Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Analysis of Forest Fragmentation in the Amazon Rainforest of Tocantins State, Brazil

The Amazon Rainforest presents intense pressure from agricultural and cattle farming activities on its forest remnants, which promotes forest fragmentation. The present study aimed to quantify structural changes in the Amazon Rainforest landscape in the state of Tocantins, Brazil, that occurred between 1985 and 2020 using Mapbiomas data and landscape metrics. MAPBIOMS images were used to delimit forest fragments and size classes. Landscape metrics were estimated using the Fragstats® software. In the state of Tocantins, the Amazon Rainforest, grassland, and savanna showed a fragmentation process between 1985 and 2020, with a reduction in their total area and the number of fragments, with less rounded shapes and loss of core areas, in need of environmental policies to protect forest remnants and interrupt the process of Forest fragmentation, especially the Amazon Rainforest.
Neuroergonomics: Research and practice

This article describes the characteristics and scope of neuroergonomics, defined as the study of brain and behaviour at work. Neuroergonomics focuses on investigations of the neural bases of mental functions and physical performance in relation to technology, work, leisure, transportation, health care and other settings in the real world. The two major goals of neuroergonomics are to use knowledge of brain function and human performance to design technologies and work environments for safer and more efficient operation, and to advance understanding of brain function underlying real-world human performance. The conceptual, theoretical and philosophical issues at the core of neuroergonomics--that lie at the confluence of cognitive science, neuroscience, and ergonomics--are discussed. Adaptive human-machine systems are then described as an illustration of neuroergonomic research. Several other examples of neuroergonomic research and practice are also described.
Using the consensus group method to select the best screening tools for autism and intellectual disability for use with Nigerian adolescents

Diagnosing autism or ID using a gold‐standard tool can be time‐consuming, costly, and requires training, which is generally limited in Nigeria, and the rest of Africa. Screening, on the other hand, can be quick and effective, with minimal training depending on the tool (Iragorri & Spackman, Public Health Reviews, 2018;39(1):17), thus making the availability of short screeners a necessity in Nigeria, and the rest of Africa. We identified four screening tools through a previously completed systematic review (Nwokolo et al., Review Journal of Autism and Developmental Disorders, 2022;1–23.), two (SCQ and AQ‐10) for autism and two (SCIL and CAIDS‐Q) for ID, which appeared appropriate for validation for use within African nations. The Nominal Group Technique was used with a purposive group of professionals, parents, and laypersons to select and adapt the existing screening tools for autism and ID for use with older children and adolescents in Nigeria. The group examined the screening tools for cultural relevance, face and content validity. Following the discussions, items were either (1) accepted in the original form or (2) more culturally appropriate examples chosen if at least 75% of participants agreed. The group selected the SCQ for autism and the SCIL for ID. The minimum agreement on all autism and ID measures items was 84%, and this indicated the measures had face and content validity for use within Nigeria. Following the recommendations and consensus of the group, the SCQ and the SCIL 14–17 were agreed on as measures to be validated with the Nigerian adolescents, with only a small number of adjustments needed to allow for different use of language, customs and environment in the Nigerian context.
Paving the Way for Memory Enhancement: Development and Examination of a Neurofeedback System Targeting the Medial Temporal Lobe

Neurofeedback (NF) shows promise in enhancing memory, but its application to the medial temporal lobe (MTL) still needs to be studied. Therefore, we aimed to develop an NF system for the memory function of the MTL and examine neural activity changes and memory task score changes through NF training. We created a memory NF system using intracranial electrodes to acquire and visualise the neural activity of the MTL during memory encoding. Twenty trials of a tug-of-war game per session were employed for NF and designed to control neural activity bidirectionally (Up/Down condition). NF training was conducted with three patients with drug-resistant epilepsy, and we observed an increasing difference in NF signal between conditions (Up–Down) as NF training progressed. Similarities and negative correlation tendencies between the transition of neural activity and the transition of memory function were also observed. Our findings demonstrate NF’s potential to modulate MTL activity and memory encoding. Future research needs further improvements to the NF system to validate its effects on memory functions. Nonetheless, this study represents a crucial step in understanding NF’s application to memory and provides valuable insights into developing more efficient memory enhancement strategies.
A Gradient Boosted ML Approach to Feature Selection for Wireless Intrusion Detection

The regular discovery of new attacks on 802.11 wireless devices emphasizes the importance of developing ML-based intrusion detection systems that generalize to such attacks. Class-imbalance issues in intrusion datasets pose a learning problem for ML solutions. Our methodology directly incorporates the class-imbalance issues to learn better feature importance measures. With features extracted using this technique from the AWID dataset, we use a gradient-boosted model to show that these features are necessary to generalize to new attack types in the AWID test dataset.
Selective Biasing with Trie-based Contextual Adapters for Personalised Speech Recognition using Neural Transducers

Neural transducer ASR models achieve state of the art accuracy on many tasks, however rare word recognition poses a particular challenge as models often fail to recognise words that occur rarely, or not at all, in the training data. Methods of contextual biasing, where models are dynamically adapted to bias their outputs towards a given list of relevant words and phrases, have been shown to be effective at alleviating this issue. While such methods are effective at improving rare word recognition, over-biasing can lead to degradation on common words. In this work we propose several extensions to a recently proposed trie-based method of contextual biasing. We show how performance of the method can be improved in terms of rare word recognition, especially in the case of very large catalogues, by introducing a simple normalisation term, how the method can be trained as an adapter module, and how selective biasing can be applied to practically eliminate over-biasing on common words.
Current Therapeutical Approaches Targeting Lipid Metabolism in NAFLD

Nonalcoholic fatty liver disease (NAFLD, including nonalcoholic fatty liver (NAFL) and nonalcoholic steatohepatitis (NASH)) is a high-prevalence disorder, affecting about 1 billion people, which can evolve to more severe conditions like cirrhosis or hepatocellular carcinoma. NAFLD is often concomitant with conditions of the metabolic syndrome, such as central obesity and insulin-resistance, but a specific drug able to revert NAFL and prevent its evolution towards NASH is still lacking. With the liver being a key organ in metabolic processes, the potential therapeutic strategies are many, and range from directly targeting the lipid metabolism to the prevention of tissue inflammation. However, side effects have been reported for the drugs tested up to now. In this review, different approaches to the treatment of NAFLD are presented, including newer therapies and ongoing clinical trials. Particular focus is placed on the reverse cholesterol transport system and on the agonists for nuclear factors like PPAR and FXR, but also drugs initially developed for other conditions such as incretins and thyromimetics along with validated natural compounds that have anti-inflammatory potential. This work provides an overview of the different therapeutic strategies currently being tested for NAFLD, other than, or along with, the recommendation of weight loss.
Adoption of Biosecurity Practices in Smallholder Dairy Farms in Ethiopia

Dairy production is an important livelihood source for smallholder dairy farmers who produce the majority of milk consumed and traded in Ethiopia. Dairy production is, however, constrained by livestock diseases that impact farm productivity, food safety, and animal welfare. Biosecurity measures (BSM) include all risk reduction strategies designed to avoid the introduction of pathogenic infections from outside and minimise the spread of diseases within dairy herds. This study used a cross-sectional survey to investigate the adoption of BSM in dairy farms in Addis Ababa and Oromia regions of Ethiopia. Using a questionnaire, scores for adopted external and internal BSM were calculated based on the Ghent’s University Biocheck tool to compare the performance of different farms in Ethiopia. The weighted external biosecurity score was 49.1%, which was below average (below 50% adoption), while the weighted internal biosecurity score was 55.5%. Low adoption of crucial BSM increases the risk of disease introduction into dairy farms and transmission within herds. Adoption of BSM at the farm level was driven by individual, demographic, and socio-economic drivers, including education, farming system, milk value chain, and farming experience among others. Results of this research reveal low adoption of BSM and the imperative to encourage farmers to implement BSM can lead to a reduction in disease pressures and, thus, a reduction in antibiotic use and increased dairy farms productivity, and improved animal health and welfare. Farmers can be encouraged through proactive engagement with veterinarians and extension professionals. Moreover, creating a favourable policy environment can support farmers to adopt and implement BSM, given the known fact that “prevention is better and cheaper than curing diseases. ”
Comparing passive and active macroinvertebrate sampling gear efficacy during biomonitoring in Southern Appalachian mountain streams

Aquatic invertebrates are important components of any aquatic ecosystem and are frequently monitored to determine the ecological integrity of those systems. Various gear types are used to collect aquatic macroinvertebrates in wadeable streams. We analyzed commonly used active and passive gear types, including kick seines, Hess samplers, drift nets, and Hester–Dendy samplers, across a watershed in the Great Smoky Mountains National Park to determine their efficacy under various habitat conditions in wadeable Southern Appalachian mountain streams. Hester–Dendy samplers were significantly different from other gear types for most univariate and multivariate analyses. Kick and Hess samples were most similar in multivariate comparisons of macroinvertebrate assemblages. Drift samples were similar in most univariate analyses but were distinct from other gear types when analyzed with multivariate techniques. Our results indicate that comprehensive assessments of macroinvertebrate assemblages require multiple gear types, various analytical approaches, and consistent taxonomic resolution. The complexity of the fluvial system in question should be thoroughly assessed prior to formulating a macroinvertebrate biomonitoring protocol to better ensure robust and meaningful results.
Collaborative Routing and Charging/Discharging Scheduling of Electric Autonomous Vehicles in Coupled Power-Traffic Networks

Autonomous vehicles (AVs) traverse on the road without active human intervention. In an AV-based public transport system, AVs can be supported by an intelligent controller to provide high-efficiency transport services. The intelligent controller manages a fleet of AVs by determining their assignments to transport requests, sending instructions concerning the optimized plans, and managing the charging/discharging schedule and locations for the governed AVs. On the other hand, vehicle-to-grid is a promising technology to benefit the power system operation by using the battery storage of electric vehicles (EVs). A well-designed request assignment and travel schedule controller can substantially reduce the unnecessary travel and energy purchase cost for the transport system and power system, respectively. In this paper, we aim at coordinating the transport request assignment, traveling and charging/discharging schedule for a fleet of electric AVs (EAVs), and the reactive power support from distributed generations (DGs) to maximize utility in both the AV-based public transportation system and power distribution system. We formulate the assignment and scheduling problem as a mixed-integer program. Experiments on a modified 15-bus distribution system and a simulated traffic network are conducted. The results show that the formulated problem effectively minimizes the total travel and energy purchase cost by controlling the EAV behaviors and DG outputs. This study may promote the integration of transport and power technologies in smart grids as well as incentivize the utilization of EAVs.
Aligning Speech Enhancement for Improving Downstream Classification Performance

Speech-based classification models in the cloud are gaining large-scale adoption. In many applications where post-deployment background noise conditions mismatch those used during model training, fine-tuning the original model on local data would likely improve performance. However, this is not always possible as the local user may not be authorized to modify the cloud-based model or the local user may be unable to share the data and corresponding labels required for fine-tuning. In this paper, we propose a denoiser stored locally on edge devices with an application-specific training scheme. It learns a custom speech enhancement scheme that aligns the local de-noiser with the downstream model, without requiring access to the cloud-based weights. We evaluate the denoiser with a common classification task – keyword spotting – and demonstrate using two different architectures that the proposed scheme outperforms common speech enhancement models for different types of background noise.
Combination of Bacillus and Low Fertigation Input Promoted the Growth and Productivity of Chinese Cabbage and Enriched Beneficial Rhizosphere Bacteria Lechevalieria

Simple Summary Chinese cabbage is often threatened by soft rot disease caused by Pectobacterium spp. , which becomes severe due to overirrigation or heavy rainfall. An improved application technique of biocontrol Bacillus agents with low fertigation input is suggested as an environment-friendly strategy to control soft rot disease and improve the cabbage yield. In this study, the effect of Bacillus velezensis B006 on cabbage growth under normal and low fertigation conditions was firstly evaluated, and then the relationships of the plant growth with the cabbage rhizosphere microbial communities and the production of endogenous hormones in cabbage roots were studied. Our results highlighted that B006 application combined with low fertigation input promoted cabbage growth and increased root vitality which were positively related to enrichment of Lechevalieria in the cabbage rhizosphere and the production of indole-3-acetic acid and salicylic acid in cabbage roots. In conclusion, we provide supporting evidence on the improvement of Bacillus application in fields to promote the healthy growth and productivity of cabbage. Abstract Long-term overfertilization increases soil salinity and disease occurrence and reduces crop yield. Integrated application of microbial agents with low fertigation input might be a sustainable and cost-effective strategy. Herein, the promoting effects of Bacillus velezensis B006 on the growth of Chinese cabbage under different fertigation conditions in field trials were studied and the underlying mechanisms were revealed. In comparison with normal fertigation (water potential of −30 kPa and soluble N, P, K of 29.75, 8.26, 21.48 Kg hm−2) without B006 application, the combination of B. velezensis B006 and reduced fertigation input (−50 kPa and N, P, K of 11.75, 3.26, 6.48 Kg hm−2) promoted cabbage growth and root development, restrained the occurrence of soft rot disease, and improved the yield. High-performance liquid chromatography (HPLC) analyses indicated that B006 application promoted the production of indole-3-acetic acid and salicylic acid in cabbage roots, which are closely related to plant growth. Rhizosphere microbiota analyses indicated that the combination of low fertigation input and B006 application promoted the enrichment of Streptomyces, Lechevalieria, Promicromonospora, and Aeromicrobium and the abundance of Lechevalieria was positively correlated with the root length and vitality. This suggested that the integrated application of reduced fertigation and Bacillus is highly efficient to improve soil ecology and productivity and will benefit the sustainable development of crop cultivation in a cost-effective way.
On the Jamming Rejection Features of Near-field Beamforming

Large multiple antenna systems have been widely used in 5G for improved spectral efficiencies. For 6G, even bigger arrays are being proposed to obtain larger gains and/or to reduce interference levels. When the physical size of the array is large compared to the propagation distance, communication might occur in the near-field regime, which requires channel models that capture the curvature of the spherical waves. In this paper, we consider the near-field communication regime and study the jamming rejection features of an extremely large array (ELAA). Due to the focusing effect available in the near-field, it is shown that the jamming rejection features in this region are much stronger than the ones associated with far-field. Therefore, near-field beam-focusing constitutes an interesting physical layer security (PLS) technique for 6G communications. 1
A Comprehensive Assessment of the Biocompatibility and Safety of Diamond Nanoparticles on Reconstructed Human Epidermis

Diamond nanoparticles, also known as nanodiamonds (NDs), exhibit remarkable, awe-inspiring properties that make them suitable for various applications in the field of skin care products. However, a comprehensive assessment of their compatibility with human skin, according to the irritation criteria established by the Organization for Economic Cooperation and Development (OECD), has not yet been conducted. The purpose of this study was to evaluate if diamond nanoparticles at a concentration of 25 μg/mL, incubated with reconstituted human epidermis (EpiDermTM) for 18 h, conform to the OECD TG439 standard used to classify chemical irritants. For this purpose, a cell viability test (MTT assay), histological assessment, and analysis of pro-inflammatory cytokine expression were performed. The results indicated that NDs had no toxic effect at the tested concentration. They also did not adversely affect tissue structure and did not lead to a simultaneous increase in protein and mRNA expression of the analyzed cytokines. These results confirm the safety and biocompatibility of NDs for application in skincare products, thereby creating a wide range of possibilities to exert an impact on the advancement of contemporary cosmetology in the future.
Unsupervised Dialogue Topic Segmentation in Hyperdimensional Space

We present HyperSeg, a hyperdimensional computing (HDC) approach to unsupervised dialogue topic segmentation. HDC is a class of vector symbolic architectures that leverages the probabilistic orthogonality of randomly drawn vectors at extremely high dimensions (typically over 10,000). HDC generates rich token representations through its low-cost initialization of many unrelated vectors. This is especially beneficial in topic segmentation, which often operates as a resource-constrained pre-processing step for downstream transcript understanding tasks. HyperSeg outperforms the current state-of-the-art in 4 out of 5 segmentation benchmarks -- even when baselines are given partial access to the ground truth -- and is 10 times faster on average. We show that HyperSeg also improves downstream summarization accuracy. With HyperSeg, we demonstrate the viability of HDC in a major language task. We open-source HyperSeg to provide a strong baseline for unsupervised topic segmentation.
Cost-Effectiveness of Exercise Therapy in Patients with Intermittent Claudication—A Comparison of Supervised Exercise, Home-Based Structured Exercise, and Walk Advice from the SUNFIT Trial

Hospital-based supervised exercise (SEP) is a guideline-recommended intervention for patients with intermittent claudication (IC). However, due to the limited availability of SEP, home-based structured exercise programs (HSEP) have become increasingly popular alongside the “go home and walk” advice. We evaluated the cost-effectiveness of walk advice (WA) with Nordic pole walking vs. SEP combined with WA or HSEP combined with WA. We used data from the SUNFIT RCT (NCT02341716) to measure quality-adjusted life-years (QALYs) over a 12-month follow-up, and economic costs were obtained from a hospital cost-per-patient accounting system. Incremental cost-effectiveness ratios (ICERs) were calculated, and uncertainty was assessed using nonparametric bootstrapping. The average health-care-cost per patient was similar in the WA (EUR 1781, n = 51) and HSEP (EUR 1820, n = 48) groups but higher in the SEP group (EUR 4619, n = 50, p-value < 0.01). Mean QALYs per patient during the follow-up were similar with no statistically significant differences. The findings do not support SEP as a cost-effective treatment for IC, as it incurred significantly higher costs without providing additional health improvements over WA with or without HSEP during the one-year observation period. The analysis also suggested that HSEP may be cost-effective compared to WA, but only with a 64% probability.
Computer-Aided Recognition of Man-Made Structures in Aerial Photographs

Abstract : Aerial image acquisition systems are producing more data than can be analyzed by human experts. Most of the images produced by remote sensing satellites, including military ones, never get seen or inspected. In this work, automated detection and recognition of buildings in aerial photos is explored. Connectivity analysis is performed on graphs derived from line segment representations of the original images, obtained with the use of the Radon Transform. The model is experimentally validated using 2-meter panchromatic aerial photographs from the National Aerial Photography Program (NAPP), which provide a marginally adequate resolution for the recognition of small buildings.