Profile, reasons for hospitalization and nursing diagnoses of refugee-native patients admitted to internal medicine clinic-an evaluation from nursing perspective.

PURPOSE: The study aims to evaluate the hospitalization diagnoses and nursing diagnoses of the refugee and local population hospitalized in internal medicine clinics, which are especially important in the early diagnosis, treatment, and rehabilitation of chronic diseases, and to emphasize their importance in nursing care. METHODS: The study was carried out in a descriptive retrospective design. The files of 3563 patients admitted to the internal medicine clinic of a training and research hospital in Türkiye in 2022 were evaluated. SPSS 26.0 program was used for data analysis. RESULTS: In the study, 95.3% of hospitalizations were native and 4.7% were refugee patients. It was determined that refugee patients admitted to the internal medicine service had a lower mean age compared to the native population (p < 0.05), but there was no difference in the duration of hospitalization (p > 0.05). When the medical diagnoses of hospitalization were examined, it was determined that the highest number of hospitalizations in the native and refugee populations were for bacterial infections in both genders. In nursing diagnoses, it was determined that both populations and genders were diagnosed with infection risk by the medical diagnoses of the patients. CONCLUSION: As a result of the study, it was observed that the duration of hospitalization, reasons for hospitalization, and nursing diagnoses of local and refugee patients were similar. In addition, it was determined that the patients' medical hospitalization diagnoses and nursing diagnoses were compatible.

Grant-Free NOMA: A Low-Complexity Power Control through User Clustering.

Non-orthogonal multiple access (NOMA) has emerged as a promising solution to support multiple devices on the same network resources, improving spectral efficiency and enabling massive connectivity required by ever-increasing Internet of Things devices. However, traditional NOMA schemes operate in a grant-based fashion and require channel-state information and power control, which hinders its implementation for massive machine-type communications. Accordingly, this paper proposes synchronous grant-free NOMA (GF-NOMA) frameworks that effectively integrate user equipment (UE) clustering and low-complexity power control to facilitate the power-reception disparity required by the power-domain NOMA. Although single-level GF-NOMA (SGF-NOMA) designates an identical transmit power for all UEs, multi-level GF-NOMA (MGF-NOMA) groups UEs into partitions based on the sounding reference signals strength and assigns partitions with different identical power levels. Based on the objective of interest (e.g., max-sum or max-min rate), the proposed UE clustering scheme iteratively admits UEs to form clusters whose size is dynamically determined based on the number of UEs and available resource blocks (RBs). Once the UEs are acknowledged with power levels and allocated RBs through random-access response (RAR) messages, UEs can transmit anytime without grant acquisition. Numerical results show that the proposed GF-NOMA frameworks can compute clusters in the order of milliseconds for hundreds of UEs. The MGF-NOMA can reach up to 96-99% of the optimal benchmark max-sum rate, and the SGF-NOMA reaches 87% of the optimal benchmark max-sum rate at the same power consumption. Since the MGF-NOMA and optimal benchmark enforce the strongest and weakest channel UEs to transmit at maximum and minimum transmit powers, respectively, the SGF-NOMA also offers a significantly higher energy consumption fairness and network lifetime as all UEs consume equal transmit powers. Although the MGF-NOMA delivers an inferior max-min rate performance, the SGF-NOMA is shown to reach 3e6 MbpJ energy efficiency compared to the 1e7 MbpJ benchmark.

Machine Learning-Based Unobtrusive Intake Gesture Detection via Wearable Inertial Sensors.

Dietary patterns can be the primary reason for many chronic diseases such as diabetes and obesity. State-of-the-art wearable sensor technologies can play a critical role in assisting patients in managing their eating habits by providing meaningful statistics on critical parameters such as the onset, duration, and frequency of eating. For an accurate yet fast food intake recognition, this work presents a novel Machine Learning (ML) based framework that shows promising results by leveraging optimized support vector machine (SVM) classifiers. The SVM classifiers are trained on three comprehensive datasets: OREBA, FIC, and CLEMSON. The developed framework outperforms existing algorithms by achieving F1-scores of 92%, 94%, 95%, and 85% on OREBA-SHA, OREBA-DIS, FIC, and CLEMSON datasets, respectively. In order to assess the generalization aspects, the proposed SVM framework is also trained on one of the three databases while being tested on the others and achieves acceptable F1-scores in all cases. The proposed algorithm is well suited for real-time applications since inference is made using a few support vector parameters compared to thousands in peer deep neural networks models.

Pain evaluation in a sample of Turkish children with cerebral palsy and its association with dependency level, verbal abilities, and the quality of life of patients and sociodemographic status, depression, and quality of life of their caregivers.

OBJECTIVES: This study aims to evaluate pain in children with cerebral palsy (CP), to investigate its association with dependency level, verbal abilities, and the quality of life (QoL) of children and sociodemographic status, depression levels, and QoL of their caregivers. PATIENTS AND METHODS: Between February 2016 and April 2016, a total of 85 children (56 males, 29 females; mean age 7.1±2.5 years; range, 4 to 12 years) with CP were included. Their sociodemographic data, gross motor functional levels, verbal abilities (verbal and non- verbal) were evaluated. The children were categorized as independent (Gross Motor Function Classification System [GMFCS] 1), partially dependent (GMFCS 2,3), and totally dependent (GMFCS 4,5). The Non-Communicating Children's Pain Checklist - Revised (NCCPC-R), the Caregiver Priorities & Child Health Index of Life with Disabilities (CPCHILD), the Nottingham Health Profile (NHP), and the Beck Depression Inventory (BDI) were used for the evaluation. RESULTS: A total of 50 (58.8%) of the children were verbal, while 35 (41.2%) were non-verbal. Of the patients, 47.1% were totally dependent, 37.6% were partially dependent, and 15.3% were independent. Pain was less frequent in the independent group, compared to the partially and totally dependent groups (p=0.04; p=0.013). There were no statistically significant difference in perceived pain according to the CPCHILD and NCCPC-R scores, when low and high socioeconomic status were compared (p>0.05). The QoL scores of children who were totally dependent and non-verbal were statistically significantly lower than those of the other children (p=0.0001, p=0.0001). A statistically significantly negative correlation between increased totally dependent children's QoL scores and decreased QoL scores of the caregivers was observed (r=-0.429 p=0.006). The children with pain had a decreased QoL (p=0.03). The QoL of caregivers decreased and the depression scores of caregivers increased, as the pain scores of the children increased (r=0.291, p=0.007; r=-0.455, p=0.0001). CONCLUSION: Our study results show that pain is more frequent in children with CP who are dependent and non-verbal, and it negatively affects the QoL of both caregivers and children. Evaluation of pain and the identification of CP patients who are at high risk of experiencing pain is worthwhile, as these individuals have difficulty in verbalizing pain due to their cognitive problems, although they are at risk of experiencing pain due to chronic physical problems, such as spasticity and contracture, as well as interventions for complicated medical problems.

Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization.

Underwater wireless technologies demand to transmit at higher data rate for ocean exploration. Currently, large coverage is achieved by acoustic sensor networks with low data rate, high cost, high latency, high power consumption, and negative impact on marine mammals. Meanwhile, optical communication for underwater networks has the advantage of the higher data rate albeit for limited communication distances. Moreover, energy consumption is another major problem for underwater sensor networks, due to limited battery power and difficulty in replacing or recharging the battery of a sensor node. The ultimate solution to this problem is to add energy harvesting capability to the acoustic-optical sensor nodes. Localization of underwater sensor networks is of utmost importance because the data collected from underwater sensor nodes is useful only if the location of the nodes is known. Therefore, a novel localization technique for energy harvesting hybrid acoustic-optical underwater wireless sensor networks (AO-UWSNs) is proposed. AO-UWSN employs optical communication for higher data rate at a short transmission distance and employs acoustic communication for low data rate and long transmission distance. A hybrid received signal strength (RSS) based localization technique is proposed to localize the nodes in AO-UWSNs. The proposed technique combines the noisy RSS based measurements from acoustic communication and optical communication and estimates the final locations of acoustic-optical sensor nodes. A weighted multiple observations paradigm is proposed for hybrid estimated distances to suppress the noisy observations and give more importance to the accurate observations. Furthermore, the closed form solution for Cramer-Rao lower bound (CRLB) is derived for localization accuracy of the proposed technique.