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The First International Conference in Systems and Network Medicine gathered together 200 global thought leaders, scientists, clinicians, academicians, industry and government experts, medical and graduate students, postdoctoral scholars and policymakers. Held at Georgetown University Conference Center in Washington D.C. on September 11-13, 2019, the event featured a day of pre-conference lectures and hands-on bioinformatic computational workshops followed by two days of deep and diverse scientific talks, panel discussions with eminent thought leaders, and scientific poster presentations. Topics ranged from: Systems and Network Medicine in Clinical Practice; the role of -omics technologies in Health Care; the role of Education and Ethics in Clinical Practice, Systems Thinking, and Rare Diseases; and the role of Artificial Intelligence in Medicine. The conference served as a unique nexus for interdisciplinary discovery and dialogue and fostered formation of new insights and possibilities for health care systems advances.

Optimizing digital twins in the Industrial Internet of Things (IIoT) requires secure and adaptable AI models. The IIoT enables digital twins, virtual replicas of physical assets, to improve real-time decision-making, but challenges remain in trust, data security, and model accuracy. This paper presents a novel framework combining blockchain technology and federated learning (FL) to address these issues. By deploying AI models on edge devices and using FL, data privacy is maintained while enabling collaboration across industrial assets. Blockchain ensures secure data management and transparency, while explainable AI (XAI) enhances interpretability. The framework improves transparency, control, security, privacy, and scalability for self-optimizing digital twins in IIoT. A real-world evaluation demonstrates the framework's effectiveness in enhancing security, explainability, and optimization, offering improved efficiency and reliability for industrial operations. [ABSTRACT FROM AUTHOR]

We proposed a GenoAnalytica model for examining the SARS's genomics sequences. The technologies make proper data extraction from genomics sequences of viruses. We use the GenoAnalytica model, i.e. GenoCompute, and IGMiner Algorithm; to classify the range of genomics sequences, including recognizing the sequence variation from the datasets. The projected algorithm computes the nucleotide patterns and represents the nucleotide genome sequence of SARS (airborne virus) by IGMiner technique and works out on the GenoCompute to calculate computation time with minimum count in second. Along with this, we proposed a UMRA algorithm to compute the mutation rate of the genome sequence with minimum count in seconds as compared to traditional method. Furthermore, we work out the different datasets (China and Algeria datasets) and determine the whole variation at the index level inside the all genome sequence. This learning also signifies the performance evaluation on altering minsup using IGMiner and Aprori-based SPM. Also, we calculate the mutation rate of the genome sequence of airborne virus using Unique Mutation Rate Analysis algorithm. The severe acute respiratory syndrome coronavirus 2 has been responsible for the deadly COVID-19 pandemic. It has ruined limitless individuals all over the globe, and along with this, it continues to harm well-being and people's health. Healthcare specialists and Researchers can obtain insight into COVID-19's inherited variation or SAR-CoV-2 through cutting-edge Artificial Intelligence and genome sequence analysis tools. [ABSTRACT FROM AUTHOR]

Purpose: Intracranial mycotic aneurysms (IMAs), rare and often life-threatening, result from arterial wall infections typically caused by bacteria such as Staphylococcus and Streptococcus. The standard treatment for ruptured aneurysms is not well-defined and often individualized. This study investigates the efficacy of endovascular glue embolization in managing ruptured IMAs, based on our center's experience. Materials and Methods: A retrospective analysis was conducted for ruptured IMAs treated with glue embolization between January 2016 and December 2023. The procedure involved aneurysm sac and parent vessel occlusion with glue delivery. Data included patient demographics, clinical presentations, and neuroimaging. Clinical outcomes were assessed using the modified Rankin scale (mRS) at 3 months, and angiographic follow-up was conducted at 6 months. Results: The study included 28 patients, predominantly male (64.3%), with a mean age of 48 years. Headache was the primary symptom in 92.9% of cases, and positive blood/cerebro spinal fluid cultures were found in 82.14% of cases. All aneurysms were located in the distal circulation, primarily in the anterior circulation system. Glue embolization was successfully performed in all cases, achieving complete aneurysm sac and parent vessel obliteration. Follow-up at 3 months indicated mRS scores of 0 or 1 in 96.5% of cases. Six-month angiographic follow-up showed no aneurysm regrowth or new formations. Conclusion: Endovascular glue embolization demonstrated high efficacy and safety in treating ruptured IMAs, with a 100% obliteration rate and favorable clinical outcomes in this single-center experience. Despite limitations such as its retrospective design and small sample size, the study supports glue embolization as a viable, less invasive alternative to traditional surgery. Further comparative studies are needed to confirm these findings and refine treatment approaches. [ABSTRACT FROM AUTHOR]

Background: The dynamics of fractional oscillators are generally described by fractional differential equations, which include the fractional derivative of the Caputo or Riemann-Liouville type. These equations induce classical oscillator equations like the harmonic oscillator equation, to include fractional order derivatives. Solving fractional differential equations numerically can be challenging due to the non-local nature of fractional derivatives. Objective: In this paper, a recently developed integral Rohit transform is utilized for solving systems of undamped and damped fractional oscillators characterized by differential equations of fractional or non-integral order involving the Caputo-fractional derivative operator. The solutions of fractional systems which include undamped-simple fractional oscillators, undamped-driven fractional oscillators, damped-driven fractional oscillators, and damped-fractional oscillators are obtained. Methods: by applying the integral Rohit transform, also written as RT. Differential equations of fractional or non-integral order are generally solved by utilizing methods which include the fractional variational iteration approach, the homotopy-perturbation method, the equivalent linearized method, the Adomian decomposition method, etc. Results: This paper demonstrates the effectiveness, reliability, and efficiency of the integral Rohit transform in solving fractional systems, which include undamped-simple fractional oscillators, undamped-driven fractional oscillators, damped-driven fractional oscillators, and damped-fractional oscillators and are characterized by differential equations of fractional or non-integral order involving the Caputo-fractional derivative operator. Conclusions: The Rohit transform brought the progressive principles or methodologies that offer new insights or views on the problems examined in the paper, distinguishing itself from existing methods and doubtlessly beginning up new research instructions. It provided precise results for the specific problems discussed in the paper, surpassing the capabilities of other methods in terms of decision, constancy, or robustness to noise and disturbances. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 virus, which induces an acute respiratory illness commonly referred to as COVID-19, had been designated as a pandemic by the World Health Organization due to its highly infectious nature and the associated public health risks it poses globally. Identifying the critical factors for predicting mortality is essential for improving patient therapy. Unlike other data types, such as computed tomography scans, x-radiation, and ultrasounds, basic blood test results are widely accessible and can aid in predicting mortality. The present research advocates the utilization of machine learning (ML) methodologies for predicting the likelihood of infectious disease like COVID-19 mortality by leveraging blood test data. Age, LDH (lactate dehydrogenase), lymphocytes, neutrophils, and hs-CRP (high-sensitivity C-reactive protein) are five extremely potent characteristics that, when combined, can accurately predict mortality in 96% of cases. By combining XGBoost feature importance with neural network classification, the optimal approach can predict mortality with exceptional accuracy from infectious disease, along with achieving a precision rate of 90% up to 16 days before the event. The studies suggested model's excellent predictive performance and practicality were confirmed through testing with three instances that depended on the days to the outcome. By carefully analyzing and identifying patterns in these significant biomarkers insightful information has been obtained for simple application. This study offers potential remedies that could accelerate decision-making for targeted medical treatments within healthcare systems, utilizing a timely, accurate, and reliable method. [ABSTRACT FROM AUTHOR]

Byline: Mahadevan. Gowtham, Devaraj. Sunilkumar, Andi. Ramesh, Bheemanathi. Srinivas, Dinesh. Verma, Krishnan. Nagarajan Rathke cleft cysts are benign lesions of the sellar and suprasellar region. Extrasellar intrasphenoidal Rathke cleft cysts [...]

The present study aimed to examine the extraction of impacted mandibular third molars by using rotatory and piezosurgery equipment. Twenty‑five patients of both genders who required surgery to remove their third molars were split into two groups (control group and research group). In the surgical extraction of the impacted mandibular third molar, in the study group, a piezotome was used to remove the bone, while in the control group, a bur was used. Patients’ acceptance of bone cutting, the length of the operation, discomfort and bleeding during the procedure, postoperative pain, swelling, trismus, and wound healing were evaluated. A statistically insignificant difference was observed for age and difficulty index. The intraoperative bleeding was similar in both groups. Both the groups tolerated the bone cutting well, with slightly better values for the piezo group. Based on Pederson’s difficulty index, the scores of difficulty of extraction of the impacted tooth were measured between the groups. The slight difficulty was observed in 9 (36%) each in the study group and control group. The moderate difficulty in 14 (56%) of control group and 15 (60%) of study group. Severe difficulty was found among 2 (8%) in the control group and 1 (4%) in the study group. The difference was not significant (P > 0.05). Intraoral bleeding was moderately seen in 25 in both groups. Among the study and control groups respectively no intraoperative pain was seen in 14 and 16, mild intraoperative pain in 11 and 7, and moderate intraoperative pain in 0 and 2. Among group I and II acceptance to bone cutting was comfortably seen in 24 and 23, bearable in 1 and 2 respectively. The difference was not significant (P > 0.05). Piezosurgery and bur were comparable in evaluating patient acceptance, intraoperative duration, intraoperative bleeding and pain, postoperative pain, edema, trismus, and wound healing. [ABSTRACT FROM AUTHOR]

Individual model verification is a common practice that increases the quality of design on the left side of the Vee model, often before costly builds and prototypes are implemented. However, verification that spans multiple models at higher levels of abstraction (e.g., subsystem, system, mission) is a complicated endeavor due to the federated nature of the data. This paper presents a tool-agnostic approach to higher-level verification tasks that incorporates tools from Semantic Web Technologies (SWTs) and graph theory more generally to enable a three-pronged verification approach to connected data. The methods presented herein use existing SWTs to characterize a verification approach using ontology-aligned data from both an open-world and closed-world perspective. General graph-based algorithms are then introduced to further explore structural aspects of portions of the graph. This verification approach enables a robust model-based verification on the left side of the Vee model to reduce risk and increase the visibility of the design and analysis work being performed by multidisciplinary teams. [ABSTRACT FROM AUTHOR]

The problem of robotic control of an advanced missile requires designing control systems for remotecontrolled missiles with sufficient abilities. These control systems need to handle navigation, guidance, propulsion, and potentially other tasks like target acquisition and avoidance of obstacles. The various aspects of missile dynamics are described by complex and non-linear differential equations. Due to this, it becomes difficult to obtain their analytical solutions. During system design and analysis, to solve these complex and non-linear differential equations, numerical techniques like Euler's technique, the Runge-Kutta technique, or more advanced mathematical techniques are generally used. The advanced missile robotic control problem is one of the problems represented by second-order linear differential equations. In this paper, the integral Rohit transform (RT) has been applied to solve an advanced missile robotic control problem. This transformation has never been applied in working out the advanced missile robotic control problem. This paper applies a new technique for solving the second-order linear differential equation representing the advanced missile robotic control problem to obtain its required turn angle so that it will be able to catch and destroy the attacker. The necessary graphs are plotted to illustrate the numerical solution of the robotic control of an advanced missile. It reveals that the integral Rohit transform (RT) technique is an effective technique to solve the advanced missile robotic control problem. [ABSTRACT FROM AUTHOR]