Industrial Internet of Things over 5G: A Practical Implementation.

The next generation of mobile broadband communication, 5G, is seen as a driver for the industrial Internet of things (IIoT). The expected 5G-increased performance spanning across different indicators, flexibility to tailor the network to the needs of specific use cases, and the inherent security that offers guarantees both in terms of performance and data isolation have triggered the emergence of the concept of public network integrated non-public network (PNI-NPN) 5G networks. These networks might be a flexible alternative for the well-known (albeit mostly proprietary) Ethernet wired connections and protocols commonly used in the industry setting. With that in mind, this paper presents a practical implementation of IIoT over 5G composed of different infrastructure and application components. From the infrastructure perspective, the implementation includes a 5G Internet of things (IoT) end device that collects sensing data from shop floor assets and the surrounding environment and makes these data available over an industrial 5G Network. Application-wise, the implementation includes an intelligent assistant that consumes such data to generate valuable insights that allow for the sustainable operation of assets. These components have been tested and validated in a real shop floor environment at Bosch Termotecnologia (Bosch TT). Results show the potential of 5G as an enhancer of IIoT towards smarter, more sustainable, green, and environmentally friendly factories.

Building an IoT Platform Based on Service Containerisation.

IoT platforms have become quite complex from a technical viewpoint, becoming the cornerstone for information sharing, storing, and indexing given the unprecedented scale of smart services being available by massive deployments of a large set of data-enabled devices. These platforms rely on structured formats that exploit standard technologies to deal with the gathered data, thus creating the need for carefully designed customised systems that can handle thousands of heterogeneous data sensors/actuators, multiple processing frameworks, and storage solutions. We present the SCoT2.0 platform, a generic-purpose IoT Platform that can acquire, process, and visualise data using methods adequate for both real-time processing and long-term Machine Learning (ML)-based analysis. Our goal is to develop a large-scale system that can be applied to multiple real-world scenarios and is potentially deployable on private clouds for multiple verticals. Our approach relies on extensive service containerisation, and we present the different design choices, technical challenges, and solutions found while building our own IoT platform. We validate this platform supporting two very distinct IoT projects (750 physical devices), and we analyse scaling issues within the platform components.

A comprehensive dataset of photonic features on spectral converters for energy harvesting.

Building integrated photovoltaics is a promising strategy for solar technology, in which luminescent solar concentrators (LSCs) stand out. Challenges include the development of materials for sunlight harvesting and conversion, which is an iterative optimization process with several steps: synthesis, processing, and structural and optical characterizations before considering the energy generation figures of merit that requires a prototype fabrication. Thus, simulation models provide a valuable, cost-effective, and time-efficient alternative to experimental implementations, enabling researchers to gain valuable insights for informed decisions. We conducted a literature review on LSCs over the past 47 years from the Web of ScienceTM Core Collection, including published research conducted by our research group, to gather the optical features and identify the material classes that contribute to the performance. The dataset can be further expanded systematically offering a valuable resource for decision-making tools for device design without extensive experimental measurements.

Predicting the efficiency of luminescent solar concentrators for solar energy harvesting using machine learning.

Building-integrated photovoltaics (BIPV) is an emerging technology in the solar energy field. It involves using luminescent solar concentrators to convert traditional windows into energy generators by utilizing light harvesting and conversion materials. This study investigates the application of machine learning (ML) to advance the fundamental understanding of optical material design. By leveraging accessible photoluminescent measurements, ML models estimate optical properties, streamlining the process of developing novel materials, offering a cost-effective and efficient alternative to traditional methods, and facilitating the selection of competitive materials. Regression and clustering methods were used to estimate the optical conversion efficiency and power conversion efficiency. The regression models achieved a Mean Absolute Error (MAE) of 10%, which demonstrates accuracy within a 10% range of possible values. Both regression and clustering models showed high agreement, with a minimal MAE of 7%, highlighting the efficacy of ML in predicting optical properties of luminescent materials for BIPV.

Proceedings from the CIHLMU 2022 Symposium: "Availability of and Access to Quality Data in Health".

Data is an essential tool for valid and reliable healthcare management. Access to high-quality data is critical to ensuring the early identification of problems, the design of appropriate interventions, and the effective implementation and evaluation of health intervention outcomes. During the COVID-19 pandemic, the need for strong information systems and the value of producing high-quality data for timely response and tracking resources and progress have been very evident across countries. The availability of and access to high-quality data at all levels of the health systems of low and middle-income countries is a challenge, which is exacerbated by multiple parallels and poorly integrated data sources, a lack of data-sharing standards and policy frameworks, their weak enforcement, and inadequate skills among those handling data. Completeness, accuracy, integrity, validity, and timeliness are challenges to data availability and use. "Big Data" is a necessity and a challenge in the current complexities of health systems. In transitioning to digital systems with proper data standards and policy frameworks for privacy protection, data literacy, ownership, and data use at all levels of the health system, skill enhancement of the staff is critical. Adequate funding for strengthening routine information systems and periodic surveys and research, and reciprocal partnerships between high-income countries and low- and middle-income countries in data generation and use, should be prioritized by the low- and middle-income countries to foster evidence-based healthcare practices.

Human Monkeypox: A Comprehensive Narrative Review and Analysis of the Public Health Implications.

Recently, numerous cases of monkeypox were reported from several non-endemic countries in Europe, North America, and Oceania, suggesting an unusual and alarming public health issue, particularly considering that the disease is not directly related to human or animal travels. Attention is currently being drawn to this phenomenon since more than 70% of the global population is no longer vaccinated against smallpox. Indeed, the smallpox vaccination also confers some indirect degree of protection against other poxviruses, including monkeypox. We performed a narrative review to describe the existing literature with regard to monkeypox using the MEDLINE, EMBASE, and Scopus databases. This review aims to provide updated evidence of findings on the epidemiology, clinical features, diagnosis, management, and prevention of monkeypox, also considering the concurrent zoonotic pandemic caused by the COVID-19 coronavirus, SARS-CoV-2.

Exposing Manipulated Photos and Videos in Digital Forensics Analysis.

Tampered multimedia content is being increasingly used in a broad range of cybercrime activities. The spread of fake news, misinformation, digital kidnapping, and ransomware-related crimes are amongst the most recurrent crimes in which manipulated digital photos and videos are the perpetrating and disseminating medium. Criminal investigation has been challenged in applying machine learning techniques to automatically distinguish between fake and genuine seized photos and videos. Despite the pertinent need for manual validation, easy-to-use platforms for digital forensics are essential to automate and facilitate the detection of tampered content and to help criminal investigators with their work. This paper presents a machine learning Support Vector Machines (SVM) based method to distinguish between genuine and fake multimedia files, namely digital photos and videos, which may indicate the presence of deepfake content. The method was implemented in Python and integrated as new modules in the widely used digital forensics application Autopsy. The implemented approach extracts a set of simple features resulting from the application of a Discrete Fourier Transform (DFT) to digital photos and video frames. The model was evaluated with a large dataset of classified multimedia files containing both legitimate and fake photos and frames extracted from videos. Regarding deepfake detection in videos, the Celeb-DFv1 dataset was used, featuring 590 original videos collected from YouTube, and covering different subjects. The results obtained with the 5-fold cross-validation outperformed those SVM-based methods documented in the literature, by achieving an average F1-score of 99.53%, 79.55%, and 89.10%, respectively for photos, videos, and a mixture of both types of content. A benchmark with state-of-the-art methods was also done, by comparing the proposed SVM method with deep learning approaches, namely Convolutional Neural Networks (CNN). Despite CNN having outperformed the proposed DFT-SVM compound method, the competitiveness of the results attained by DFT-SVM and the substantially reduced processing time make it appropriate to be implemented and embedded into Autopsy modules, by predicting the level of fakeness calculated for each analyzed multimedia file.

Laparoscopy in Low-Income Countries: 10-Year Experience and Systematic Literature Review.

Laparoscopy is a procedure that ultimately reduces hospital stay time and speeds up post-operative recovery. It is mainly performed in high-income countries but its implementation in many low- and middle-income countries (LMICs) is increasing. However, no aggregate data exist regarding the outcomes of this procedure in resource-limited settings. We retrospectively reviewed all cases of laparoscopy recorded from January 2007 to March 2017 at the Department of Surgery of Beira to assess the related outcomes. Moreover, we performed a systematic review of the laparoscopic practices and outcomes in low-income countries. Data from the Department of Surgery of Beira identified 363 laparoscopic procedures, mainly relating to gynecological diseases, cholelithiasis, and appendicectomy with only a 1.6% complication rate (6 cases) and a 1.9% conversion rate (7 cases) to open surgery. The systematic review showed a pooled risk of overall complications significantly lower in laparoscopic vs. open appendicectomy (OR = 0.43; 95% CI 0.19-0.97; I2 = 85.7%) and a significantly lower risk of infection (OR = 0.53; 95% CI 0.43-0.65; I2 = 0.00%). The pooled SMD in operation duration in laparoscopic vs. open appendectomy was 0.58 (95% CI -0.00; 1.15; I2 = 96.52), while the pooled SMD in hospitalization days was -1.35 (95% CI -1.87; -0.82; I2 = 96.41). Laparoscopy is an expensive procedure to adopt as it requires new equipment and specialized trained health workers. However, it could reduce post-operative costs and complications, especially in terms of infections. It is crucial to increase its accessibility, acceptability, and quality particularly in LMICs, especially during this COVID-19 era when the reduction of patient hospitalization is essential.

Temporal anomaly detection: an artificial immune approach based on T cell activation, clonal size regulation and homeostasis.

This paper presents an artificial immune system (AIS) based on Grossman's tunable activation threshold (TAT) for temporal anomaly detection. We describe the generic AIS framework and the TAT model adopted for simulating T Cells behaviour, emphasizing two novel important features: the temporal dynamic adjustment of T Cells clonal size and its associated homeostasis mechanism. We also present some promising results obtained with artificially generated data sets, aiming to test the appropriateness of using TAT in dynamic changing environments, to distinguish new unseen patterns as part of what should be detected as normal or as anomalous. We conclude by discussing results obtained thus far with artificially generated data sets.

Anal high-grade and late-stage cancer management in low-income setting: a case report.

The burden of cancer is increasing in sub-Saharan Africa due to ageing, common risk factors and population growth. Anal cancer is a human papillomavirus-related rare disease with an incidence rate of 1.8 per 100 000 persons overall with an increasing incidence of by 2% per year in the last three decades. Despite that gold standard management is well described, in low-income countries, there is no possibility for a proper management. We presented a late-stage anal cancer case that reflects the urgent necessity to create the adequate condition for the development of effective oncologic approach including prevention, diagnosis and management.

Giant mesenteric cyst: Successful management in low-resource setting.

INTRODUCTION: Mesenteric cysts are rare, generally benign intra-abdominal lesions with a wide range of presentation in terms of size, clinical presentation, etiology, radiological features, and pathological characteristics. PRESENTATION OF CASE: We reported a case of giant mesenteric cyst in a 16-month-old girl successfully managed in a low-resource setting. DISCUSSION: This case is particularly important not only due to the rarity of the presented case, but also for the highlighted aspects from a public health point of view. We faced of the problem of a late stage disease and the lack of preoperative diagnosis due to cultural and economic reasons and the weaknesses of healthcare systems, as in the majority of low- and middle-income countries. CONCLUSION: Despite all these limitation, this case illustrates that complex, rare diseases can also be managed successfully in a low-resource setting. It is mandatory to strengthen and improve the health system both in terms of equipment both in terms of public health policies in order to offer a better and more effective quality of care to patients also in low-income countries.

Conservative management of Cantrell syndrome: a case report.

Cantrell syndrome (CS) is defined as congenital combination of five anomalies: defects at the lower part of the sternum, anterior diaphragm, midline supraumbilical abdominal wall, diaphragmatic pericardium and ectopia cordis. Antenatal screening should be performed to make an accurate prenatal diagnosis. The prognosis is usually poor with a high mortality early in life. The gold standard management is surgery but its prognosis remains poor. In many low-income settings prenatal examinations and surgery treatment are not possible. In the present case, we report a not surgery managed baby affected by CS, with good clinical conditions after 5 months.

Coronavirus Diseases (COVID-19) Current Status and Future Perspectives: A Narrative Review.

At the end of 2019 a novel virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing severe acute respiratory syndrome expanded globally from Wuhan, China. In March 2020 the World Health Organization declared the SARS-Cov-2 virus a global pandemic. We performed a narrative review to describe existing literature with regard to Corona Virus Disease 2019 (COVID-19) epidemiology, pathophysiology, diagnosis, management and future perspective. MEDLINE, EMBASE and Scopus databases were searched for relevant articles. Although only when the pandemic ends it will be possible to assess the full health, social and economic impact of this global disaster, this review represents a picture of the current state of the art. In particular, we focus on public health impact, pathophysiology and clinical manifestations, diagnosis, case management, emergency response and preparedness.

Giant ovarian fibroma with associated Meigs syndrome in low resources setting.

Meigs' syndrome is a rare condition characterized by the presence of a benign fibroma of the ovary, ascites and pleural effusion. It very uncommon and diagnosis is made difficult by symptoms that usually mimic disseminated malignancy. The gold standard treatment is laparotomy and, by definition of the syndrome, after tumor removal, the symptoms resolves and the patients become asymptomatic. We presented a giant ovarian fibroma with associated Meigs syndrome, successfully managed in a low resources setting.

Successful Surgery Management of Giant Periorbital Malignant Schwannoma in a Low-Income Setting.

Schwannoma can arise from any cranial, peripheral or autonomic nerve, except the olfactory and optic. About 25-45% of extracranial schwannomas lie in the head and neck. Data on malignant schwannoma from low-income settings are inconsistent. We reported a case of giant periorbital malignant schwannoma successfully treated in a low-income setting. The strength of our case is given not only by the rarity and the size of the disease but also for highlighting the weakness of health system in low-resource settings. It is mandatory to strengthen the health system with particular attention to physical, psychologic and social aspects and to promote comprehensive programs including all these aspects.

Successful management of giant hydrocolpos in a limited-resource setting.

Abdominal distention and urinary retention are rare manifestations in newborns. The differential diagnosis of a female neonate presenting these signs, especially when combined, should include hydrocolpos due to imperforate hymen. The prognosis of imperforate hymen is generally good, although it can be associated with serious nephro-urologic and infectious complications. Early diagnosis and drainage of hydrocolpos allow prevention and/or improvement of these possible complications. In limited-resource settings, diagnostic imaging is more difficult to obtain, and, therefore, increased caution and an accurate physical exam with perineal inspection are essential. We report the case of a 8-day-old female neonate showing abdominal distention and urinary retention. She had a final diagnosis of imperforate hymen with giant hydrocolpos, complicated by obstructive uropathy and following urosepsis and bladder perforation.

Bilateral Mastectomy as Radical Treatment of Gynecomastia Secondary to Antiretroviral Therapy in a Low-Income Setting: A Case Report.

Gynecomastia is a common finding in males, with an incidence that varies widely globally. In 10-25% of cases, it is caused by drugs. Its pathophysiologic mechanism includes exposure to exogenous estrogens and medications that cause hypogonadism, antiandrogenic effects and hyperprolactinemia. Gynecomastia is associated with exposure to antiretroviral therapy (ART), particularly efavirenz. Sometimes surgery may be required as treatment. We report a case of a 46-year-old man receiving ART presenting with a marked bilateral breast enlargement who underwent bilateral mastectomy as the only successful treatment in a low-income setting.