Several arboviral diseases have been known to be endemic (e.g., Crimean-Congo hemorrhagic fever, Rift Valley fever) or are emerging (dengue fever, chikungunya, O\'nyong-nyong) in human populations in Mauritania, while others have become rare in recent years (e.g. yellow fever). Moreover, domestic animals, especially cattle, camels, goats, and sheep, are also known to be infected with some of these arboviruses (e.g. Crimean-Congo hemorrhagic fever, Rift Valley fever). For these reasons, viral hemorrhagic fever surveillance in Mauritania is part of the Integrated Disease Surveillance and Response (IDSR). However, limited information is available on the efficacy of the viral hemorrhagic fever surveillance system in the Assaba region of Mauritania. The aim of the present study was to assess the performance of the surveillance system, in particular its general utility, simplicity, flexibility, acceptability, and reactivity.
A descriptive cross-sectional study was conducted from July to August 2022 in the Assaba region with the objective of evaluating the characteristics of the system by interviewing key actors involved in the surveillance of viral hemorrhagic fevers, with a focus on Rift Valley fever and Crimean-Congo hemorrhagic fever, using questionnaires developed following the guidelines of the Centers for Disease Control and Prevention (Atlanta, Georgia, USA). Data from 2020-2022 on viral hemorrhagic fevers from the National Institute of Public Health laboratory were analyzed. Medians, interquartile ranges, and proportions were calculated using Epi Info® 7.2.5.0 and Excel® 2021.
The questionnaire was answered by all twenty-six persons involved in the viral hemorrhagic fever surveillance system in Assaba region. The majority of survey respondents found the system to be useful (51%), simple (63%), acceptable (46%), responsive (64%), and flexible (46%). An analysis of the data revealed a positive predictive value of 28% for Rift Valley Fever. The weekly distribution of cases within the wilaya indicates that the moughataa of Kiffa recorded the highest number of cases in September, with a notable weekly peak during that month in 2020. According to the analysis of the National Institute of Public Health database, cases of viral hemorrhagic fevers were promptly handled. Survey responses and database analysis revealed issues related to data quality and data management mechanisms. These limitations in the surveillance system are likely to be due to insufficient resources and training of the personnel, in particular with regards to data collection and management, which in turn led to incomplete or missing data and invalid data entry. These weak points can be ascribed, at least in part, to financial constraints and inadequate attribution of priority to arboviral diseases. Despite these limitations, disease data generated by the surveillance system were generally reliable.
The viral hemorrhagic fever surveillance system in the Assaba region adheres to the organization and functioning of the national viral hemorrhagic fever surveillance system, which is part of the IDSR. The characteristics of utility, simplicity, responsiveness, and flexibility of the viral hemorrhagic fever surveillance system are good, but acceptability and flexibility need further improvement. The earlier the first arboviral human or animal cases are detected, the more likely an active intervention can be organized in response to the emerging epidemic or epizootic and prevent the spread of the disease. An efficient viral surveillance system is the key to reducing the negative impact of arboviral diseases in Assaba region.

BACKGROUND: Knee arthritis is a destructive disorder that affects the knee joints and causes pain and reduced mobility. Drug treatments, weight loss, and exercise control the symptoms of the disease, but these methods only delay the disease process and eventually, knee joint replacement surgery will be needed. After surgery, with the help of a proper physiotherapy program, full recovery takes an average of 6-12 months. However, currently, there is no similar tool to facilitate this process in Iran.
OBJECTIVE: The purpose of this research is to design and develop the prototype of a rehabilitation system for patients after knee replacement surgery, which provides patients with information and appropriate physiotherapy programs.
METHODS: This study was development-applied and was done in three stages. In the first stage, the needs and content of education and therapeutic exercises were prepared in the form of a checklist, and then the necessity of each item was checked in the evaluation sessions. In the second stage, the prototype of the system was developed using Adobe XD software and based on the requirements approved from the previous stage. In the third stage, the usability of the program was analyzed from the point of view of experts using the exploratory evaluation method with Nielsen\'s 13 principles of usability.
RESULTS: At first, the system requirements were extracted and prepared in two checklists of content (51 exercises) and capabilities (60 items). Then after a survey of experts based on the Delphi technique, content information (43 exercises) and functional and non-functional requirements (53 items) were obtained. A survey of experts helped to finalize the information elements, categorize them, and prepare the final version of the checklists. Based on this, the system requirements were classified into 11 categories, and the training content was classified into 3 training periods. Finally, the design and development of the system was done. This system has admin, physician, and patient user interfaces. The result of usability showed that this system is efficient and there are only a few problems in the feature of helping users to detect and recover from errors.
CONCLUSIONS: It seems necessary to develop a system based on the principles of usability by therapists and rehabilitation specialists to train and monitor the remote rehabilitation process of patients after knee joint replacement at home. And the importance of involving stakeholders in the design and development of remote rehabilitation systems is not hidden from anyone. Kara system has all of the above.

This research addresses the rapid aging phenomenon prevalent in Asian societies, which has led to a significant increase in elderly individuals relocating to nursing homes due to health-related issues. This trend has resulted in social isolation and loneliness among the elderly, contributing to physical and mental ailments such as hypertension and cardiovascular diseases, as highlighted by the World Health Organization (WHO). To mitigate these issues, the research proposes leveraging technology, specifically the integration of robotics, to alleviate the caregiver shortage and enhance human interaction for the elderly. The novel approach involves developing a social robot designed to bridge the gap between humans and machines, combining knowledge from embedded systems, robotics, and essential soft skills for effective interaction. The authors found that this technological solution holds promise in addressing the caregiver shortage and improving the well-being of elderly individuals by reducing their sense of isolation, fostering better mental and physical health outcomes, and potentially transforming the landscape of elderly care through innovative technological applications. Future work includes expanding pilot studies and collaborating with healthcare institutions to further validate the effectiveness of the solution.

This article discusses fundamental issues associated with the functional reliability of selected fire alarm systems (FASs) in operation during building fires. FASs operate under diverse external or internal natural environmental conditions, and the operational process of FAS should take into account the impacts of physical phenomena that occur during fires. Their operation is associated with the constant provision of reliability. FAS designers should also consider the system\'s reliability when developing fire control matrices, tables, algorithms, or scenarios. All functions arising from an FAS control matrix should be implemented with a permissible reliability level, RDPN(t), prior to, as well as during, a fire. This should be assigned to the controls saved in the fire alarm control unit (FCP). This article presents the process by which high temperatures generated during a fire impact the reliability of FAS functioning. It was developed considering selected critical paths for a specific scenario and the control matrix for an FAS. Such assumptions make it possible to determine the impact of various temperatures generated during a fire on the reliability of an FAS. To this end, the authors reviewed that the waveform of the R(t) function changes for a given FAS over time, Δt, and then determined the fitness paths. The critical paths are located within the fire detection and suppression activation process, using FAS or fixed extinguishing devices (FEDs), and the paths were modeled with acceptable and unacceptable technical states. The last section of this article defines a model and graph for the operational process of a selected FAS, the analysis of which enables conclusions to be drawn that can be employed in the design and implementation stages.

Few prior works study self-driving cars by deep learning with IoT collaboration. SDC-Net, which is an end-to-end multitask self-driving car camera cocoon IoT-based system, is one of the research areas that tackles this direction. However, by design, SDC-Net is not able to identify the accident locations; it only classifies whether a scene is a crash scene or not. In this work, we introduce an enhanced design for the SDC-Net system by (1) replacing the classification network with a detection one, (2) adapting our benchmark dataset labels built on the CARLA simulator to include the vehicles\' bounding boxes while keeping the same training, validation, and testing samples, and (3) modifying the shared information via IoT to include the accident location. We keep the same path planning and automatic emergency braking network, the digital automation platform, and the input representations to formulate the comparative study. The SDC-Net++ system is proposed to (1) output the relevant control actions, especially in case of accidents: accelerate, decelerate, maneuver, and brake, and (2) share the most critical information to the connected vehicles via IoT, especially the accident locations. A comparative study is also conducted between SDC-Net and SDC-Net++ with the same input representations: front camera only, panorama and bird\'s eye views, and with single-task networks, crash avoidance only, and multitask networks. The multitask network with a BEV input representation outperforms the nearest representation in precision, recall, f1-score, and accuracy by more than 15.134%, 12.046%, 13.593%, and 5%, respectively. The SDC-Net++ multitask network with BEV outperforms SDC-Net multitask with BEV in precision, recall, f1-score, accuracy, and average MSE by more than 2.201%, 2.8%, 2.505%, 2%, and 18.677%, respectively.

BACKGROUND: Strokes pose a substantial health burden, impacting 1 in 6 people globally. One-tenth of patients will endure a second, often more severe, stroke within a year. Alarmingly, a younger demographic is being affected due to recent lifestyle changes. As fine motor and cognitive issues arise, patient disability as well as the strain on caregivers and health care resources is exacerbated. Contemporary occupational therapy assesses manual dexterity and cognitive functions through object manipulation and pen-and-paper recordings. However, these assessments are typically isolated, which makes it challenging for therapists to comprehensively evaluate specific patient conditions. Furthermore, the reliance on one-on-one training and assessment approaches on manual documentation is inefficient and prone to transcription errors.
OBJECTIVE: This study examines the feasibility of using an interactive electronic pegboard for stroke rehabilitation in clinical settings.
METHODS: A total of 10 patients with a history of stroke and 10 healthy older individuals were recruited. With a limit of 10 minutes, both groups of participants underwent a series of challenges involving tasks related to manual operation, shape recognition, and color discrimination. All participants underwent the Box and Block Test and the Purdue Pegboard Test to assess manual dexterity, as well as an array of cognitive assessments, including the Trail Making Test and the Mini-Mental Status Examination, which served as a basis to quantify participants\' attention, executive functioning, and cognitive abilities.
RESULTS: The findings validate the potential application of an interactive electronic pegboard for stroke rehabilitation in clinical contexts. Significant statistical differences (P<.01) were observed across all assessed variables, including age, Box and Block Test results, Purdue Pegboard Test outcomes, Trail Making Test-A scores, and Mini-Mental Status Examination performance, between patients with a history of stroke and their healthy older counterparts. Functional and task testing, along with questionnaire interviews, revealed that patients with a history of stroke demonstrated prolonged completion times and slightly inferior performance. Nonetheless, most patients perceived the prototype as user-friendly and engaging. Thus, in the context of patient rehabilitation interventions or the evaluation of patient cognition, physical functioning, or manual dexterity assessments, the developed pegboard could potentially serve as a valuable tool for hand function, attention, and cognitive rehabilitation, thereby mitigating the burden on health care professionals.
CONCLUSIONS: Health care professionals can use digital electronic pegboards not only as a precise one-on-one training tool but also as a flexible system that can be configured for online or offline, single-player or multiplayer use. Through data analysis, a more informed examination of patients\' cognitive and functional issues can be conducted. Importantly, patient records will be fully retained throughout practices, exercises, or tests, and by leveraging the characteristics of big data, patients can receive the most accurate rehabilitation prescriptions, thereby assisting them in obtaining optimal care.

OBJECTIVE: To develop the mathematical model with high sensitivity and specificity to assess the malignant potential of adrenal cortical tumors, which can be used to diagnose adrenocortical carcinoma (ACC) in adults.
METHODS: Pathomorphological examination of surgical and consultative material of adrenocortical neoplasms was carried out. All cases were verified according to the WHO Classification of adrenal gland tumors (5th ed., 2022), the tumor\'s histogenesis was confirmed by immunohistochemical examination. Statistical analysis of the histological and immunohistochemical factors in terms of their value in relation to the diagnosis of ACC was carried out on Python 3.1 in the Google Colab environment. ROC analysis was used to identify critical values of predictors. The cut-off point was selected according to the Youden`s index. Logistic regression analysis using l1-regularisation was performed. To validate the model, the initial sample was divided into training and test groups in the ratio of 9:1, respectively.
RESULTS: The study included 143 patients divided into training (128 patients) and test (15 patients) samples. A prognostic algorithm was developed, which represent a diagnostically significant set of indicators of the currently used Weiss scale. The diagnosis is carried out in 3 stages. This mathematical model showed 100% accuracy (95% CI: 96-100%) on the training and test samples.
CONCLUSIONS: The developed algorithm could solve the problem of subjectivity and complexity in the interpretation of some of the criteria of current diagnostic algorithms. The new model is unique in that, unlike others, it allows verification of all morphological variants of ACC.
UNASSIGNED: Разработать математическую модель с высокой чувствительностью и специфичностью для определения злокачественного потенциала опухолей коры надпочечника, которая может быть использована для диагностики адренокортикального рака (АКР) у взрослых.
UNASSIGNED: Проведено патоморфологическое исследование операционного и консультативного материала новообразований коры надпочечника. Все случаи верифицировали в соответствии с Классификацией опухолей надпочечника Всемирной организации здравоохранения (5-е изд., 2022), гистогенез новообразования подтверждался с помощью иммуногистохимического исследования. Выполнен статистический анализ гистологических и иммуногистохимических признаков с точки зрения их информативности в отношении диагностики АКР. Статистический анализ проведен на языке программирования Python 3.1 в среде Google Colab. Для выявления критических значений предикторов использовали ROC-анализ. Отрезная точка выбиралась согласно критерию Юдена. Выполнен логистический регрессионный анализ с использованием l1-регуляризации. Для валидации модели использовали разделение исходной выборки на обучающую и тестовую в соотношении 9:1 соответственно.
UNASSIGNED: В исследование включено 143 пациента, которые были разделены на обучающую (n=128) и тестовую (n=15) выборки. Разработан прогностический алгоритм, представляющий диагностически значимый комплекс показателей используемой в настоящее время шкалы Weiss, с помощью которого диагностика осуществляется в 3 этапа. Данная математическая модель показала 100% точность (95% ДИ: 96%—100%) на обучающей и тестовой выборке.
UNASSIGNED: Разработанный алгоритм позволит решить проблему субъективности и сложности в интерпретации некоторых из критериев существующих алгоритмов диагностики. Новая модель уникальна тем, что в отличие от других позволяет верифицировать все морфологические варианты АКР.

With the utilization of advanced microfluidic techniques, the microfluidic particle counter demonstrates significant potential due to its high efficiency, precise manipulation, and portability. This work focuses on a photodetection counter based on optical absorption. To achieve precise particle detection, a Christmas tree-like structure was implemented to separate a single particle from a cluster, which was then detected in independent multiple parallel channels. The system exhibits a high degree of reliability, as evidenced by a linear correlation coefficient over 0.99 obtained during testing with gradient-concentrated beads. Furthermore, when the calculated density of NIH 3T3 cells is compared with that of a traditional flow cytometer, the system achieves a substantial agreement percentage ranging from 87.5 to 99.9%. The system\'s ability to perform high-throughput analysis with a high acquisition rate positions it as a promising tool for real-time point-of-care testing.

Arsenic contamination not only complicates mineral processing but also poses environmental and health risks. To address these challenges, this research investigates the feasibility of utilizing Hyperspectral imaging combined with machine learning techniques for the identification of arsenic-containing minerals in copper ore samples, with a focus on practical application in sorting and processing operations. Through experimentation with various copper sulfide ores, Neighborhood Component Analysis (NCA) was employed to select essential wavelength bands from Hyperspectral data, subsequently used as inputs for machine learning algorithms to identify arsenic concentrations. Results demonstrate that by selecting a subset of informative bands using NCA, accurate mineral identification can be achieved with a significantly reduced the size of dataset, enabling efficient processing and analysis. Comparison with other wavelength selection methods highlights the superiority of NCA in optimizing classification accuracy. Specifically, the identification accuracy showed 91.9% or more when utilizing 8 or more bands selected by NCA and was comparable to hyperspectral data analysis with 204 bands. The findings suggest potential for cost-effective implementation of multispectral cameras in mineral processing operations. Future research directions include refining machine learning algorithms, exploring broader applications across diverse ore types, and integrating hyperspectral imaging with emerging sensor technologies for enhanced mineral processing capabilities.

UNASSIGNED: A notable shift in healthcare policy is healthcare privatization, which refers to the transfer of ownership, management, or provision of healthcare services from the public sector to private entities.
UNASSIGNED: To provide a narrative examination of the impact of privatization on various dimensions of healthcare, including quality, equity, accessibility, and cost-effectiveness. Policymakers can utilize the findings of this study to make well-informed decisions regarding privatization strategies.
UNASSIGNED: A systematic review was implemented using the following databases: PubMed, Scopus, and Google Scholar. Studies conducted from January 2000 to January 2023 in developing or developed countries that assessed the impact of healthcare privatization on population health within public sector institutions were included.
UNASSIGNED: Eleven studies were included. The findings revealed diverse perspectives on the impact of healthcare privatization, with four studies (36.4%) supporting privatization (two of these were conducted in Saudi Arabia), six studies (54.5%) opposing it (three of these were conducted in European countries), and one study (9.1%) taking a neutral stance. Two studies investigated the impact on healthcare quality, and both revealed that privatization negatively impacts uninsured patients and low-income populations. In addition, five studies investigated the healthcare access and equity dimensions following privatization: one was in favor, one was neutral, and three were opposing it. Four studies investigated the cost-effectiveness dimension, with three in favor and one study opposing it.
UNASSIGNED: This review highlights different perspectives on healthcare privatization. While studies, as those from Saudi Arabia, suggest benefits in terms of efficiency and innovation, others, particularly from European countries, emphasize negative consequences such as inequity and reduced quality. This emphasizes the need for more investigations to understand privatization\'s impact on healthcare.