This study assessed the potential application of biochar as a renewable nutrient source to enhance soil fertility and crop productivity in small-scale farming systems in Narathiwat Province, Thailand. A field experiment using five treatments—control (no amendment), rice husk biochar, rubber wood biochar, oil palm bunch biochar, and raw organic residues—was conducted on baby corn (Zea mays L. var. saccharata) grown in sandy soil. Oil palm bunch biochar significantly improved plant height, biomass, and yield compared to the other treatments, while also increasing soil organic matter and nutrient availability. A complementary survey of 30 local farmers revealed that 30% had already adopted biochar, and 43.33% expressed interest in its future use. Among the interested group, 73% believed adoption could increase if simpler application methods and more accessible equipment were developed. These findings demonstrate the promise of oil palm bunch biochar as an effective and sustainable soil amendment, particularly for improving sandy soil conditions in smallholder farming systems.

The study of quantum dots has opened up a plethora of possibilities for advanced applications in various fields. One crucial aspect of understanding these nanomaterials is exploring their nonlinear optical properties, which can be influenced by a range of factors, including the solvent environment in which they are dispersed during nonlinear optical tests. This article delves into the effects of different solvents, namely ethanol, benzene, toluene, and chloroform, on the nonlinear optical capabilities of CTAB@CdTe quantum dots probed by the Z-scan technique when exposed to a continuous-wave Nd:YAG laser emitting at 532 nm. High-quality water soluble TGA-capped CdTe quantum dots were successfully synthesized and subsequently modified for transfer in an organic solvent through a ligand exchange process using CTAB. The products were characterized using high-resolution transmission electron microscopy, UV-Vis spectroscopy, photoluminescence measurements, and Fourier transform infrared spectroscopy. The analysis revealed an average quantum dot size of approximately 3 nm. Furthermore, following the ligand exchange, the quantum dots exhibited excellent dispersion in the organic solvent, demonstrating the effectiveness of the modification process.

Rising damp is one of the most common problems affecting older buildings. This phenomenon also leads to material degradation, reduced indoor air quality, increased energy consumption, and possible respiratory diseases in people who are exposed to such an environment for long periods of time. This article presents the results of long-term research focused on assessing the effectiveness of undercutting masonry as a remediation measure against rising damp. The moisture condition of the structure was monitored for several years at several designated locations, both before and after remediation. The results obtained show a gradual but permanent reduction in moisture. This fact confirms the high effectiveness of the proposed remediation technology. The study further discusses the consequences of possible residual moisture for the possibility of subsequent application of thermal insulation. It pays particular attention to the limitations of some contact insulation systems and the potential of active thermal protection as a possible alternative approach. This proposal is identified as a promising strategy for improving the thermal and moisture properties of the structure.

LLM (large language model) families have a big powerful as one of many AI models that has the potential to revolutionize research and scientific, including general purposes.That is, current LLMs assist not only general purposes but also domains such as scientific research and engineering, the prediction of economic trend, health diagnosis, and policy decision.It also enables graphic works, data analysis, coding workface.However, unfortunately, the characteristics and application possibilities of the several structure of LLMs did not work through study on application areas.Current LLMs as well as GAI (General AI) models respond to question of user by re-organization these data after learn vast data (Text, Number, Image).That is, inference algorithms of current LLMs do not have the cause on why this task happen.To recover this problem, causal inference is quite important as core technology.The causal inference by structures of LLM such as standard LLM, offline causal RL with backdoor adjustment (Confounder control), offline causal model with explicit confounder, and backdoor adjustment intervention must fully study through step by step of case because these responses is quite different from structures.This paper offers how to apply and what structures of LLMs is useful for user case, depending structure of LLM causal inference.The learning capabilities of LLM is quite different from causal inference model of LLM on application.To research on purpose, what is the core approaches by causal inference model, what is the tuned parameter and structure, and how they obtain practical data for application.These methodologies of LLM causal inference on application should be provided through research and simulation of real-world LLM applications across the target.The prediction of Korea economic growth based on the causal inference of LLM should be performed through a review of current S. Korean situation and data on purpose because we do not fully study and effectively use by the causal inference of LLM for research target.This paper shows the prediction about GDP growth of S. Korea, China, and world, and stock market trend as example, and compares these graphs to see the results by the causal inference of LLM.

Upscaling protocols to produce exosomes from human neural precursor cells (NPCs) are crucial for enabling broader therapeutic applications for neurodegenerative diseases with associated inflammation. Exosomes are small extracellular vesicles measuring between 30 and 150 nm in diameter that are emerging as promising delivery systems in cell-free therapies. An analysis of the US-NIH clinical trials database identifies 246 studies focused on exosome diverse applications, underscoring the growing importance of both naïve and engineered exosomes, specifically those enriched with miRNAs. NPC transplantation has faced several challenges, including immunogenicity and limitations associated with the ineffectiveness of single-dose administration. However, NPC exosomes are emerging as more promising therapeutic tools due to the possibility of multiple applications and their unique properties such as low immunogenicity, biocompatibility, ability to penetrate biological barriers and neuroregenerative properties. To tackle the challenge of producing large quantities of high-quality exosomes, our research used advanced three-dimensional cultivation techniques in vertical-wheel (PBS) and stirred-tank (DASbox) bioreactors. Bioreactor-upscaled ReNcell® VM human NPCs enhanced exosomal yield while maintaining essential stem NPC characteristics. DASbox bioreactor produced smaller, more uniformly sized neurospheres than the PBS system. DASbox-generated exosomes demonstrated superior transfection efficiency with pre-miR-124-3p, here used as a promising neuroprotective strategy, and improved microglia uptake than those from PBS or adherent cultures. Moreover, DASbox-derived exosomes were shown to be internalized by neurons and glial cells and to differently regulate inflammatory mediators upon stress conditions, while exerting better modulatory activity when transfected with pre-miR-124-3p. These results highlight the potential of exosomes from bioreactor-upscaled human NPCs as innovative therapeutic agents for targeting neuron-glia dyshomeostasis and dysfunctional miRNAs in neurodegenerative diseases, meeting the growing demand for their therapeutic application and supporting the development of more effective strategies.

As a new research technology, pulsed high magnetic fields (PHMF) have been gradually used in many fields such as industry, communication, and medical treatment. However, there is still a lack of studies on the safety of PHMF and their effects on autoimmune inflammation. Our work aims to expand the possibilities of PHMF application in autoimmune disease therapy and provide a foundation for further refined research into the safety of PHMF. We used 30 DBA/1J mice to investigate the effects of 7.25 T PHMF exposure for 10 days on normal mice and collagen-induced arthritis (CIA) mice, a kind of autoimmune inflammation model. White blood cell counts, neutrophil counts, eosinophil counts, basophil counts, neutrophil percentage, the variable coefficient of red blood cell distribution width, and the standard deviation of red blood cell distribution width of normal mice were significantly up-regulated and lymphocyte percentage was significantly down-regulated by twice-daily 7.25 T PHMF. The body weight, other blood routine indexes, and organ pathological manifestations were not significantly affected by the PHMF. Moreover, different doses of PHMF exposure did not have significant effects on body weight, arthritis score, joint pathology, synovitis score, and the expression of inflammatory cytokines in CIA mice. In general, daily short-term exposure to 7.25 T PHMF does not have severe effects on normal mice and CIA model mice, and more studies are needed to verify its safety and efficacy.

Abstract Secondary raw materials are a key prerequisite for a functioning circular economy; however, their use is limited by technical, economic, and regulatory challenges. From a technical perspective, it is crucial that secondary raw materials exhibit sufficiently high and consistent quality in order to replace primary resources in demanding applications. Sensor-based sorting technologies play a central role in this context as they enable automated, rapid, and material-specific separation. This publication presents several case studies demonstrating how sensor-based sorting can be successfully applied to a wide range of tasks and waste streams. (a) The sorting of post-consumer textiles is technically challenging. In the ‘StraTex’ project, AI-based methods were developed to evaluate feeding and singulation systems, along with datasets for second-hand textiles to enable automated, high-quality sorting. (b) The ‘KIRAMET’ project improves the quality of steel scrap through AI-based detection and removal of copper-containing particles. The developed system achieves more than 99.9% separation efficiency and enables the use of the scrap in low-CO 2 steelmaking processes. (c) In the ‘ReWaste F’ project, the Smart Waste Factory was implemented, enabling quality-driven process control through networked sensor systems and data platforms. For the first time, polypropylene from mixed waste was successfully recycled along the entire value chain into end products. (d) ‘ReSoURCE’ demonstrates the automated sorting of refractory waste using multi-sensor systems and AI, including small particle sizes. This results in purer fractions and new application possibilities as secondary raw materials. In sensor-based sorting systems, the trend is clearly moving toward multi-sensor solutions and AI-supported, data-driven approaches. At the same time, there is still a need for standardized quality criteria, interoperable data and sensor interfaces, and economically viable solutions. Overall, the further development of sensor-based sorting represents a key lever for transforming waste management into a central, resource-efficient component of a functioning circular economy.

In the theory of problems of mathematical physics associated with singularly perturbed equations, a substantial body of fundamental results has been accumulated, primarily devoted to direct problems and their analytical investigation. However, this cannot be fully attributed to singularly perturbed inverse transport problems in an unbounded domain, which determines the relevance of the research presented in this work. Additional difficulties arise in the study of multi-velocity inverse transport problems, since the implementation of most known solution methods is associated with the need to construct special coordinate systems that depend on the specific features of the problem under consideration. These systems may be either rectangular Cartesian coordinates, in which the problem is originally formulated, or curvilinear coordinates to which an appropriate transformation is performed. In this case, additional conditions are required for new functions, in which new variables are related to each other by certain relationships, depending on the given areas. In this paper, an n-velocity singularly perturbed inverse Kac-type transport problem in an unbounded domain is investigated. The proposed solution method makes it possible to remain within the Cartesian coordinate system regardless of the number of phase space variables, thereby significantly simplifying the analytical study and expanding the possibilities for practical application of the obtained results. For a singularly perturbed inverse transport problem of the Katz type in an unbounded domain, results are obtained in weighted space. The solution is constructed using a modified asymptotic method. Within the framework of this approach, estimates of the proximity of solutions of a singularly perturbed inverse problem and a degenerate inverse problem in a selected weight space are proven.

Abstract Background In today’s rapidly changing society, the prevalence of emotional disorders is on the rise, including depression, anxiety and stress, etc. In recent years, fashion design is no longer limited to functionality but has gradually developed in the direction of emotional regulation. The application of multi-sensory stimulation in fashion design may provide effective regulatory means for emotional disorders by awakening emotions, enhancing self-identity and positive emotions. Therefore, the research will evaluate the effectiveness of this design concept in practical application through empirical analysis and explore the regulatory effect of multi-sensory emotion-awakening clothing design on emotional disorders. The research aims to provide theoretical support and practical guidance for future clothing design and emotional health intervention programs. Methods The study adopted a randomized controlled experimental design, recruiting a total of 120 participants with a balanced distribution of gender, age, occupation and health status. Volunteers who meet the requirements are screened through questionnaires and interviews to ensure that they do not have serious emotional disorders such as major depressive disorder or generalized anxiety disorder. Participants were randomly divided into an experimental group and a control group, with 60 participants in each group, to eliminate the influence of selection bias. The experimental group used clothing that adopted multi-sensory stimulation, including: using brightly colored and strongly contrasting fabrics and designs; Choose the appropriate fabric to provide a comfortable touch; Equip the clothing with a light and elegant fragrance to enhance the body’s emotional response. The control group wore ordinary clothing, which was not specially designed in terms of material, color or smell. Participants filled out the “Positive and Negative Affect Schedule (PANAS)” and the “Self-rating Scale of Depression and Anxiety (SAS)” both before and after the experience. PANAS is used to assess participants’ scores on positive and negative emotions, while SAS measures levels of depression and anxiety. Results In the experimental results, the PANAS score of the experimental group increased from 24.1 ± 5.2 at the baseline to 38.5 ± 6.4, while that of the control group remained almost unchanged. The baseline was 24.3 ± 5.3, and after the intervention, it was 25.1 ± 5.0. The p=.202, which did not reach statistical significance. Meanwhile, in the change of SAS score in the experimental group, it decreased from 20.3 ± 4.8 to 13.2 ± 4.1, while in the control group, there was almost no change. The baseline was 20.1 ± 4.6, and after the intervention, it was 19.8 ± 4.5, with a p value of 0.088, which did not reach significance. Discussion The research results show that multi-sensory emotion-awakening clothing design can effectively enhance the positive emotions of participants and significantly reduce the levels of depression and anxiety, demonstrating the potential of such design in the regulation of mood disorders. This discovery not only provides new ideas for the field of fashion design, but also offers practical application basis for clinical mental health intervention. Future research can further explore the combination of different sensory stimuli, the differences in effects under various cultural backgrounds, and the application possibilities in other fields.

This article presents a highly efficient and cost-effective approach to develop tunable broadband microwave absorber. The proposed absorber consists of non-metallic Expanded Graphite (EG) unit cells fabricated on Linear Low-Density Polyethylene (LLDPE) substrate. The tunability of the absorber is achieved by injecting distilled water into the fluidic channel engraved on the LLDPE substrate. The developed absorber shows a bandwidth of 2.10 GHz of -10 dB with > 90% of absorption with air filled channels and can be tuned by 1.10 GHz with the water filled channels. Rigorous numerical calculations and experimental measurements are carried out to establish the efficiency of the absorber. Prior to fabrication process, simulations of the electric and magnetic fields are carried out for different configurations to figure out the physical origin of the absorption property. Being fabricated on a flexible substrate with non-metallic expanded graphite, the absorber caters the advantages of being ultra-thin, flexible, non-corrosive and free from processing conundrums. Current progression of the unit cells in microwave absorber design in modern state of the art communication systems, stealth technology and electromagnetic shielding circuits, indicates offers strong possibilities for future applications of the developed flexible absorber.

The relevance of this review is due to the necessity of analyzing the current state of platelet-rich plasma (PRP) therapy in various areas of medicine and the possibilities of using this method of therapy. Objective: to make the literature review that covers the problem of PRP therapy application in medical practice. PRP has been used in medicine for more than 50 years. Recently, the interest in PRP drugs application has increased worldwide. This is due to the high efficiency, safety for the patient and the low cost of the method. Currently, PRP therapy is used in dermatology and cosmetology, traumatology and orthopedics, dentistry, surgical practice (cardiac surgery, maxillofacial surgery, plastic surgery, purulent surgery, and others), obstetrics and gynecology. The sphere of platelet-rich plasma application is constantly expanding, and the list of diseases for which PRP drugs are prescribed is constantly being updated. Despite the widespread use of PRP therapy in medical practice, the potential of this treatment method has not yet been fully explored and further study of the possibilities of application in various fields of medicine is required.

Grain trade plays a key role in global food security, but it faces a number of challenges, including climate change, geopolitical conflicts, and market volatility. These challenges have led to increasing attention on the agricultural applications of artificial intelligence (AI). AI technologies (such as predictive analytics, automated logistics, and precision agriculture) are helping to increase efficiency, reduce losses, and enhance sustainability. The aim of this article is to examine the role of AI in grain trade and its future potential.

Two-dimensional (2D) van der Waals (vdW) magnets offer a versatile platform to explore fundamental physics and low-dimensional functionalities. Metal thiophosphates (MTPs) with mobile Cu+ ions exhibit a ferroionic state, where polarization arises from ionic redistribution among multiple nearly degenerate sites. CuVP2S6 uniquely combines intrinsic ferromagnetism from the V sublattice with Cu+-driven ferroionic configurational freedom, enabling direct exploration of how ionic dynamics influence magnetic interactions. Herein, high-quality CuVP2S6 single crystals are synthesized, and their structural and physical properties are systematically investigated. Temperature-dependent neutron diffraction elucidates a ferroionic structure with dynamic distributions of copper ions across multiple crystallographic sites. The versatile occupations are driven by local symmetry-controlled orbital interactions between copper ions and surrounding ligands through a second-order Jahn-Teller mechanism. Magnetic measurements identify a ferromagnetic (FM) transition below 3.3K. The pressure-controlled magnetocrystalline anisotropy and interlayer exchange interactions mediated by Cu+ migration are demonstrated, boosting the Curie temperature remarkably by over 60% and inducing a soft-to-hard FM transition unparalleled within the MTP family. These results demonstrate that ionic configurational freedom provides an efficient route to control magnetism, opening new possibilities for spintronic applications.

Rare-earth dimers with single-electron metal-metal bonds feature strong hyperfine and exchange interactions between metal atoms and an unpaired valence electron. Their stabilization inside carbon cages opens the possibility for applications in quantum information processing and sensing but requires investigation of their unusual spin states and dynamics. Herein, we present a systematic EPR study of dimetallofullerenes M2@C80(CH2Ph) with M25+ dimers, whose composition varied from Y2 (S = 1/2) through YGd (S = 4) to Gd2 (S = 15/2). High-field W-band EPR spectroscopy facilitated the analysis of different molecular orientations and T1 anisotropy and allowed resolution of individual |mS⟩ → |mS + 1⟩ transitions in high-spin Gd dimetallofullerenes. For Y2@C80(CH2Ph), we demonstrated that fullerene isomerism strongly influences the molecular and spin dynamics of metal dimers. The pronounced difference in spin-lattice relaxation times T1 was found for the Ih and D5h cage isomers and rationalized by Raman spectroscopic study and calculations of spin-phonon couplings. Lateral metal modes, which play the main role in the spin-lattice relaxation of endohedral metallofullerenes, were found at higher frequencies in the D5h isomer, in line with its longer T1. The orientational dependence of spin decoherence was ascribed to the rotational motion of the Y2@C80(CH2Ph) molecules in glassy toluene. Substitution of Y with Gd accelerated spin-lattice relaxation and decoherence. A similar T1 and phase-memory relaxation time (Tm) of YGd@C80(CH2Ph) and Gd2@C80(CH2Ph) proved that the effect does not scale with the number of Gd atoms. While the Rabi frequency is constant across the whole spectrum in Y2@C80(CH2Ph), YGd@C80(CH2Ph) and Gd2@C80(CH2Ph) exhibit variations in the Rabi frequencies for their |mS⟩ → |mS + 1⟩ transitions.

Droplet-based microfluidics has witnessed tremendous progress in the past two decades, and this technique has been demonstrated as one of the most promising technologies to synthesize high-end and versatile materials with advanced functions, which provide great possibilities for numerous applications. Herein, the recent progress in preparing high-value natural microspheres by droplet-based microfluidic techniques is summarized comprehensively. We start with an in-depth articulation of the working principles of droplet-based microfluidics and surface modification for microdevices. Subsequently, droplet-based microspheres' fabrication methods have been discussed and summarized. Furthermore, the emerging representative biomedical applications of the different types of natural microspheres are outlined systematically. After that, we consider the challenges that hinder droplet-based microfluidic improvement in academic and industrial applications. Eventually, we will point out the perspectives of droplet-based microfluidics and aim to advance droplet-based microfluidics and sophisticated applications. The scope of this review is not only to offer an in-depth understanding of droplet-based microfluidics but also to open new pathways for versatile applications.

Abstract In this study, we evaluated the transient characteristics of a steep-subthreshold-slope device, a gate-controlled carrier-injection silicon-on-insulator transistor (GCCI SOI-Tr). The GCCI SOI-Tr exhibited a delay time that decreased exponentially with increasing voltage. In particular, a near-zero delay was observed at a low overdrive voltage (0.2 V), raising the possibility of an ultralow-power CMOS applications. Furthermore, the carrier-ejection time was measured using the double-pulse measurement method. Consequently, the carriers are ejected exponentially. The results of tendencies in carrier-charging and ejection times can contribute to estimating the “integration” and “leaky” as the neuronal behavior for the spiking neural networks.

Purpose 4D textiles are created through 3D printing on pre-stressed textiles. The flexible textiles generate tension during the printing process, while the 3D-printed structure acts as a reinforcement, storing this tension within the material. These components undergo state transitions in response to external stimuli. In such cases, it is essential to ensure that the printed elements remain reliably bonded to the textile substrate and do not delaminate. Therefore, the interface between the 3D-printed structure and the textile property plays a crucial role. In order to solve this potential problem and reduce material and time consumption in the experimental phase, a systematic investigation of adhesion performance is necessary. Design/methodology/approach This study systematically examines the key parameters influencing adhesion at this interface, including textile type, printing parameters, printed materials and pre-tensioning. A comprehensive 2ˆn factorial experimental design was employed to analyse the interactions between these parameters. Adhesion strength was evaluated using 180° peel tests. Mathematical evaluation methods, including Analysis of Variance (ANOVA), effect calculation and confidence interval analysis, are applied to systematically assess the influence of each factor. The evaluation is performed using an organized results table, which simplifies the analysis process and facilitates the interpretation of significant effects and interactions. Findings In this study, nine factors influencing interfacial adhesion in 4D textiles were analysed using a 2ˆn factorial design. Key findings show that nozzle distance critically affects adhesion, with 0.05 mm being optimal. Higher textile pre-tension reduced adhesion, while lower pre-tension enabled 4D shape transformation. Nozzle temperature significantly influenced adhesion, with 220 °C optimal for PLA. Textile structure was also critical: dual tricot fabrics with elastane improved mechanical interlocking. The interaction between pre-tension and textile type also affects adhesion. Material choice affected adhesion, with silicone rubber achieving the highest values. In contrast, mesh orientation, printing speed and bed temperature showed no significant effects within the tested ranges. Originality/value The findings of this study provide a systematic analysis of interfacial adhesion, supporting the efficient production of 4D textiles and 3D-printed structures on textile substrates. By applying the identified key parameters, strong adhesion can be achieved without repeated trial-and-error testing, ensuring structural integrity for repeated state transformations in 4D textiles. Additionally, improved process reliability contributes to more sustainable manufacturing by reducing material waste and the need for auxiliary bonding agents. These insights enhance the robustness and scalability of 4D-textile production and open new application possibilities in wearable electronics, soft robotics and adaptive architectural structures.

Nanogels (as well as other polymer networks) can absorb nanoparticles that give them new properties and expand their application possibilities. The resulting hybrid entities constitute a kind of polymer nanocomposites, which have become an emerging area of research. In this work, coarse-grained simulations have been used to study how certain properties of these nanocomposites (size, number of nanoparticles inside, net charge, and surface potential) change with temperature. Four nanocomposites with different values of charge anchored to the polymer network (known as bare charge) were simulated. The degree to which nanocomposites shrink and expel the particles they contain depends strongly on the bare charge, which, in turn, could be related to the pH in pH-sensitive micro- and nanogels. Our results also reveal that nanoparticles are responsible for nanocomposites exhibiting much richer and more complex behavior than nanogels. Furthermore, the strong correlations that nanoparticles experience when the polymer network shrinks should not be ignored in mean-field theories that try to predict how nanocomposites behave.

Background/Objectives: Blood biomarkers for estimating alcohol consumption are useful for preventing alcohol-related harms. Although there are conventional blood biomarkers of heavy alcohol drinkers, it remains to be clarified whether their combination is useful for estimation of excessive alcohol consumption from the viewpoint of preventing hypertension. Methods: Participants included 8172 men aged from 31 to 70 years who had undergone health checkups. Overall, participants were classified into three groups of nondrinkers, occasional drinkers, and regular drinkers by frequency; regular drinkers were further classified into four groups of light (<22 g/day), moderate (≥22 and <44 g/day), heavy (≥44 and <66 g/day), and very heavy drinkers (≥66 g/day) according to the amount of average daily alcohol consumption. The relationships of blood biomarkers (mean corpuscular volume [MCV], gamma glutamyl transferase [GGT], and HDL cholesterol [HDL-C]) and their products with alcohol consumption were investigated by using correlation analysis and receiver-operating characteristics (ROC) analysis. Results: Seven variables of blood biomarkers and their products were significantly correlated with frequency and amount of alcohol consumption, and the degrees of the correlations were stronger in the following order: HDL-C alone < product of MCV and HDL-C < MCV alone < GGT alone < product of MCV and GGT < product of GGT and HDL-C < product of MCV, HDL-C and GGT. In the ROC analysis, the area under the ROC curve for the relationship between the product of MCV, HDL-C, and GGT (named the alcohol consumption index [ACI]) and excessive alcohol intake (22 g/day or more) was 0.819 (95% confidence interval: 0.809–0.830), and the cutoff of this index was 194,863 with a sensitivity and specificity of 0.745 and 0.751, respectively. The positive predictive value was 69.2%. Conclusions: Among the three conventional blood biomarkers and their combinations, ACI demonstrated the strongest associations with alcohol consumption and excessive alcohol intake in men. Although the combined biomarkers are unlikely to be useful as a diagnostic tool, there is a possibility of future application by integrating ACI with recent biomarkers including carbohydrate-deficient transferrin for estimation of alcohol consumption.

The purpose of the present study was to verify whether the go/no‐go procedure with compound stimuli would establish derived asymmetric and transitive relations between stimuli under the control of contextual cues. In Experiment 1, nonarbitrary relational training and tests established red and blue background colors as the contextual cues. Subsequently, arbitrary relational training established relations between pairs of stimuli under the control of the contextual cues. Finally, tests evaluated the emergence of new relations under contextual control. All four participants, university students, met the learning criterion during training and demonstrated the derived relations that were tested. Experiment 2 replicated Experiment 1, without the nonarbitrary relational training and testing. All three participants also exhibited the derived performances. The effectiveness of the procedure for establishing derived relations, the implications of the necessity of nonarbitrary relational training, and the possibilities for application are discussed.