The University of Jena, officially the Friedrich Schiller University Jena (German: Friedrich-Schiller-Universität Jena, abbreviated FSU, shortened form Uni Jena), is a public research university located in Jena, Thuringia, Germany.The university was established in 1558 and is counted among the ten oldest universities in Germany. It is affiliated with six Nobel Prize winners, most recently in 2000 when Jena graduate Herbert Kroemer won the Nobel Prize for physics. It was renamed after the poet Friedrich Schiller who was teaching as professor of philosophy when Jena attracted some of the most influential minds at the turn of the 19th century. With Karl Leonhard Reinhold, Johann Gottlieb Fichte, G. W. F. Hegel, F. W. J. Schelling and Friedrich Schlegel on its teaching staff, the university was at the centre of the emergence of German idealism and early Romanticism.As of 2014[update], the university has around 19,000 students enrolled and 375 professors. Its current president, Walter Rosenthal, was elected in 2014 for a six-year term. W. F.
Autonomous driving is about to change the transportation of people and goods. However, due to very high safety requirements, even in exceptional situations, autonomous driving may take longer than anticipated to reach the necessary technological maturity and finally be approved for use on public roads. Remote driving, in which humans in the loop teleoperate vehicles, can be a key bridging technology. Even when autonomous driving is the standard, remote human supervisors will remain crucial for solving problems in exceptional situations. In this context, this paper extends the classical vehicle routing problem with time windows (VRPTW) by the specificities of remote driving. Remote drivers need not remain in their vehicles during service times at customer locations. Instead, they can switch to other vehicles, opening the possibility of operating a fleet with fewer drivers. We model a basic version of the resulting VRPTW with remote drivers (VRPTWRD) as a mixed integer program and resort to Benders decomposition to derive effective branch-and-Benders-cut approaches. Our computational experiments analyze both algorithmic performance and managerial insights, including changes in routing costs, driver utilization measured through idleness rates, and operational flexibility quantified by driver switches. The results reveal that remote driving can be an effective lever to reduce the workforce in times of human driver shortages.
In this study, we investigate the structural changes in dynamic metallopolymers during stimulus application, i.e. thermal treatment. For this purpose, we focused on the synthesis of polymers containing terpyridine moieties as ligands in the side chains that were complexed with either iron(ii) or zinc(ii) salts. The resulting crosslinked metallopolymers were characterized using differential scanning calorimetry (DSC), thermogravimetric analysis (TGA) and elemental analysis (EA). Rheology experiments, including dynamic mechanical thermal analysis (DMTA), frequency sweeps, stress relaxation and time-temperature superposition were conducted to study the stimuli-responsive mechanical properties. Hereby, the activation energy determined by stress relaxation as a combination of the metal complex and the polymer matrix could be determined. Additionally, computational master curves were obtained and the resulting relaxation spectra were analyzed. Beside the macroscopic material properties, temperature-dependent Raman spectroscopy and density functional theory (DFT) calculations were utilized to gain information on the changes on the molecular level. In this context, morphological changes in the polymer matrix were observed, which might be correlated to the presence of supramolecular aggregates. The changes on the molecular level could be linked to the macroscopic properties.
Stretching is extensively studied and implemented to improve flexibility, as a component of warm-ups to prepare for activity, to mitigate musculotendinous injury risk, improve cardiovascular health parameters, and optimize post-exercise recovery, among others. However, less attention has focused on the effects of stretching on neurological, neurovascular, and psychological outcomes. Stress represents a pervasive element in contemporary life, with chronic mental and physical stress contributing to adverse health consequences. Evidence suggests that a bout of stretching can acutely increase sympathetic activation stimulation. However, following both acute and chronic stretching, direct and indirect measures of stress show improvements, including reductions in heart rate, blood pressure, sympathetic‒parasympathetic control, cortisol release, and psychological stress (e.g., anxiety scales) along with modifications in heart rate variability and other related changes. This review synthesizes current knowledge regarding stretch-induced adaptations to neurological, neurovascular, and psychological parameters and is intended to assist researchers, health and fitness professionals, university level students, and informed fitness enthusiasts. Based on this growing literature base, we propose evidence-based practical applications that still warrant further scientific investigation.
Accurate prediction of NMR chemical shifts in transition metal complexes remains challenging due to the wide range of coordination environments and complex electronic structures of these systems. In this work, we present a machine learning approach (ML) for rapid and accurate prediction of H-1 NMR shifts in zinc complexes across multiple solvent environments. We systematically selected a diverse set of zinc complexes from the transition metal quantum mechanics (tmQM) database using K-means clustering on SOAP descriptors, and performed DFT NMR calculations across five solvents to generate training data. We combine smooth overlap of atomic positions (SOAP) descriptors with tree-based ensemble methods to predict proton chemical shifts. Among several ML algorithms evaluated, LightGBM achieved the best performance on held-out test complexes (MAE = 0.016 ppm, RMSE = 0.028 ppm, R-2 = 0.99), demonstrating excellent generalization to unseen molecular structures. External validation against experimental NMR data across multiple solvents revealed strong predictive performance (R-2 = 0.90, MAE = 0.56 ppm), with exceptional accuracy in methanol (R-2 = 0.96) and acetonitrile (R-2 = 0.91). Notably, the model demonstrated robust transferability to acetonitrile despite this solvent not being included in the training set. This approach provides a computationally efficient alternative to expensive quantum chemical calculations for predicting H-1 NMR shifts in transition metal complexes, offering prediction times that are orders of magnitude faster while maintaining accuracy comparable to DFT methods, potentially accelerating the characterization and design of organometallic compounds.
Flupyradifurone is a butenolide insecticide with growing agricultural significance as a substitute for classical neonicotinoids, which have been partially banned in the EU and other countries. It also acts as an agonist on acetylcholine receptors in the insect central nervous system, but its neurophysiological effects on individual interneurons are poorly understood. Here, we investigated the effects of sublethal flupyradifurone intoxication on the neural processing of auditory information in the field cricket Gryllus bimaculatus. Using a suction electrode on the brain surface, we recorded the spiking activity of the two ascending auditory interneurons AN1 and AN2 while injecting the insecticide into the prothorax. Flupyradifurone caused a dose-dependent decrease in sound-evoked spike responses and an increase in spontaneous spiking activity in both ascending auditory interneurons, eventually leading up to a collapse of all spiking activity the highest dose tested (20 µl of 10− 3 mol/l). The substantial increase of spontaneous spiking activity in AN1 and AN2, which started with 20 µl of 10− 5 mol/l flupyradifurone treatment, will mask the neuronal coding of relevant acoustic signals required for auditory feature detection in the higher brain circuits. Field crickets rely on AN1 to transmit low-frequency auditory information for pattern recognition in phonotactic mate finding, and on AN2 to transmit high-frequency auditory information for sound source localization in predator avoidance behaviour. The disruption of auditory processing at the level of ascending interneurons by this insecticide will negatively impact the chances for reproduction and survival in crickets.
