Caterpillar (United States)

Previous research has empirically demonstrated that personal resilience and organizational level interventions are important considerations when studying workplace adversity such as burnout and excessive workloads in the context of a pandemic. To explore interest in participating in a wellness initiative program designed specifically for advanced practice registered nurses (APRNs) and physician associates (PAs), with a voluntary option for participation in research. An anonymous, brief, online survey with Likert-response type items and one free-text question was delivered with passive informed consent via email to all APRNs and PAs (N = 312 participants) within one health care system. A positive correlation between likelihood of seeking well-being care and having sufficient time (0.36, p < .01) was found, which indicates that APRNs or PAs who felt they had sufficient time to get counseling were actually more likely to seek counseling services. Results from the path model show that the comfort-level of seeking counseling completely mediates the relationship between fear of negative perceptions of receiving counseling and the likelihood of participating in a wellness program. Well-being strategies are important for organizations to engage in to support their employees; however, confidentiality concerns and competing priorities for time may influence engagement. Interest among APRNs and PAs for participation in research exists if enough information is provided and anonymity can be assured. Organizations should focus on finding innovative ways to integrate wellness into APRN and PA practice that mitigates the belief around the burden of time commitment and seeks ways to provide psychological safety for its employees.

This study aims to examine the implementation of job reclassification as part of the bureaucratic simplification policy in the Government of Yogyakarta City. The national bureaucratic simplification policy, mandated through the Regulation of the Minister of Administrative and Bureaucratic Reform No. 25 of 2021 and the President’s instruction in 2019, requires changes in organizational structure, job adjustments, and work systems across government institutions, including the Yogyakarta City Government. This research employs a qualitative method with a descriptive approach through observation, in-depth interviews, and documentation. Data analysis is conducted using the model of Miles, Huberman, and Saldana, which includes data condensation, data display, and conclusion drawing. The findings indicate that the implementation of job reclassification was carried out through three main stages: structural simplification, conversion of administrative positions into functional positions, and adjustments to a cross-sector team-based work system. The Yogyakarta City Government reclassified 177 officials in 2021 and continued the process in 2022 with an additional five officials. The study identifies several challenges, including the limited readiness of human resources, concerns about credit points, mismatch between technical competencies and job requirements, and worries related to income changes. Nevertheless, the reclassification also provides benefits such as improved organizational efficiency, faster decision-making, greater work system flexibility, and strengthened collaborative work culture. Overall, job reclassification in the Yogyakarta City Government has been carried out effectively and aligns with national bureaucratic reform objectives. However, further efforts are required to enhance ASN competencies, develop more accurate functional position planning, and strengthen the work system to ensure that the goals of bureaucratic simplification can be fully achieved. ABSTRAK Penelitian ini bertujuan untuk mengetahui bagaimana implementasi penyetaraan jabatan dalam rangka penyederhanaan birokrasi di Pemerintah Kota Yogyakarta. Kebijakan nasional penyederhanaan birokrasi yang ditetapkan melalui Peraturan Menteri Pendayagunaan Aparatur Negara dan Reformasi Birokrasi Nomor 25 Tahun 2021 dan instruksi Presiden tahun 2019 menuntut perubahan struktur, penyesuaian jabatan, serta sistem kerja di berbagai instansi pemerintah, termasuk Pemerintah Kota Yogyakarta. Penelitian ini menggunakan metode kualitatif dengan pendekatan deskriptif melalui teknik observasi, wawancara mendalam, serta dokumentasi. Analisis data dilakukan menggunakan model Miles, Huberman, dan Saldana yang mencakup kondensasi data, penyajian data, serta penarikan kesimpulan. Hasil penelitian menunjukkan bahwa implementasi penyetaraan jabatan dilaksanakan melalui tiga tahapan utama, yaitu penyederhanaan struktur organisasi, konversi jabatan administrasi menjadi jabatan fungsional, serta penyesuaian sistem kerja berbasis tim lintas bidang. Pemerintah Kota Yogyakarta menyetarakan 177 pejabat pada tahun 2021 dan melanjutkan proses tersebut pada tahun 2022 dengan tambahan lima pejabat. Penelitian menemukan beberapa hambatan seperti keterbatasan kesiapan sumber daya manusia, kecemasan terkait angka kredit, ketidaksesuaian kompetensi teknis, serta kekhawatiran tentang pendapatan. Meskipun demikian, penyetaraan jabatan juga memberikan manfaat berupa peningkatan efisiensi kerja, percepatan proses pengambilan keputusan, fleksibilitas sistem kerja, dan penguatan budaya kolaboratif. Secara keseluruhan, penyetaraan jabatan di Pemerintah Kota Yogyakarta telah berjalan cukup baik dan selaras dengan arah reformasi birokrasi nasional. Namun masih diperlukan peningkatan kompetensi ASN, perencanaan formasi jabatan fungsional yang lebih matang, serta penguatan sistem kerja agar tujuan penyederhanaan birokrasi dapat tercapai secara optimal.

In the labyrinthine landscape of macroeconomic data, where subtle anomalies can herald significant economic shifts, this paper proposes a novel hybrid framework for their accurate and timely detection. The framework effectively combines an Autoregressive Integrated Moving Average (ARIMA) model for solid baseline forecasting with Long Short-Term Memory (LSTM) networks and a variational autoencoder that incorporates LSTM layers (VAE-LSTM) to capture complex residual patterns analysis. A unique dynamic weighting method, which includes temporal smoothing and differences in macroeconomic states, adaptively fuses the outputs of these models, leveraging their strengths across diverse economic scenarios. The proposed hybrid framework’s efficacy was evaluated on a dataset of 348 macroeconomic indicators from Brazil, Russia, India, China, and South Africa (BRICS) nations, covering 1970 to 2020. Empirical results show the framework outperforms other state-of-the-art (SoTA) methods: ARIMA, LSTM, VAE-LSTM, Autoencoder (AE), Isolation Forest (IF) and One-Class Support Vector Machine (OCSVM) achieving an F1-score of 0.915 with AUC of 0.926 and PR-AUC of 0.839. Furthermore, sensitivity analysis substantiates the framework’s robustness across different weighting configurations, maintaining consistent F1-scores between 0.887 and 0.915. The proposed framework offers a robust and adaptive approach to anomaly detection in complex macroeconomic time series, with potential applications in risk management, policy formulation, and economic forecasting.

Semantic web service composition considers semantics for finding better solutions than syntactic web service composition. This paper focuses on hierarchical relationships among parameters of web services. A comprehensive mathematical model for semantic web service composition, into which hierarchical parameter relationships are incorporated, is presented as a general mathematical formulation. Experimental results demonstrate that the mathematical model for semantic composition finds hidden and better solutions that syntactic composition cannot find. The optimality of the solutions is empirically verified through extensive experiments. Furthermore, the scalability of the model is tested by comprehensive experiments to explore the impacts of eight key factors on web service composition. The mathematical model and the provided datasets are expected to serve as benchmarking tools for performance evaluation of heuristic algorithms for semantic web service composition. Finally, a web application is presented to visualise the semantic web service composition process, which is developed using the Django framework.

We present a chance-constrained model predictive control (MPC) framework under Gaussian mixture model (GMM) uncertainty. Specifically, we consider the uncertainty that arises from predicting future behaviors of moving obstacles, which may exhibit multiple modes (for example, turning left or right). To address multimodal uncertainty distribution, we propose three MPC formulations: nominal chance-constrained planning, robust chance-constrained planning, and contingency planning. We prove that closed-loop trajectories generated by the three planners are safe. The approaches differ in conservativeness and performance guarantee. In particular, the robust chance-constrained planner is recursively feasible under certain assumptions on the propagation of prediction uncertainty. On the other hand, the contingency planner generates a less conservative closed-loop trajectory than the nominal planner. We validate our planners using state-of-the-art trajectory prediction algorithms in autonomous driving simulators.

This research describes developing a Fuzzy Logic based weight prediction system (FL-eWPS) during the process of injection molding. The main purpose is to apply Fuzzy Logic to predict defects during injection molding operations while processing parameters, such as shot size, barrel temperature, cooling time, and holding pressure. The parameters are varied within a shorter range when using Delrin 511 DP plastic from DuPont Engineering Polymers. eDART data logging system was used for real-time data collection for the different parameters by using the sensors during the injection filling stages. A Fuzzy Logic reasoning algorithm was applied to gain the threshold values of weight prediction with various processing parameter settings. During the injection molding process, the FL-eWPS system was shown to predict weight with 99% accuracy.