Henry Kissinger, one of the most influential and controversial diplomats of the late 20th and early 21st centuries, played a pivotal role in shaping the geopolitical landscape of the Middle East through his shuttle diplomacy following the Yom Kippur War. This article explores the multidimensional and layered nature of Kissinger?s strategy, grounded in the geostrategic concepts of Saul Bernard Cohen. While Kissinger?s mission aimed to end hostilities between Israel and its Arab neighbours, it also sought to counterbalance the expanding Soviet influence in the region, a critical aspect often overlooked in the existing scholarly work. By examining the geopolitics of oil, power, and influence through the lens of the Carter Doctrine, this study illustrates how Kissinger?s realpolitik not only influenced the Arab-Israeli peace negotiations but also altered the dynamics of the Cold War, thereby reshaping the course of history.

This research aimed to examine the applicability of the Pivot Profile (PP) technique in detecting adulteration in acacia honey from the Tuzla region, Bosnia and Herzegovina (B&H). The PP technique captured the relative meaning of descriptors and gathered free descriptions of differences between a target product and a pivot product (PVT), which served as a standard. Four pairs of samples were evaluated: original acacia honey (PVT) versus honey samples adulterated with 20%, 40%, 60%, and 80% fructose-glucose syrup. The sensory assessment involved 72 participants (25 women and 47 men), all acacia honey producers aged from 20 to 55 years of age. The chi-square test (ch² = 3032.37, p < 0.001) revealed significant statistical differences among values, indicating that the consumer panel effectively distinguished the samples. The chi-square test per cell was used to explore variation within the data matrix, identifying descriptors significantly differing from PVT in citation frequency. A total of 48 sensory attributes were generated (5 for appearance, 14 for odours, 4 for basic tastes, 3 for aftertastes, 16 for flavours, 2 for trigeminal effects, and 4 for texture). Correspondence Analysis (CA) was employed to visually represent sensory changes in honey samples based on adulteration levels, illustrating consumer perception of samples and attributes. CA effectively explained nearly 60% of the variability observed across the initial two dimensions, thus emphasizing the connection between sensory alterations and consumer perception. The results revealed a reduction in aroma and appearance attributes, along with occurrences of sensory defects such as off-flavours, unpleasant trigeminal effects, and altered viscosity properties. PP technique provided detailed information about each sample, assessing similarities and differences compared to PVT in a single session using multivariate techniques, contrasting with traditional trained or expert assessments. The PP technique appears promising for further exploration in vocabulary use and data analysis, not only for other honey types but also for various food products susceptible to adulteration.

Sleep deprivation is a significant contributor to various diseases, leading to poor cognitive function, decreased performance, and heart disorders. Insomnia, the most prevalent sleep disorder, requires more effective diagnosis and screening for proper treatment. Actigraphic data and its combination with physiological sensors like electroencephalogram (EEG), electrocardiogram (ECG), and body temperature have proven significant in predicting insomnia using machine learning methods. Studies focusing solely on actigraphic data achieved an accuracy of 84%, combining it with other wearable devices increased accuracy to 88%, and 2-channel EEG alone yielded an accuracy of 92%, but limits scalability and practicality in real-world settings. Here we show that using the hybrid approach of incorporating both recursive feature elimination (RFE) and principal component analysis (PCA) on sleep and heart data features yields outstanding results, with the multi-layer perception (MLP) achieving an accuracy of 95.83% and an F1 score of 0.93. The top-ranked features are predominantly sleep-related and time-domain RR interval. The dependent variables in our study have been extracted from the self-report Pittsburgh Sleep Quality Index questionnaire responses. Our findings emphasize the importance of tailoring feature sets and employing appropriate reduction techniques for optimal predictive modeling in sleep-related studies. Our results demonstrate that the ensemble classifiers generalize well on the dataset regardless of the feature count, while other algorithms are hindered by the curse of dimensionality.

Wastewater must be treated before discharge into the recipient to such an extent that it meets standards and regulations on wastewater quality, so as not to damage the environment. Depending on the quality of the influent, different technological procedures are applied, which are more or less energy intensive. Also, with the tightening of the conditions related to the quality of the effluent, the application of more energy-intensive purification technologies occurs, and thus the energy consumption at the plants increases. Wastewater treatment plants are among the biggest consumers of energy. In this paper, electric energy consumption at wastewater treatment plants was analyzed depending on different indicators of specific energy consumption, the applied technological process, and the level of purification.