Machine learning (ML) techniques learn a system by observing it. Events and occurrences in the network define what is expected of the network’s operation. It is for this reason that ML techniques are ...used in the computer network security field to detect unauthorized intervention. In the event of suspicious activity, the result of the ML analysis deviates from the definition of expected normal network activity and the suspicious activity becomes apparent. Support vector machines (SVM) are ML techniques that have been used to profile normal network activity and classify it as normal or abnormal. They are trained to configure an optimal hyperplane that classifies unknown input vectors’ values based on their positioning on the plane. We propose to use SVM models to detect malicious behavior within low-power, low-rate and short range networks, such as those used in the Internet of Things (IoT). We evaluated two SVM approaches, the C-SVM and the OC-SVM, where the former requires two classes of vector values (one for the normal and one for the abnormal activity) and the latter observes only normal behavior activity. Both approaches were used as part of an intrusion detection system (IDS) that monitors and detects abnormal activity within the smart node device. Actual network traffic with specific network-layer attacks implemented by us was used to create and evaluate the SVM detection models. It is shown that the C-SVM achieves up to 100% classification accuracy when evaluated with unknown data taken from the same network topology it was trained with and 81% accuracy when operating in an unknown topology. The OC-SVM that is created using benign activity achieves at most 58% accuracy.
The Internet of Things (IoT) provides the ability to extend the Internet into devices and everyday objects, in a way that they are uniquely addressable. Sensors, actuators, as well as everyday ...devices and objects, such as cellphones, cars, and homes, are interconnected and form a network that can be accessed, monitored, and controlled remotely. Security is an important subject in the IoT networks since the devices and the networks can be used as a means of invading the users' privacy. The current work examines the issue of security agent location using indicative intrusion detection techniques for network layer attacks. We analyze the methodology, operation, as well as the complexity of each technique. Through the extensive implementation and experimentation, we are able to conclude that the local security agents have the same performance results with centralized and decentralized approaches, but with negligible overhead. As such, they are useful when internal network communication, or network augmentation with monitoring nodes, is not feasible.
The operation of the Internet of Things (IoT) networks and Wireless Sensor Networks (WSN) is often disrupted by a number of problems, such as path disconnections, network segmentation, node faults, ...and security attacks. A method that gains momentum in resolving some of those issues is the use of mobile nodes or nodes deployed by mobile robots. The use of mobile elements essentially increases the resources and the capacity of the network. In this work, we present a Node Placement Algorithm with two variations, which utilizes mobile nodes for the creation of alternative paths from source to sink. The first variation employs mobile nodes that create locally-significant alternative paths leading to the sink. The second variation employs mobile nodes that create completely individual (disjoint) paths to the sink. We then extend the local variation of the algorithm by also accounting for the energy levels of the nodes as a contributing factor regarding the creation of alternative paths. We offer both a high-level description of the concept and also detailed algorithmic solutions. The evaluation of the solutions was performed in a case study of resolving congestion in the network. Results have shown that the proposed algorithms can significantly contribute to the alleviation of the problem of congestion in IoT and WSNs and can easily be used for other types of network problems.
Introduction: Chronic pain is increasingly recognized as part of long COVID syndrome, mainly in the form of myalgias. However, chronic pain has several forms, and according to our clinical ...experience, COVID-19 survivors suffer from numerous painful syndromes, other than myalgias. The aim of our study was to estimate the prevalence of chronic pain, describe the commonest painful syndromes and identify pain determinants in a random population of COVID-19 survivors. Methods: This was a cross-sectional study conducted at the Medical School, University of Cyprus. A random population of 90 COVID-19 survivors was recruited. Demographic and COVID-19 related clinical characteristics were recorded. The painDETECT and DN4 questionnaires were used to evaluate the painful syndromes. Results: The prevalence of chronic pain was estimated to be 63.3%. The most common site of pain was low back (37.8%), followed by joints (28.9%) and neck (12.2%). Patients with chronic pain compared to subjects without pain were older (50.5 ± 15.9 versus 42.2 ± 12.6, p = 0.011) and more likely to be female (71.9% versus 45.5%, p = 0.013). One in six subjects (16.7%) reported new-onset pain post COVID-19. The prevalence of neuropathic pain was estimated to be 24.4%. After adjusting for age and gender, headache during COVID-19 was a statistically significant predictor of neuropathic pain, increasing 4.9 times (95% 1.4–16.6, p = 0.011) the odds of neuropathic pain. Conclusion: Chronic pain—especially neuropathic—is widely prevalent in COVID-19 survivors. One in six subjects will develop new-onset pain that will persist beyond the acute phase of the disease and, therefore, should be considered a symptom of long COVID syndrome.
The assignment of alleles to haplotypes in prenatal diagnostic assays has traditionally depended on family study analyses. However, this prevents the wide application of prenatal diagnosis based on ...haplotype analysis, especially in countries with dispersed populations. Here, we present an easy and fast approach using Droplet Digital PCR for the direct determination of haplotype blocks, overcoming the necessity for acquiring other family members’ genetic samples. We demonstrate this approach on nine families that were referred to our center for a prenatal diagnosis of β-thalassaemia using four highly polymorphic single nucleotide variations and the most common pathogenic β-thalassaemia variation in our population. Our approach resulted in the successful direct chromosomal phasing and haplotyping for all nine of the families analyzed, demonstrating a complete agreement with the haplotypes that are ascertained based on family trios. The clinical utility of this approach is envisaged to open the application of prenatal diagnosis for β-thalassaemia to all cases, while simultaneously providing a model for extending the prenatal diagnostic application of other monogenic diseases as well.
Industrial Control Systems (ICS) can be remotely controlled allowing easy access for better management and increasing productivity, but with the cost of becoming susceptible to attacks. Disrupting ...operation in critical systems can be catastrophic and lead to different types of disasters. It is, therefore, of a paramount importance to detect abnormalities of the operation process at an early stage before irreversible damages occur. Intrusion Detection Systems can detect operational faults when trained based on how the system is expected to operate, but also on what is not considered a normal operation. In the current work we propose SOTAD, a System for Operational Technology Attack Detection, to detect malicious interventions in Industrial IoT. SOTAD outlines the steps to be taken for creating the detection models, the data to be used for training and the monitoring time periods that allow high detection rates. The SOTAD detection mechanisms evaluated were the Threshold Baseline detection and the Binary Logistic Regression (BLR). The models were conducted using values from the field devices. The experimental validation of the proposed method was performed using two datasets obtained from iTrust, namely Secure Water Treatment (SWaT) and Water Distribution (WADI). The two datasets represent real world industrial processes and consists of benign and malicious data samples.
Physical exercise appeared to be effective, when implemented as an adjuvant to the pharmacotherapy option, in a variety of painful conditions. Peripheral neuropathic pain (PNP) is very prevalent and ...affects up to two-thirds of individuals with polyneuropathy (PN), regardless of etiology. The aim of this systematic review was to evaluate the currently available studies that assess adjuvant physical exercise for the management of PNP.
A systematic literature search was conducted in the PubMed international database. For the systematic search, three medical subject headings (MeSH) were used. Term A was 'physical exercise' OR 'exercise' OR 'activity' OR 'workout' OR 'training'; term B was 'pain' OR 'painful'; term C was 'neuropathy' OR 'polyneuropathy.' Additionally, three filters were used: human subjects, English language, and full text. The reference lists of eligible papers and relevant reviews were also meticulously searched in order to include further relevant studies. Six papers eligible to be included were identified.
Physical exercise in various forms can be of benefit in the management of PNP when used as an adjuvant to the standard care. Overall, using the American Society of Interventional Pain Physicians (ASIPP) criteria, the current best available evidence exists for both aerobic and muscle strengthening exercise programs (level II evidence). The intensity of the exercise seems to play a significant role, with higher intensity interval training programs being more promising, though this remains to be confirmed in future studies.
Physical exercise is a promising non-pharmacological intervention for the management of PNP. Future RCTs should be conducted to make a face-to-face comparison of the available exercise treatments with the aim to design specific exercise programs for patients with PNP.
Introduction
Wheelchair users are at a high risk of experiencing non-neuropathic pain of musculoskeletal origin as a result of being wheelchair-bound. The aim of this systematic review was to ...establish the prevalence of musculoskeletal pain in wheelchair users that is attributable to wheelchair use, and to describe the different pain syndromes and discuss risk factors and management options.
Methods
After a systematic MEDLINE search, we identified 40 papers eligible for inclusion.
Results
The pooled prevalence of musculoskeletal pain at any location was 50% (95% CI 33–67%). The most common pain syndrome was shoulder pain (pooled prevalence 44%, 95% CI 36–52%). Wheelchair users were 5.8 times as likely to suffer from shoulder pain as controls (95% CI 2.7–12.2,
p
< 0.0001). Other pain syndromes included neck, elbow, wrist, hand and low back pain.
Older age and increased duration of wheelchair use were the most significant determinants of pain in wheelchair users.
Conclusions
Musculoskeletal pain as a result of wheelchair use is very common amongst wheelchair users. Management of pain should follow national and international guidelines. Optimal adjustment of seating position may prevent pain, and is important to be taken into consideration.
Machine learning models have long be proposed to detect the presence of unauthorized activity within computer networks. They are used as anomaly detection techniques to detect abnormal behaviors ...within the network. We propose to use Support Vector Machine (SVM) learning anomaly detection model to detect abnormalities within the Internet of Things. SVM creates its normal profile hyperplane based on both benign and malicious local sensor activity. An important aspect of our work is the use of actual IoT network traffic with specific network layer attacks implemented by us. This is in contrast to other works creating supervised learning models, with generic datasets. The proposed detection model achieves up to 100% accuracy when evaluated with unknown data taken from the same network topology as it was trained and 81% accuracy when operating in an unknown topology.
Haemoglobinopathies are the most common monogenic diseases, posing a major public health challenge worldwide. Cyprus has one the highest prevalences of thalassaemia in the world and has been the ...first country to introduce a successful population-wide prevention programme, based on premarital screening. In this study, we report the most significant and comprehensive update on the status of haemoglobinopathies in Cyprus for at least two decades. First, we identified and analysed all known 592 β-thalassaemia patients and 595 Hb H disease patients in Cyprus. Moreover, we report the molecular spectrum of α-, β- and δ-globin gene mutations in the population and their geographic distribution, using a set of 13824 carriers genotyped from 1995 to 2015, and estimate relative allele frequencies in carriers of β- and δ-globin gene mutations. Notably, several mutations are reported for the first time in the Cypriot population, whereas important differences are observed in the distribution of mutations across different districts of the island.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
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