The selective determination of tryptophan in pasteurized milk is an indicator of quality, as its presence indicates that the process has been carried out at the correct temperatures. Tryptophan analysis is costly for industries, but the use of a sensor would reduce both costs and analysis times. A sensor using l-tryptophan dehydrogenase (TrpDH) was optimized, with the system immobilized using an electrode to quantify the tryptophan content in industrial milk. The stability of the enzyme was studied at a controlled temperature of 4 °C, and it was observed that it maintained 90
Behavioral evaluation is the dominant paradigm for assessing alignment in large language models (LLMs). In current practice, observed compliance under finite evaluation protocols is treated as evidence of latent alignment. However, the inference from bounded behavioral evidence to claims about global latent properties is rarely analyzed as an identifiability problem. In this paper, we study alignment evaluation through the lens of statistical identifiability under partial observability. We allow agent policies to condition their behavior on observable signals correlated with the evaluation regime, a phenomenon we term evaluation awareness. Within this framework, we formalize the Alignment Verifiability Problem and introduce Normative Indistinguishability, which arises when distinct latent alignment hypotheses induce identical distributions over evaluator-accessible observations. Our main theoretical contribution is a conditional impossibility result: under finite behavioral evaluation and evaluation-aware policies, observed compliance does not uniquely identify latent alignment, but only membership in an equivalence class of conditionally compliant policies, under explicit assumptions on policy expressivity and observability. We complement the theory with a constructive existence proof using an instruction-tuned LLM (Llama-3.2-3B), demonstrating a conditional policy that is perfectly compliant under explicit evaluation signals yet exhibits degraded identifiability when the same evaluation intent is conveyed implicitly. Together, our results show that behavioral benchmarks provide necessary but insufficient evidence for latent alignment under evaluation awareness.
Intelligent Transportation Supply Chain Systems has continued to metamorphose forward-looking supply chain management (SCM). Using data from the Intelligent Transportation Systems and Supply Chain Management Systems on real-time information Transportation services is critical to survival. The new protocol proposes to fulfill this deficiency gap in Intelligent Transportation Systems and Supply Chain Management. This research suggested the two-tier Tokenization for Intelligent Transportation Supply Chain Systems Using a Hybrid optimized query expansion strategy and Smart Contracts to automatically handle the Intelligent Transportation Supply Chain Systems vehicle alignment on live driving. This helps to enhance the vehicle conditions and traffic to enhance overall transportation systems. This Proposed System has three modules to improve the Intelligent Transportation Supply Chain Systems. Firstly, the Intelligent Transportation Supply Chain Systems for cloud resources scheduling of semantic driving with vehicle alignment. It Shows the Intelligent Transportation activities and Supply Chain Systems management based on "Brent's algorithm. " This encourages the two-tier tokenization. Broadly, the two-tier Tokenization Enabled Smart Contracts show process verification. The formation of Tier 1 is Fungible Tokens based process verification, and Tier 2 is Non-Fungible Tokens based process verification. Also, the Smart Contracts are adaptable to Security concerns in the Intelligent Transportation Supply Chain Systems. It is Forming the "Hybrid Optimized Query Expansion Strategy " for Intelligent Transportation ranking. The simulation result of effective two-tier tokenization for intelligent transportation supply chain systems using hybrid optimized query expansion strategy and smart contracts improves the supply chain management system in planning at 34.5%, optimization at 56.8%, the level of system management at 60.23%, also the analysis 72.3% and finally at the execution stage by 75%.
Evaluating open-ended questions is a common and time-consuming task in education. With the continuous advances in natural language processing (NLP), large language models (LLMs) trained on massive datasets can assist in this process. This study evaluates the use of LLMs, complemented by retrieval-augmented generation (RAG), for the numerical grading of open-ended answers of approximately 250 words. We focus on two Spanish-language technical courses and assess general-purpose LLMs. Our results show that RAG improves grading accuracy, achieving reductions in mean absolute error (MAE) of up to 19.47% compared to using LLMs alone, with the best configuration reaching a MAE of 1.19. We also note that LLMs tend to assign high grades, reflecting the dataset's imbalance toward higher scores. This work demonstrates the potential of combining RAG with general-purpose LLMs to evaluate specialised Spanish language content, avoiding the cost and bias of model fine-tuning.
A comorbidade entre dor crônica e transtornos depressivos representa um desafio clínico de alta complexidade na prática médica contemporânea. Esta revisão sistemática teve como objetivo analisar os mecanismos neurobiológicos e psicossociais compartilhados entre ambas as condições, investigando a bidirecionalidade dessa relação. A metodologia baseou-se na análise de evidências científicas publicadas em bases de dados nacionais e internacionais nos últimos dez anos. Os resultados demonstram que existe uma sobreposição significativa nos substratos neuroanatômicos e nos sistemas de neurotransmissão, particularmente envolvendo a serotonina e a noradrenalina. Além disso, a inflamação sistêmica crônica atua como um mediador central na manutenção do ciclo de dor e depressão. Conclui-se que o tratamento eficaz exige uma abordagem multidisciplinar e integrada, focando na modulação dos sistemas de dor e do humor de forma concomitante para garantir a recuperação funcional e o bem estar do paciente.
