Miracle-dz/UniFAR-FTUs huggingface dataset:
An Environment Control Management System for Container-Type Energy Storage System

The most widely used energy storage system in current industrial applications and commercialization is Battery Energy Storage System (BESS). Due to its fast response capability, BESS has been accepted as an energy storage system worldwide. However, there are still high risks associated with large-scale BESS installations. System malfunctions can lead to battery damage, and inadequate environmental control of BESS operation can result in incidents such as battery leakage, flooding, or prolonged exposure to high temperatures. To prevent disasters and proactively prepare for them, we proposed the planning and design of an Environmental Control System (ECS) for BESS. The ECS adopted sensors to monitor the BESS site and the batteries, preparing for potential disasters. It continuously collected environmental monitoring data and was integrated with a Battery Management and Monitoring System (MBMS) to protect the internal batteries and prevent the spread of disasters. The experiment was conducted in an actual BESS installation, and Modbus communication protocol was used for the communication of sensors and the management system. The experimental results demonstrated that this planned ECS maintained the temperature within the working range for the batteries inside the container, and the relevant components designed for the system effectively interacted with the MBMS to protect the energy storage system.
Multi-objective optimization of the management of a waterworks using an integrated well field model

This study uses multi-objective optimization of an integrated well field model to improve the management of a waterworks. The well field model, called WELLNES (WELL field Numerical Engine Shell) is a dynamic coupling of a groundwater model, a pipe network model, and a well model. WELLNES is capable of predicting the water level and the energy consumption of the individual production wells. The model has been applied to Sonderso waterworks in Denmark, where it predicts the energy consumption within 1.8% of the observed. The objectives of the optimization problem are to minimize the specific energy of the waterworks and to avoid inflow of contaminated water from a nearby contaminated site. The decision variables are the pump status (on/off), and the constraint is that the waterworks has to provide a certain amount of drinking water. The advantage of multi-objective optimization is that the Pareto curve provides the decision-makers with compromise solutions between the two competing objectives. In the test case the Pareto optimal solutions are compared with an exhaustive benchmark solution. It is shown that the energy consumption can be reduced by 4% by changing the pumping configuration without violating the protection against contamination.
Bioremediation of Sewage using Specific Microbial consortium and Biogenic Filter materials

Municipal sewage problems are more complex as the volume of the wastewater is large and it requires huge area. The objective of this work is to evaluate a set of microbial consortium and various kinds of filter materials for domestic wastewater treatment. The present study involves defining an economic solution utilizing various kinds of filter materials. The materials taken into study are selected to work on the basis of adsorption, immobilization and centres for bioconversion. Biofiltration experiments were under taken with various biofilter materials inoculated with a defined microbial community. A lab scale submerged attached growth bioreactors using, natural biogeneic materials as supporting material for the immobilization of specific consortium was applied for the domestic wastewater treatment. The units are tested for their individual capacities and as consortia with an ultimate aim achieving safer environmental standards. However, the key component of the research study is the role of microbial flora.
Cluster Analysis of Glucose Fluctuations in the Pre-Morning and Early Morning Hours in Patients with Type 1 Diabetes

Background and Aim: In patients with type 1 diabetes, glucose dynamic in the pre-morning and early morning hours may vary requiring a differentiated approach to glycemic management. In this study, we clustered data of continuous glucose monitoring from subjects with type 1 diabetes managed with basal-bolus insulin therapy in order to identify glucose fluctuation patterns. Materials and Methods: We used continuous glucose monitoring data obtained from 570 patients. Time intervals from 4:00 am to 8:00 am were analyzed. Clustering of time series was performed using a hierarchical clustering algorithm. Statistical significance of obtained cluster structure was evaluated using Monte Carlo method and the Silhouette Score. Results: We have identified 11 clusters, including 3 ones with at least one episode of hypoglycemia. Differences between the clusters included initial and final glucose levels, as well as the presence of uptrend or downtrend. Among clusters without hypoglycemia, two clusters showed normal or near normal glucose levels, two clusters were characterized by a slightly elevated glucose, other demonstrated moderate or severe hyperglycemia. In all clusters with hypoglycemia episode(s) the mean glucose level at the start of the time interval was close to the hypoglycemia threshold; patterns with an uptrend with a stable glucose level and a downtrend occurred in approximately equal proportions. Conclusion: Patients with type 1 diabetes demonstrate a variety of glucose patterns in the pre-morning and early morning hours. The data highlight the importance of differentiated approaches to therapy.
FERGI: Automatic Annotation of User Preferences for Text-to-Image Generation from Spontaneous Facial Expression Reaction

Researchers have proposed to use data of human preference feedback to fine-tune text-to-image generative models. However, the scalability of human feedback collection has been limited by its reliance on manual annotation. Therefore, we develop and test a method to automatically annotate user preferences from their spontaneous facial expression reaction to the generated images. We collect a dataset of Facial Expression Reaction to Generated Images (FERGI) and show that the activations of multiple facial action units (AUs) are highly correlated with user evaluations of the generated images. Specifically, AU4 (brow lowerer) is reflective of negative evaluations of the generated image whereas AU12 (lip corner puller) is reflective of positive evaluations. These can be useful in two ways. Firstly, we can automatically annotate user preferences between image pairs with substantial difference in these AU responses with an accuracy significantly outperforming state-of-the-art scoring models. Secondly, directly integrating the AU responses with the scoring models improves their consistency with human preferences. Finally, this method of automatic annotation with facial expression analysis can be potentially generalized to other generation tasks. The code is available at https://github.com/ShuangquanFeng/FERGI, and the dataset is also available at the same link for research purposes.
Temporal coupled-mode theory in nonlinear resonant photonics: From basic principles to contemporary systems with 2D materials, dispersion, loss, and gain

Temporal coupled-mode theory (CMT) is an acclaimed and widely used theoretical framework for modeling the continuous wave (CW) response and temporal dynamics of any integrated or free-space photonic resonant structure. It was initially employed to understand how energy is coupled into and out of a cavity and how it is exchanged between different resonant modes. In the 30 years that followed its establishment, CMT has been expanded to describe a broad range of nonlinear interactions as well (self- and cross-phase modulation, saturable absorption, frequency generation, gain, etc.). In this tutorial, we thoroughly present the basic principles and the evolution of CMT throughout the years, showcasing its immense capabilities for the analysis and design of linear and nonlinear resonant photonic systems. Importantly, we focus on examples of modern, open nanophotonic resonators incorporating contemporary bulk or sheet (2D) materials that may be lossy and dispersive. For each linear/nonlinear effect under study we follow a meticulous, step-by-step approach, starting from an accurate model of the physical phenomenon and proceeding to its introduction in the CMT framework all the way to the efficient solution of the resulting system of equations. Our work highlights the merits of CMT as an efficient, accurate, and versatile theoretical tool. We envision that it can serve both as an introductory reference for any reader, as well as a comprehensive handbook on how to incorporate a broad range of linear and nonlinear effects in the CMT framework.
High-Intensity Focused Ultrasound Ablation by the Dual-Frequency Excitation

High-intensity focused ultrasound (HIFU) has emerged as an effective and noninvasive therapeutic modality for cancer and solid tumor. Despite its promising clinical outcomes and the approval of the Food and Drug Administration of many countries, the ablation time of a large target is long, so enhancement of the lesion production is highly desired. In this study, dual-frequency (or amplitude modulation) excitation was evaluated both numerically and experimentally, and its performance was compared with that using single-frequency excitation at the same power output. The nonlinear wave propagation model was used to simulate the acoustic field of HIFU exposure, the Gilmore model was used to determine the induced bubble dynamics, and then absorbed acoustic energy and bubble-enhanced heating were put into the BioHeat equation to calculate the temperature elevation. HIFU-produced lesion in the bovine serum albumin-embedded polyacrylamide was recorded photographically. It is found that dual-frequency excitation (3.16 + 3.20MHz) can increase the lesion area by 35%–65% compared to single-frequency excitation (3.18 MHz) at the same power output. The lesion enhancement increases with the pulse repetition frequency, duty cycle, and modulation depth and decreases with the frequency difference. In summary, dual-frequency excitation can increase the bubble cavitation and the associated heating for HIFU ablation for large lesion production.
Science to Practice : Can Contrast-enhanced US with Targeted Microbubbles Monitor the Response to Antiangiogenic Therapies ?

The widespread clinical introduction of antiangiogenic therapies continues to stimulate a search for surrogate imaging biomarkers for monitoring treatment response. One promising approach takes advantage of the ability of ultrasonographic (US) contrast material–enhancing microbubbles to be targeted to specific endothelial cell surface receptors in vivo. Specific endothelial cell ligands (such as monoclonal antibodies or engineered peptides) can be conjugated to the outer shell of contrast-enhancing microbubbles to selectively direct the bubbles to receptors residing in specific vascular beds, permitting in vivo imaging of angiogenesis at the cellular and molecular level. In this issue of Radiology, Willmann et al (1) have used US to detect contrast-enhancing microbubbles targeted to specific receptors on newly formed endothelial cells in tumors growing in live mice to indirectly image angiogenic activity.
Integrating cellular automata and Markov techniques to generate urban development potential surface: a study on Kolkata agglomeration

Abstract Uncontrolled, yet fragmented peripheral urban expansion has emerged as a menace to urban development. To cope with this rapid urban expansion process, identification of the forces responsible for this rapid urban expansion is a pre-requisite, especially when its threats to habitability are taken into consideration. This study tries to evaluate fragmented uncontrolled urban expansion faced by Kolkata using cellular automata-Markov chain. Urban growth patterns, land use/land cover transformations and spatial allocation correspondence with planning strategy is the main theme of this study. Depending upon the driving forces, the study result indicates a bi-directional urban development potential surface, which might be a result of the biased planning initiative along with middle-class residential demand. This simulation result provides evidence for the planning authority to evaluate the effectiveness of spatial allocation and urban expansion trends and provide flexibility to modify the current allocation scenario.
IDENTIFIKASI MINERAL BATUAN PADA DAERAH MANIFESTASI MATA AIR PANAS DI KOYA KECAMATAN TONDANO SELATAN KABUPATEN MINAHASA MENGGUNAKAN SEM-EDX DAN FTIR

Indonesia's geological conditions make a real contribution to the availability of geothermal energy in Indonesia, the types of rocks that exist in the area of ​​manifestation vary in metal and mineral content. North Sulawesi Province is one of the regions that has alteration rocks which are found in many areas of manifestation of geothermal. The purpose of this study was to determine the composition of elements and types of minerals in altered rocks in the manifestation area of ​​Koya Hot Springs, South Tondano District, North Sulawesi Province. The method used is the electron beam method on SEM, and EDX uses the energy dispersive X-ray and FTIR methods. The results of the analysis on SEM-EDX showed that in sample-1 the rocks in the hot springs had elements containing 30.51% silica (Si) minerals and 69.49% oxygen (O) while in sample-2 oxygen-O 71,89 %, silica (Si) 15.28 % and aluminum (Al) 12.83%. The FTIR spectra of sample-1 with 778 cm-1 and sample-2 779 cm-1 wavenumber are quartz. This result is consistent with SEM-EDX analysis with the mineral composition is Silica (Si) and Oxygen (O).
The origin and genesis of major Jurassic unconformities within the triple junction area of the North Sea, UK

Three Jurassic unconformities of intra-Aallenian, intra-Callovian and intra-middle Oxfordian age are consistently identified within the literature for the North Sea region but despite their common recognition these unconformities remain poorly understood and they are essentially spatially and temporally unconstrained. The recent development of a sequence stratigraphic framework for the Jurassic of the North Sea allows for a thorough investigation of the spatial and temporal characteristics of the intra-Callovian and intra-Oxfordian unconformities and these data have important implications for Middle and Late Jurassic rifting within the North Sea region. The intra-Aalenian unconformity is believed to have been generated as a result of an increase in geothermal gradient within the area and subsequent thermal uplift that preceded rifting. The stratigraphic gap associated with this unconformity increases towards the proposed location of a Middle Jurassic, triple junction centred dome. In contrast the stratigraphic break associated with the intra-Callovian and the intra-middle Oxfordian unconformities is found to increase onto what are now intra-rift highs and rift margin areas. These unconformities occur at the same time as distinct changes in the geometry of the fill of the triple junction and also occur at the same time as seismic scale extension. They do not appear to correlate to any supposed global fall in relative sea-level. Stratigraphic data indicate that three phases of extension occurred during the Callovian, middle and late Oxfordian and lastly during the Kimmeridgian and Volgian. Although the intra-Callovian and intra-middle Oxfordian unconformities can be confused with the effects of the Middle Jurassic North Sea dome unconformity, they are in fact the result of extension during the initial Callovian and Middle/late Oxfordian rift phases. The recognition of these regional unconformities suggests that the Middle and Late Jurassic rifting process was punctuated rather than being continuous.
Design and Implementation of Wearable IMU Sensor System for Heel-Strike and Toe-Off Gait Parameter Measurement

Heel-strike (HS) and toe-off (TO) events are crucial in gait analysis, assessing various parameters like gait cycle time, stance time, swing time, single support time, and double support time. These metrics aid in diagnosing medical conditions and post-surgery biomechanical stability. However, current evaluation methods often rely on costly laboratory setups, limiting accessibility. We propose an innovative approach using wearable sensor technology, integrating an Inertial Measurement Unit (IMU) sensor (MPU6050) and the NodeMCU microcontroller (ESP8266). Our primary goal is to accurately detect HS and TO events during normal human gait analysis. We employ two MPU6050 sensors on both shanks, collecting gait signals as participants walk a 40-meter distance. The collected data is wirelessly transmitted by the ESP8266 microcontroller to the ‘Blynk’ IoT application through a Wi-Fi module. The Blynk app displays precise timing (in seconds) of HS and TO events on a smartphone, offering insights into biomechanical stability and disease diagnosis. This project emphasizes continuous gait monitoring through IoT integration, enabling more flexible and accessible gait analysis with wearable sensors. Our results demonstrate the promising applications of this approach in research, enhancing understanding and accessibility of human gait dynamics.
Applying Virtual Reality for Port Logistics Learning

This article describes an educational innovation by applying a virtual reality simulator called DELIVRIT in learning port logistics. The objective is to investigate if immersive technology can improve the understanding of logistics concepts and give a more interactive and personalized approach to training. A Likert scale survey was used to find out the appreciation of the participants about the use of this 3D platform, as well as a logistics knowledge questionnaire to observe their learning of basic concepts. This study conducted in a virtual port found that participants who used DELIVRIT for port logistics education and training performed well on logistics tasks where they used virtual reality technology. Furthermore, most of the participants reported a positive user experience. The results suggest that this virtual reality simulator can be an effective tool for learning port logistics, improving learning outcomes, and providing a positive user experience. Virtual reality technology has the potential to improve education and training in the logistics and transportation industry, which can lead to improvements in the efficiency and effectiveness of operations at ports and other logistics hubs.
Effects of Facial Expressions and Parts on Changes in Facial Attractiveness by Brief Presentation

In the recent development of computer graphics and avatar generation, the synthesis of attractive faces and bodies has gained interest. In cognitive psychology, it is known that the attractiveness of the whole face and facial parts changes with the duration of exposure. Also, facial expressions influence the attractiveness of the whole face and facial parts. In the present study, we investigated how attractiveness judgments for whole faces and parts with facial expressions would change with exposure time. Participants rated the attractiveness of whole faces and facial parts. The faces and face parts expressed either smiling, angry, or neutral expressions. The face stimuli were presented for either 20 ms or 1000 ms. The results showed that the attractiveness ratings of the eyes, mouth, and whole face were higher with the shorter exposure duration. However, the nose became more attractive with the longer exposure. Smiling faces tended to be rated as more attractive and angry faces as less attractive. However, this pattern did not always hold for facial parts. The present results have implications not only for understanding attractiveness judgments in humans, but also for computer graphics and avatar generation in the fields of artificial intelligence and computer vision.
Pneumonia Detection Using Snapshot Ensemble Model

Pneumonia is a serious and potentially life-threatening disease that predominantly affects the elderly population. Early diagnosis plays a crucial role in saving lives. Medical imaging, particularly the use of deep learning techniques, has shown promise in aiding the detection and classification of pneumonia in patients based on their chest X-rays. This paper presents a novel system for pneumonia detection and classification, utilizing a deep learning snapshot ensemble model. The EfficientNet-B0 technique is employed to address the pneumonia identification problems. The system is trained on the chest X-ray images (pneumonia) Kermany dataset, which is a well-known dataset in the field. The proposed system demonstrates impressive performance, achieving an accuracy of 97% for pneumonia classification.
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes

Change-point detection (CPD) is crucial for identifying abrupt shifts in data, which influence decision-making and efficient resource allocation across various domains. To address the challenges posed by the costly and time-intensive data acquisition in CPD, we introduce the Derivative-Aware Change Detection (DACD) method. It leverages the derivative process of a Gaussian process (GP) for Active Learning (AL), aiming to pinpoint change-point locations effectively. DACD balances the exploitation and exploration of derivative processes through multiple data acquisition functions (AFs). By utilizing GP derivative mean and variance as criteria, DACD sequentially selects the next sampling data point, thus enhancing algorithmic efficiency and ensuring reliable and accurate results. We investigate the effectiveness of DACD method in diverse scenarios and show it outperforms other active learning change-point detection approaches.
Using Habitat Suitability Models Considering Biotic Interactions to Inform Critical Habitat Delineation: An Example with the Eastern Hog-nosed Snake ( Heterodon platirhinos) in Ontario, Canada

Habitat suitability models have been used in a variety of fields, including conservation, and are considered a powerful tool to model the potential niche of species. Presence-only models have been particularly useful to define suitable habitat for rare species at a landscape scale. In Canada, the Species at Risk Act not only protects species at risk, but also their residences and critical habitats. It is thus necessary to identify which geographic areas species at risk depend on and which habitats can be considered suitable. In this study, we identify areas of high suitability for the eastern hog-nosed snake, Heterodon platirhinos, in Ontario, Canada. We employ three models - Maxent, Boosted Regression Trees, and the Genetic Algorithm for Rule Set Production (GARP) - to model the current distribution of the species. Because the eastern hog-nosed snake is a diet specialist, we also assess the importance of biotic interactions in habitat suitability models by including variables representing prey availability. The best models were combined using a consensus approach and categorical maps showing 4 conservation scenarios were built. Maxent and Boosted Regression Trees performed better than GARP. While forest density is positively related to habitat suitability, cropland density limits the distribution of this snake. Climate also played an important role in shaping the distribution of this species. Biotic variables allowed better interpretation of the predictions made by the models by reflecting spatial bias in sampling. We discuss how habitat suitability models can help delineate the critical habitat of species at risk and whether variables representing biotic interactions should be included.