Miracle-dz/UniFAR-FTUs huggingface dataset:
Divisible Task Offloading for Multiuser Multiserver Mobile Edge Computing Systems Based on Deep Reinforcement Learning

Mobile edge computing (MEC) is a promising computing paradigm that enables offloading tasks to edge servers to decrease the load on user equipment (UE) and the latency of services. However, blind offloading of data-intensive tasks may cause edge node congestion and increase the task’s time delay. Therefore, developing a new incentive deep reinforcement learning-based scheme is imperative to solve such divisible task offloading in this end-edge-cloud orchestrated environment. First, we build a novel MEC-based state space framework containing multiple servers and UE, and introduce a new dynamic task division into multiple subtasks mode to execute in parallel on multiple nodes with multiple service providers. Next, we model the process of the divisible task offloading by the Markov Decision Process (MDP) and derive a sufficient condition in the subtask offloading to maximize the quality of physical experience (QoPE) of the divisible task. Finally, we propose a new self-adaptive Q-network with prioritized experience replay (SQ-PER) algorithm based on double deep Q-network (DDQN), in which the experience replay technique and the traditional $\varepsilon $ -greedy exploration strategy are optimized to improve learning efficiency and stability. Simulation results show that the SQ-PER algorithm makes better utilization of environmental resources and significantly reduces time delay of the task than other methods in complex scenarios.
Real and Reactive Power Flow Analysis of a Practical Indian Power Distribution Network

In the present scenario, the day to day increase in the use of electrical energy necessitates the investigation of energy flow in a radial distribution network (RDN). The implementation of Newton Raphson Method to estimate the load flow solutions thereby minimizing the fuel cost of generators in an RDN. This approach has been tested on an IEEE 33-bus system using the Electrical Transient Analyzer Program (ETAP) and the results are discussed.
Unsupervised segmentation of human placenta tissues using hyperspectral image analysis

Hyperspectral imaging (HSI) is an emerging modal-ity in medical imaging. Large-scale clinical utilization of HSI warrants data sets for learning and dedicated classification algorithms, which are currently under research and development. In this work, we present a new unsupervised segmentation method for HSI data of human placenta tissues. The proposed method employs convolutional autoencoders and it was customized for a placenta hyperspectral image dataset, although it is also applicable to other surgical datasets. A high overall accuracy of 0.88 was obtained compared to other methods tested on the same data.
Dulaglutide Protects Mice against Diabetic Sarcopenia-Mediated Muscle Injury by Inhibiting Inflammation and Regulating the Differentiation of Myoblasts

Background Type 2 diabetes mellitus increases the risk of sarcopenia, which is characterized by decreased muscle mass, strength, and function. However, there are no effective drugs to treat diabetic sarcopenia, and its underlying mechanism remains unknown. Here, we aimed to determine whether the GLP-1 receptor agonist (GLP-1RA) dulaglutide (Dul) affects the progression of diabetic sarcopenia. Methods db/db mice were injected intraperitoneally with 0.6 mg/kg dulaglutide for 10 weeks. Mouse muscle tissues were then pathologically evaluated and stained with F4/80 or MPO to detect macrophages and neutrophils, respectively. In addition, inflammatory factors and FNDC5 in the muscle tissues were detected using qRT-PCR. Moreover, C2C12 cells were induced to enable their differentiation into skeletal muscle cells, and muscle factor levels were then detected. Furthermore, changes in muscle factor levels were detected at various glucose concentrations (11 mM, 22 mM, and 44 mM). Results In vivo, dulaglutide alleviated muscle tissue injury; reduced levels of the inflammatory factors, IL-1β, IL-6, CCL2, and CXCL1; and reversed the level of FNDC5 in the muscle tissues of db/db mice. In vitro, a C2C12 cell differentiation model was established through the observation of cell morphology and determination of myokine levels. Upon stimulation with high glucose, the differentiation of C2C12 cells was inhibited. Dulaglutide improved this inhibitory state by upregulating the levels of both FNDC5 mRNA and protein. Conclusions Treatment with the GLP-1RA dulaglutide protects db/db mice against skeletal muscle injury by inhibiting inflammation and regulating the differentiation of myoblasts. High glucose inhibited the differentiation of C2C12 cells and decreased the mRNA and protein levels of myokines. Dulaglutide could reverse the differentiation state induced in C2C12 cells by high glucose.
A Robust CNN-based Approach for Skin Lesion Detection and Classification

The abnormal cell growth that most frequently develops on skin that has in some way been exposed to the sun is called skin cancer. Yet, areas of our skin that are not commonly exposed to sunlight might potentially acquire this sort of cancer. The three main kinds of skin cancer are Melanoma, Basal cell carcinoma and squamous cell carcinoma. On an estimate around 9,500 people are affected daily by skin cancer in the United States. Early screening and detection of warning signs can help to detect and cure cancer before it becomes serious. Therefore, deep diving in the world of Deep Learning aims to build an easily accessible technology that can detect skin cancer based on the type of dermatoscopic lesions. In this paper, we have explored a similar venture, where we have built a classification model on the type of skin lesions using CNN. We have trained our model on MNIST HAM-10000 dataset. The efficiency of our model has been assessed using accuracy, recall, precision and f1-score. We have achieved maximum accuracy of 99.13% for classification of seven types of skin lesions.
Hybrid and adaptive control of functional electrical stimulation to correct hemiplegic gait for patients after stroke

Introduction: Gait, as a fundamental human movement, necessitates the coordination of muscles across swing and stance phases. Functional electrical stimulation (FES) of the tibialis anterior (TA) has been widely applied to foot drop correction for patients with post-stroke during the swing phase. Although the gastrocnemius (GAS) during the stance phase is also affected, the Functional electrical stimulation of the gastrocnemius received less attention. Methods: To address this limitation, a timing- and intensity-adaptive Functional electrical stimulation control strategy was developed for both the TA and GAS. Each channel incorporates a speed-adaptive (SA) module to control stimulation timing and an iterative learning control (ILC) module to regulate the stimulation intensity. These modules rely on real-time kinematic or kinetic data during the swing or stance phase, respectively. The orthotic effects of the system were evaluated on eight patients with post-stroke foot drop. Gait kinematics and kinetics were assessed under three conditions: no stimulation (NS), Functional electrical stimulation to the ankle dorsiflexor tibialis anterior (SA-ILC DS) and FES to the tibialis anterior and the ankle plantarflexor gastrocnemius (SA-ILC DPS). Results: The ankle plantarflexion angle, the knee flexion angle, and the anterior ground reaction force (AGRF) in the SA-ILC DPS condition were significantly larger than those in the NS and SA-ILC DS conditions (p < 0.05). The maximum dorsiflexion angle during the swing phase in the SA-ILC DPS condition was similar to that in the SA-ILC DS condition, with both being significantly larger than the angle observed in the NS condition (p < 0.05). Furthermore, the angle error and force error relative to the set targets were minimized in the SA-ILC DPS condition. Discussion: The observed improvements can be ascribed to the appropriate stimulation timing and intensity provided by the SA-ILC DPS strategy. This study demonstrates that the hybrid and adaptive control strategy of functional electrical stimulation system offers a significant orthotic effect, and has considerable potential for future clinical application.
The Optimal Tumor Mutational Burden Cutoff Value as a Novel Marker for Predicting the Efficacy of Programmed Cell Death-1 Checkpoint Inhibitors in Advanced Gastric Cancer

Purpose The optimal tumor mutational burden (TMB) value for predicting treatment response to programmed cell death-1 (PD-1) checkpoint inhibitors in advanced gastric cancer (AGC) remains unclear. We aimed to investigate the optimal TMB cutoff value that could predict the efficacy of PD-1 checkpoint inhibitors in AGC. Materials and Methods Patients with AGC who received pembrolizumab or nivolumab between October 1, 2020, and July 27, 2021, at Samsung Medical Center in Korea were retrospectively analyzed. The TMB levels were measured using a next-generation sequencing assay. Based on receiver operating characteristic curve analysis, the TMB cutoff value was determined. Results A total 53 patients were analyzed. The TMB cutoff value for predicting the overall response rate (ORR) to PD-1 checkpoint inhibitors was defined as 13.31 mutations per megabase (mt/Mb) with 56% sensitivity and 95% specificity. Based on this definition, 7 (13.2%) patients were TMB-high (TMB-H). The ORR differed between the TMB-low (TMB-L) and TMB-H (8.7% vs. 71.4%, P=0.001). The progression-free survival and overall survival (OS) for 53 patients were 1.93 (95% confidence interval [CI], 1.600–2.268) and 4.26 months (95% CI, 2.992–5.532). The median OS was longer in the TMB-H (20.8 months; 95% CI, 2.292–39.281) than in the TMB-L (3.31 months; 95% CI, 1.604–5.019; P=0.049). Conclusions The TMB cutoff value for predicting treatment response in AGC patients who received PD-1 checkpoint inhibitor monotherapy as salvage treatment was 13.31 mt/Mb. When applying the programmed death ligand-1 status to TMB-H, patients who would benefit from PD-1 checkpoint inhibitors can be selected.
Experiences of Parents of Pre-Adolescents Coping with Online Learning, Socialization and Navigating Critical Media Literacy

COVID-19 school closures necessitated shifts in how students engaged in learning and connected socially. For pre-adolescents and their families, these closures added urgency to an already identified challenge for parents trying to navigate their children’s engagement with digital platforms. Researchers utilized interviews to explore parent and child experiences related to online learning and behavior. Online workshops provided parents with critical media literacy (CML) knowledge and skills for navigating media texts and platforms. This paper employed emergent design-based research methodology and interpretive qualitative case-study methods. Data from interviews, recordings, and field notes were analyzed thematically. The analyses identified that parents noted increased time spent online by their children due to COVID-19 lockdowns. They also highlighted concerns for child safety, issues connected to CML, acknowledgment of the benefit of a support community amongst parents, and parent-child conversations about online actions. This study affirmed the need for parent support regarding CML and digital tools in educational and social online environments and provided suggestions for ways to promote this type of support.
Non-Traditional Agricultural Export Performance in Ghana. Exporting SMEs’ Characteristics, Export Mode, and Institutions

Increasing focus of African economies, including Ghana, on non-traditional agricultural exporting (NTAE) small and medium enterprises (SMEs) to counter declining earnings from traditional exports fall short to expectation. This study investigates this gap by looking at unstructured data, and the drivers of NTAE SMEs that can enhance performance and support sector policy initiatives. The research question to be answered is: Which SME characteristics, export mode and institutions (services and finance) are positively related to export performance in the NTAE sector in Ghana? This study applied PLS SEM analysis. The drivers of significance are export knowledge, corporate governance, product development, and professional and technical support. Although building social networks and export modes were found to be significant, they negatively affected the export performance. The cost implications limit the research to a cross-sectional study. Perceived export performance was used because the respondents did not provide export statistics. Most research on NTAEs in Ghana has focused on either subsectors or SMEs. This study adds value by focusing on SMEs in the NTAE sector, providing insights to policymakers and the government for intervention and policy formulation. Government policy implications include direct investments in shipping and air freight, tax rebates, concessions, and access to finance for NTAE SMEs. Managerial implications show that knowledge of export processes, export market regulations, and international consumer preferences is essential
First molecular detection of Babesia vulpes and Babesia capreoli in wild boars from southern Italy

Introduction Following the increase of wild boar (Sus scrofa) populations in Europe, a potential risk of emerging infections by vector-borne pathogens may occur. Despite this, the circulation of piroplasmid species in these ungulates is still a neglected topic, particularly in the Mediterranean basin. Therefore, this study aimed to investigate the presence of Babesia/Theileria spp. in wild boars from southern Italy to assess the epidemiological role of these ungulates in the circulation of piroplasmids. Methods By using a citizen science approach among hunters and veterinarians, wild boar spleen samples were collected in the Campania region (southern Italy) between 2016 and 2022. A combined semi-nested PCR/sequencing analysis targeting the V4 hyper-variable region of 18S rRNA was run to detect Babesia/Theileria spp. DNA. Results Out of 243 boars, 15 (i.e., 6.2, 95% CI: 3.4–9.9) tested positive to Babesia/Theileria spp. , Babesia vulpes (n = 13, 5.3, 95% CI: 3.1–8.9) the most prevalent, followed by Babesia capreoli (n = 2, 0.8, 95% CI: 0.2–2.9). Three different B. vulpes sequence types were identified (i.e., ST1, ST2, ST3), with the most representative as ST1 (60%), and a single B. capreoli sequence type. No statistically significant difference (p > 0.05) were found between the presence of the pathogens and boar age, sex, province and sample collection year. Discussion Data demonstrate for the first time the occurrence of B. vulpes and B. capreoli in wild boars, which may play a role in the biological cycle of piroplasmids. We emphasize the importance of monitoring these ungulates to prevent potential foci of infection. The engagement of hunters in epidemiological scientifically based surveys can constitute a technically sound control strategy of piroplasmids in a One Health perspective.
Flow Structures With High Lagrangian Coherence Rate Promote Diatom Blooms in Oligotrophic Waters

Diatoms are among the most efficient autotrophic organisms for oceanic primary production and carbon sequestration. Yet, the spatial distributions of these planktonic organisms remain puzzling and the underlying physical processes poorly known, especially in oligotrophic open waters. Here we investigate what dynamical conditions are conducive to episodic diatom blooms in oceanic deserts based on Lagrangian diagnosis and satellite‐derived phytoplankton functional types and currents. The coherence of the flow is diagnosed in space and time simultaneously through the Lagrangian coherence rate (LCR) to identify which dynamical structures favor diatom growth. Observations evidence that flow structures with high LCR (40 days or longer) in areas with elevated eddy kinetic energy and vorticity sustain high diatom concentrations in the sunlit layers. Our findings show that the integration of Eulerian kinematic variables into a Lagrangian frame reveals new dynamical aspects of geophysical turbulence and unveil their biological impacts.
Construction and validation of an instrument for Forensic Nursing and similar graduation disciplines

Objective: to construct and validate a questionnaire for tracking disciplines and content of Forensic Nursing and similar disciplines in undergraduate courses. Methods: methodological study for the construction and validation of content and semantics. The items were constructed and categorized using content analysis, based on the national and international theoretical framework of Forensic Nursing, structured in three sections with dichotomous and subjective questions. Content and semantics were validated using the brainstorming technique and an 80% concordance index among the judges. Results: the sections were: identification of courses, of disciplines of Forensic Nursing and Violence, and contents of Forensic Nursing discipline. Conclusion: the Forensic Nursing Discipline and Content Supervision Questionnaire for Graduation Nursing Courses was considered easy to use and dynamic to assess the situation of Forensic Nursing and Violence-related subjects in Nursing courses at college Institutions.
Applying real-world data from expanded-access (“compassionate use”) patients to drug development

Abstract Our drug development process has produced many life-saving medications, but patients experiencing rare diseases and similar conditions often are left with limited options for treatment. For an approved treatment to be developed, research on a new candidate or existing drug must validate safety and efficacy based on contemporary research expectations. Randomized clinical trials are conducted for this purpose, but they are also costly, laborious, and time-consuming. For this reason, The 21st Century Cures Act mandates that the US Food and Drug Administration look for alternative methods for approving drugs, in particular exploring the uses of real-world data and evidence. Expanded access (“compassionate use”) is a pathway for the clinical treatment of patients using drugs that are not yet approved for prescribing in the United States. Using real-world evidence generated from expanded-access patients presents an opportunity to provide critical data on patient outcomes that can serve regulatory approval in conjunction with other observational datasets or clinical trials, and in limited circumstances may be the best data available for regulatory review. In doing so, we may also support and encourage patient-centered care and a personalized medicine approach to drug development.
Wi-Diag: Robust Multisubject Abnormal Gait Diagnosis With Commodity Wi-Fi

The existing commodity Wi-Fi-based human gait recognition systems mainly focus on a single subject due to the challenges of multisubject walking monitoring. To tackle the problem, we propose Wi-Diag, the first commodity Wi-Fi-based multisubject abnormal gait diagnosis system that leverages only one pair of off-the-shelf commercial Wi-Fi transceivers to separate each subject’s gait information and maintains an excellent performance when the scenario changes. It is an intelligent multisubject gait diagnosis system that can release an experienced doctor from heavy load work. Multisubject abnormal gait diagnosis is modeled as a blind source separation (BSS) issue, and multisubject walking mixed signals are efficiently separated by IC analysis (ICA) approach. This fact is verified by comprehensive theoretical derivation and experimental validation. In addition, CycleGAN is leveraged to mitigate the environmental dependency so that Wi-Diag can be robust when the scenario changes. The excellent performance of Wi-Diag is verified by extensive experiments. The average mean diagnosis accuracy with a maximum group size of four and various scenarios is 87.77%.
Synergic Exploitation of TCAD and Deep Neural Networks for Nonlinear FinFET Modeling

Accurate large-signal (LS) modeling of Fin Field-Effect Transistors (FinFETs) plays an important role in designing microwave circuits for the next generation of communication systems and quantum sensing. In this work we propose preliminary results on a novel approach to LS FinFET modeling, that translates the X-parameters from physical TCAD analysis into deep neural networks (DNNs). The proposed method includes two phases. First, the X-parameters of nonlinear active device are extracted trough accurate TCAD physical simulations. Then, a long short-term memory (LSTM)-based DNN is employed for ANN modelling, to reproduce the scattered waves for any given incident waves up to the 5th harmonic. Similarly to X-parameters, the proposed DNN model simulates the transistor behavior around the large-signal operating point. Unlike the original X-parameter method, though, the DNN approach can incorporate the dependency on bias or other technological and physical parameters in a seamless and numerically efficient way. Hence, once implemented into circuit simulators, it allows for faster and more accurate circuit design.
Necrotising Inflammation of Peri-Pancreatic Tissue With Normal Appearance of Pancreas Is a Distinct Entity of Acute Pancreatitis: Report of Two Cases With Review of Literature

Improved insights into the pathophysiology of acute pancreatitis have paved the way for identification of distinct entities in the spectrum of the disease. The presence of necrotising inflammation limited to peripancreatic tissue with a normal appearance of pancreas is one such entity. This entity, described as extrapancreatic necrotising pancreatitis (EPN), is considered a less aggressive form of acute necrotising pancreatitis. This entity needs to be recognized precisely and managed accordingly among patients with acute pancreatitis. However, EPN has not been highlighted in the revised classification of acute pancreatitis. Here we report two patients with EPN with varied presentations and diverse management and outcome.
A Computationally Efficient Predistortion and Segment Thresholding for Distributed PA Arrays

Multipath MIMO systems have emerged, providing faster and more reliable wireless communications. These systems require distributed arrays of nonlinear power amplifiers, which in turn require novel digital predistortion solutions. In this paper the authors propose a novel technique called, variation in the slope, to reduce the computational overhead of multipath segmented DPD for distributed arrays of power amplifiers. Additionally the approach to calculate the threshold for the segments requires only the second derivative calculation of gain versus input power and thus avoids computationally expensive clustering techniques. This approach was validated using a 100MHz5G NR signal and a hybrid beam former module, resulting in an achieved −50dB ACLR on average. This value meets the spectral mask requirement for 5GFR2 standards. This method requires fewer training samples and DPD parameters compared to state-of-the-art techniques for multipath systems.