Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Engineering and Biosynthesis Technology: Keys to Unlocking the Chains of Phage Therapy

Phages possess the ability to selectively eliminate pathogenic bacteria by recognizing bacterial surface receptors. Since their discovery, phages have been recognized for their potent bactericidal properties, making them a promising alternative to antibiotics in the context of rising antibiotic resistance. However, the rapid emergence of phage-resistant strains (generally involving temperature phage) and the limited host range of most phage strains have hindered their antibacterial efficacy, impeding their full potential. In recent years, advancements in genetic engineering and biosynthesis technology have facilitated the precise engineering of phages, thereby unleashing their potential as a novel source of antibacterial agents. In this review, we present a comprehensive overview of the diverse strategies employed for phage genetic engineering, as well as discuss their benefits and drawbacks in terms of bactericidal effect.
Time Variant Directional Multi-Link Channel Sounding and Estimation for V2X

We present our new scalable multi-channel and multi-node sounder, the ILMSoundG3. It is configurable in terms of the number of switched Tx and parallel Rx nodes. The basic structures of the Tx and Rx nodes are given with consideration of the measurement system requirements.The ILMSoundG3 is validated via a proof of concept measurement at 2.53 GHz in an urban environment. The system was configured as a sounder with two Tx nodes and one Rx node, which records two MIMO links simultaneously. Each node uses an antenna array. The transmitters were moved by cars whereas the receiver was elevated 20 m above the ground by a lifting platform acting as a base station. The used system configuration results in a snapshot rate of approx. 0.5 kHz, which covers the expected time variance of the chosen environment.The measurement results were analyzed using HRPE, providing a parametric description of the specular propagation paths of the radio channel per measurement link. Each estimated path is described by its directions of departure and arrival, delay, Doppler shift, and fully polarimetric complex path weights.
Wearable Smart Rings for Multifinger Gesture Recognition Using Supervised Learning

This article presents a wearable smart ring with an integrated Bluetooth low-energy (BLE) module. The system uses an accelerometer and a gyroscope to collect fingers’ motion data. A prototype was manufactured, and its performance was tested. To detect complex finger movements, two rings are worn on the point and thumb fingers while performing the gestures. Nine predefined finger movements were introduced to verify the feasibility of the proposed method. Data preprocessing techniques, including normalization, statistical feature extraction, random forest recursive feature elimination (RF-RFE), and $k$ -nearest neighbors sequential forward floating selection (KNN-SFFS), were applied to select well-distinguished feature vectors to enhance gesture recognition accuracy. Three supervised machine-learning algorithms were used for gesture classification purposes, namely support vector machines (SVMs), $k$ -nearest neighbors (KNNs), and naïve Bayes (NB). We demonstrated that when utilizing the KNN-SFFS recommended features as the machine-learning input, our proposed finger gesture recognition approach not only significantly decreases the dimension of the feature vector, results in faster response time, and prevents overfitted model, but also provides approximately similar machine-learning prediction accuracy compared to when all elements of feature vectors were used. Using the KNN as the primary classifier, the system can accurately recognize six one-finger and three two-finger gestures with 97.1% and 97.0% accuracy, respectively.
Satisfactory Driving Mode Classification Based on Pedal Operation Characteristics

Since the vehicle's response according to the driver's gas pedal operation varies greatly depending on the driving mode, the selection of the driving mode significantly affects the driver's satisfaction. This article presents a satisfactory driving mode classification that enhances the driver's satisfaction by providing a suitable driving mode to the driver. Unlike the conventional algorithm based on driving style recognition, the proposed approach determines the changes required for the current driving mode, such as mode-up, -stay, -down. Features suitable for classification are extracted from the driver's pedal operation characteristics during specific situations, such as launch, acceleration while driving. The performance of the proposed algorithm is evaluated through nested cross-validation, compared with conventional algorithms based on driving style recognition, demonstrating its superiority, generality. The proposed algorithm is event-based, operates in real-time while driving. As a result, it provides a more reliable, effective solution for enhancing driver satisfaction by providing an appropriate driving mode.
ROOKERY BAY, FLORIDA BENTHIC COMMUNITY ASSESSMENT

An operator activates a document editing program or editing document data including character strings, graphics and image data with the aid of an input device and a display. In the course of edition of the document data by the operator, application programs are activated. The operator can write the characters, graphics and others into the document data being edited by making use of the application programs.
A New Method for Electrical Impedance Tomography With Incomplete Electrode Array

The possibility to design wearable devices based on electrical impedance tomography (EIT) has recently given a boost to this technology. However, the coverage of the sensor array will vary with the size difference of the measured object, resulting in an incomplete measurement set. If this measurement set is directly invested in the solution of the inverse problem, the imaging quality will be significantly degraded. In this case, it is necessary to take measures to reduce the impact of missing measurement data on reconstruction. In this article, a new method (MinR-DFF) is proposed which provides a novel solution to the problem of local measurement information loss due to incomplete electrode arrays and achieves high-quality image reconstruction. The effectiveness of the proposed method is verified by numerical simulation and solid–liquid two-phase experiments. By using this method, the correct images of the phantoms are successfully recovered with an incomplete electrode array and both the correlation coefficients (CORs) and the relative errors (REs) do not differ from the results using the complete measurement set by more than 5%.
Cattle Behavior Classification Based on Integration of Acceleration Sensor and Position Information

In order to monitor the behavior of cattle, we designed an intelligent cattle collar, and completed the experiments at the National Breeding Center of Beef Cattle in YangLing, Shaanxi. The collar is composed of a control chip nRF52832 based on Bluetooth 5.0 and MPU6050 sensor, which can realize the collection of cattle behavior (acceleration, angular velocity) and position information. The micro controller calculates the position information of the cattle, according to the Received Signal Strength Indicator (RSSI) broadcast by the Bluetooth beacon in the cowshed. We set the dataset with 3-axis acceleration, 3-axis angular velocity and three-dimensional position coordinates as dataset A, and the dataset without adding location information as dataset B. These two datasets were input into SVM, KNN and DeepConvLSTM classification models to identify the walking, standing, eating and lying behaviors of cattle. The accuracy of dataset B on SVM, KNN and DeepConvLSTM is 84.65%, 98.96% and 99.13% respectively, while the accuracy of dataset A is 85.23%,99.01 % and 99.53% respectively. Compared with dataset B, it has increased by 0.58%, 0.05% and 0.40%. At the same time, the F1 scores of walking, standing, eating and lying behaviors in dataset A are higher than those in dataset B. Among them, the F1 score of standing behavior is 0.77%, 0.15% and 0.68% higher than that of dataset B in SVM, KNN and DeepConvLSTM classifiers. The experimental results show that the accuracy of cattle behavior classification is improved by adding position information. The integration of acceleration sensor and position information can monitor the behavior of cattle more accurately.
Privacy-preserving Representation Learning for Speech Understanding

Existing privacy-preserving speech representation learning methods target a single application domain. In this paper, we present a novel framework to anonymize utterance-level speech embeddings generated by pre-trained encoders and show its effectiveness for a range of speech classification tasks. Specifically, given the representations from a pre-trained encoder, we train a Transformer to estimate the representations for the same utterances spoken by other speakers. During inference, the extracted representations can be converted into different identities to preserve privacy. We compare the results with the voice anonymization baselines from the VoicePrivacy 2022 challenge. We evaluate our framework on speaker identification for privacy and emotion recognition, depression classification, and intent classification for utility. Our method outperforms the baselines on privacy and utility in paralinguistic tasks and achieves comparable performance for intent classification.
Improving emergency vehicles flow in urban environments through SDN-based V2X communications

Vehicular networks have emerged in the past years as one of the most promising technologies in the context of Intelligent Transportation Systems. Several applications have recently been envisaged to improve the lives of citizens in different ways, particularly in the field of safety. In this work we focus on a different direction by leveraging on vehicular communication technologies and software-defined networking (SDN) to achieve a solution able to improve the travel time of emergency vehicles (EVs) when traveling in urban environments. In particular, we devise a solution where the route to be followed by EVs is shared with an SDN-enabled infrastructure, being then smartly rebroadcast using the roadside units (RSUs) that are near the region the EV will pass by in a near future. This way, standard vehicles that are ahead of the EV are notified about its coming, and clear the required lane to facilitate a faster and obstacle free path for the EV’s journey. Simulation experiments based on the SUMO traffic simulator for the city of Valencia, Spain, have shown that, if properly tuned, the proposed solution is able to reduce the travel time of EVs by nearly 15%, while maintaining the travel time of other vehicles mostly unaltered.
Attack-Agnostic Deep Face Anti-Spoofing

The task of face anti-spoofing (FAS) is to determine whether the captured face from a face recognition system is live or fake. Current methods which are trained with existing fake faces ignore the generalization and perform poorly on unseen attacks. To tackle this problem, a novel Attack-agnostic Face Anti-spoofing framework is proposed. Different from previous methods that can be treated as a defense system, we regard face anti-spoofing as a unified framework with the attack and defense systems and optimize the defense system against unseen attacks via adversarial training with the attack system. Concretely, the attack system consists of two modules: an Adversarial learning-based Attack Pattern Generation (Adv-APG) module and a Supervised learning-based Attack Pattern Drift (Sup-APD) module. The Adv-APG module generates a series of spoofing samples by recombining a live face with known attack patterns in a generative way. The Sup-APD module pulls the generated spoofing samples in a supervised way to an unknown domain that makes the defense system ineffective. The defense system is free to choose and compatible with our attack system. Extensive experiments are conducted by using three different defense architectures to verify that the proposed attack system can improve the performance on both seen- and unseen attacks on multiple datasets.
Numerical Analysis on the Transient Inductance Gradient of the Resistive Overlay Rail on the Sliding Electrical Contact

A resistive overlay rail for an electromagnetic launcher was tested and its inductance gradients were analyzed using 2-D and 3-D finite-element methods (FEMs). The overlay rail was consisted with outside copper rail for good conduction of current and inside steel one for the prevention of rail wear. When the armature slides along the steel rail, electrical current flows mainly through the copper rail before it reaches the armature tail and it penetrates the steel rail at the armature tail due to the velocity skin effect. The rail inductance gradient <inline-formula> <tex-math notation="LaTeX">$L^{\prime }_{R}$ </tex-math></inline-formula> is obtained with 2-D FEM in both frequency and time domains without an armature and the propulsive inductance gradient <inline-formula> <tex-math notation="LaTeX">$L^{\prime }_{P}$ </tex-math></inline-formula> are obtained with 3-D transient FEM with a moving armature. A C-shaped aluminum armature was launched with the velocity over 2 km/s on the resistive overlay rail and gouging was not induced, while it appeared in the experiment with a copper rail. Using the two obtained inductance gradients, the circuit simulation was conducted and it explains the experiment result with a good agreement.
Analysis of Mobile Device Dual Tasking on the Move: Normal Cognitive Decline of Aging as Ground Truth for Mild Cognitive Impairment

The widespread use of mobile phones in daily life makes them a fundamental tool for the study of human behavior. In particular, they can be used as a source of additional information to help to diagnose diseases. This work is based on contrasted dual-tasking tests where cognitive performance is studied by performing tasks of high cognitive load while walking. In this case, we study significant differences in mobile device use among groups of people of different ages and examine whether they are more characteristic when the interaction takes place on the move. A study is conducted by monitoring the interaction with the mobile device for one consecutive week and analyzing the correlations between these interactions and the participants’ ages. Additionally, a user profiling model is designed to help to use this ground truth in future works focused on the early diagnosis of cognitive deficits. The results obtained contribute to preliminarily characterizing how age-related normotypical cognitive decline affects interactions with mobile devices. In addition, the pilot study generates a dataset with monitored events and interactions of 45 users that includes more than 4.5 million records.
Adaptive Nonsingular Terminal Sliding Model Control for Permanent Magnet Synchronous Motor Based on Disturbance Observer

In order to shorten the response time and improve robustness for a permanent magnet synchronous motor (PMSM) control system, an adaptive nonsingular terminal sliding model control (NTSMC) equipped with a disturbance observer is presented for PMSM. First, an improved exponential reaching law is adopted to adaptively adjust exponential and constant approach speed in the adaptive NTSMC. Second, the disturbance observer is devised to observe the load torque and external disturbance and feeds back them to the adaptive NTSMC to compensate. Simulation and experimental results applied to PMSM show that the proposed approach can quickly track load torque and external disturbance and the system has advantages of small overshoot, static error, rapidity, and higher robustness.
Severity Classification of Parkinson's Disease from Speech using Single Frequency Filtering-based Features

Developing objective methods for assessing the severity of Parkinson's disease (PD) is crucial for improving the diagnosis and treatment. This study proposes two sets of novel features derived from the single frequency filtering (SFF) method: (1) SFF cepstral coefficients (SFFCC) and (2) MFCCs from the SFF (MFCC-SFF) for the severity classification of PD. Prior studies have demonstrated that SFF offers greater spectro-temporal resolution compared to the short-time Fourier transform. The study uses the PC-GITA database, which includes speech of PD patients and healthy controls produced in three speaking tasks (vowels, sentences, text reading). Experiments using the SVM classifier revealed that the proposed features outperformed the conventional MFCCs in all three speaking tasks. The proposed SFFCC and MFCC-SFF features gave a relative improvement of 5.8% and 2.3% for the vowel task, 7.0%&1.8% for the sentence task, and 2.4% and 1.1% for the read text task, in comparison to MFCC features.
Coarticulation of Sibe Vowels and Dorsal Fricatives in Spontaneous Speech: An Acoustic Study

Previous phonological analyses of dorsal segments in Sibe (Tungusic; Xinjiang, China) have treated the velar and uvu-lar series as allophonic, with velar segments adjacent to /i @ u/ and uvular segments adjacent to /a o/. In this paper, we use a spontaneous speech corpus to examine the acoustic correlates of coarticulation of dorsal fricatives and vowels in Sibe. The first two spectral moments and mid-frequency spectral peak of dorsal fricatives were measured over the fricative onset in VC sequences and offset in CV sequences. Differences in spectral measures suggest that dorsal fricatives coarticulate with both preceding and following vowels primarily in terms of tongue dorsum backness, but with some role of height possible. These findings reflect a more complex relationship between Sibe vowels and dorsals than previously described; the distribution of labels used for the dorsal fricatives also suggests a gradient assimilation process.
Artificial Intelligence, ChatGPT and Organizational Studies

Abstract The advancement of the use of Artificial intelligence in the scientific field, such as Connectedpapers and ChatGPT, has allowed us to reflect on how technological tools have become mediators and participants in the context of education and academia. In the field of organizational theories, despite the different perspectives on understanding the incorporation of AIs in academic practice, we highlight two challenges in our daily academic life. The first challenge refers to confronting the digital colonialism that AIs impose on us, considering that they constitute themselves through the reproduction of language models programmed in countries of the "global north” The second challenge concerns its unfoldings in the process of automation of academic writing in administration. We consider the need to reflect on how the uses of AIs can contemporarily reproduce our place in the field of science as one of scientific data extractivism, the limitation of the teaching of academic writing in administration as the reproduction of an "assisted programming" of hegemonic language models, and the possibilities of disentangling as a way of counteracting this dynamic of automation of article writing in administration.
Automatic speaker recognition with variation across vocal conditions: a controlled experiment with implications for forensics

Automatic Speaker Recognition (ASR) involves a complex range of processes to extract, model, and compare speaker-specific information from a pair of voice samples. Using heavily controlled recordings, this paper explores the impact of specific vocal conditions (i.e. vocal setting, disguise, accent guises) on ASR performance. When vocal conditions are matched, ASR performance is generally excellent ( whisper is an exception). When conditions are mismatched, as in most forensic cases, we see an increase in discrimination and calibration error in some cases. The most problematic mismatches are those involving whisper and supralaryngeal vocal settings; these produce the greatest phonetic changes to speech. Mismatches involving high pitch also produce poor performance, although this appears to be driven by speaker-specific differences in articulatory implementation. We discuss the implications of the findings for the use of ASR in forensic casework and the interpretability of system output.
RECO: On-Demand Recharging and Data Collection for Wireless Rechargeable Sensor Networks

The emerging Wireless Energy Transfer (WET) is a promising technology that has the potential to thwart the energy limitation challenge in the field of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). WET-enabled WSNs are known as Wireless Rechargeable Sensor Networks (WRSNs). Over the last decade, various solutions have been suggested to minimize energy consumption and data delivery latency in WSNs. Among them, employing a mobile data collector has proven to be the most efficient solution. Motivated by this fact, we propose an on-demand recharging and data collection scheme called RECO, which employs dual-functioning mobile chargers (MCs) for both operations. RECO includes three essential procedures. 1) It clusters the network into multiple regions and defines two nonoverlapping itineraries on each partition; 2) It designs an efficient scheduling algorithm based on an Adapted Graham-Scan Algorithm (A-GSA); and 3) It defines anchor points to integrate the recharging and data dissemination processes. RECO is ideal for data delivery delay non-tolerant applications that were ignored by prior periodic recharging and data collection schemes. The experimental results demonstrate that RECO outperforms benchmarks regarding recharging and data collection efficiency.
An Automated Deep Learning Approach for Spine Segmentation and Vertebrae Recognition Using Computed Tomography Images

Spine image analysis is based on the accurate segmentation and vertebrae recognition of the spine. Several deep learning models have been proposed for spine segmentation and vertebrae recognition, but they are very computationally demanding. In this research, a novel deep learning model is introduced for spine segmentation and vertebrae recognition using CT images. The proposed model works in two steps: (1) A cascaded hierarchical atrous spatial pyramid pooling residual attention U-Net (CHASPPRAU-Net), which is a modified version of U-Net, is used for the segmentation of the spine. Cascaded spatial pyramid pooling layers, along with residual blocks, are used for feature extraction, while the attention module is used for focusing on regions of interest. (2) A 3D mobile residual U-Net (MRU-Net) is used for vertebrae recognition. MobileNetv2 includes residual and attention modules to accurately extract features from the axial, sagittal, and coronal views of 3D spine images. The features from these three views are concatenated to form a 3D feature map. After that, a 3D deep learning model is used for vertebrae recognition. The VerSe 20 and VerSe 19 datasets were used to validate the proposed model. The model achieved more accurate results in spine segmentation and vertebrae recognition than the state-of-the-art methods.