Miracle-dz/UniFAR-FTUs huggingface dataset:
Achieving Stable Service-Level Targets in Time-Varying Queueing Systems: A Simulation-Based Offline Learning Staffing Algorithm

In this paper, we develop a new staffing algorithm for achieving stable service-level targets in queues with time-varying arrivals. Specifically, we aim to stabilize the tail probability of delay, which is the probability that the waiting time exceeds a designated target τ > 0. We integrate reinforcement learning into the decision making in queueing models; our new method recursively evolves the staffing decision by alternating between two phases: (i) we generate simulated queueing data by operating the system under the present staffing function (exploitation), and (ii) we utilize the newly generated data to devise improved staffing decision (exploration). We demonstrate the effectiveness of our new method using various numerical examples.
Water Balance in Oil Palm Plantation with Ridge Terrace and Nephrolepis biserrata as Cover Crop

The existence of oil palm plantations as a possible cause of drought in the surrounding areas in Indonesia is a critical issue. Therefore, information related to the effects of oil palm plantations on the surrounding environment in terms of soil water content (SWC) availability is needed. Soil and water conservation techniques in the form of ridge terracing and cover crops, such as Nephrolepis biserrata , can be expected to potentially improve soil water reserves, especially in the dry-season, by accumulating water in the rainy season. This study aimed to study the effects of N. biserrata as cover crop, together with the potential effects of ridge terraces, on the water balance in mature oil palm plantations. The research was conducted in mature oil palm plantations, Afdeling III block 375 (planted in 1996) and block 415 (planted in 2005), Rejosari Unit, PT Perkebunan Nusantara (PTPN) VII in Natar District, South Lampung Regency, Indonesia, from August 2014 to January 2015. The research was based on of setting up 15 m x 20 m experimental plots with the following treatments: (i) without ridge terraces and without N. biserrata (G0T0); (ii) without ridge terraces but with N. biserrata (G0T1); (iii) with ridge terraces but without N. biserrata (G1T0); (iv) with ridge terraces and with N. biserrata (G1T1). Hydrology parameter data were collected for each treatment plot; water balance was calculated using a water balance equation. The results showed that the use of the cover crop N. biserrata in combination with ridge terraces helped improving SWC reserves by approximately 71% and 12%, respectively. The use of N. biserrata as a cover crop reduced the rate of water loss by percolation and run-off, by approximately 36% and 80%, respectively, in an area where the annual rainfall is above 2,400 mm per year. The presence of N. biserrata shortened the period of SWC deﬁcit by extending the period of a water surplus by 70 days when compared with ridge terracing alone (which reduced the period of SWC by 50 days).
Analysis of Communication Delays in Roadside Detection Systems for Cooperative AEB Implementation

The recent development in the framework of Intelligent Transportation Systems (ITS) relies on the availability of ultra-fast networks for achieving low-latency communication. In fact, for the actual implementation of the Cooperative-Advanced Driving Assistance System(C-ADAS) based on V2X communication, a limited delay can be tolerated for safety-critical control algorithms such as Automatic Emergency Braking (AEB). This work proposes a detailed evaluation of the latency involved in a roadside detection system based on a commercial thermal camera aimed at communicating with surrounding connected vehicles through Message Queue Telemetry Transport (MQTT) protocol. In particular, the focus is pointed on the proposed communication architecture that has been designed and implemented, as well as on how delay can affect the performance of an AEB control logic implementation, highlighting limitations and possible ways to cope with this issue. Experimental results show that the SG-based communication architecture proposed limits E2E latency below 200 ms, which, on the basis of simulation tests for a large public transportation vehicle, still represents a relevant amount of time for safety-critical ADAS implementation, thus requiring some methodologies for delay compensation.
Nucleus Detection Based on Adversarial Domain Adaptation with Cross-Domain Consistency

Automatic cell/nucleus detection is a prerequisite for various quantitative analyses on microscopy image. However, previous deep learning methods require enough annotated microscopy images for better performance, which is a great challenge for microscopy image due to limited annotation and high cost. This paper proposes an end-to-end adversarial learning model with unsupervised domain adaptation for cell/nucleus detection. Different staining microscopy images transformation and cell/nucleus detection are merged into one end-to-end model to achieve mutual restriction of accuracy. Furthermore, a cross-domain consistency loss is introduced, which can refine the results of image transformation and localize cells synchronously. The experiments conclude that proposed method achieves the best F1 scores compared with other methods on cell/nucleus detection of different staining microscopy images. Moreover, ablation study also approves the effectiveness of cross-domain consistency loss.
Effect of Genetically Modified Organisms (Gmos) on Health and Environment in Southwestern Nigeria: Scientists' Perception

The study was carried out in South Western Nigeria to determine the perception of scientists in Universities and Research Institutes on the effect of GMOs on health and environment. This ispredicatedon the fact that scientists'perception wouldinfluence the on-going debate as a prelude to Nigeria being a signatory to the use of GMOs. Using a simple random sampling technique a total of one hundred and eighty respondents were selected from a population of 760 and then interviewed. Data were collected through the use of a structured questionnaire with a reliability coefficient of 0.92 and analysis was done using frequency counts, percentages andprobit regression model. Scientists were between 31and 40years (59.40%) wereMSCholders (44.44%), got theirinformation on GMOs from journals (89.60%) and were males (56.70%). Majority of the scientists had low awareness of the GMOproducts (52.8%), and lowperception (54.5percent). There was a significant relationship between awareness, age, religion, radio, newspaper, scientific periodicals and their perception toward GMOs. There was no significant difference in perception, and awareness between scientists in Universities andresearch institutes.
Leveraging Data-Driven Process Mining Insights for Efficiency, Compliance, and Accountability: Enhancing Safety Inspections in a Specified City

Safety inspections play a pivotal role in ensuring the well-being of individuals and the structural integrity of buildings. This study delves into the analysis of safety inspections within a specified city, leveraging a dataset encompassing over 100,000 records. The analysis was executed using Disco Fluxicon, a process mining tool, which provided insights into critical facets of the safety inspection process. The methodology involved the redefinition of attributes, revealing bottleneck activities, highlighting exceptional behavior cases, and employing data-driven decision-making. The study identified major bottleneck activities in the inspection process, including ‘issued,’ ‘permit finaled,’ and ‘approved,’ indicating the need for targeted optimization. It also unveiled the significance of exceptional behavior cases and their role in guiding tailored approaches. The analysis empowered regulatory bodies with data-driven insights to enhance resource allocation and decision-making. These findings offer the potential for an efficient and adaptive safety inspection process, translating to quicker project approvals and improved resource allocation, ultimately boosting the construction sector's confidence and economic growth. The study's insights foster transparency, accountability, safety, and compliance in the specified city. A streamlined inspection process contributes to economic growth, job creation, and enhanced quality of life for residents and workers. Leveraging these findings, the city can create a safer and more efficient inspection environment, promoting confidence and progress.
Enhanced Pedestrian Trajectory Prediction via the Cross-Modal Feature Fusion Transformer

We address the challenge of predicting pedestrian trajectories in videos, a task inherently complex due to the diverse and intricate nature of human motion and interactions within their environment. The accurate anticipation of trajectories necessitates a holistic comprehension of the temporal evolution of past events in videos. Regrettably, existing methods often neglect the fusion of critical features, such as human behavior, motion, and interaction, thereby limiting their efficacy in tackling these challenges. To overcome these limitations, we propose the Cross-modal Feature Fusion Transformer, a novel approach for pedestrian trajectory prediction. Our model seamlessly integrates multimodal features, including human behavior, position, speed, and interaction with surroundings, to effectively encapsulate the temporal progression of observed frames. It consists of transformer-based cross-modal fusion encoder and decoder modules, adeptly melding the interactions between the multimodal features through a multi-head co-attentional mechanism. This enables the precise prediction of future trajectories. Additionally, we incorporate auxiliary self-supervised future prediction losses to learn the temporal evolution of past and future multimodal features. We evaluate our approach on ETH/UCY and ActEV/VIRAT datasets and demonstrate its superior performance compared to state-of-the-art methods.
Fuzzy Dynamic Controller for Wheeled Mobile Robots Considering Lateral and Longitudinal Wheels Slippage

Wheeled Mobile Robots (WMRs) are known for their simplicity, ease of movement on hard and flat terrain, and effective control, which has led many researchers to investigate their challenges. In this paper, a fuzzy controller is developed to address the trajectory-tracking problem of WMRs. The study takes into account environmental disturbances, such as longitudinal and lateral slippage, and presents a fuzzy controller to compensate for the effects of these disturbances. The method’s efficiency will be demonstrated through the simulation results, and this fuzzy approach is compared with previous research in this field.
Electrical and Structural Characterization of (002) Oriented Aluminum Scandium Nitride

In this paper, Al<inf>0.8</inf>Sc<inf>0.2</inf>N films with optimal orientation of (002) were deposited by DC magnetron sputtering in a N2 and Ar environment. (002) Al<inf>0.8</inf>Sc<inf>0.2</inf>N can be controlled by adjusting the substrate temperature. Al<inf>0.8</inf>Sc<inf>0.2</inf>N films grown were of the best quality with a smaller FWHM (0.22°) at 400°C. Based on this, the films grow with thickness spanning 364 nm to 945 nm. AFM analysis showed that roughness of Al<inf>0.8</inf>Sc<inf>0.2</inf>N films declined from 11 to 1.72 as thickness increased, indicating films became smooth while thickening. Dielectric permittivity of Al<inf>0.8</inf>Sc<inf>0.2</inf>N films also demonstrated a thickness dependence. The dielectric permittivity increased from 11.7 to 28.2 as thickness rose. Moreover, electric resistivity of Al<inf>0.8</inf>Sc<inf>0.2</inf>N was 22 MΩ due to its lower dielectric loss, which was slightly lower than that of pure AlN (28.2 MΩ). Our research is important for preparing high-quality piezoelectric films to improve performance of high-frequency devices.
On the Correlations between Performance of Deep Networks and Its Robustness to Common Image Perturbations in Medical Image Interpretation

The robustness of medical image interpretation deep learning models to common image perturbations is crucial, as the medical images in clinical applications may be from different institutions and contain various perturbations that did not appear in training data, decreasing the interpretation performance. In this paper, we investigate the correlations of the robustness of 28 ImageNet models under 6 image perturbation types over 10 severity levels on the CheXpert chest X-ray (CXR) classification dataset. The results demonstrate that: (1) If a model has a higher ImageNet accuracy, after fine-tuning it on CheXpert for CXR classification, it tends to be more robust on perturbed CXRs. (2) If a model has a higher CXR classification performance after fine-tuning on CheXpert, it is not necessarily more robust on perturbed CXRs, depending on the severity levels of the perturbations. Under stronger perturbations, lower CXR performance models tend to be more robust instead. (3) The model architectures may be a key factor to the robustness. For instance, no matter how large the models are, EfficientNet and EfficientNetV2 models tend to be more robust, while ResNet models tend to be more vulnerable. Our work can help select or design robust models for medical image interpretation to improve the capability for clinical applications.
Integrated Policy-Driven Adaptive Control Framework for Grid-Tied Inverters per Frequency Decomposition Based Optimization

This paper presents a unique control architecture for grid-connected inverters. By adding vendor-provided static controllers to an identification-based adaptive control strategy, the proposed method expands the controller’s operating range for dynamic grid conditions. The optimal control policy, based on the nonlinear system dynamics per energy signals at the inverter terminals, determines each controller’s contribution, respectively. These dynamic energy signals are transformed into a physically meaningful signal that represents the dynamics that influence the inverter output power quality via a frequency mode decomposition algorithm per a discrete transform. To enhance the grid’s power quality and overall stability over a wide range of operating conditions, the proposed integrated control architecture eliminates the undesirable high-frequency content via the decomposed energy signals. The proposed method is validated based on experimental results, which showcase the effectiveness of the proposed control over conventional static proportional–integral control (e.g., settling time reduced >56%).
Power Loss Simulation due to Reconfiguration of Shipboard AC to DC Electrical Distribution System with Hybrid Energy Sources

According to study data released by International Maritime Organization (IMO), emissions from ships contribute 2.2% of the greenhouse gas CO2, as well as 13% of SOx pollutants and 15% of NOx pollutants from the total air pollution in the world. Thus, innovation by utilizing renewable energy such as the addition of photovoltaic (PV) is required. Several studies also have shown that an electric distribution system using direct current (DC) has several advantages over the conventional alternating current (AC) power distribution system. However, the investigation of power losses between both systems in non-electric propelled ship, further supplied with hybrid PV -diesel generator power has not been performed. This research is conducted based on power flow simulation during four ship's four operation modes in power system simulator. Based on the simulations that have been carried out, the power loss value in a DC distribution system is always smaller than the power loss value in an AC distribution system. When photovoltaic (PV) panels are installed instead of a diesel generator alone, power loss is also demonstrated to be lessened. In loading-unloading mode without using photovoltaic, the AC distribution system exhibits the maximum power loss value, with a power loss value of 3,267 kW and 2,223 kVAR or about 0.23% of the total power.
The Informed Elastic Net for Fast Grouped Variable Selection and FDR Control in Genomics Research

Modern genomics research relies on genome-wide association studies (GWAS) to identify the few genetic variants among potentially millions that are associated with diseases of interest. Only reproducible discoveries of groups of associations improve our understanding of complex polygenic diseases and enable the development of new drugs and personalized medicine. Thus, fast multivariate variable selection methods that have a high true positive rate (TPR) while controlling the false discovery rate (FDR) are crucial. Recently, the T-Rex+GVS selector, a version of the T-Rex selector that uses the elastic net (EN) as a base selector to perform grouped variable election, was proposed. Although it significantly increased the TPR in simulated GWAS compared to the original T-Rex, its comparably high computational cost limits scalability. Therefore, we propose the informed elastic net (IEN), a new base selector that significantly reduces computation time while retaining the grouped variable selection property. We quantify its grouping effect and derive its formulation as a Lasso-type optimization problem, which is solved efficiently within the T-Rex framework by the terminated LARS algorithm. Numerical simulations and a GWAS study demonstrate that the proposed T-Rex+GVS (IEN) exhibits the desired grouping effect, reduces computation time, and achieves the same TPR as T-Rex+GVS (EN) but with lower FDR, which makes it a promising method for large-scale GWAS.
MACEdge: Real-Time Video Analytics Based on Multi-Access Collaborative Edge Computing

Video analysis typically requires a significant amount of computing resources and energy. Traditional cloud-based video analysis relies on concentrating computing resources in the cloud, which puts a tremendous load on network bandwidth and introduces poor user experience due to latency. Edge computing offers a solution by offloading a portion of the video analysis tasks to the edge, significantly reducing the pressure on bandwidth and latency. However, the fixed video configurations (resolution, frame rate, etc.) uploaded to the edge may not be optimal for specific situations. Moreover, using a low video configuration makes it difficult for edge video analysis systems to recognize environmental changes, while using a high video configuration poses challenges to latency and bandwidth. Therefore, in this paper, we propose the MACEdge-an edge video analysis framework by leveraging the multi-access capability of the edge gateway to enhance edge perception by accessing heterogeneous sensors. We offer an algorithm based on Q Learning for dynamic adaptive adjustment of sensor threshold and video configurations. In experiments using real-world data, our collaborative solution improved accuracy and reduced high bandwidth costs compared to other video configuration adjustment benchmarks.
Securing Smart UAV Delivery Systems Using Zero Trust Principle-Driven Blockchain Architecture

This paper presents a novel architecture that utilizes the Zero Trust Principle to facilitate blockchain-enabled Unmanned Aerial Vehicle (UAV) delivery systems, emphasizing recipient privacy, verification, and continuous authentication. By integrating blockchain’s decentralization and transparency with a Zero Trust model, the architecture ensures robust security and access control. The blockchain-based architecture guarantees data integrity and resilience, while advanced cryptographic techniques, including homomorphic encryption, ensure long-term security. Ongoing security assessment and improvement are facilitated through the maturity model’s integration with blockchain technology. This innovative approach effectively mitigates cyber risks, safeguards recipient data privacy, it initiative promotes the secure and efficient use of UAVs in logistics. It emphasizes the importance of maintaining strict access controls and trust boundaries. Through adaptable security evaluations, the model addresses evolving threats, ensuring a secure environment for UAV integration into the modern supply chain while prioritizing recipient privacy and security.
An Efficient and Cost-Effective Approach for Targeted Influence Maximization

Targeted Influence Maximization (TIM) aims to find a k subset from a given set of nodes which can influence a maximum number of nodes in a graph representing a social network. This paper proposes an efficient, cost-effective approach for Targeted Influence Maximization (TIM) within a given budget. The main objective of this work is to identify good seeds (targets) for a given budget so that maximum spread can be achieved within the given budget. The gain calculation for individual nodes is done and then the selected node gain is used as input along with the budget to our proposed seed selection algorithm. This seed selection algorithm is based on a greedy approach to the 0/1 Knapsack Problem which is time efficient as well. The selected nodes are then used as input for the proposed Influence Propagation algorithm, a modified version of the Linear Threshold (LT) model. This modified LT model caters to a particular context while measuring the influence spread. The performance of this model is evaluated with the existing baseline work. It is observed that given a lower budget, our model is performing way better than the existing baseline work.
A Simplified Analytical Approach for NVH Assessment of Permanent Magnet Synchronous Motors

The paper deals with NVH (Noise Vibration and Harshness) analysis of Permanent Magnet Synchronous Electric Motors (PMSMs) for automotive applications. A simplified analytical model is presented and used to simulate the acoustic emission of PMSMs. The simplified model is based on a 2D circular curved beam of generalized cross section. The input radial and tangential electromagnetic force distribution is computed by means of the Maxwell stress tensor. The differential equations describing the vibrational behavior of the curved beam are analytically solved and explicit, closed-form expressions of the vibration and sound pressure levels are derived. The novelty of the presented model relies in the introduction of the lever-arm effect induced by tangential electromagnetic forces in the stator vibration. The developed model is employed to compare different architectures of PMSMs, particularly referring to their NVH performances.
A Loop Closure Detection SLAM System by Encoding Visual and Point Cloud Features

Loop closure detection is a vital aspect of simultaneous localization and mapping (SLAM) and plays a very crucial role in minimizing cumulative mistakes. The hostile and unpredictable environment in which the robot operates makes it unfortunate that the original LIO-SAM technique for loop closure identification utilizing a single sensor is not always accurate. In this work, we describe how vision cameras and LiDAR can be merged. A novel global descriptor, visual scanning context (VSC), is proposed based on the fused data, which retains the geometric properties of the 3D spatial structure described by the visual features and the LiDAR information, and does not rely on training and odometry results, and can be used efficiently for location recognition. To improve the efficiency of retrieving candidate locations, an efficient two-layer search technique is suggested that is viewpoint-independent and rotationally invariant. The performance of our technology is tested and evaluated on publicly available datasets, and trials reveal that our system surpasses previous methods in terms of efficiency, accuracy, and robustness.