We develop a general framework for various lattice-valued, Probabilistic and approach uniform convergence spaces. To this end, we use the concept of s-stratified LM- filter, where L and M are suitable frames. A stratified LMN-uniform convergence tower is then a family of structures indexed by a quantale N. For different choices of L; M and N we obtain the lattice valued, Probabilistic and approach uniform convergence spaces as examples. We show that the resulting category sLMN-UCTS is topological, well- bred and Cartesian closed. We furthermore de ne stratified LMN-uniform tower spaces and show that the category of these spaces is isomorphic to the subcategory of stratified LMN-principal uniform convergence tower spaces. Finally we study the underlying stratified LMN-convergence tower spaces.

Introduction: The ethical dimensions of agriculture need to be explored, distinguishing between ethics in agriculture and ethics of agriculture. Ethics in agriculture is traditionally based on abstract moral theory, formulated through logical reasoning, but often found inadequate to address the complex ethical challenges within agriculture due to its abstract nature and lack of practical visibility. Conversely, ethics of agriculture is approached within a context-specific framework derived from the research topic with the aim of providing more tailored solutions. Materials and Methods: A comprehensive review of the relevant literature is conducted, using a descriptive-analytical approach to provide a conceptual framework for understanding agricultural ethics. Conclusion: The article highlights the critical need for a comprehensive ethical framework to address the multifaceted ethical challenges in the agricultural sector. The importance of analyzing and resolving ethical dilemmas at all stages of agriculture is underscored in order to align practices with human ideals and to ensure ethical decision-making by stakeholders involved in agriculture.

Background and Objectives: Distributed generations (DGs) based on renewable energy, such as PV units, are becoming more prevalent in distribution networks due to technical and environmental benefits. However, the intermittency and uncertainty of these sources lead to technical and operational challenges. Energy storage application, uncertainty analysis, and network reconfiguration are apt therapies to resist these challenges. Methods: Energy management of modern, smart, and renewable-penetrated distribution networks is tailored here considering the uncertainties correlations. Network operation costs including switching operations, the expected energy not served (EENS) index as the reliability objective, and the node voltage deviation suppression as the technical objective are mathematically modeled. Multi-objective particle swarm optimization (MOPSO) is considered as the optimization engine. Scenario generation method and Nataf transformation are used in Probabilistic evaluations of the problem. Moreover, the technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) is deployed to make a final balance between different objectives to yield a unified solution.Results: To show the effectiveness of the proposed approach, the IEEE 33-node distribution network is put under extensive simulations. Different cases are simulated and interrogated to assess the performance of the proposed model.Conclusion: For different objectives dealing with different aspects of the network, remarkable achievements are attained. In brief, the final solution shows 4.50% decrease in operation cost, 13.07% improvement in reliability index, and 18.85% reduction in voltage deviation compared to the initial conditions.

In this study, first a supervised version for Probabilistic principal component analysis mixture model (SPPCAMM) is proposed. Then, considering projection penalty in learning of a predictive model, a method for face recognition using a dimensionality reduction without loss framework is proposed. In the proposed method, first a locally linear underlying manifold of data samples is obtained using supervised Probabilistic principal component analysis mixture model. Then, a support vector machine with projection penalty is trained as the mentioned predictive model using this locally linear underlying manifold. In this way, the benefits of dimensionality reduction are used in the predictive model, while using the projection penalty idea, the loss of useful information is prevented. To train and evaluate the proposed method, well-known face databases are used. Gabor feature extraction method is applied on the face images. The experimental results show that the proposed method has the most average classification accuracy compared to many traditional methods, and also compared to the projection penalty idea used for linear and non-linear kernel-based dimensionality reduction techniques.