (WCNSFET) and its circuit-level performance. In this work, a single nanosheet is transformed into a wave-like structure to enhance the physical device area, thus showing an improvement in device performance. The device and circuit level performances are analyzed using 3D TCAD simulation tools. Furthermore, the wavy channel nanosheet FETs are analyzed with different number of waves in a single sheet and compared with flat sheet-based transistors for different channel materials (Ge and GaAs). The results reveal the improvement in drive current, low propagation delay, a smooth voltage transfer characteristic, high noise margin, and low energy. The results achieved with this novel device make the device a promising candidate for next-generation low power CMOS applications.
Improving Fairness of NDN Congestion Control from Resource Pooling Perspective

A practical congestion control scheme for Named Data Networking (NDN), called PCON, has been proposed for efficient data retrieval from multiple sources. Although PCON has been proposed for a multi-path/-source data retrieval, it does not discuss resource pooling viewpoint which is an important fairness concept in resource sharing. In this paper, we first evaluate the fairness performance of PCON and show that PCON cannot achieve the global fairness based on the resource pooling concept. To improve the fairness, we proposed a method that enhances AQM mechanisms of PCON to improve fairness among flows sharing multiple communication resources. Through performance evaluation using a ns-3-based NDN simulator, we reveal that our proposed method can balance the congestion, improve the global fairness, and achieve high-level performance from the resource pooling perspective.
Intelligent Energy-Efficiency Trajectory Planning of Heterogeneous Air Base Stations for 6G Emergency Communication

With the development of 6G, emergency communication services upgrade and the need for edge intelligence is increasing. However, today’s 6G emergency communication scenarios are changeable and complex, and existing methods cannot achieve large-scale and QoS guarantees. So we proposed a heterogeneous coverage compensation mechanism with static complete deployment and dynamic enhancement deployment. To enhance the capacity of hotpots determined by the clustering algorithm, we jointly optimize the UAV trajectory and users’ connectivity to maximize the UAV’s energy efficiency(EE). The optimized problem is NP-hard and is solved by deep reinforcement learning(DRL) algorithm. Simulation results show our method can significantly improve EE, which is much higher than Q-learning, PSO, and GA.
Optimizing Antenna Radiation Efficiency using N-Port Networks

In this work, a method is proposed for enhancing the radiation efficiencies of ultra-thin cell phone antennas. The proposed method incorporates the concept of N-port network and employs the NSGA-II algorithm to strategically optimize the status of the loads at the ports. The effectiveness of the approach is demonstrated by improving the antenna efficiencies at the GSM 900 band, with the commonly used inverted-F antenna (IFA) serving as a representative example.
Resource Allocation and Deployment Optimization for UAV-Assisted SPC-Based MEC Systems

We consider an unmanned aerial vehicle (UAV)assisted mobile edge computing (MEC) system, where a UAV-carried MEC server provides computation offloading service to multiple ground user equipment (UEs). To satisfy the low-latency requirement of the MEC, the short-packet communication (SPC) is used for the task offloading from the UEs to the UAV. The maximum computation latency among all the UEs is minimized by jointly optimizing the UAV’s deployment location, offloading bandwidths of all UEs, and computing times and CPU frequencies of the UEs and UAV. To solve the resulting challenging optimization problem, we propose an algorithm that decomposes it into two subproblems, which optimize the UAV’s deployment location, and the offloading bandwidths and CPU frequencies, respectively. In solving these subproblems, the data rate expression for SPC is approximated in a logarithmic form, and the equivalent problem reformulation and successive convex approximation techniques are used. Simulation results are presented to verify the effectiveness of the proposed algorithm in comparison with some benchmark schemes. Furthermore, the results show that the proposed algorithm is prominent for the system with SPC. based offloading, especially when the communication capability of the system is not strong.
Joint Processing of Pilot and Data in UAV-aided Maritime Communications

Considering the channel aging effect in maritime communications, we explore the potential gain of joint processing for maritime system capacity enhancement. Besides using a typical composite channel model including both small-scale fading and large-scale fading, we study a dynamic channel model which channel fluctuation is common offshore. We first introduce two unmanned aerial vehicle (UAV) aided maritime communication models. By analyzing the mutual information of joint processing, we derive the closed-form expression of lower bound for the achievable rate. Simulation results indicate that joint processing can take full advantage of the channel state information contained in received data signals, and it gives rise to a satisfying spectral efficiency. Meanwhile, the joint processing shows a good performance when the ship user sails far away from the serving UAV. In addition, joint processing is not noise-limited, when the signal-to-ratio is low at sea, the joint processing can still provide a relatively high spectral efficiency.
Energy-Efficient Differential Spin Hall Effect Ternary Content Addressable Memory

In this paper, a ternary content addressable memory (TCAM) design based on the differential spin Hall effect (DSHE)-magnetoresistive random access memory (MRAM) is proposed. The proposed TCAM comprises 3 transistors and 2 magnetic tunnel junctions (3T-2MTJs) structure and utilizes DSHE technique that enables high-density memory with reduced power consumption and latency. DSHE technique is an energy efficient method as it stores complementary bits in MTJs placed on both sides of a heavy metal layer by passing a single spin orbit torque (SOT) current through it. The findings demonstrate that the efficiency in terms of energy and the latency for the search operation of the proposed DSHE-TCAM is improved by 30% and 13.9%, respectively, when compared to SOT-MRAM-based TCAM. Also, the write energy efficiency of the proposed TCAM is enhanced by 47.5% and area is reduced by 42.1% in comparison to SOT-MRAM based TCAM. Furthermore, the proposed TCAM shows better performance in comparison to other counterparts that are based on different memory technologies such as static RAM (SRAM), ferroelectric field-effect transistor (FeFET), and spin transfer torque-MRAM (STT-MRAM). Hence, DSHE-MRAM-based TCAM are potential candidature for high-speed and energy-efficient search operations in future computing systems.
Participants recruitment for coverage maximization by mobility predicting in mobile crowd sensing

Mobile Crowd Sensing (MCS) is an emerging paradigm that leverages sensor-equipped smart devices to collect data. The introduction of MCS also poses some challenges such as providing high-quality data for upper layer MCS applications, which requires adequate participants. However, recruiting enough participants to provide the sensing data for free is hard for the MCS platform under a limited budget, which may lead to a low coverage ratio of sensing area. This paper proposes a novel method to choose participants uniformly distributed in a specific sensing area based on the mobility patterns of mobile users. The method consists of two steps: (1) A second-order Markov chain is used to predict the next positions of users, and select users whose next places are in the target sensing area to form a candidate pool. (2) The Average Entropy (DAE) is proposed to measure the distribution of participants. The participant maximizing the DAE value of a specific sensing area with different granular sub-areas is chosen to maximize the coverage ratio of the sensing area. Experimental results show that the proposed method can maximize the coverage ratio of a sensing area under different partition granularities.
SPAD values estimation in jujube leaves based on lasso-based optimized spectral indices and a Stacking Algorithm

Jujube is an important economic forestry product, with Xinjiang being the leading producer in China, known for its high-quality fruits. Monitoring jujube growth is crucial for agroforestry management and Xinjiang’s ecological economy. Chlorophyll content, a vital pigment associated with plant physiological activity and photosynthesis, can be non-destructively measured using the portable chlorophyll meter SPAD-502 Plus. However, its limitations in measuring SPAD values over a large area necessitate efficient estimation methods. Hyperspectral data and optimal band combination techniques are commonly employed to estimate SPAD values. However, the extensive hyperspectral data and time-consuming band calculations call for data dimensionality reduction methods. The LASSO method has shown promise in estimating SPAD values by selecting important hyperspectral bands. Nevertheless, its application in constructing vegetation indices for SPAD prediction in trees or vegetation leaves remains limited. To address this gap, this study utilizes LASSO-based optimized spectral indices and a Stacking Algorithm to estimate SPAD values in jujube leaves. The results demonstrate that LASSONDI and LASSO-RI are effective predictors of SPAD values in jujube trees. Additionally, the Stacking Algorithm combining SVR, GBDT, and Ridge models yields the best prediction performance with an R$^{2}$ of 0.98 and RMSE of 0.38.
Design and Preliminary Evaluation of a Virtual Reality with Haptic Feedback Tool for Spatial Neglect Assessment and Rehabilitation

The present paper presents the design and evaluation of a virtual reality (VR) with haptic feedback based HEMISPATIAL NEGLECT assessment and rehabilitation tool. It benefits from an increasing number of studies exploiting the potentialities of VR technologies. The proposed tool consists of a home-made force feedback interface connected to a built-in VR environment with the required controller and interface. The proposed VR environment is intuitive for users since it represents the hospital that patients are usually visiting. The joystick is used as an input for the user that allows the displacement and navigation inside the virtual hospital, and simultaneously it creates vibrations and force feedback to the hand of the users. Several modules have been implemented and evaluated for hemispatial neglect assessment and rehabilitation. Promising results have been obtained.
On Disjoint Difference Sets and Their Associated QC-LDPC Codes with Large Girth

The disjoint difference set (DDS) has been utilized in recent years to construct quasi-cyclic (QC) low-density parity-check (LDPC) codes without small cycles. However, the existing DDS-based QC-LDPC codes have no all-zero circulants, which limits the decoding performance of such codes to some extent. In this paper, two new types of DDS-based QC-LDPC codes are proposed, which contain all-zero circulants and possess girth at least eight and girth at least ten, respectively. Numerical simulations show that the new codes have good performance in AWGN channels. Besides, a new result on existence for DDSs and two novel deterministic constructions for DDSs are put forward.
CA-LIME: An Algorithm that Explains Classification Results of Cardiomyocyte Beating

Cardiovascular diseases are the leading cause of death and form a growing health concern. For the development and screening of novel drugs in a high-throughput manner, classification systems are developed to classify beating cardiomyocytes as healthy or diseased. With outstanding performance on other medical tasks, the use of machine learning for this application is increasingly explored. However, the predictions of such system are difficult to interpret for humans and provide no insight into the difference between healthy and diseased cardiomyocytes. We propose Contraction Analysis LIME (CA-LIME) to make the decisions made by machine learning algorithms more interpretable. CA-LIME is specifically designed to handle contraction analysis data. The applicability of CA-LIME is shown using samples treated with drugs, showing the explanation are in accordance with their known effects. Finally, explanations are generated for samples derived from a patient to show the applicability to patient specific diseased condition.
A Miniaturized and Circularly Polarized Antenna with Improved Radiation Efficiency

In this paper, a miniaturized and circularly polarized (CP) antenna with improved radiation efficiency is proposed. The antenna proposed in this paper is based on a printed dipole antenna with its two arms bent around to grow the current path for miniaturization, and the bending process provides two orthogonal directions of current to achieve circular polarization. This leads to a compact design that can better utilize the limited space to achieve good performance. In addition, the radiation efficiency of the proposed antenna is improved by optimizing the winding of the arms. Finally, a highly miniaturized CP antenna is realized with a decent radiation efficiency. The size of the antenna is 0.13 λ0× 0.14 λ0× 0.01 λ0, where λ0is the wavelength in vacuum and the simulated radiation efficiency is around 60% across the working bandwidth.
A New Optimal Centralized Demand Side Management for a Campus Smart Microgrid

Based on demand response and energy theft detection, a novel optimal demand side management (DSM) is proposed for a smart microgrid (SMG). A campus SMG containing photovoltaic (PV), wind turbine, diesel station, smart meters, inverters, batteries, sensors, domestic loads, and central} {DSM unit is built in MATLAB. The SMG elements are communicated using Wi-Fi protocol and internet of things (IoT) technology. The proposed DSM is optimally designed using Grey Wolf Optimization (GWO) to minimize the customer’s electricity bill, maximize the user comfort, and obtain the optimal load patterns in the hypothetical campus SMGs. It inspires consumers to alter their daily consumption patterns over a variable tariff according to real-time pricing (RTP), time of use (ToU), and critical peak pricing (CPP) methods. The results proved the proposed technique effectiveness for reducing the user’s electricity bill and conserving utility resources through energy theft detection. Finally, the proposed GWO technique is compared with firefly algorithm (FFA) and bacteria foraging algorithm (BFA) to verify its superiority.
Analysis of Ship Rotation Effects on the Electromagnetic Scattering of Wake

To assess the impact of ship rotational motion on radar cross-sectional (RCS), dynamic electromagnetic (EM) scattering simulations were conducted for the wake generated by a ship undergoing roll, pitch, and yaw motions. A computational fluid dynamics tool was utilized to establish the ship motion model and calculate the wake resulting from the rotational motions. The wake data was then converted into an EM simulation model using three-dimensional modeling techniques. The EM scattering of the ship wake was computed using full-wave EM methods. The results demonstrate that the ship wake causes directional changes in the RCS, providing insights into the influence of ship rotational motion on the EM scattering characteristics of the wake. This analysis supports the assessment of wake EM detection.