When do parties respond to their political rivals and when do they ignore them? This article presents a new computational framework to detect, analyze and predict partisan responsiveness by showing when parties on opposite poles of the political spectrum react to each other’s agendas and thereby contribute to polarization. Once spikes in responsiveness are detected and categorized using latent Dirichlet allocation, we utilize the terms that comprise the topics, together with a gradient descent solver, to assess the classifier’s predictive accuracy. Using 10,597 documents from the official websites of radical right and ethnic political parties in Slovakia (2004–2014), the analysis predicts which political issues will elicit partisan reactions, and which will be ignored, with an accuracy of 83% (F-measure) and outperforms both Random Forest and Naive Bayes classifiers. Subject matter experts validate the approach and interpret the results.

Diabetic peripheral neuropathy (DPN) is a common and debilitating complication of diabetes mellitus which affects individuals with both type 1 and type 2 diabetes mellitus (T2DM), presenting with sensory loss, pain, and progressive nerve dysfunction. DPN pathogenesis is multifactorial: chronic hyperglycemia activates the polyol, hexosamine, and protein kinase C (PKC) pathways, increases advanced glycation end-products, and drives oxidative stress, mitochondrial dysfunction, inflammation, and impaired neurotrophic signaling. In addition to hyperglycemia-driven mechanisms, dyslipidemia and microvascular insufficiency exacerbate neural ischemia and metabolic stress. Recent mechanistic, animal, and associative human studies further implicate amyloidogenic toxicity, particularly from human islet amyloid polypeptide (hIAPP), as a plausible contributory factor in peripheral nerve degeneration in T2DM, linking protein misfolding and aggregation to axonal damage and demyelination in DPN. Despite increased understanding of these mechanisms, current treatments remain mainly symptomatic. Emerging therapeutic strategies, including antioxidants, anti-inflammatory agents, modulators of mitochondrial function, amyloid oligomer modulators, neurotrophic enhancers, and regenerative approaches such as stem cells and gene-based therapies, offer potential to modify disease progression. The strength of evidence across these methods varies, ranging from mechanistic and animal studies to early human research and, in some cases, randomized clinical trials. Therefore, although several candidates show potential to alter the disease, few have demonstrated consistent benefits on objective measures of nerve structure or function in large clinical trials. This review summarizes the key mechanisms driving DPN in T2DM and highlights promising therapeutic innovations poised for clinical translation.

This paper addresses the issue of polynomial iterative learning tracking control (Poly-ILC) for continuous-time linear systems (LTI) operating repetitively. It explores the design of an iterative learning control law by examining the stability along the pass theory of 2D repetitive systems. The obtained result is a generalization of the notion of stability along passages, taking into account transient performances. To strike a balance between stability along passages and transient performance, we extend our developed result in the discrete case, relying on some numerical tools. Specifically, in this work we investigate the convergence of tracking error with given learning controller gains. The key contribution of this structure of control lies in establishing an LMI (linear matrix inequality) condition that ensures both pole placement according to desired specifications and the convergence of output error between iterations. Furthermore, new sufficient conditions for stability regions along the pass addressing the tracking problem of differential linear repetitive processes are developed. Numerical results are provided to demonstrate the effectiveness of the proposed approaches.