Miracle-dz/UniFAR-FTUs huggingface dataset:
A Mixed Logit Model of Homeowner Preferences for Wildfire Hazard Reduction

People living in the wildland-urban interface (WUI) are at greater risk of suffering major losses of property and life from wildfires. Over the past several decades the prevailing view has been that wildfire risk in rural areas was exogenous to the activities of homeowners. In response to catastrophic fires in the WUI over the past few years, recent approaches to fire management and prevention in the WUI have emphasized activities that can be taken by landowners and communities to reduce the risk of catastrophic wildfires. These activities include fuel reduction via mechanical thinning and controlled burning of forests surrounding communities and the creation of defensible space around homes. Promotion of community and homeowner-based risk reduction activities represents a new direction in wildland fire management and prevention. We developed a survey instrument to evaluate the value to homeowners in Florida of public and private programs to reduce wildfire risk. A random stratified random sample was drawn to evaluate potential differences in preferences between people living in low, medium, and high fire risk zones. A choice experiment was designed that allowed respondents to choose between public and private fire risk reduction programs that varied across three attributes: wildfire risk, economic loss, and program cost. The survey was implemented using a phone-mail-phone protocol. Results show that people living in communities they considered to be at high risk of wildfires were willing to pay a substantial premium for public wildfire mitigation programs, but had modest willingness-to-pay (WTP) for fuel reduction programs on their own property. Risk preference is related to demographic characteristics, and households that are risk seeking are more likely to make risky choices regarding wildfire mitigation programs. The results suggest that low income households, households without homeowners insurance, and African-American households living in the WUI may be good candidates for assistance.
Archean melt inclusions in zircon from quartzite and granitic orthogneiss from South Africa; magma compositions and probable sources of protoliths

Results are presented of analyses of melt inclusions in >3.0 Ga zircon grains from three lithologies in South Africa: the Orange Grove Quartzite of the Witwatersrand Supergroup, quartzite of the Beit Bridge Group and the S-type Singelele orthogneiss, the latter both of the Central Zone of the Limpopo Belt. In each case, the presence of melt inclusions indicates that the zircon is magmatic in origin. No melt inclusions occur in metamorphic overgrowths in some grains. Melt inclusions in detrital zircon grains from the two quartzite units indicate that both tonalitic-trondhjemitic and granitic magmas were being emplaced in the source areas at about 3.2 Ga. Granitic magmatism at that time is among the earliest recognized in southern Africa. Inclusions in zircon grains from the Singelele orthogneiss are compositionally similar to the host rock, showing that they are a magmatic phase rather than a xenocrystic phase.
Flight toward Sustainability in Poultry Nutrition with Black Soldier Fly Larvae

Simple Summary This review article comprehensively explores black soldier fly larvae (BSFL), Hermetia illucens, as a sustainable alternative protein source in poultry nutrition. It discusses the ecological distribution, nutritional composition, and benefits of BSFL, including their high digestibility and nutrient availability. This article highlights the impact of BSFL on broiler growth performance, meat quality, and gut health and underscores the importance of proper inclusion levels for optimal results. In conclusion, this study emphasizes that BSFL offer a nutritionally rich, environmentally sustainable solution, presenting a viable alternative to conventional feed sources and contributing significantly to sustainable agriculture and food security. Abstract Black soldier fly larvae (BSFL), Hermetia illucens (L.) (Diptera: Stratiomyidae), have emerged as a promising feed ingredient in broiler chicken diets, known for their high protein content, nutritional richness, and environmental sustainability. This review examines the effects of integrating BSFL into broiler feeds, focusing on aspects such as growth performance, nutrient digestibility, physiological responses, and immune health. The ability of BSFL to transform waste into valuable biomass rich in proteins and lipids underscores their efficiency and ecological benefits. Protein levels in BSFL can range from 32% to 53%, varying with growth stage and diet, offering a robust source of amino acids essential for muscle development and growth in broilers. While the chitin in BSFL poses questions regarding digestibility, the overall impact on nutrient utilization is generally favorable. The inclusion of BSFL in diets has been shown to enhance growth rates, feed efficiency, and carcass quality in broilers, with the larvae’s balanced amino acid profile being particularly advantageous for muscle development. BSFL may also support gut health and immunity in broilers due to its bioactive components, potentially influencing the gut’s microbial composition and enhancing nutrient absorption and overall health. Moreover, the capacity of BSFL to efficiently convert organic waste into protein highlights their role as an environmentally sustainable protein source for broiler nutrition. Nonetheless, further research is necessary to fully understand the long-term effects of BSFL, ideal inclusion rates, and the impact of varying larval diets and rearing conditions. It is crucial for poultry producers to consult nutritionists and comply with local regulations when incorporating new feed ingredients like BSFL into poultry diets.
Numerical Analysis of Ion Flow in One-Bipole HVDC Transmission Line Using Revised Charge Injection Methods

In the evolving field of electric power transmission networks, high-voltage direct current (HVDC) transmission has garnered attention for its efficacy in long-distance power delivery. However, HVDC systems are susceptible to corona discharges, which generate ions that disrupt the electric field distribution and pose safety concerns. To address these challenges, this study introduces new calculation techniques for predicting the electric field and ion current density around HVDC transmission lines using the finite-element method. The onset fields for the corona discharge were established at 14 and 13 kV/cm for positive and negative ions, respectively. Three novel techniques—average (A), cosine (C), and average–cosine combination (AC)—were introduced for continuous charge distribution. Additionally, an enhancement factor $\beta$ was incorporated to reflect the various climatic conditions, enhancing the model’s adaptability. This approach streamlines the analysis by reducing the reliance on complex parameters such as conductor roughness coefficient and climate constants. The techniques were validated across four different bundle configurations of transmission lines, with the AC technique demonstrating superior accuracy in predicting the electric field and ion current density, affirming its robustness in diverse scenarios, including under wind conditions. This research marks a significant advancement in modeling electrical discharge phenomena in HVDC environments, providing a simplified yet precise tool for ensuring electrical safety.
The importance of root strength and deterioration rates upon edaphic stability in steepland forests

The additional strength provided by roots to the soil is generally considered to be in the form of a cohesive strength hC which may range in magnitude from 1 kPa to 20 kPa. Studies of the tensile strength of tree roots show that small roots sampled from living trees range in mean tensile strength from about 10 MPa to about 60 MPa. After tree felling small roots lose their strength at average rates between 300 and 500 kPa per month. Root biomass also decreases rapidly after clearfelling. The reduction in K after forest removal is a prime cause of landsliding on many steep slopes.
EMI-Net: An End-to-End Mechanism-Driven Interpretable Network for SAR Target Recognition Under EOCs

Most existing synthetic aperture radar (SAR) target recognition methods based on deep learning are of black box structure and data-drive networks, which are faced with the issue of severe performance degradation under extended operating conditions (EOCs). To address the issue, this article proposes an end-to-end mechanism-driven interpretable network (EMI-Net) for SAR target recognition under EOCs. The EMI-Net achieves the integration of scattering center feature extraction and target recognition in an end-to-end framework to avoid the mismatch between scattering center features with the classifier and also explore the representative electromagnetic characteristics that are useful for recognition under EOCs. In EMI-Net, by unfolding a sparse-solving algorithm and integrating the scattering center model into deep networks, our model contains feature encoding and decoding procedures of SAR images. Thus, EMI-Net is an interpretable deep unfolding network that is driven by physical mechanisms to precisely learn scattering center features reflecting locations and amplitude of SAR targets based on the deep learning mode. Our EMI-Net divides input images into multiple patches based on the image-domain scattering center model to reduce its computation and space complexities. In addition, EMI-Net treats the scattering centers as discrete 3-D point data and designs a point cloud network (PCN) as a classifier to explore permutation-invariant representations for recognition. Results on the measured dataset validate that EMI-Net gains superior scattering center extraction and target recognition results under EOCs, and also shows high time-efficiency and low memory requirement.
Effect of greenhouse heating on quality of tomato drip irrigated with a water pumped from a photovoltaic pumping system

In remote agricultural areas, diesel generators are commonly used to pump water. These technics contribute to global warming, environmental pollution, and greenhouse gas emissions. On account of these harmful consequences, the concept of sustainability is suggested by the researcher. An independent source available almost world-wide. Sources like wind energy, solar energy, geothermal and biomass energies represent a very interesting potential for renewable energy. Solar energy is one of the most abundant renewable energies obtained from the sun which produces the clean electrical energy required for electrical pumps to supply water for plants. Photovoltaics generators represent an excellent alternative to producing electricity. The electricity generated by the PV modules is employed in the photovoltaic water pumping system, operating the pump to deliver water. The water supplied by the photovoltaic water pumping system is for agriculture applications, where it’s used to irrigate tomatoes. In the arid region of Adrar, Algeria, agriculture is facing temperature drops, especially in winter. Tomato is one of the most consumed vegetables and are widely cultivated in greenhouses. Tomato is very sensitive to the transition between cold and warm temperatures. Therefore, the quality of tomatoes is getting attention from several researchers.The main objective of our study is to carry out an experiment to analyze the effect of heating inside the greenhouse to provide the heat necessary for the growing environment, on the quality of tomatoes.The work focused on the physicochemical and microbiological quality of the tomatoes from the greenhouses. The obtained results demonstrate excellent quality compared to standards.
A Novel Tendon Injury Model, Induced by Collagenase Administration Combined with a Thermo-Responsive Hydrogel in Rats, Reproduces the Pathogenesis of Human Degenerative Tendinopathy

Patellar tendinopathy is a common clinical problem, but its underlying pathophysiology remains poorly understood, primarily due to the absence of a representative experimental model. The most widely used method to generate such a model is collagenase injection, although this method possesses limitations. We developed an optimized rat model of patellar tendinopathy via the ultrasound-guided injection of collagenase mixed with a thermo-responsive Pluronic hydrogel into the patellar tendon of sixty male Wistar rats. All analyses were carried out at 3, 7, 14, 30, and 60 days post-injury. We confirmed that our rat model reproduced the pathophysiology observed in human patients through analyses of ultrasonography, histology, immunofluorescence, and biomechanical parameters. Tendons that were injured by the injection of the collagenase–Pluronic mixture exhibited a significant increase in the cross-sectional area (p < 0.01), a high degree of tissue disorganization and hypercellularity, significantly strong neovascularization (p < 0.01), important changes in the levels of types I and III collagen expression, and the organization and presence of intra-tendinous calcifications. Decreases in the maximum rupture force and stiffness were also observed. These results demonstrate that our model replicates the key features observed in human patellar tendinopathy. Collagenase is evenly distributed, as the Pluronic hydrogel prevents its leakage and thus, damage to surrounding tissues. Therefore, this model is valuable for testing new treatments for patellar tendinopathy.
Insights into the management of Lewy body dementia: a scoping review

Lewy body dementia (LBD) is situated at the convergence of neurodegenerative disorders, posing an intricate and diverse clinical dilemma. The accumulation of abnormal protein in the brain, namely, the Lewy body causes disturbances in typical neural functioning, leading to a range of cognitive, motor, and mental symptoms that have a substantial influence on the overall well-being and quality of life of affected individuals. There is no definitive cure for the disease; however, several nonpharmacological and pharmacological modalities have been tried with questionable efficacies. The aim of this study is to figure out the role of different interventional strategies in the disease. Donepezil, rivastigmine, memantine, and galantamine were the commonly used drugs for LBD. Together with that, levodopa, antipsychotics, armodafinil, piracetam, and traditional medications like yokukansan were also used, when indicated. Talking about nonpharmacological measures, exercise, physical therapy, multicomponent therapy, occupational therapy, psychobehavioral modification, transcranial stimulation, and deep brain stimulation have been used with variable efficacies. Talking about recent advances in the treatment of LBD, various disease-modifying therapies like ambroxol, neflamapimod, irsenontrine, nilotinib, bosutinib, vodobatinib, clenbuterol, terazosin, elayta, fosgonimeton, and anle138b are emerging out. However, there drugs are still in the different phases of clinical trials and are not commonly used in clinical practice. With the different pharmacological and nonpharmacological modalities we have for treatment of LBD, all of them offer symptomatic relief only. Being a degenerative disease, definite cure of the disease can only be possible with regenerative measures.
DeepCamouflageAI: Deep Learning-Based Camouflage People Detection and Segmentation

Camouflage hides information about any object in the background by efficiently merging foreground and background pixels. However, differentiating between these two-level pixels is a challenging task. Existing techniques work on camouflage object detection (COD), but camouflage people detection (CPD) needs to be explored more. Therefore, we have developed a CPD model for People detection and segmentation. The proposed model is based on deep convolutional neural network layers and transfer learning techniques. We exploited Mobile Netv2 as a base technique and presented a model that efficiently detects People from camouflage backgrounds. The model is trained and evaluated using the CAMO and COD10K standard datasets. Later evaluation is performed using the CPD dataset of 1K camouflaged people images. Comparative analysis between different techniques is performed and evaluated. The proposed model performed very well compared to the previous SOTA model.
Strategies to Promote Empowerment Status of Breast Cancer Women

Background As the second leading cause of death in women in the world, breast cancer has several physical and psychological effects. Nowadays, nonclinical approaches such as patient empowerment have been considered by physicians along with clinical care. Given the increasing number of breast cancer women worldwide, promoting the empowerment of these patients is one of the key factors affecting their survival and quality of life. Therefore, because of no comprehensive research on the empowerment needs and related improvement strategies, this study is aimed at determining the empowerment status of breast cancer patients referred to the Shahid Motahari Breast Cancer Clinic in Iran, Shiraz, and at providing strategies to improve their empowerment in 2021. Methods This applied study was conducted in two phases. In the quantitative phase, 310 Cancer-Related Patient Empowerment Scale questionnaires (Persian format) were distributed among the studied patients selected through the random sampling method in the clinic, and the items with “unacceptable status” became the basis for determining the empowerment strategies through the scoping review and semistructured interviews with 22 medical staff and patients through the thematic analysis. The collected data were analyzed using the SPSS 20.0 and MAXQDA10 software. Results The mean score of the participants' empowerment strategies was 3.58. The results showed that trust in the physician, family support, and spiritual beliefs could affect the empowerment of the studied patients. Moreover, the participants needed empowerment strategies in 11 scale items with unacceptable status, for which 46 strategies were determined in the scoping review and interview phase. Conclusion The results of this study provided useful strategies for empowering breast cancer patients, the most important of which were classified into five categories of financial support, informational support, interaction with the physician, occupational support, and complementary therapies, the use of which by the stakeholders could help to improve the patients' quality of life while improving their empowerment.
A Microwave Differential Dielectric Sensor Based on Mode Splitting of Coupled Resonators

This study explores the viability of using the avoided mode crossing phenomenon in the microwave regime to design microwave differential sensors. While the design concept can be applied to any type of planar electrically small resonators, here, it is implemented on split-ring resonators (SRRs). We use two coupled synchronous SRRs loaded onto a two-port microstrip line system to demonstrate the avoided mode crossing by varying the distance between the split of the resonators to control the coupling strength. As the coupling becomes stronger, the split in the resonance frequencies of the system increases. Alternatively, by controlling the strength of the coupling by materials under test (MUTs), we utilize the system as a microwave differential sensor. First, the avoided mode crossing is theoretically investigated using the classical microwave coupled resonator techniques. Then, the system is designed and simulated using a 3D full-wave numerical simulation. To validate the concept, a two-port microstrip line, which is magnetically coupled to two synchronous SRRs, is utilized as a sensor, where the inter-resonator coupling is chosen to be electric coupling controlled by the dielectric constant of MUTs. For the experimental validation, the sensor was fabricated using printed circuit board technology. Two solid slabs with dielectric constants of 2.33 and 9.2 were employed to demonstrate the potential of the system as a novel differential microwave sensor.
Multilingual Intent Recognition: A Study of Crosslingual Transfer Learning

Intent recognition aids conversational AI in understanding the meaning of user messages, which has to be realised in multiple languages for multilingual scenarios. In this paper, we explore the effectiveness and limitations of massively multilingual language representational models like mBERT and XLM-R, in expressing their crosslingual transfer learning ability for multilingual intent recognition. It is noted that models based on BERT can be fine-tuned for multiple downstream tasks with a limited domain-specific text dataset, in order to achieve task and domain adaptation. We analyse the particulars of text data that assist these models in effectively expressing crosslingual transfer, without an explicit fine-tuning. We noticed that when the text content across multiple languages is “inherently parallel fine-tuning with domain-specific text from one language successfully exhibit crosslingual transfer to other languages for recognising intents from short text messages. Inherently parallel text data has content in multiple languages with similar meaning, though not in parallel sentences. Such data can be found commonly in messages received by conversational assistants, in the form of customer’s queries, reviews, or requests related to same products or services offered by companies/providers. The analysis presented in this paper can benefit numerous business and industrial applications in rich multilingual societies, where it is easier to collect and prepare text data for fine-tuning in one major language, and build conversational assistants catering to multiple target low-resource languages.
Recent Developments in Polyurea Research for Enhanced Impact Penetration Resistance and Blast Mitigation

Polyurea has gained significant attention in recent years as a functional polymer material, specifically regarding blast and impact protection. The molecular structure of polyurea is characterized by the rapid reaction between isocyanate and the terminal amine component, and forms an elastomeric copolymer that enhances substrate protection against blast impact and fragmentation penetration. At the nanoscale, a phase-separated microstructure emerges, with dispersed hard segment microregions within a continuous matrix of soft segments. This unique microstructure contributes to the remarkable mechanical properties of polyurea. To maximize these properties, it is crucial to analyze the molecular structure and explore methods like formulation optimization and the incorporation of reinforcing materials or fibers. Current research efforts in polyurea applications for protective purposes primarily concentrate on construction, infrastructure, military, transportation and industrial products and facilities. Future research directions should encompass deliberate formulation design and modification, systematic exploration of factors influencing protective performance across various applications and the integration of numerical simulations and experiments to reveal the protective mechanisms of polyurea. This paper provides an extensive literature review that specifically examines the utilization of polyurea for blast and impact protection. It encompasses discussions on material optimization, protective mechanisms and its applications in blast and impact protection.