This study critically examines the multifaceted dynamics of foreign employee integration within the Czech Republic, with a specific focus on the Mladá Boleslav region. Conducted prior to the Ukrainian crisis, this research serves as a crucial baseline for understanding integration in a pre-crisis context and provides comparative insights into the evolving challenges and opportunities amid the subsequent migration movements. The study explores various aspects of integration and inclusion, drawing upon migration theories, economic factors, and sociological perspectives to understand the motivators and challenges faced by foreigners, particularly in light of the majority society’s perception, which often leans towards skepticism and negativity. The research methodology builds on grounded theory and integrates both quantitative and qualitative approaches, utilizing surveys and semi-structured interviews to explore the experiences of foreign nationals, with an emphasis on immigrant women. A key finding of the study is the significant role of employers in facilitating integration. The paper discusses how businesses, through inclusive policies and practices, can profoundly influence the integration experience. Cooperation between employers, local integration centers, and other relevant organizations emerges as vital, providing additional resources and support systems to enhance the integration process. The study concludes by emphasizing the critical role of various stakeholders, particularly employers, in shaping sustainable human resources practices that foster a more inclusive and harmonious society.

To rejuvenate the country through science and education, the university is an important position of China's personnel training system and a base for the production of human resources in our country. The higher education in the popularization stage has made a profound change in the employment mode of graduates, which makes the discipline structure and personnel training mode of colleges and universities adapt to the requirements of the market and society. Based on the employment situation of colleges and universities, this paper analyzes the significance, dilemma and suggestions of constructing a feedback mechanism for the quality of graduates, so as to help colleges and universities cultivate more high-quality talents.

In this study, the authors propose a method that combines CNN and LSTM networks to recognize facial expressions. To handle illumination changes and preserve edge information in the image, the method uses two different preprocessing techniques. The preprocessed image is then fed into two independent CNN layers for feature extraction. The extracted features are then fused with an LSTM layer to capture the temporal dynamics of facial expressions. To evaluate the method's performance, the authors use the FER2013 dataset, which contains over 35,000 facial images with seven different expressions. To ensure a balanced distribution of the expressions in the training and testing sets, a mixing matrix is generated. The models in FER on the FER2013 dataset with an accuracy of 73.72%. The use of Focal loss, a variant of cross-entropy loss, improves the model's performance, especially in handling class imbalance. Overall, the proposed method demonstrates strong generalization ability and robustness to variations in illumination and facial expressions. It has the potential to be applied in various real-world applications such as emotion recognition in virtual assistants, driver monitoring systems, and mental health diagnosis.

The study evaluated 33 accessions of groundnut in the field, consisting of 23 landraces from Nasarawa communities in Nigeria and 10 inbred lines. Assessment entailed the determination of plant survivorship, yield related parameters and pathological indices while genetic diversity study was undertaken using SSR and RAPD molecular markers. Data analysis was done on the Minitab 17.0 software. Significant variability was noted in all traits except in pod sizes, seed sizes and % infected seeds. About 33.3% of the accessions had a survival rate of ≥ 70.0% where 9/10 Inbred lines were found with overall yield (kg/ha) ranging from 4.0 ± 1.6 in Akwashiki-Doma to 516.8 ± 46.9 kg/ha in Samnut 24 × ICGV–91328. Five accessions (15.5%) had pathological indices of zero indicating no traces of any disease of any type and they included one landrace called Agric-Dazhogwa and four Inbred lines: Samnut 25 × ICGV–91317, Samnut 26 × ICGV–19324, Samnut 26 × ICGV–91328 and Samnut 26 × ICGV–91319. Coefficients of yield determination R2 by survivorship and pathological index were 50.6% and 15%, respectively. A fit model was established (Yield in kg/ha = –146 − 7.94 × Pi + 5.88 × S). Susceptibility to diseases depends on the type of variety (χ2(32) = 127.67, P = 0.00). Yield was significantly affected by BNR@30 (F = 5.47, P = 0.025, P < 0.05) and DSV@60*RUST@60 interaction effect (F = 4.39, P = 0.044, P < 0.05). The similarity coefficient ranged from 28.57 to 100 in plant morphology. Four varieties had no amplified bands with SSR primers whereas amplified bands were present only in four landraces accessions using the RAPD primer. The dendrogram generated by molecular data gave three groups where genetic similarity ranged from 41.4 to 100.0. The Inbred lines were noted for their high survivorship, good yield and disease resistance. Samnut 24 × ICGV–91328, an inbred line, had the highest yield but was susceptible to diseases. Among the landraces, Agric-Musha, Bomboyi-Dugu and Agric-Dazhogwa were selected for high survivorship and disease resistance. The selected inbred lines and landraces are valuable genetic resources that may harbour useful traits for breeding and they should be presented to the growers based on their unique agronomic values. The highest yielding inbred lines should be improved for resistance to late leaf spot diseases while the outstanding landraces should be improved for yield.