Search Results (3,517)

The transfer-matrix method is employed to investigate a spin-1/2 Ising chain under open and periodic boundary conditions. It is demonstrated that finite-size Ising chains with antiferromagnetic coupling may exhibit significantly distinct magnetic behavior under open and periodic boundary conditions. While the open Ising chains display intriguing magnetic features regardless of the system size, mainly due to a specific contribution of boundary spins, the magnetic behavior of closed Ising chains depends basically on the number of spins. The closed Ising chains with an odd number of spins are subject to a geometric spin frustration leading to an additional plateau in the magnetization curve, which is naturally absent in the closed Ising chains with an even number of spins. Despite different microscopic origins, the magnetization curves of open and closed Ising chains with an odd number of spins exhibit an identical intermediate plateau, with only small quantitative differences appearing at moderate temperatures, which means that a geometric spin frustration of odd-membered rings is somewhat similar to the effect of open boundaries. The magnetization curves of the open Ising chains with an even number of spins differ drastically from those of the closed Ising chains due to the presence of an additional intermediate magnetization plateau. Furthermore, the initial susceptibility, inverse initial susceptibility, and susceptibility–temperature product are examined in detail as functions of temperature. These magnetic response functions demonstrate that the Curie constant and Weiss temperature represent fundamental characteristics of the magnetic system that are independent of the choice of boundary conditions. Full article

We introduce and study a class of multi-composite sigmoid neural network operators for Banach space-valued approximation. The proposed operators are generated by density-type kernels induced by finite compositions of seven standard sigmoid-type activation functions. The approximation is considered for continuous functions on compact intervals of the real line and on the whole real line, with values in an arbitrary Banach space $(X,\parallel ·\parallel )$. We prove quantitative pointwise and uniform convergence results by means of Jackson-type inequalities expressed through the first modulus of continuity. Higher-order and fractional approximation results are also obtained in terms of Banach space-valued derivatives and Caputo–Bochner fractional derivatives. The associated feed-forward neural network representation has one hidden layer and uses the multi-composite sigmoid function as its activation. Numerical experiments are presented to validate the theoretical estimates and to illustrate the approximation behavior of the proposed operators. In particular, we compare classical tanh-based operators, normalized self-composed activation operators, and heterogeneous multi-composite activation operators. The results show that self-composition and heterogeneous composition may improve the uniform approximation error for certain activation families and parameter choices, while also indicating that the observed improvement is activation-dependent and influenced by the composition order, kernel localization, and the regularity of the target function. Full article

Field-Programmable Gate Array (FPGA) technology allows for the creation of unique hardware implementations based on mass-produced chips. The process of project prototyping for such systems using Hardware Description Languages (HDLs) remains complex, even with modern tools. The comparison of HDL coding styles, for example, a behavioral case statement against a structural binary-tree decomposition, shows that the choice is capable of affecting post-implementation timing and area. The performed study, using the open-source yosys/nextpnr toolchain, shows that the validity of such a comparison is decided by the fabric domain. Logic that falls through to generic Look-Up Table (LUT) mapping is governed by the mapper’s heuristic fixed point rather than by source intent: on the crossbar, the behavioral and structural netlists become identical in cell composition; on the priority encoder, the verdict reverses; and on the barrel shifter, the LUT area collapses, so the comparison does not isolate the coding-style variable. It was measured that the keep_hierarchy attribute restores a meaningful comparison at ~17% LUT cost (N = 8) and provides a structural invariant to the ABC mapper variant, but the behavioral result is mapper-sensitive and the N = 4 verdict reverses under the legacy -noabc9 mapper (Cohen’s d from +2.4 to −1.6). By contrast, logic that involves a dedicated primitive before LUT mapping—an adder bound to the carry chain or a multiplier bound to a DSP block—yields source-meaningful verdicts that do not reverse with a mapper. Replication on a second fabric (Lattice iCE40) confirms that this behavior is fabric- rather than vendor-specific. The main contribution of this work is the proposed first fabric-domain characterization of synthesis canonicalization as a methodological hazard for cross-HDL FPGA studies on open-source toolchains, which identifies the two-phase synthesis mechanism that delimits it and supplies a decision rule (inspect post-synthesis composition) to identify whether a given comparison is susceptible. Full article

This paper introduces a multiplicative Atangana–Baleanu–conformable fractional integral operator in the setting of multiplicative calculus. The proposed operator is formulated by applying the Atangana–Baleanu–conformable fractional integral structure to the logarithmic representation of positive functions, thereby combining multiplicative behavior, nonsingular memory effects, and conformable scaling in a single framework. Appropriate function-space assumptions are imposed to ensure that the operator is well defined. Based on this operator, we establish a new auxiliary identity and derive several multiplicative Milne–Mercer-type inequalities for multiplicatively convex functions. The obtained results include multiplicative Riemann–Liouville-type, multiplicative Atangana–Baleanu-type, and conformable-type inequalities as special cases under suitable choices of the parameters. To clarify the role of the fractional parameters, numerical examples are provided together with logarithmic gap values, relative-error comparisons, heatmaps, contour plots, and parameter-sensitivity analyses. These computations illustrate the validity of the derived inequalities and compare the proposed bounds with their reduced special cases. Full article

Olfactory detection of essential oils (EOs), natural plant-derived mixtures of odorous volatile compounds, stimulates neural pathways involved in emotion, cognitive function, and memory in humans and significantly influences insect behavior (inducing attractiveness or repellency). In this study, the olfactory perception of rose (EO 1, a synthetic mixture with rose aroma), eucalyptus (EO 2), lemon (EO 3), clove (EO 4), rosemary (EO 5), and caraway (EO 6) EOs in untrained human participants was compared to the behavioral responses induced in Tenebrio molitor (adult insects) by EO exposure. Significant differences emerged in the perception of EO odor dimensions (pleasantness, intensity, and familiarity) using a Likert-type scale in untrained participants. The tested EOs elicited different behavioral responses in T. molitor insects, as assessed by repellency, escape, and choice tests. A positive correlation (r = 0.7861, p < 0.05) emerged between EO odor intensity perceived by participants and escape induction in T. molitor adults. GC–MS analysis revealed citronellol, 1,8-cineole, limonene, eugenol, α-pinene, and carvone as the most abundant volatile compounds in EO 1, EO 2, EO 3, EO 4, EO 5, and EO 6, respectively. The EO odor dimensions in participants and insect behavioral responses were also related to the in silico physicochemical/pharmacokinetic properties of the main EO components. Our results provide new insights into the chemical basis of olfactory preferences both in T. molitor adults and humans. Full article

This study investigates how AI-assisted plagiarism changes dishonest academic behavior in a socially interactive learning environment under different educational conditions. To this end, this study develops a scenario-based simulation to examine how AI-assisted plagiarism influences dishonest academic behavior in socially interactive learning environments. The model represents students as autonomous agents embedded in local peer networks who adapt their weekly behavior under academic pressure, institutional intervention, and available cheating options. Two behavioral scenarios are considered: a conventional plagiarism environment, in which agents choose between honest submission and direct copying, and an AI-augmented environment, in which AI-assisted plagiarism is introduced as an additional dishonest strategy. Intervention is modeled through environmental and institutional conditions, specifically detection probability and sanction severity, rather than through direct internal reward manipulation. Q-learning is used as a simplified adaptive mechanism for repeated agent choice. Experimental results show that the possibility of producing and assessing a simulation to see the availability of AI-assisted plagiarism substantially changes the behavioral composition of misconduct by increasing total dishonest behavior and shifting a large share of it toward the AI-assisted category. In the simulation, active intervention reduces dishonest behavior overall but does not eliminate AI-assisted plagiarism as the dominant dishonest strategy in the AI-augmented environment. These observations in the simulation suggest that academic misconduct in the AI era should be understood not only as a problem of deterrence but also as a problem of behavioral adaptation under changing technological and institutional conditions. To support the realism assessment of the simulation design, the study also conducts a structured qualitative plausibility review using multiple large language models under a shared prompt. Across these reviews, the model is judged to be acceptable as a first-stage stylized baseline, while important limitations are identified in agent heterogeneity, social influence depth, and the use of Q-learning as a simplified adaptive heuristic to reproduce the behaviors of actors in there. Full article

Background/Objectives: Intranasal tumors are common malignancies in dogs, characterized by locally aggressive behavior and clinical signs such as epistaxis, nasal discharge, and facial deformity. Radiotherapy (RT) is considered the treatment of choice due to anatomical limitations to surgical resection. This study aimed to evaluate clinical outcomes, toxicity, and prognostic factors in dogs with primary malignant intranasal tumors treated with cobalt-60–based megavoltage radiotherapy. Methods: This retrospective study included 40 dogs with histopathologically confirmed primary malignant intranasal tumors treated between September 2018 and February 2025 at a veterinary radiotherapy clinic in Rio de Janeiro, Brazil. Medical records were reviewed for patient demographics, tumor characteristics, treatment protocols, response, toxicity, and survival outcomes. Tumors were staged using modified Adams criteria based on computed tomography. Definitive-intent protocols (n = 32) delivered 48–54 Gy in 10–13 fractions administered three to five times weekly, while palliative protocols consisted of either four fractions of 8 Gy delivered once weekly or five fractions of 4 Gy delivered daily. Results: Adenocarcinoma was the most common histologic subtype (42.5%), and 82.5% of dogs had stage III–IV disease. The objective response rate was 82.5% (CR: 17.5%; PR: 65.0%), with clinical benefit observed in 92.5% of cases. Acute toxicity was frequent but manageable, primarily affecting skin, oral mucosa, and eyes. Overall median progression-free interval (PFI) and survival time (MST) were 382 days and 430 days, respectively. Stage IV disease was significantly associated with shorter survival when compared to stage I-III (MST 345 vs. 1063 days, respectively; p = 0.016). Treatment response was significantly associated with PFI in univariate analysis (p < 0.05). Conclusions: Radiotherapy provided high response rates and meaningful clinical benefit with acceptable toxicity in dogs with malignant intranasal tumors, highlighting the importance of early diagnosis and treatment. Further prospective studies with standardized protocols are warranted. Full article

Animals with knockout of the dopamine transporter gene (DAT-KO) display hyperdopaminergic phenotypes, including attention-deficit/hyperactivity-like behaviors. A previous behavioral analysis of heterozygous rats with partial knockout (DAT-HET) suggested increased susceptibility to addictive behaviors. The aim of this study was to investigate elements of addictive behaviors and the mechanisms underlying dopamine release in DAT-HET rats. Offspring derived from DAT-knockout breeding underwent genotyping and behavioral assessment using the marble burying test, a manipulative behavior test using nesting material, and a modified version of the Iowa Gambling Task. Feeding behavior was studied using a binge-eating model. Reinforcing properties were investigated using intracranial self-stimulation under fixed-ratio (FR) and variable-ratio (VR) schedules. Dopamine (DA) release and clearance dynamics were assessed using fast-scan cyclic voltammetry (FSCV). DAT-HET rats exhibited moderate hyperactivity, increased impulsive choice, and compulsive responses. Male DAT-HET rats also showed increased compulsive overeating compared with wild-type (WT) rats of both sexes and female DAT-HET rats. In addition, DAT-HET rats demonstrated a preference for VR self-stimulation, which resembles risk- and thrill-seeking behavior in humans. In DAT-KO rats, impaired DA clearance resulted from complete loss of dopamine transporter function. In DAT-HET rats, increased DA release amplitude was observed, and dopamine persisted longer in the extracellular space than in WT rats. These findings underscore the importance of the DAT-HET model for studying impulsivity, compulsivity, and factors underlying the predisposition to addictive behavior. Full article

Appendiceal mucinous neoplasms (AMNs) are a rare but clinically significant category of gastrointestinal tumors, ranging from low-grade appendiceal mucinous neoplasm (LAMN), the main precursor of pseudomyxoma peritonei (PMP), to high-grade appendiceal mucinous neoplasm (HAMN), poorly differentiated and signet-ring-cell adenocarcinomas, and goblet cell adenocarcinoma. Although current WHO and PSOGI classifications provide well established diagnostic criteria, controversies persist regarding the biological behavior and prognostic significance of the most aggressive subtypes and the relationship between HAMN and mucinous adenocarcinoma. While appendectomy is sufficient for localized LAMN, cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) is the treatment of choice for peritoneal dissemination This review integrates the histopathological and molecular classification of AMN and PMP with the evolution of intraperitoneal chemotherapy. Key findings indicate that KRAS and GNAS mutations are central drivers of mucin overproduction and peritoneal spread, that tumor grade and mucin cellularity remain the strongest prognostic determinants, and that the evidence supporting HIPEC and PIPAC derives largely from observational rather than randomized data. As a novel insight, we highlight the emerging role of patient-derived organoids as translational models for functional drug testing. Progress will depend on integrating molecular characterization, critical appraisal of intraperitoneal therapies, and organoid-based testing to advance individualized treatment for peritoneal surface malignancies. Full article

In the context of China’s stock-based urban renewal, public space regeneration in old urban areas increasingly requires attention to everyday use, inclusive access, local memory, and collaborative governance alongside physical upgrading. Drawing on catalyst theory, this study builds an analytical framework linking catalyst classification, potential element identification, effectiveness evaluation, actor collaboration, and renewal strategy transformation. The Guibeicheng area of Wuxi, China, is examined using semi-structured interviews, cognitive maps, qualitative coding, space syntax, the analytic hierarchy process, and actor collaboration analysis. The analysis indicates that behavioral and narrative catalysts are closely associated with residents’ everyday use and place identity. Event catalysts may generate phased amplification effects under specific conditions, while organizational and rule-based governance catalysts mainly provide support conditions for sustaining catalytic effects. Comparing space syntax results with cognitive-map and interview evidence further points to mismatches between configurational potential and perceived everyday activation. These include high-integration spaces with limited evidence of repeated everyday use, high-choice nodes mainly associated with pass-through use, weak everyday connections to historical resources, and limited independent organizational support for high-priority catalysts. On this basis, the study proposes a renewal pathway that combines everyday behavior guidance, event transformation, local narrative embedding, and organizational governance coordination. The findings provide a case-based reference for catalyst-oriented public space regeneration in historic urban areas and suggest potential implications for social sustainability, cultural continuity, and community resilience through spatial activation and long-term collaborative governance. Full article

Large language models (LLMs) are increasingly used in programming education to provide on-demand conceptual clarification, yet how students actually use this feature in mastery learning systems (in which learners must demonstrate conceptual competence before progressing)—and whether clarification interactions relate to subsequent learning—has received limited empirical study. This paper analyzes 732 student remediation episodes (366 students, 43 assignments) to examine how students move through the remediation branch of an LLM-assisted programming course, whether their behavioral pathway choices are associated with subsequent coding challenge efficiency, and what theoretical role the clarification function plays. The results show that 78.0% of remediation episodes follow a pure retesting strategy, with only 22.0% involving any clarification interaction. Clarification is highly concentrated on conceptual questions (84.7%) and occurs mostly in the first remediation round (86.3%). An effect size analysis reveals a large difference in remediation rounds between single immediate and single delayed clarifiers (Cliff’s $\delta =-0.912$), suggesting that the timing of clarification is more strongly associated with remediation efficiency than its occurrence alone. mixed-effect linear models show no significant pathway effects on coding challenge process efficiency (active time and number of code snapshots; all $p>0.05$), a null result that is further examined through code-variability subgroup analyses. We argue that the clarification feature acts as a selective process-support mechanism: its observable value appears to lie in a shorter remediation process rather than in improved subsequent task efficiency, and this association is clearest when clarification occurs early. The findings have practical implications for the design of clarification features in AI-assisted learning systems and for instructional intervention strategies. Full article

A central question in contemporary bail reform is whether the different forms of monetary release used in large U.S. jurisdictions, commercial surety bonds, deposit bonds, full cash bonds, and property bonds, produce systematically different pretrial outcomes. The commercial bail industry has long defended its role on the grounds that bondsman-supervised release produces superior pretrial outcomes through a private enforcement function not available under alternative mechanisms. The present study tests this claim using data from the 2009 State Court Processing Statistics program on 5271 felony defendants released on financial conditions in 35 large urban counties. Logistic regression models with county fixed effects and cluster-robust standard errors estimate the association between release mechanism and two outcomes, pretrial rearrest and failure to appear (FTA), net of bail amount, prior criminal record, seriousness of offense, criminal justice status at arrest, time from arrest to release, type of legal representation, and demographic characteristics. Three findings emerge. First, defendants released on deposit bonds exhibit substantially lower odds of pretrial rearrest than otherwise comparable defendants released on commercial surety bonds, a finding that is robust across a battery of sensitivity analyses. Second, defendants released on full cash bonds exhibit substantially lower odds of FTA than otherwise comparable defendants released on commercial surety bonds, although this finding is somewhat sensitive to specification choice and is partly mediated by bail amount. Third, no specification supports the public-safety claim made on behalf of commercial bail because surety bonds do not outperform the alternatives for either outcome. These findings indicate that the principal empirical justification for the commercial bail industry is not supported by nationally representative data, and that a shift away from commercial bail toward court-administered alternatives is unlikely to impose behavioral costs and may produce modest public-safety gains. Full article

AI technologies are changing the world of work in ways that are hard to predict, and this uncertainty is felt particularly strongly by young people who are just beginning to think about their futures. This study explores how high school students in Turkey perceive AI’s potential impact on their career choices, using Social Cognitive Career Theory (SCCT) and Uncertainty Management Theory (UMT) as interpretive lenses rather than formally tested models. SCCT helps frame AI as an environmental force that shapes how students think about their career options, while UMT helps explain how students emotionally and cognitively respond to uncertainty that cannot easily be resolved. Using a cross-sectional survey of 354 12th-grade students, we developed and validated the AI-Related Career Perception Questionnaire (AICP-Q), which yielded four factors: AI Anxiety and Career Precarity, AI Literacy and Technological Awareness, Proactive Career Adaptation, and Socio-Technical Uncertainty. Students showed moderate AI awareness but relatively high levels of socio-technical uncertainty. Academic track emerged as an exploratory statistical correlate of AI Anxiety, a descriptive association suggesting that students’ sense of threat from AI may relate more to the specific skill demands of their chosen field than to the prestige of their school, though no causal inference can be drawn from these cross-sectional data. A key finding is “the planning gap”: students recognized the potential career disruptions associated with AI but did not consistently respond with adaptive behaviors. Drawing on UMT, we advance the tentative hypothesis, to be tested in future research, that this pattern may relate to a lack of the appraisal resources needed to translate awareness into action; because these constructs were not directly measured, this remains an interpretive suggestion rather than an empirical finding. Full article

Despite recent advances, medical question answering systems still struggle with domain-specific reasoning and data efficiency. This paper presents Med-LLaMA3, a family of medical large language models developed by parameter-efficient fine-tuning of the LLaMA-3.1 (8 billion) and LLaMA-3.2 (1 and 3 billion) architectures using quantized low-rank adaptation (QLoRA) and low-rank adaptation (LoRA) with 4-bit quantization. Beyond model training, this work contributes the following: (1) a formalized dataset curation taxonomy (source type × clinical granularity × task format) with a source-category ablation confirming that the multi-source combination drives benchmark gains beyond any single category; (2) a systematic characterization of low-rank-adaptation rank-scaling behavior for the LLaMA-3 family in the medical domain (monotonic improvement up to rank 128, with no observed plateau); and (3) statistically validated comparisons using McNemar’s test and 95% bootstrap confidence intervals. We curated a medical instruction dataset of over 1.5 million samples spanning medical examinations, clinical dialogues, and biomedical literature. Our approach trains only ∼4% of the base model’s parameters and, consistent with prior studies of parameter-efficient methods in the medical domain, achieves performance comparable to full fine-tuning at a fraction of the memory footprint. Evaluated with five in-context examples per prompt, the 8-billion-parameter model attains a mean accuracy of 75.71% across the eight medical-domain subsets of the Massive Multitask Language Understanding benchmark; improvements over the unmodified LLaMA-3.1-8B-Instruct baseline are statistically significant on the medical multiple-choice benchmark MedMCQA and, after Bonferroni correction across the eight subsets, on three subsets (Clinical Knowledge, Medical Genetics, and Nutrition), with two further subsets being significant only before correction. A structured named-entity-recognition evaluation on 100 hospital discharge summaries (macro-averaged F1 0.94; dual-annotator agreement $\kappa =0.87$) provides complementary evidence of clinical-text utility. A safety mitigation pilot shows that context-disambiguation preprocessing reduces the highest-severity abbreviation-ambiguity error rate from 30% to 10% on a 30-case held-out set. These results show that parameter-efficient fine-tuning can deliver high-performance medical large language models while training only ∼4% of the model’s parameters and reducing memory use by roughly 75%, enabling development on low-cost consumer-grade hardware. Full article

This study investigates the use of interpretable machine learning techniques to identify behavioral and demographic patterns associated with diabetes, based on structured population survey data from the Canadian Community Health Survey (CCHS). A decision tree classifier was applied to a dataset comprising $16,824$ respondents and 38 preprocessed features covering lifestyle, well-being, and sociodemographic factors. The model was optimized through grid search with five-fold stratified cross-validation, achieving a test accuracy of $61.3$% (mean $62.6$% ±$0.6$% across a $10\times 5$ repeated stratified cross-validation). Feature importance analysis revealed that age, alcohol consumption patterns, daily energy expenditure, and physical activity were the most influential factors associated with diabetes status, with the top three features exhibiting stable importance across all cross-validation folds. The model produced a set of 32 human-readable decision rules; a sensitivity analysis confirmed that these rules are stable across encoding choices and cross-validation folds. Several model variants were evaluated: a class-weighted decision tree, a logistic regression baseline, an age-only decision tree, and an age and sex logistic regression. The class-weighted model improved minority-class recall (from $0.25$ to $0.53$) at the cost of overall accuracy. A one-hot encoding sensitivity analysis showed that replacing ordinal label encoding of nominal variables with one-hot encoding produces virtually identical results (accuracy: $61.4$% vs. $61.3$%), confirming that the main rules are not artifacts of the encoding choice. Although the classification accuracy is moderate and not significantly better than a majority-class baseline (McNemar’s test, $p=0.455$), the extracted rules confirmed several known associations and revealed interactions between social and lifestyle variables. These rules are intended as hypothesis-generating population-level descriptors rather than validated clinical decision tools, and no causal inference is claimed. This approach demonstrates the value of rule-based models for exploratory public health research. Full article