Patients exposed to a surgical safety checklist experience better postoperative outcomes, but this could simply reflect wider quality of care in hospitals where checklist use is routine.

Abstract

Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results.

Abstract-The paper is devoted to supervise method approach to identify the vessel anomaly behavior in waterways using the Automated Identification System (AIS) vessel reporting data. In this work, we describe the use of SVMs to detect the vessel anomaly behavior. The SVMs is a supervised method that needs some pre knowledge to extract the maritime movement patterns of AIS raw data into information. This is the basis to remodel information into a meaningful and valuable form. The result of this work shows that the SVMs technique is applicable to be used for the identification of vessel anomaly behavior. It is proved that the best accuracy result is obtained from dividing raw data into 70% for training and 30% for testing stages.

Abstrak Penelitian ini bertujuan untuk mengetahui tingkat kelayakan dan kepraktisan modul pembelajaran kimia materi asam basa berbasis Problem Based Learning (PBL) untuk meningkatkan motivasi belajar. Penelitian ini adalah jenis Penelitian pengembangan yang mengadopsi model 4-D (define, design, develop, disseminate), namun dibatasi hanya pada sampai tahap pengembangan (develop). Tingkat kelayakan modul diukur melalui lembar validasi ahli dengan 6 aspek penilaian yang dinilai oleh 3 validator. Tingkat kepraktisan modul diukur melalui angket respon yang diisi oleh 22 orang siswa kelas XI IPA SMAN 8 Mataram. Hasil penelitian menunjukkan tingkat kelayakan modul yang dihitung dengan rumus Aiken V adalah 0.83 dalam kategori sangat layak dan sangat praktis dengan persentase praktikalitas sebesar 89.14%. Berdasarkan hasil tersebut dapat disimpulkan bahwa modul yang dikembangkan bersifat layak dan praktis untuk meningkatkan motivasi belajar.