Background: Monitoring the lifestyles of older adults helps promote independent living and ensure their well-being. The common technologies for home monitoring include wearables, ambient sensors, and smart household meters. While wearables can be intrusive, ambient sensors require extra installation, and smart meters are becoming integral to smart city infrastructure. Research Gap: The previous studies primarily utilized high-resolution smart meter data by applying Non-Intrusive Appliance Load Monitoring (NIALM) techniques, leading to significant privacy concerns. Meanwhile, some Japanese power companies have successfully employed low-resolution data to monitor lifestyle patterns discreetly. Scope and Methodology: This study develops a lifestyle monitoring system for older adults using low-resolution smart meter data, mapping electricity consumption to appliance usage. The power consumption data are collected at 15-min intervals, and the background power threshold distinguishes between the active and inactive periods (0/1). The system quantifies activity through an active score and assesses daily routines by comparing these scores against the long-term norms. Key Outcomes/Contributions: The findings reveal that low-resolution data can effectively monitor lifestyle patterns without compromising privacy. The active scores and regularity assessments calculated using correlation coefficients offer a comprehensive view of residents’ daily activities and any deviations from the established patterns. This study contributes to the literature by validating the efficacy of low-resolution data in lifestyle monitoring systems and underscores the potential of smart meters in enhancing elderly people’s care. Full article

A 2-year-old, intact female Pomeranian presented with bilateral forelimb lameness, characterized by the olecranon making contact with the ground. The patient experienced two separate incidents of falling, occurring four and three weeks before admission, respectively. Following each episode, non-weight-bearing lameness was initially observed in the left forelimb, followed by the development of crouch gait. Based on the physical examination, radiographic, and ultrasonographic findings, bilateral triceps brachii tendon disruption was diagnosed. Intraoperatively, excessive granulation tissue at the distal end of the tendon was excised. The footprint region of each triceps brachii tendon was decorticated with a high-speed burr until bleeding was observed. The triceps brachii tendon was reattached to completely cover its footprint on the olecranon using the Krackow suture technique. This method involves anchoring the suture through bone tunnels in the ulna. Trans-articular external skeletal fixation was applied to both forelimbs to immobile and stabilize the elbow joints for nine weeks. Subsequently, the dog gradually increased its walking activities while on a leash over a six-week period. At the three-year follow-up, the patient exhibited improved forelimb function and maintained a normal gait without signs of lameness. Suture-mediated anatomic footprint repair proved useful in this single case and may be an effective surgical alternative for the management of chronic triceps brachii tendon disruption in dogs. Full article

Diblock conjugated oligomers are π-conjugated molecules that contain two segments having distinct frontier orbital energies and HOMO-LUMO gap offsets. These oligomers are of fundamental interest to understand how the distinct π-conjugated segments interact and modify their excited state properties. The current paper reports a study of two series of diblock oligomers that contain oligothiophene (T_n) and 4,7-bis(2-thienyl)-2,1,3-benzothiadiazole (TBT) segments that are coupled by either ethynyl (-C≡C-) or trans-(-C≡C-)₂Pt(II)(PBu₃)₂ acetylide linkers. In these structures, the T_n segment is electron rich (donor), and the TBT is electron poor (acceptor). The diblock oligomers are characterized by steady-state and time-resolved spectroscopy, including UV-visible absorption, fluorescence, fluorescence lifetimes, and ultrafast transient absorption spectroscopy. Studies are compared in several solvents of different polarity and with different excitation wavelengths. The results reveal that the (-C≡C-) linked oligomers feature a delocalized excited state that takes on a charge transfer (CT) character in more polar media. In the (-C≡C-)₂Pt(II)(PBu₃)₂-linked oligomers, there is weak coupling between the T_n and TBT segments. Consequently, short wavelength excitation selectively excites the T_n segment, which then undergoes ultrafast energy transfer (~1 ps) to afford a TBT-localized excited state. Full article

Drilling with prestressed concrete (DPC) pipe pile is a nonsqueezing pile sinking technology, employing drilling, simultaneous pile sinking, a pipe pile protection wall, and pile side grouting. The unloading induced by drilling, the pipe pile supporting effect, and the dissipation of the negative excess pore-water pressure after pile sinking, all of which have significant effects on the recovery of soil pressure on the pile side, are the main concerns of this study, which aim to establish a method to reasonably evaluate the timing selection of pile side grouting. The theoretical solutions for characterizing the unloading and dissipation of the negative excess pore-water pressure are presented based on the cylindrical cavity contraction model and the separated variable method. By inverse-analyzing the measured initial pore pressure change data from borehole unloading, initial soil pressures on the pile side of each soil layer are determined using the presented theoretical solutions. Then, the presented theoretical solutions were verified through a comparative analysis with the corresponding measured results. Moreover, by introducing time-dependent coefficients α_t1 and α_t2 to characterize the pore pressure dissipation and rheology effects, the effects of the negative excess pore-water pressure dissipation on the pile-side soil pressure recovery are discussed in detail. Full article

Permissioned blockchains are widely used in scenarios such as digital assets, supply chains, government services, and Web 3.0, but their development is hindered by low throughput and scalability. Blockchain sharding addresses these issues by dividing the ledger into disjoint shards that can be processed concurrently. However, since cross-shard transactions require the collaboration of multiple shards, blockchain sharding needs a commit protocol to ensure the atomicity of executing these transactions, significantly impacting system performance. To this end, by exploiting the characteristics of deterministic ordering, we propose a cross-shard transaction processing protocol called cross-reserve, which eliminates this costly cross-shard coordination while providing the same consistency and atomicity guarantee. Based on the ordering-free execute–validate (EV) architecture, we implemented a blockchain prototype called NeuChain+, which further reduces the cross-shard transaction processing overhead using the pipelined read sets transmission. Experimental results show that NeuChain+ is scalable and outperforms state-of-the-art blockchain systems with $1.7$–$75.3\times$ throughput under the SmallBank workload. Full article

Limited resources for edge computing platforms in airborne and spaceborne imaging payloads prevent using complex image processing models. Model pruning can eliminate redundant parameters and reduce the computational load, enhancing processing efficiency on edge computing platforms. Current challenges in model pruning for remote-sensing object detection include the risk of losing target features, particularly during sparse training and pruning, and difficulties in maintaining channel correspondence for residual structures, often resulting in retaining redundant features that compromise the balance between model size and accuracy. To address these challenges, we propose the L1 reweighted regularization (L1RR) pruning method. Leveraging dynamic and self-adaptive sparse modules, we optimize L1 sparsity regularization, preserving the model’s target feature information using a feature attention loss mechanism to determine appropriate pruning ratios. Additionally, we propose a residual reconstruction procedure, which removes redundant feature channels from residual structures while maintaining the residual inference structure through output channel recombination and input channel recombination, achieving a balance between model size and accuracy. Validation on two remote-sensing datasets demonstrates significant reductions in parameters and floating point operations (FLOPs) of 77.54% and 65%, respectively, and a 48.5% increase in the inference speed on the Jetson TX2 platform. This framework optimally maintains target features and effectively distinguishes feature channel importance compared to other methods, significantly enhancing feature channel robustness for difficult targets and expanding pruning applicability to less difficult targets. Full article

The body condition index (BCI) is an indicator of both reproductive success and health in small mammals and might help to understand ecological roles of species. We analyzed BCI data from 28,567 individuals trapped in Lithuania between 1980 and 2023. We compared BCIs between species and examined differences in age groups, gender, and reproductive statuses within each species. Seven out of eighteen species had sample sizes with N < 100. In terms of species, we found that seven of the eight species with the highest average BCIs are granivores or omnivores, which can consume animal-based food at least seasonally. The two contrasting (decreasing or increasing) BCI patterns observed during ontogeny can be related to diet differences among juveniles, subadults, and adult animals. Our results demonstrate that reproductive stress has a negative impact on the BCI of adult females in all analyzed species and nearly all adult males. Although the animals with extremely low BCI consisted mostly of shrews, for the first time we found 23 common and pygmy shrews exhibiting the Chitty effect, i.e., a very high body mass resulting in a BCI > 5.0. This is the first multi-species approach of body condition at middle latitudes. The results increase our understanding of how changing environmental conditions are affecting small mammals. Full article

There is something queer about Symbolism. Art historians have long acknowledged the links between Symbolist aesthetics and contemporaneous ideas about human sexuality, and even a cursory examination of artworks by male Symbolist artists working across the continent reveals an eyebrow-raising number of muscled nudes, lithe ephebes, and intimate male couplings. The sensual male body could register the artist’s erotic desire, even as he put it forth as an idealized emblem of transcendental truth. But perhaps Symbolism’s queerness extended beyond subject matter. Scholars have argued that Symbolism was in part defined by a subversive approach to visual semiotics: a severing—we might say a queering—of the ties binding a sign to its established cultural meaning. Similarly, male homosexual subcultures were sustained by endowing established signs and pictures with a uniquely queer significance. This paper seeks to tease out the relationship between Symbolist aesthetics and male homosexuality in terms of a shared sensibility towards pictorial interpretation. Taking as a case study the work of the Swedish Symbolist artist Eugène Jansson, I argue that Symbolism held appeal for homosexual artists precisely because queer subcultures were primed to read subversive meaning into normative pictures. Offering a new reading of Symbolism’s sexual valences, I contextualize the movement’s attendant artworks within the broader cultural landscape of homosexual signs and symbols and articulate the parallels between Symbolist approaches to the image and queer modes of seeing in the late nineteenth century. Full article

In this work, we present an innovative calibration technique leveraging differentiable programming to enhance energy resolution and reduce the energy scale systematic uncertainty in X-ray spectroscopic experiments. This approach is demonstrated using synthetic data and is applicable in general to various spectroscopic measurements. This method extends the scope of differentiable programming for calibration, employing Kernel Density Estimation (KDE) to achieve a target Probability Density Function (PDF) for a fully differentiable model of the calibration. To assess the effectiveness of the calibration, we conduct a toy simulation replicating the entire detector response chain and compare it with a standard calibration. This ensures a robust and reliable calibration methodology, holding promise for improving energy resolution and providing a more versatile and efficient approach without the need for extensive fine-tuning. Full article

A lack of natural resources drives the oil dependency in Pacific Island Countries and Territories (PICTs), hampering energy security and imposing high electricity tariffs in the region. Nevertheless, the Western Equatorial Pacific is known for its large Sea Surface Temperature (SST) and deep-sea water (DSW) temperature difference favorable for harvesting thermal energy. In this study, we selected 18 PICTs in the western Equatorial Pacific to estimate Annual Energy Production (AEP) for a 1 MW class Ocean Thermal Energy Conversion (OTEC) plant. We combined the DSW temperature from the mean in situ Argo profiles and 1 km resolution satellite SST data to estimate the thermal energy resource resolving the fine features of the island coastline. Furthermore, the twenty-year-long SST dataset was used to analyze the SST variability. The analysis showed that Equatorial islands and Southern islands have the highest inter-annual variability due to El Nino Southern Oscillation (ENSO). The power density varied from 0.26 to 0.32 W/m² among the islands, with the lowest values found for the southernmost islands near the South Equatorial Countercurrent. Islands within the South Equatorial Current, Equatorial Undercurrent, and North Equatorial Countercurrent showed the highest values for both power density and gross power. Considering a 1 MW class OTEC plant, Annual Energy Production (AEP) in 2022 varied from 7 GWh to 8 GWh, with relatively low variability among islands near the Equator and in low latitudes. Considering the three variables, AEP, SST variability, and distance from the shore, Nauru is a potential candidate for OTEC, with a net power of 1.14 MW within 1 km from the shore. Full article

According to data from the World Health Organization (WHO), cancer is considered to be one of the leading causes of death worldwide, and new therapeutic approaches, especially improved novel cancer treatment regimens, are in high demand. Considering that many chemotherapeutic drugs tend to have poor pharmacokinetic profiles, including rapid clearance and limited on-site accumulation, a combined approach with tumor-homing peptide (THP)-functionalized magnetic nanoparticles could lead to remarkable improvements. This is confirmed by an increasing number of papers in this field, showing that the on-target peptide functionalization of magnetic nanoparticles improves their penetration properties and ensures tumor-specific binding, which results in an increased clinical response. This review aims to highlight the potential applications of THPs in combination with magnetic carriers across various fields, including a pharmacoeconomic perspective. Full article

Semiconductors continue to shrink in die size because of benefits like cost savings, lower power consumption, and improved performance. However, this reduction leads to more defects due to increased inter-cell interference. Among the various defect types, customer-found defects are the most costly. Thus, finding the root cause of customer-found defects has become crucial to the quality of semiconductors. Traditional methods involve analyzing the pathways of many low-yield wafers. Yet, because of the extremely limited number of customer-found defects, obtaining significant results is difficult. After the products are provided to customers, they undergo rigorous testing and selection, leading to a very low defect rate. However, since the timing of defect occurrence varies depending on the environment in which the product is used, the quantity of defective samples is often quite small. Unfortunately, with such a low number of samples, typically 10 or fewer, it becomes impossible to investigate the root cause of wafer-level defects using conventional methods. This paper introduces a novel approach to finding the root cause of these rare defective chips for the first time in the semiconductor industry. Defective wafers are identified using rare customer-found chips and chip-level EDS (Electrical Die Sorting) data, and these newly identified defective wafers are termed vBADs (virtual bad wafers). The performance of root cause analysis is dramatically improved with vBADs. However, the chip-level analysis presented here demands substantial computing power. Therefore, MPP (Massive Parallel Processing) architecture is implemented and optimized to handle large volumes of chip-level data within a large architecture infrastructure that can manage big data. This allows for a chip-level defect analysis system that can recommend the relevant EDS test and identify the root cause in real time even with a single defective chip. The experimental results demonstrate that the proposed root cause search can reveal the hidden cause of a single defective chip by amplifying it with 90 vBADs, and system performance improves by a factor of 61. Full article

Solid polymer electrolytes (SPEs) are the key components of lithium metal batteries to overcome the obstacle of insecurity in conventional liquid electrolytes; however, the trade-off between their ionic conductivity and mechanical properties remains a significant challenge. In this work, two-dimensional ZSM-5 nanosheets as fillers are incorporated into a poly(ethylene oxide) (PEO) matrix and lithium salts to obtain composite polymer electrolytes (CPEs). The improved physicochemical and electrochemical properties of the CPE membranes are characterized in full detail. Stripping/plating measurements in symmetric Li/Li cells and cyclic charge/discharge tests are performed to investigate the cyclability and stability of the CPEs. All-solid-state LiFePO₄/Li batteries deliver excellent cycling performance with an initial discharge capacity of 152.3 mAh g⁻¹ and 91.4% capacity retention after 200 cycles at 0.2 C, with a discharge specific capacity of 118.8 mAh g⁻¹ remaining after 350 cycles at 0.5 C. Therefore, CPEs containing ZSM-5 nanosheets are a promising option for all-solid-state lithium-ion batteries. Full article

Over the past decades, the research on optically active polymers (OAPs) has significantly grown, and extensive studies have been carried out on their syntheses, conformations, and applications. The most commonly used OAPs are based on natural products such as sugars or amino acids, which limits their scope. A broader range of applications can be achieved by synthesizing lab-tailored monomers, which allow precise control over structure and properties. This research developed a four-step synthetic route to a previously unreported chiral [2.2]paracyclophane-based epoxide monomer. An aluminum catalyst and an alkylammonium initiating system were applied and optimized for its polymerization to provide access to a novel class of chiral polyethers. Furthermore, we demonstrated the copolymerization viability of the (4-[2.2]paracyclophanyl)oxirane monomer using phthalic anhydride. Full article

Ensuring fairness and equity in value distribution is crucial for the sustainability of platform ecosystems. However, existing approaches to distributing benefits among cooperative entities often find it difficult to accurately assess each stakeholder’s contributions. This paper tackles this challenge through a case study of the Haier COSMOPlat IIoT platform. By analyzing its value creation and value distribution processes, the research uncovers how platform enterprises can overcome existing limitations by quantifying and revealing intangible customer relationships alongside financial metrics. This revised value-sharing mechanism encourages a shift from “post-event value-sharing” to “mid-event adjustment”, promoting a fair and equitable profit distribution framework that motivates stakeholders toward sustainable value co-creation. Full article

This study assessed the effectiveness of a trastuzumab-targeted ¹⁷⁷Lu-labeled mesoporous Carbon@Silica nanostructure (DOTA@TRA/MC@Si) for HER2-positive breast cancer treatment, focusing on its uptake, internalization, and efflux in breast cancer cells. The synthesized PEI-MC@Si nanocomposite was reacted with DOTA-NHS-ester, confirmed by the Arsenazo(III) assay. Following this, TRA was conjugated to the DOTA@PEI-MC@Si for targeting. DOTA@PEI-MC@Si and DOTA@TRA/MC@Si nanocomposites were labeled with ¹⁷⁷Lu, and their efficacy was evaluated through in vitro radiolabeling experiments. According to the results, the DOTA@TRA/MC@Si nanocomposite was successfully labeled with ¹⁷⁷Lu, yielding a radiochemical yield of 93.0 ± 2.4%. In vitro studies revealed a higher uptake of the [¹⁷⁷Lu]Lu-DOTA@TRA/MC@Si nanocomposite in HER2-positive SK-BR-3 cells (44.0 ± 4.6% after 24 h) compared to MDA-MB-231 cells (21.0 ± 2.3%). The IC₅₀ values for TRA-dependent uptake in the SK-BR-3 and BT-474 cells were 0.9 µM and 1.3 µM, respectively, indicating affinity toward HER-2 receptor-expressing cells. The lipophilic distribution coefficients of the radiolabeled nanocomposites were determined to be 1.7 ± 0.3 for [¹⁷⁷Lu]Lu-DOTA@TRA/MC@Si and 1.5 ± 0.2 for [¹⁷⁷Lu]Lu-DOTA@PEI-MC@Si, suggesting sufficient passive transport through the cell membrane and increased accumulation in target tissues. The [¹⁷⁷Lu]Lu-DOTA@TRA/MC@Si nanocomposite showed an uptake into HER2-positive cell lines, marking a valuable step toward the development of a nanoparticle-based therapeutic agent for an improved treatment strategy for HER2-positive breast cancer. Full article

Streptococcus gordonii (S. gordonii, Sg) is one of the early colonizing, supragingival commensal bacterium normally associated with oral health in human dental plaque. MicroRNAs (miRNAs) play an important role in the inflammation-mediated pathways and are involved in periodontal disease (PD) pathogenesis. PD is a polymicrobial dysbiotic immune-inflammatory disease initiated by microbes in the gingival sulcus/pockets. The objective of this study is to determine the global miRNA expression kinetics in S. gordonii DL1-infected C57BL/6J mice. All mice were randomly divided into four groups (n = 10 mice/group; 5 males and 5 females). Bacterial infection was performed in mice at 8 weeks and 16 weeks, mice were euthanized, and tissues harvested for analysis. We analyzed differentially expressed (DE) miRNAs in the mandibles of S. gordonii-infected mice. Gingival colonization/infection by S. gordonii and alveolar bone resorption (ABR) was confirmed. All the S. gordonii-infected mice at two specific time points showed bacterial colonization (100%) in the gingival surface, and a significant increase in mandible and maxilla ABR (p < 0.0001). miRNA profiling revealed 191 upregulated miRNAs (miR-375, miR-34b-5p) and 22 downregulated miRNAs (miR-133, miR-1224) in the mandibles of S. gordonii-infected mice at the 8-week mark. Conversely, at 16 weeks post-infection, 10 miRNAs (miR-1902, miR-203) were upregulated and 32 miRNAs (miR-1937c, miR-720) were downregulated. Two miRNAs, miR-210 and miR-423-5p, were commonly upregulated, and miR-2135 and miR-145 were commonly downregulated in both 8- and 16-week-infected mice mandibles. Furthermore, we employed five machine learning (ML) algorithms to assess how the number of miRNA copies correlates with S. gordonii infections in mice. In the ML analyses, miR-22 and miR-30c (8-week), miR-720 and miR-339-5p (16-week), and miR-720, miR-22, and miR-339-5p (combined 8- and 16-week) emerged as the most influential miRNAs. Full article

Cryptocurrencies are becoming increasingly prominent in financial investments, with more investors diversifying their portfolios and individuals drawn to their ease of use and decentralized financial opportunities. However, this accessibility also brings significant risks and rewards, often influenced by news and the sentiments of crypto investors, known as crypto signals. This paper explores the capabilities of large language models (LLMs) and natural language processing (NLP) models in analyzing sentiment from cryptocurrency-related news articles. We fine-tune state-of-the-art models such as GPT-4, BERT, and FinBERT for this specific task, evaluating their performance and comparing their effectiveness in sentiment classification. By leveraging these advanced techniques, we aim to enhance the understanding of sentiment dynamics in the cryptocurrency market, providing insights that can inform investment decisions and risk management strategies. The outcomes of this comparative study contribute to the broader discourse on applying advanced NLP models to cryptocurrency sentiment analysis, with implications for both academic research and practical applications in financial markets. Full article

Evapotranspiration (ET) accounts for over half of the moisture source of Asian monsoon rainfall, which has been significantly altered by anthropogenic forcings. However, how individual anthropogenic forcing affects the ET over monsoonal Asia is still elusive. In this study, we found a significant decline in ET over the Asian monsoon region during the period of 1950–2014 in Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The attribution analysis suggests that anthropogenic aerosol forcing is the primary cause of the weakening in ET in the historical simulation, while it is only partially compensated by the strengthening effect from GHGs, although GHGs are the dominant forcings for surface temperature increase. The physical mechanisms responsible for ET changes are different between aerosol and GHG forcings. The increase in aerosol emissions enhances the reflection and scattering of the downward solar radiation, which decreases the net surface irradiance for ET. GHGs, on the one hand, increase the moisture capability of the atmosphere and, thus, the ensuing rainfall; on the other hand, they increase the ascending motion over the Indian subcontinent, leading to an increase in rainfall. Both processes are beneficial for an ET increase. The results from this study suggest that future changes in the land–water cycle may mainly rely on the aerosol emission policy rather than the carbon reduction policy. Full article

Today, many individuals read the daily news from social media platforms. Research has shown that news with negative valence might influence the well-being of individuals. Existing research that examined the impact of headlines on individuals’ well-being has primarily focused on examining the positive or negative polarity of words used in the headlines. In the present study, we adopt a different approach and ask participants to categorize the headlines themselves based on the emotions they experienced while reading them and how their choice impacts their well-being. A total of 306 participants were presented with 40 headlines from main news sites that were considered popular based on the number of public reactions. Participants had to rate their emotional experience of the headlines following five emotional states (i.e., happiness, anger, sadness, fear, and interest). Emotion regulation strategies and resilience were also measured. In line with our hypotheses, we found that participants reported experiencing negative emotions more intensively while reading the headlines. Emotion regulation was not found to influence the emotional states of individuals, whereas resilience did. These findings highlight that individuals can experience heightened emotions without reading the entire news story. This effect was observed regardless of the headline’s emotional valence (i.e., positive, negative, or neutral). Furthermore, our study highlights the critical role of interest as a factor in news consumption. Interest significantly affects individuals’ engagement and reactions to headlines, regardless of valence. The findings underscore the complex interplay between headline content and reader engagement and stress the need for further research into how headlines are presented to protect individuals from potential emotional costs. Full article

(1) Background: Neurofeedback training (NFT) has emerged as a promising approach for enhancing cognitive functions and reducing anxiety, yet its specific impact on university student populations requires further investigation. This study aims to examine the effects of NFT on working memory improvement and anxiety reduction within this demographic. (2) Methods: A total of forty healthy university student volunteers were randomized into two groups: an experimental group that received NFT and a control group. The NFT protocol was administered using a 14-channel Emotiv Epoc X headset (EMOTIV, Inc., San Francisco, CA 94102, USA) and BrainViz software version Brain Visualizer 1.1 (EMOTIV, Inc., San Francisco, CA 94102, USA), focusing on the alpha frequency band to target improvements in working memory and reductions in anxiety. Assessment tools, including the Corsi Block and Memory Span tests for working memory and the State-Trait Anxiety Inventory-2 (STAI-2) for anxiety, were applied pre- and post-intervention. (3) Results: The findings indicated an increase in alpha wave amplitude in the experimental group from the second day of NFT, with statistically significant differences observed on days 2 (p < 0.05) and 8 (p < 0.01). Contrary to expectations based on the previous literature, the study did not observe a concurrent positive impact on working memory. Nonetheless, a significant reduction in state anxiety levels was recorded in the experimental group (p < 0.001), corroborating NFT’s potential for anxiety management. (4) Conclusions: While these results suggest some potential of the technique in enhancing neural efficiency, the variability across different days highlights the need for further investigation to fully ascertain its effectiveness. The study confirms the beneficial impact of NFT on reducing state anxiety among university students, underscoring its value in psychological and cognitive performance enhancement. Despite the lack of observed improvements in working memory, these results highlight the need for continued exploration of NFT applications across different populations and settings, emphasizing its potential utility in educational and therapeutic contexts. Full article