Miracle-dz/UniFAR-FTUs huggingface dataset:
Negative Thermal Expansion Metamaterials: A Review of Design, Fabrication, and Applications

Most materials conventionally found in nature expand with an increase in temperature. In actual systems and assemblies like precision instruments, this can cause thermal distortions which can be difficult to handle. Materials with a tendency to shrink with an increase in temperature can be used alongside conventional materials to restrict the overall dimensional change of structures. Such structures, also called negative-thermal-expansion materials, could be crucial in applications like electronics, biomedicine, aerospace components, etc., which undergo high changes in temperature. This can be achieved using mechanically engineered materials, also called negative thermal expansion (NTE) mechanical metamaterials. Mechanical metamaterials are mechanically architected materials with novel properties that are rare in naturally occurring materials. NTE metamaterials utilize their artificially engineered architecture to attain the rare property of negative thermal expansion. The emergence of additive manufacturing has enabled the feasible production of their intricate architectures. Industrial processes such as laser powder bed fusion and direct energy deposition, both utilized in metal additive manufacturing, have proven successful in creating complex structures like lattice formations and multimaterial components in the industrial sector, rendering them suitable for manufacturing NTE structures. Nevertheless, this review examines a range of fabrication methods, encompassing both additive and traditional techniques, and explores the diverse materials used in the process. Despite NTE metamaterials being a prominent field of research, a comprehensive review of these architected materials is missing in the literature. This article aims to bridge this gap by providing a state-of-the-art review of these metamaterials, encompassing their design, fabrication, and cutting-edge applications.
Implementation and Analyses of Yaw Based Coordinated Control of Wind Farms

This paper presents, with a live field experiment, the potential of increasing wind farm power generation by optimally yawing upstream wind turbine for reducing wake effects as a part of the SmartEOLE project. Two 2MW turbines from the Le Sole de Moulin Vieux (SMV) wind farm are used for this purpose. The upstream turbine (SMV6) is operated with a yaw offset ( α ) in a range of − 12 ° to 8° for analysing the impact on the downstream turbine (SMV5). Simulations are performed with intelligent control strategies for estimating optimum α settings. Simulations show that optimal α can increase net production of the two turbines by more than 5%. The impact of α on SMV6 is quantified using the data obtained during the experiment. A comparison of the data obtained during the experiment is carried out with data obtained during normal operations in similar wind conditions. This comparison show that an optimum or near-optimum α increases net production by more than 5% in wake affected wind conditions, which is in confirmation with the simulated results.
Inflammation mechanism and anti-inflammatory therapy of dry eye

Dry eye is a widespread chronic inflammatory disease that causes fatigue, tingling, burning, and other symptoms. Dry eye is attributed to rheumatic diseases, diabetes, hormone disorders, and contact lenses, which activate inflammatory pathways: mitogen-activated protein kinases (MAPK) and nuclear factor-B (NF-κB), promote macrophage inflammatory cell and T cell activation, and inflammation factors. Clinicians use a combination of anti-inflammatory drugs to manage different symptoms of dry eye; some of these anti-inflammatory drugs are being developed. This review introduces the dry eye inflammation mechanisms and the involved inflammatory factors. We also elucidate the anti-inflammatory drug mechanism and the detection limits.
Collagen isolation with acid soluble method from the skin of Red Snapper (lutjanus sp.)

Collagen is an extracellular matrix connective tissue that account for about 30% of the total protein. Red snapper skin has the potential as an alternative source of collagen that can increase the added value of food and fishery industrial waste. This research aimed to isolate collagen by acid-soluble method and collagen characterization. Isolation of red snapper skin collagen consists of two stages, which are pretreatment with 0.1 M NaOH over 12 hours and hydrolysis with 0.5 M acetic acid over 48 hours. The pretreatment process resulted a non-collagen protein content at 0.2576% mg/mL. Hydrolisis with acetic acid yielded 9.71% (wet) and 41.19% (dry). Chemical analysis by amino acid analysis showed the dominant amino acid glycine, proline, arginine and alanine. Physical characterization of collagen was done by FTIR analysis and showed the presence of amide group A, B, I, II and III which belong to typical group of collagen.
Time-Stamp Attacks on Remote State Estimation in Cyber-Physical Systems

In this article, we consider the security of remote state estimation in cyber-physical systems (CPS), where a wireless sensor sends the measurement of the considered system to a remote state estimator using a wireless channel. In this scenario, time-stamp technology is commonly used to record the time characteristic of the data for different objectives, such as realizing time synchronization and dealing with time delays. We consider a potential threat to this mechanism, and investigate a time-stamp attack, where a malicious adversary manipulates data packets to degrade the estimation performance of the remote estimator. In particular, we assume that the attacker can alter the time-stamps of any two packets. We consider two types of sensor transmission—namely, innovation and local estimation, respectively, and analyze the evolution of the remote estimation error covariance for both cases. Furthermore, we characterize the optimal attack strategy that maximizes the estimation error covariance from the attacker's point of view. Finally, we present numerical simulations to demonstrate the effectiveness of our results.
Emerging Roles of Epigenetics in Grapevine and Winegrowing

Epigenetics refers to dynamic chemical modifications to the genome that can perpetuate gene activity without changes in the DNA sequence. Epigenetic mechanisms play important roles in growth and development. They may also drive plant adaptation to adverse environmental conditions by buffering environmental variation. Grapevine is an important perennial fruit crop cultivated worldwide, but mostly in temperate zones with hot and dry summers. The decrease in rainfall and the rise in temperature due to climate change, along with the expansion of pests and diseases, constitute serious threats to the sustainability of winegrowing. Ongoing research shows that epigenetic modifications are key regulators of important grapevine developmental processes, including berry growth and ripening. Variations in epigenetic modifications driven by genotype–environment interplay may also lead to novel phenotypes in response to environmental cues, a phenomenon called phenotypic plasticity. Here, we summarize the recent advances in the emerging field of grapevine epigenetics. We primarily highlight the impact of epigenetics to grapevine stress responses and acquisition of stress tolerance. We further discuss how epigenetics may affect winegrowing and also shape the quality of wine.
Utilization of Infrared Thermography in Assessing Thermal Responses of Farm Animals under Heat Stress

Simple Summary Heat stress is an event that causes health alterations and decreases the productive performance of farm animals. Therefore, it is important to establish methods that can help to evaluate the thermal state of animals noninvasively. According to the physiological response of vasodilation, when the brain detects an increase in body temperature, infrared thermography can be used to detect these peripheral changes and predict heat stress. The present review aims to analyze the neurobiological response associated with heat stress and how thermal imaging in different thermal windows can be used to recognize heat stress in farmed ungulates. Abstract Heat stress is a condition that can affect the health, performance, and welfare of farm animals. The perception of thermal stress leads to the activation of the autonomic nervous system to start a series of physiological and behavioral mechanisms to restore thermostability. One of these mechanisms is vasodilation of peripheral blood vessels to increase heat loss through the skin. Due to this aspect, infrared thermography has been suggested as a method to assess the thermal state of animals and predict rectal temperature values noninvasively. However, it is important to consider that predicting rectal temperature is challenging, and its association with IRT is not always a direct linear relationship. The present review aims to analyze the neurobiological response associated with heat stress and how thermal imaging in different thermal windows can be used to recognize heat stress in farmed ungulates.
Design and Optimization of Delphi-Based Electromagnetic Coilgun

In classical gun and satellite launching systems, noise, flame, and hazardous gasses occur during the explosion of gunpowder, and it is possible to change the muzzle velocity of projectile only during the production phase. In addition, negative aspects of classical guns include the storage of munitions, their decay in time, high amount of their mass, and their high cost. In this paper, “A New Electromagnetic Coilgun” is designed to allow acceleration of a ferromagnetic projectile with the help of instantaneous alternate current applied on four sequential stator coils without using any mechanical boosters. programmable interface controllers microcontroller software and Delphi software were used to obtain the data from optic systems placed at the beginning of stator coils for gradual velocity measurement of the projectile, to process the same, and to perform triggering checks. Furthermore, such Delphi-based software allowed to improve the muzzle velocity by changing the triggering time for coils and, thus, to optimize the system. This paper is distinguished from other studies in the literature in terms of the software used and the design of accelerating coil. Data communication was ensured through the serial port of the PC during the launching process. The study examined the effects of variables such as the voltage applied on coils, number of turns of the coil, and the number of capacitors on the maximum muzzle velocity that may be obtained through the launcher and these were discussed in detail in the scope of the paper.
Preliminary study on early diagnosis of Alzheimer’s disease in APP/PS1 transgenic mice using multimodal magnetic resonance imaging

Alzheimer’s disease (AD) has an insidious onset and lacks clear early diagnostic markers, and by the time overt dementia symptoms appear, the disease is already in the mid-to-late stages. The search for early diagnostic markers of AD may open a critical window for Alzheimer’s treatment and facilitate early intervention to slow the progression of AD. In this study, we aimed to explore the imaging markers for early diagnosis of AD through the combined application of structural magnetic resonance imaging (sMRI), resting-state functional magnetic resonance imaging (rs-fMRI), and 1H-magnetic resonance spectroscopy (1H-MRS) multimodal magnetic resonance imaging (MRI) techniques at the animal experimental level, with the aim to provide a certain reference for early clinical diagnosis of AD. First, sMRI scans were performed on 4-month-old amyloid beta precursor protein/presenilin 1 (APP/PS1) transgenic AD model mice and wild type mice of the same litter using a 7.0 T animal MRI scanner to analyze the differential brain regions with structural changes in the gray matter of the brain by voxel-based morphometry (VBM). Next, rs-fMRI scans were performed to analyze the differential brain regions between groups for local spontaneous brain activity and functional connectivity (FC) between brain regions. Finally, 1H-MRS scans were performed to quantify and analyze intergroup differences in the relative concentrations of different metabolites within regions of interest (cortex and hippocampus). Compared with wild type mice, the volume of the left hippocampus, and right olfactory bulb of APP/PS1 transgenic AD model mice were reduced, the functional activity of the bilateral hippocampus, right piriform cortex and right caudate putamen was reduced, the functional network connectivity of the hippocampus was impaired, and the relative content of N-acetylaspartate (NAA)in the hippocampus was decreased. In addition, this study found that imaging changes in olfactory-related brain regions were closely associated with AD diagnosis, and these findings may provide some reference for the early diagnosis of AD.
Contribution of tourist hotels in socio-economic development of local communities in Monduli District, Northern Tanzania

A study to assess the contribution of tourist hotels in socio-economic development was carried out in Mto wa Mbu Ward in Monduli District. Survey data were collected using household questionnaire survey, focus group discussion, semi-structured interviews, and by physical visits. The study revealed that tourist hotels and/or lodges realized TZS 665, 496,000 (US $ 443,664) per annum and offered employment to 143 people, out of which 19% were females. In addition, hotels and/or lodges investors helped village governments in implementing different socio-economic development projects such as schools, dispensaries, and provision of clean water. Despite these livelihood achievements, only 9.3% of the local communities had the opinion that these investments were of beneficial to them. The study concludes that these investments have the potential of improving local communities’ livelihood. To achieve this, the study recommends active local participation in the tourism development process by involving government and other tourist stakeholders such as tour companies and travel agencies.
An Entity Extraction Pipeline for Medical Text Records Using Large Language Models: Analytical Study.

BACKGROUND The study of disease progression relies on clinical data, including text data, and extracting valuable features from text data has been a research hot spot. With the rise of large language models (LLMs), semantic-based extraction pipelines are gaining acceptance in clinical research. However, the security and feature hallucination issues of LLMs require further attention. OBJECTIVE This study aimed to introduce a novel modular LLM pipeline, which could semantically extract features from textual patient admission records. METHODS The pipeline was designed to process a systematic succession of concept extraction, aggregation, question generation, corpus extraction, and question-and-answer scale extraction, which was tested via 2 low-parameter LLMs: Qwen-14B-Chat (QWEN) and Baichuan2-13B-Chat (BAICHUAN). A data set of 25,709 pregnancy cases from the People's Hospital of Guangxi Zhuang Autonomous Region, China, was used for evaluation with the help of a local expert's annotation. The pipeline was evaluated with the metrics of accuracy and precision, null ratio, and time consumption. Additionally, we evaluated its performance via a quantified version of Qwen-14B-Chat on a consumer-grade GPU. RESULTS The pipeline demonstrates a high level of precision in feature extraction, as evidenced by the accuracy and precision results of Qwen-14B-Chat (95.52% and 92.93%, respectively) and Baichuan2-13B-Chat (95.86% and 90.08%, respectively). Furthermore, the pipeline exhibited low null ratios and variable time consumption. The INT4-quantified version of QWEN delivered an enhanced performance with 97.28% accuracy and a 0% null ratio. CONCLUSIONS The pipeline exhibited consistent performance across different LLMs and efficiently extracted clinical features from textual data. It also showed reliable performance on consumer-grade hardware. This approach offers a viable and effective solution for mining clinical research data from textual records.
Detection and Localization of Small Moving Objects in the Presence of Sensor and Platform Movement

In this paper, we address the challenge of detecting small moving targets in dynamic environments characterized by the concurrent movement of both platform and sensor. In such cases, simple image-based frame registration and optical flow analysis cannot be used to detect moving targets. To tackle this, it is necessary to use sensor and platform meta-data in addition to image analysis for temporal and spatial anomaly detection. To this end, we investigate techniques that utilize inertial data to enhance frame-to-frame registration, consistently yielding improved detection outcomes when compared against purely feature-based techniques. For cases where image registration is not possible even with metadata, we propose single-frame spatial anomaly detection and then estimate the range to the target using the platform velocity. The behavior of the estimated range over time helps us to discern targets from clutter. Finally, we show that a KNN classifier can be used to further reduce the false alarm rate without a significant reduction in detection performance. The proposed strategies offer a robust solution for the detection of moving targets in dynamically challenging settings.
Casting Light on Early Heart Failure: Unveiling the Prognostic Potential of the E/(e′ × s′) Index

It has been shown that patients with NYHA class I and II have a high morbidity and mortality burden. We investigated the value of a new tissue Doppler index, E/(e′ × s′), to predict cardiac events in the long-term follow-up of patients at an early stage of heart failure (HF). Sequential echocardiography was conducted on a consecutive cohort of 212 hospitalized HF patients, pre-discharged and with three-month follow-up. The primary end point consisted of cardiac death or readmission due to HF worsening. During follow-up, cardiac events occurred in 99 patients (46.7%). The first cardiac event was represented by cardiac death in 8 patients (3.8%) and readmission for HF in 91 patients (42.9%). A Kaplan–Meier analysis did not show a significantly different event-free survival rate between patients with NYHA class I and II. The composite end point was significantly higher in patients with an E/(e′ × s′) >1.6. The E/(e′ × s′) at discharge was the best independent predictor of cardiac events. Those exhibiting an E/(e′ × s′) > 1.6 at discharge, with a subsequent deterioration after three months, displayed the poorest prognosis concerning cardiac events, HF-related rehospitalization, and cardiac mortality (all p < 0.05). In early-stage HF patients, an E/(e′ × s′) > 1.6 emerged as a robust predictor of clinical outcomes, especially when coupled with a deterioration in condition.
Overexploitation of Abalone at Libong Island, Trang Province, Southern Thailand

Abalone is generally known as a fishery resource of high economic value. The wild abalone from Libong Island is widely known for its potential as a cocktail-size and high-quality broodstock for hatcheries. The high market price and external demand have encouraged local fisherman to catch the wild abalone without proper management, resulting in a near extinction crisis in the abalone population in this area. The present evaluation of abalone management at Libong Island, Trang Province, Thailand, was conducted using local user perceptions. Sixteen performance indicators included effectiveness indicators, equity indicators, and sustainable indicators. These were measured to determine whether the abalone management activities had achieved the set objectives in terms of better conditions for abalone cultivation and sustainability. The results revealed that the abalone population has undergone degrading and decline due to lack of proper management measures in this area. The findings suggest that practical management is needed for the abalone population at Libong Island.
Hidden Mountain

This article considers the neglected area of disposition, the nature of our relationship with waste. Marketing tactics are complicit in a throwaway culture, so how can we better theorize our relationship to waste? The authors submit that to maintain control, we are encouraged to keep waste in its place—out of sight and out of mind. This is achieved through systemic smoothing mechanisms such as our socialization against waste, the role of trash cans, and the work of garbage collectors. By exposing the detritus of consumption, the “waste mountain,” a macromarketing analysis helps us confront the systemic avoidance of waste. As such, this constitutes an initial contribution to marketing as social engagement and also to future policy development. We connect the rendering invisible or hidden aspect of waste to what Bauman has termed the economics of deception prevalent within consumer society.
Development of Probabilistic Viscoelastic Continuum Damage Model for Asphalt Concrete

The main objective of this paper is to develop a realistic probabilistic framework for characterization of different types of asphalt concrete using advanced material modeling. The adopted methodology builds on and enhances a viscoelastic continuum damage (VECD) material model by utilizing a suite of associated experimental testing protocols and incorporating the uncertainties associated with the different material properties. The modeled uncertainties address the variabilities and errors associated with the linear viscoelastic (LVE) functions achieved from the complex modulus test and damage characteristic curves obtained from constant crosshead rate testing. A probabilistic scheme using First Order approximations and Monte Carlo simulations is developed to characterize the inherent uncertainty of each of the LVE functions over the time domain of their mastercurves. For damage characteristic curves, the uncertainty in normalized pseudostiffness increases as the level of damage becomes larger. The uncertainties of LVE properties are propagated along with those of C versus stress curves to yield a probabilistic viscoelastic continuum damage model (P-VECD). The P-VECD not only predicts the average viscoelastic response to a given loading input, but it can also provide its distribution, which is essential for a reliability-based pavement design.