Abstract: We establish a version of Pontryagin’s maximum principle for optimal control problems with impulses and phase constraints. Using the Dubovitskii-Milyutin theory, we construct a conic variational framework that handles impulsive dynamics and general state constraints. The main difficulty lies in working with piecewise continuous functions, required by the impulsive nature of the system. This setting also demands an extension of the classical result on the existence of nonnegative Borel measures, which leads to an adjoint equation formulated as a Stieltjes integral. Theoretical results are illustrated with examples, and key results by I. Girsanov are extended to the impulsive context.

Abstract: Stroke continues to carry an enormous morbidity and mortality burden worldwide. Stroke survivors often incur debilitating consequences that impair motor function, independence in activities of daily living and quality of life. Rehabilitation is a pivotal intervention to minimize disability and promote functional recovery following a stroke. The Internet of Medical Things, a network of connected medical devices, software and health systems that collect, store and analyze health data over the internet is an emerging resource in neurorehabilitation for stroke survivors. This manuscript reviews emerging rehabilitation technologies such as robotic devices, virtual reality, brain-computer interfaces and telerehabilitation in the setting of neurorehabilitation for stroke patients.

Abstract: Methanogens, also known as methanogenic archaea, are among the most ancient and widespread microorganisms, despite their particular requirements for growth. These oxygen-sensitive microorganisms have impacted climate and biogeochemical cycles throughout Earth’s history, although their specific roles in the long-term carbon cycle remain little explored. Methanogens evolved early during Earth’s history, likely during the Archaean Eon, in layered benthic microbial communities called microbial mats. These ancient mats, when lithified, form microbialites that represent some of the earliest evidence of life in the fossil record dating back > 3.5 Gy. Contemporary microbial mats experience a wide range of fluctuating conditions, including dramatic diel shifts in oxygen, sulfide, redox, temperature, salinity and pH. Methanogens are an integral part of marine and freshwater microbial mats and have been identified in the oxic zone of these sedimentary ecosystems; however, their adaptations to apparently unfavorable conditions and their role in long-term CO2 sequestration through precipitation of carbonate are unclear. Furthermore, the importance and coevolution of methanogens and microbial mats may explain the global role these organisms had on Earth’s major climate events during the Archean and Proterozoic eons, notably in the ending of icehouse periods and recovery of mats following mass extinctions – often in conditions with low or no oxygen. In addition to an important role in the evolution of our planet, methanogens may also produce biosignatures that are relevant for astrobiology research [and space exploration]. This review will discuss the diversity, physiology, and ecology of methanogens in order to clarify their role in biogeochemical processes through geologic time.

Abstract: This paper presents a definitive synthetic proof of the impossibility of trisecting an arbitrary angle within Euclidean geometry. The proof centers not on algebraic abstractions, but on an intrinsic geometric inconsistency revealed through the lens of the canonical 90° angle. This angle serves not merely as a counterexample, but as a diagnostic lever that fractures the very concept of a universal trisection property. A new “Principle of Operational Dissonance” is formulated from an analysis of foundational operations, such as doubling and cubing a square’s diagonal. These operations, while producing congruent final magnitudes, violate the core Euclidean doctrine of proportional similarity, demonstrating that (a:b ≠ c:d) in a strict geometric sense. This dissonance mirrors the logical structure of the trisection problem. The proof demonstrates that assuming the existence of a universal trisection procedure forces a specific geometric condition-the equality of certain lengths-when applied to a 90° angle. This condition arises solely from the angle’s axiomatic status and the constraints of compass-and-straightedge constructions. However, this forced condition is not preserved under variation of the angle measure, rendering any purported universal procedure internally inconsistent. The resulting contradiction proves the impossibility of trisecting a 90° angle with a universal method. This failure, stemming from a fundamental incompatibility within the geometric system rather than the peculiarities of a single angle, extends to all angles, conclusively resolving the classical problem. The proof thus delineates the exact boundary of classical constructive geometry, indicating that any future universal solution must arise from the introduction of new geometric properties innately compatible with Euclidean theory. It reaffirms the self-contained sufficiency of Euclidean geometry for resolving its celebrated problems and challenges the methodological necessity of importing non-geometric techniques to establish geometric impossibilities. The presented framework offers a purely synthetic geometric perspective, one that aligns with the foundational spirit of Euclid’s Elements.

Abstract: A hypergraph generalizes an ordinary graph by allowing an edge to connect any nonempty subset of the vertex set. By iterating the powerset operation one step further, one obtains nested (higherorder) vertex objects and, consequently, a finite SuperHyperGraph whose vertices and edges may themselves be set-valued at multiple levels. Thus, many hierarchical graph structures exist in the literature. Moreover, not only in graph theory but also in broader fields—such as through concepts like Decision Trees and Tree Soft Sets—it is well known that tree structures are effective tools for representing hierarchical concepts. In this paper, we define a new class of graphs called Tree-Vertex Graphs. In this framework, a tree structure is imposed on the vertex set, and the edge set is defined in a manner consistent with the tree-structured vertex set. The tree structure therefore serves as a key concept for representing hierarchical graphs.

Abstract: Background: In Japan, the number of older adults living alone has been increasing, raising the risk of unnoticed health decline or solitary death. Continuous monitoring using sensors can help detect behavioral changes indicating health issues and has the potential to support both older adults and their families. Methods: We obtained behavior and temperature data, continuously recorded over a long period at 15-min intervals from sensors installed in the homes of nine older adults living alone. After data cleaning, behavioral signals were analyzed using Fourier spectral analysis and multiple regression to extract 13-dimensional behavioral characteristic vectors. We whitened a portion of these behavioral characteristic vectors as benchmark data. We applied the same whitening process to the comparing data using the matrix obtained during this whitening process. By analyzing misclassification rates using boundary variance for benchmark and comparing data, we attempted to detect temporal changes in user behavior and differences between individuals. Results: Spectral analysis revealed 24-hour periodicity in all users’ behavior. By analyzing the misclassification rate using boundary variance for long-term signals, we identified users who maintain consistent behavioral patterns and those exhibiting significant temporal variation. We were also able to detect differences in these behavioral patterns. Conclusions: This study demonstrates that long-term temporal changes in the daily behavior of older adults living alone can be detected using simple continuous sensor data. Our approach is applicable not only for monitoring behavior changes in older adults living alone, but also for observing behavior changes in people with disabilities and children within the home environment.

Abstract: The sum-of-divisors function σ(n) has been studied since antiquity, most often in connection with perfect and abundant numbers, yet its behavior under additive divisibility constraints has not been systematically classified. The paper considers the problem of determining, for a fixed integer a, the positive integers n for which σ(n) | n + a. It is shown that for every fixed integer a ≥ 2, only finitely many positive integers n satisfy this relation. The proof reduces the divisibility condition to a size dichotomy: either n < a, yielding only finitely many possibilities, or σ(n) = n + a, which is equivalent to a fixed-value equation for the sum of proper divisors. It is then shown that this equation admits only finitely many solutions for each fixed a. Special cases are described explicitly. When a = 1, the relation σ(n) | n + 1 holds only for n = 1 and for prime n. When a = 0, the condition reduces to σ(n) = 2n, recovering the classical perfect numbers. For a < 0, the inequality σ(n) > n for all n > 1 excludes all but trivial cases. These results complete the classification of shifted divisibility for σ(n) and close the sequence initiated by analogous investigations of φ(n) and λ(n), identifying σ as the terminal case in which multiplicative divisibility collapses to finiteness.

Abstract: Generative recommendation models often struggle with two key challenges: (1) the superficial integration of collaborative signals, and (2) the decoupled fusion of multimodal features. These limitations hinder the creation of a truly holistic item representation. To overcome this, we propose CEMG, a novel Collaborative-Enhanced Multimodal Generative Recommendation framework. Our approach features a Multimodal Fusion Layer that dynamically integrates visual and textual features under the guidance of collaborative signals. Subsequently, a Unified Modality Tokenization stage employs a Residual Quantization VAE (RQ-VAE) to convert this fused representation into discrete semantic codes. Finally, in the End-to-End Generative Recommendation stage, a large language model is fine-tuned to autoregressively generate these item codes. Extensive experiments demonstrate that CEMG significantly outperforms state-of-the-art baselines.

Abstract: Coastal salt marshes are essential for climate change mitigation due to their high carbon storage capacity, which is influenced by soil type, hydrology, and floristic composition. Over the past century, invasive Phragmites australis has displaced native Spartina alterniflora across salt marshes on the Long Island Sound, and it is widely hypothesized that its larger biomass and rapid growth enhance soil carbon sequestration. This study tested that hypothesis by comparing TOC stocks and physical soil properties in two southern Connecticut marshes over multiple seasons. Our results show that mean soil bulk density was significantly higher under P. australis than S. alterniflora at both locations. However, this did not translate to superior carbon storage. Analysis showed significant seasonal effect but no significant overall difference in median TOC between species, indicating that P. australis is competitive in total mass only due to its higher soil density. Notably, Levene’s test for homogeneity of variance was significant (P = 0.039), revealing that P. australis creates highly heterogeneous "hot spots" of carbon storage compared to the relatively uniform distribution found in native stands. These findings suggest that while P. australis invasion results in a more physically dense and potentially resilient marsh platform—relevant for surviving sea-level rise and filtering nutrient runoff—it may simultaneously compromise the stability and uniformity of regional carbon sinks. Management strategies should consider these tradeoffs when prioritizing the protection of native S. alterniflora for consistent carbon sequestration.

Abstract: Laser Powder Bed Fusion (LPBF) of 316L stainless steel is highly sensitive to laser power, scan speed, and beam size, which makes property prediction challenging especially when only small, scattered experimental datasets are available. Traditional machine-learning models trained directly on such limited data often struggle with overfitting and poor generalization. In this study, we present a lightweight, physics-Guided surrogate modelling framework designed specifically for small-data LPBF environments. Starting from 74 literature-reported microhardness measurements, we create an expanded training set using a cluster-aware Kernel Density Estimation (KDE) strategy that generates new samples only within physically meaningful regions of the P–v–spot space. A SAFE_DIST constraint ensures that surrogate points do not become near-duplicates of actual experiments, while a ±3 HV noise model preserves realistic hardness variability seen in LPBF studies. To incorporate first-order thermal behaviour without resorting to computationally expensive simulations, we construct three analytical descriptors: an energy-density proxy, a Rosenthal-inspired thermal-gradient indicator, and a thermo-mechanical efficiency (TME) metric. Together, these features improve interpretability and guide the model toward thermally consistent predictions. Ensemble regressors trained solely on the surrogate dataset demonstrate strong predictive capability on unseen real measurements, achieving an independent real-only test R² of 0.84. A strict real-only leave-one-out cross-validation (LOOCV) yields a conservative R² of 0.64, consistent with the inherent scatter of LPBF microhardness data. When trained on the full augmented dataset, the model achieves an overall R² of 0.91, reflecting the smooth, physically coherent nature of the surrogate space. The resulting process maps and learning-curve trends align closely with established LPBF thermal–microstructural behaviour, confirming that the framework learns underlying physics rather than memorizing datapoints. Overall, this work provides a simulation-free, data-efficient, and thermally grounded approach for predicting microhardness in LPBF 316L, offering a practical foundation for rapid parameter exploration, process optimization, and extension to other materials and LPBF responses.

Abstract: Autism Spectrum Disorder (ASD) is a neurological condition that impairs communication skills, with individuals often experiencing mild to severe challenges that may require specialised care. While numerous researchers are developing automated ASD recognition systems, achieving high performance remains challenging due to the lack of effective features. In this study, we propose a novel dual-stream model that combines handcrafted facial-landmark features and pixel-level deep learning features to classify ASD and non-ASD faces. The system processes images through two distinct streams to capture complementary features. In the first stream, facial landmarks are extracted using Mediapipe, initially capturing 478 points and selecting 137 symmetric landmarks. The face position is determined by applying in-plane rotation using the angles calculated from the outer eye corners (landmarks 33 and 263). Geometric features and 52 blendshape features are then fed into Dense layers (128 units) with dropout for regularisation. These features are merged and refined through additional Dense layers (128 and 64 units) to produce the final output for Stream-1. In the second stream, the RGB image is resized, normalised using the preprocessing function corresponding to the chosen backbone (e.g., ResNet50V2, DenseNet121, InceptionV3), and then extracted features using a Convolutional Neural Network (CNN) enhanced with Squeeze-and-Excitation (SE) blocks. Global Average Pooling (GAP) reduces dimensionality, followed by DenseNet (256 units with dropout) and a final Dense layer (64 units) to extract features for Stream-2. The outputs from both streams are concatenated, and a softmax gate with weighted concatenation is applied to combine the features. A final Dense layer (128 units with dropout) refines the features before passing them through a softmax layer to produce the probabilistic classification score. This hybrid approach, integrating landmark-based and RGB-based features, significantly enhances the model’s ability to distinguish between ASD and Non-ASD faces. Using the Kaggle dataset, the model achieved an accuracy of 96.43%, with a precision of 97.10%, recall of 95.71%, and an F1 score of 96.40%. On the YTUIA dataset, the accuracy increased to 97.83%, with a precision of 97.78%, recall of 97.78%, and an F1 score of 97.78%. Although these results are promising, they fall short of surpassing the highest reported performance of 95.00\% for Kaggle and 95.90\% for YTUIA. Future work will focus on optimizing the model’s performance to exceed these benchmarks.

Abstract: This paper presents a general algorithm for rapidly generating all N×N Latin squares, along with its precise counting framework and isomorphic (quasi-group) polynomial algorithms. It also introduces efficient algorithms for solving Latin square-filling problems. Numerous combinatorial isomorphism problems, including Steiner triple systems, Mendelsohn triple systems, 1-factorization, networks, affine planes, and projective planes, can be reduced to Latin square isomorphism. Since groups are true subsets of quasigroups and group isomorphism is a subproblem of quasi-group isomorphism, this makes group isomorphism an automatically P-problem. A Latin square of order N is an N × N matrix where each row and column contain exactly N distinct symbols, with each symbol appearing only once. A matrix derived from such a multiplication table forms an N-order Latin square. In contrast, a binary operation derived from an N-order Latin square as a multiplication table constitutes a pseudogroup over the Q set. I discovered four new algebraic structures that remain invariant under permutation of rows and columns, known as quadrilateral squares. All N×N Latin squares can be constructed using three or all four of these quadrilateral squares. Leveraging the algebraic properties of quadrilateral squares that remain unchanged by permutation, we designed an algorithm to generate all N × N Latin squares without repetition when permuted, resulting in the first universal and nonrepetitive algorithm for Latin square generation. Building on this, we established a precise counting framework for Latin squares. The generation algorithm further reveals deeper structural aspects of Latin squares (pseudogroups). Through studying these structures, we derived a crucial theorem: two Latin squares are isomorphic if their subline modularity structures are identical. Based on this important and key theorem, and combined with other structural connections discussed in this paper, a polynomial-time algorithm for Latin square isomorphism has been successfully designed. This algorithm can also be directly applied to solving quasigroup isomorphism, with a time complexity of 5/16(n⁵−2n⁴−n³+2n²)+2n³ Furthermore, more symmetrical properties of Latin squares (pseudogroups) were uncovered. The problem of filling a Latin grid is a classic NP-complete problem. Solving a fillable Latin grid can be viewed as generating grids that satisfy constraints. By leveraging the connections between parametric group algebra structures revealed in this paper, we have designed a fast and accurate algorithm for solving fillable Latin grids. I believe the ultimate solution to NP- complete problems lies within these connections between parametric group algebra structures, as they directly affect both the speed of solving fillable Latin grids and the derivation of precise counting formulas for Latin grids.

Abstract: Abstract Low-frequency pulsed magnetic fields (LFPMFs) are a recently developed modality for managing pain and promoting wound healing. The term LFPMF is used to describe low-intensity fields in wound and tissue studies, and is referred to as magnetic peripheral nerve stimulation (mPNS) in pain-related studies. The recent clearance of the first mPNS device for treating pain due to diabetic neuropathy by the FDA marks a watershed event in the clinical acceptance of these modalities. In addition to being within the frequency range of 0.5-100 Hz, the use of electromagnetic fields rather than electrical current, which dissipates in tissues, results in several therapeutic advantages of magnetic fields. These fields permeate tissues and affect a larger area. Most dramatically, patients (approximately 60-75%) (1) can experience neuronal blockade immediately upon application and have a resulting dramatic pain reduction even if they have had neuropathic pain symptoms for years. Interestingly, it is thought that the neuronal blockade effect may potentiate the peripheral reconditioning of the CNS in terms of long-term pain control.

Abstract: Aurantii Fructus Immaturus (AFI), a traditional Qi-regulating Chinese medicine, is commonly utilized in clinical prescriptions to treat diverse gastrointestinal diseases. Recent studies have demonstrated that the flavonoids in AFI effectively enhance gastrointestinal motility. But the effects of other active components in AFI on gastrointestinal motility remain unclear. Meanwhile, numerous studies have found that gastrointestinal motility is closely related to gut microbiota. To explore the effects improving gastrointestinal motility and influences on gut microbiota of Aurantii Fructus Immaturus extract and its ingredient stachydrine, the mice with gastrointestinal motility disorder (GIMD) induced by loperamide hydrochloride were orally treated with AFI extract, its fraction and stachydrine for three weeks. After the treatment, the therapeutic effects of AFI extract, its fraction and stachydrine were evaluated by measuring gastrointestinal motility related indexes including gastrointestinal transit time, gastric emptying and small intestine propulsion rate. Simultaneously, the effects of AFI extract, its fraction and stachydrine on gut microbiota were evaluated by 16S rRNA gene sequencing. Short-chain fatty acids (SCFAs) and bile acids (BAs), two gut microbial metabolites, were quantitatively analysed using gas chromatography (GC) and high-performance liquid chromatography tandem mass spectrometry (HPLC-MS). The correlation between gastrointestinal motility indexes, differential gut microbiota, and their metabolites was investigated using Spearman analysis. The results showed that AFI extract, its fraction and stachydrine improved the physiological state of the mice and exhibited significant improvement in gastrointestinal motility. Moreover, AFI extract, its fraction and stachydrine treatment reshaped the composition of gut microbiota by reducing pro-inflammatory bacteria and increasing bacteria that produce SCFAs in GIMD mice. In addition, AFI extract, its fraction and stachydrine regulated the levels of gut microbial metabolites, including SCFAs and BAs that participate in regulating gastrointestinal motility. The Spearman's correlation indicated a certain association between gastrointestinal motility indexes, gut microbiota and their metabolites. Therefore, AFI extract and its fraction can significantly improve gastrointestinal motility, suggesting that other active components are improving gastrointestinal motility besides flavonoids in AFI. Furthermore, stachydrine is a new bioactive ingredient of AFI extract improving gastrointestinal motility. AFI extract, its fraction and stachydrine may indirectly improve gastrointestinal motility by altering the composition and metabolism of gut microbiota to restore gastrointestinal function. This study establishes scientific evidence for the mechanism of AFI in treating GIMD.

Abstract: If we aim to create AGI, our first job is to enable it understand the world. The key to understanding has a name and that name is world model. This is what AGI must look for. In fact, rather than looking for a model, we will aim to find a description of the world. For this purpose, we need a language for description of worlds. We will use the game of chess to create the language we need. We have already done this in a previous paper, but then the agent was able to see the chessboard, while now it will play blind. Playing without seeing the chessboard makes the problem more complex and requires the addition of abstract ED models. The result will be a world model which will enable AGI think in its mind and plan its actions.

Abstract: Generative artificial intelligence (GenAI) is a paradigm shift that redefines knowledge retrieval, development, communication, and verification. However, GenAI’s capabilities for mimicking the work of human scholars have advanced, generating content indistinguishable from that of human authors, making pre-publication editing and peer review challenging. Through an extensive bibliographic exploration of the Scopus and HeinOnline databases, we investigated academic, ethical, and legal attitudes toward GenAI and scientific authorship. A backward reference search was conducted, beginning with bibliographic evidence published from 2022 to 2025, to identify earlier contributions that demonstrate stakeholders' positions. Index keyword co-occurrence analysis was performed to identify trends and attitudes among the scientific and legal professional communities. It is well recognized that GenAI impacts the traditional ideas and practices of authorship, creativity, ownership, and copyright, but accountability and responsibility remain with the authors. Although the need for reforming guidelines and laws related to these subjects is unanimously recognized, academic scholars tend to debate theoretical and doctrinal subjects, while legal professionals focus on the deliberate misuse of GenAI, legal schemes, ethical compliance, verification of the origin of content, and unauthorized use of resources protected by proprietary rights. The ongoing technological developments in GenAI powerfully shape opinions and drive new ideas for the scientific and legal community.

Abstract: Tropical cyclones pose major risks to life and property, especially as coastal populations grow and climate change increases the likelihood of intense storms, making seasonal prediction of tropical cyclones an important scientific and societal goal. This study uses HURDAT best-track records from 1950–2024 to quantify annual tropical cyclone, hurricane, and major hurricane counts across the Atlantic basin, Caribbean Sea, and Gulf of Mexico. These nine targets are paired with 34 monthly climate predictors from NOAA and NASA GISS—including SST and ENSO indices, Main Development Region (MDR) wind and pressure fields, and latent heat flux empirical orthogonal functions—evaluated under nine predictor-set configurations. Four forecasting approaches are developed and tested under operationally realistic conditions: Lasso regression, K-nearest neighbors (KNN), an artificial neural network (ANN), and XGBoost, using a 30-year sliding-window cross-validation design and a Poisson log-likelihood skill score relative to climatology. Lasso performs reliably with concise, physically interpretable predictors, while XGBoost provides the most consistent overall skill, particularly for basin-wide total cyclone and hurricane counts. The skill of ANN is limited by small sample sizes, and KNN offers only marginal improvements. Forecast skill is the highest for basin-wide storm totals and decreases for regional and major-hurricane targets due to lower event frequencies and stronger predictability limits.

Abstract: The generation of synthetic human genomic data offers immense potential for biomedical research and data sharing, while theoretically safeguarding individual privacy. However, existing methods, including deep generative models, struggle to achieve a robust balance between data utility and privacy protection. State-of-the-art evaluations like PRISM-G reveal vulnerabilities such as proximity, kinship replay, and trait-linked leakage. This paper introduces GenProtect-V, an end-to-end privacy-preserving synthetic human genomic data generation framework based on a Variational Autoencoder architecture. GenProtect-V integrates multi-layered privacy mechanisms: a Differentially Private Encoder to mitigate Proximity Leakage, Decoupled Latent Space Learning to address Kinship Replay, and a Rare Variant Smoother to counter Trait-linked Leakage. Through extensive experiments on the 1000 Genomes Project dataset, we demonstrate that GenProtect-V consistently achieves significantly lower PRISM-G composite scores compared to state-of-the-art baselines. Crucially, GenProtect-V simultaneously maintains or improves key utility metrics, including Allele Frequency fidelity, Population Structure preservation, and GWAS reproducibility. An ablation study further confirms the independent and significant contributions of its privacy mechanisms. GenProtect-V establishes a new benchmark for balancing privacy and utility, offering a more secure and practical paradigm for synthetic genomic data generation.

Abstract: Assembly theory defines structural complexity as the minimum number of steps required to construct an object in an assembly space. We formalize the assembly space as an acyclic digraph of strings. Key results include analytical bounds on the minimum and maximum assembly indices as functions of string length and alphabet size, and relations between the assembly index (ASI), assembly depth, depth index, Shannon entropy, and expected waiting times for strings drawn from uniform distributions. We identify patterns in minimum- and maximum-ASI strings and provide construction methods for the latter. While computing ASI is NP-complete, we develop efficient implementations that enable ASI computation of long strings. We establish a counterintuitive, inverse relationship between a string ASI and its expected waiting time. Geometric visualizations reveal that ordered decimal representations of low ASI bitstrings of even length N naturally cluster on diagonals and oblique lines of the squares with sides equal to 2^N/2. Comparison with grammar-based compression (Re-Pair) shows that ASI provides superior compression by exploiting global combinatorial patterns. These findings advance complexity measures with applications in computational biology (where DNA sequences must violate Chargaff's rules to achieve minimum ASI), graph theory, and data compression.

Abstract: The house cricket Acheta domesticus is found globally. It is an agricultural pest causing economic damage to a wide variety of crops including cereal seedlings, vegetable crops, fruit plants, and stored grains. Additionally, crickets act as mechanical vectors of pathogens by harboring bacteria, fungi, viruses and toxins causing foodborne illnesses. They can contaminate stored grains, packaged foods, or animal feed due to deposition of their feces, lowering the quality of the food and creating food safety risks. Synthetic insect repellents, such as pyrethroids and carbamates, have been used previously in integrated pest management practices to control crickets. Though successful as repellents, they have been associated with health and environmental risks and concerns. The use oforganic green repellents, such as plant essential oils, may be a viable alternative in pest management practices. In this study, we tested the behavioral effects of 27 plant-based essential oils on the behavioral effects of the house crickets, Acheta domesticus in dual choice bioassays. Crickets were introduced into an open arena to allow them unrestricted movement. A transparent plastic bottle containing an essential oil treatment was placed in the arena to allow voluntary entry by crickets. Following a predetermined observation period, the number of crickets that entered the bottle was recorded, and percent entry was calculated as the proportion of individuals inside the bottle relative to the total number in the arena. Analysis of the percentage entry into the bottle allowed for a comparative assessment of repellency of the panel of essential oils that were tested. Essential oils that elicited high levels of entry into the bottle were categorized as having weak or no repellency, while those that produced reduced entry were classified as moderate or strong repellents. This ranking system enabled a clear differentiation among essential oils with respect to impact on cricket behavior. Our results indicated that house crickets responded with a strong repellent behavior to nearly half of the essential oils tested, while four essential oils and two synthetic repellents evoked no significant repellent responses. Four strong repellent essential oils were tested at different concentrations and showed a clear dose-dependent repellent effect. The results suggest that selected essential oils can be useful in the development of more natural “green” insect repellents.