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INTRODUCTION. Overactive bladder syndrome is caused by many factors including obesity, insulin resistance and poor dietary habits. Since it is a chronic disease and needs time to be treated, introducing telepilates in addition to Mediterranean diet would encourage better adherence and results to the treatment program. AIM. To assess the impact of a virtual group-based telerehabilitation program on overactive bladder symptoms and metabolic health in women with obesity. MATERIAL AND METHODS. Eighty obese women (BMI 30.0–34.9 kg/m2) between the ages of 35 and 45 were allocated into two equal groups, 40 for each: (A) supervised telepilates and (B) unsupervised telepilates. The supervised group participated in a 12-week Pilates workout program over videoconference platforms three times a week. Meanwhile, the unsupervised group only received four online meetings. The groups’ diet was the Mediterranean style. The Hemostatic Model of Insulin Resistance (HOMA-IR), body mass index (BMI), waist circumference (WC), Patient Perception of Urgency Scale (PPIUS), Overactive Bladder Questionnaire Short Form (OAB-q SF), and Telehealth Usability Scale (TUS) were measured. RESULTS AND DISCUSSION. The supervised telepilates group exhibited statistically significant amelioration of overactive bladder symptoms and a reduction in HOMA-IR (p 0.001), while the unsupervised telepilates group showed insignificant changes in these measures (p 0.05). Furthermore, the supervised telepilates group showed significantly greater reductions in BMI and WC (p 0.001) than the unsupervised telepilates group (p 0.05). Additionally, the supervised telegroup outperformed the unsupervised telegroup on all parameters of TUS (p 0.001) CONCLUSION. Women with obesity experienced decrease in overactive bladder symptoms and improved metabolic health after completing a 12-week telepilates training program.

Abstract Software-defined networks (SDNs) have been growing rapidly due to their ability to provide an efficient network management approach compared to traditional methods. However, one of the major challenges facing SDNs is the threat of Distributed Denial of Service (DDoS) attacks, which can severely impact network availability. Detecting and mitigating such attacks is challenging, given the constantly evolving range of attack techniques. In this paper, a novel hybrid approach is proposed that combines statistical methods with machine-learning capabilities to address the detection and mitigation of DDoS attacks in SDN environments. The statistical phase of the approach utilizes an entropy-based detection mechanism, while the machine-learning phase employs a clustering mechanism to analyze the impact of active users on the entropy of the system. The k-means algorithm is used for clustering. The proposed approach was experimentally evaluated using three modern datasets, namely, CIC-IDS2017, CSE-CIC-2018, and CICIDS2019. The results demonstrate the effectiveness of the system in detecting and blocking sudden and rapid attacks, highlighting the potential of the proposed approach to significantly enhance security against DDoS attacks in SDN environments.

Abstract Triple-negative breast cancer (TNBC) is often treated with neoadjuvant systemic therapy (NAST). We investigated if radiomic models based on multiparametric Magnetic Resonance Imaging (MRI) obtained early during NAST predict pathologic complete response (pCR). We included 163 patients with stage I-III TNBC with multiparametric MRI at baseline and after 2 (C2) and 4 cycles of NAST. Seventy-eight patients (48%) had pCR, and 85 (52%) had non-pCR. Thirty-six multivariate models combining radiomic features from dynamic contrast-enhanced MRI and diffusion-weighted imaging had an area under the receiver operating characteristics curve (AUC) > 0.7. The top-performing model combined 35 radiomic features of relative difference between C2 and baseline; had an AUC = 0.905 in the training and AUC = 0.802 in the testing set. There was high inter-reader agreement and very similar AUC values of the pCR prediction models for the 2 readers. Our data supports multiparametric MRI-based radiomic models for early prediction of NAST response in TNBC.

Abstract Extreme weather and climate events can have substantial impacts on society and the environment. Compound extremes (two or more extreme events occurring simultaneously or successively) may exert even larger impacts than individual events. Here we examine physical drivers behind variability in hydrometeorological (precipitation and temperature) compound extremes on subseasonal-to-seasonal timescales. Observational evidence presented here through composite analysis indicates that compound extreme frequency is linked to the Madden-Julian oscillation, a unique type of organized tropical convection varying primarily on subseasonal-to-seasonal timescales. The linkage between Madden-Julian oscillation and compound extremes is largely dependent on ENSO phases, which can be seen through different magnitudes or changes in sign of the canonical relationship conditioned on ENSO states. Similarly, the Madden-Julian oscillation can interrupt the canonical ENSO-compound extreme relationship. Our results suggest a potential route to improve subseasonal-to-seasonal prediction of western US compound extremes by considering the combined effect of both Madden-Julian oscillation and ENSO.

Leonardo Kapural,1 Jim Melton,2 Billy Kim,3 Priyesh Mehta,4 Abindra Sigdel,5 Alexander Bautista,6 Erika A Petersen,7 Konstantin V Slavin,8,9 John Eidt,10 Jiang Wu,11 Said Elshihabi,12 Jason Matthew Schwalb,13 H Edward Garrett Jr,14 Elias Veizi,15 Giancarlo Barolat,16 Ravi R Rajani,17 Peter C Rhee,18 Maged Guirguis,19 Nagy Mekhail20 1Carolinas Pain Institute and Center for Clinical Research, Winston-Salem, NC, USA; 2Department of Vascular Surgery, Cardiovascular Health Clinic, Oklahoma City, OK, USA; 3Department of Vascular Surgery, The Surgical Clinic, Nashville, TN, USA; 4Department of Pain Medicine, Meta Medical Research Institute, Dayton, OH, USA; 5Department of Surgery, University of Louisville, Louisville, KY, USA; 6Department of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY, USA; 7Department of Neurosurgery, University of Arkansas, Little Rock, AR, USA; 8Department of Neurosurgery, University of Illinois at Chicago, Chicago, IL, USA; 9Department of Neurology, Jesse Brown VA Medical Center, Chicago, IL, USA; 10Department of Vascular Surgery, Baylor Scott and White Heart and Vascular Hospital Dallas, Dallas, TX, USA; 11Department of Anesthesiology & Pain Medicine, University of Washington Medical Center, Seattle, WA, USA; 12Department of Neurosurgery, Legacy Brain & Spine Surgical Center, Atlanta, GA, USA; 13Department of Neurosurgery, Henry Ford Medical Group, Detroit, MI, USA; 14Department of Vascular Surgery, University of Tennessee-Memphis, Memphis, TN, USA; 15Department of Pain Medicine, VA Northeast OH Healthcare System, Cleveland, OH, USA; 16Department of Neurosurgery, Barolat Neuroscience, Presbyterian/St Luke’s Medical Center, Denver, CO, USA; 17Department of Vascular Surgery, Emory University and Grady Memorial Hospital, Atlanta, GA, USA; 18Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA; 19Department of Interventional Pain Management, Ochsner Health System, New Orleans, LA, USA; 20Department of Pain Management, Cleveland Clinic, Cleveland, OH, USACorrespondence: Leonardo Kapural, Carolinas Pain Institute and Center for Clinical Research, 145 Kimel Park Dr #330, Winston-Salem, NC, 27103, USA, Email lkapuralmd@gmail.comPurpose: This multicenter, randomized, double-blinded, active sham-controlled pivotal study was designed to assess the efficacy and safety of high-frequency nerve block treatment for chronic post-amputation and phantom limb pain.Patients and Methods: QUEST enrolled 180 unilateral lower-limb amputees with severe post-amputation pain, 170 of whom were implanted with the Altius device, were randomized 1:1 to active-sham or treatment groups and reached the primary endpoint. Responders were those subjects who received ≥ 50% pain relief 30 min after treatment in ≥ 50% of their self-initiated treatment sessions within the 3-month randomized period. Differences between the active treatment and sham control groups as well as numerous secondary outcomes were determined.Results: At 30-min, (primary outcome), 24.7% of the treatment group were responders compared to 7.1% of the control group (p=0.002). At 120-minutes following treatment, responder rates were 46.8% in the Treatment group and 22.2% in the Control group (p=0.001). Improvement in Brief Pain Inventory interference score of 2.3 ± 0.29 was significantly greater in treatment group than the 1.3 ± 0.26-point change in the Control group (p = 0.01). Opioid usage, although not significantly different, trended towards a greater reduction in the treatment group than in the control group. The incidence of adverse events did not differ significantly between the treatment and control groups.Conclusion: The primary outcomes of the study were met, and the majority of Treatment patients experienced a substantial improvement in PAP (regardless of meeting the study definition of a responder). The significant in PAP was associated with significantly improved QOL metrics, and a trend towards reduced opioid utilization compared to Control. These data indicate that Altius treatment represents a significant therapeutic advancement for lower-limb amputees suffering from chronic PAP.Keywords: post-amputation pain, phantom limb pain, neuromodulation, peripheral nerve stimulation, high-frequency nerve block

BackgroundMethylphenidate (MPH), a central nervous system stimulant primarily prescribed for attention-deficit/hyperactivity disorder (ADHD), has seen increasing rates of misuse and abuse, particularly in patients with dual diagnosis (co-occurring psychiatric disorders and substance use disorders/SUDs). The heightened risk of dependence and adverse effects in these vulnerable populations warrants a systematic review to assess the prevalence and pattern of abuse/misuse of MPH among patients within this population and to understand potential risk factors, patterns of misuse, and outcomes, including the impact on psychiatric symptoms and overall mental health, the effects on SUD (e.g., exacerbation or mitigation of symptoms), and the incidence of adverse events and complications (e.g., cardiovascular issues, psychological effects).MethodologyA systematic review was conducted in August-September 2024 using both PubMed and Scopus databases. The following search strategy was used: TITLE-ABS-KEY (methylphenidate OR Ritalin OR Concerta) AND TITLE-ABS-KEY (abuse OR misuse OR dependency OR addiction) AND TITLE-ABS-KEY (dual diagnosis OR comorbid psychiatric disorder OR psychiatric disorder AND substance use disorder). The systematic review was structured in accordance with the PRISMA guidelines and identified studies were assessed by title/abstract and full text screening against eligibility criteria.ResultsA total of 12 studies were selected for analysis after screening for relevance, quality, and adherence to inclusion criteria. Findings indicated that individuals with psychiatric disorders, particularly conduct disorder (N=593/1551 individuals), mood disorder (N=90/1551 individuals), anxiety disorder (N=66/1551 individuals), personality disorder (N=44/1551 individuals) and major depression disorder (N=40/1551 individuals), were more likely to misuse MPH. Co-occurring SUD, especially involving Alcohol Use Disorder (N=475/1551 individuals), Cannabis Use Disorder (N=371/1551 individuals), Nicotine Use Disorder (N=343/1551 individuals), Cocaine Use Disorder (N=68/1551 individuals), significantly elevated the risk. Misuse often involved higher doses than prescribed (N=84/1551 individuals) or using non-oral routes of administration (N=20/1551 individuals; e.g., snorting). Adverse outcomes included heightened risk of gastrointestinal events (N=201/1551 individuals), cardiovascular events (N=108/1551 individuals), psychosis (N=69/1551 individuals), and exacerbation of psychiatric symptoms (N=1082/1551 individuals).ConclusionMPH misuse and abuse are significant concerns in patients with psychiatric disorders and SUD. Risk factors include impulsivity, history of substance abuse, and access to prescription stimulants. Integrated therapeutic approaches and stricter prescription monitoring are recommended to mitigate misuse risks.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42024576724.

Les investigations archéologiques menées à Kerkouane, dans le cadre d’une collaboration scientifique entre l’Institut National du Patrimoine (Tunisie) et l’Université de Sassari (Italie), ont apporté de nouvelles perspectives à l’histoire de cette cité punique du Cap Bon. Dans cet exposé préliminaire, nous présentons les premiers résultats obtenus lors des recherches effectuées entre 2022 et 2023, et définissons quelques axes d’étude visant à replacer cette nouvelle documentation archéologique dans un contexte élargi, celui du Cap Bon entre les périodes archaïque et hellénistique (VIIe/VIe-IIIe siècles av. J.-C.). Nous tenons compte de l’influence orientale introduite et diffusée en Afrique par Carthage, ainsi que de l’élément autochtone libyque, qui prédomine et reste très actif. Ces nouvelles données, combinées aux découvertes antérieures, inciteront à des investigations archéologiques plus approfondies pour une meilleure compréhension de la ville de Kerkouane.

E-government describes governments’ efforts to use information technology to improve the efficiency of government services. While the positive aspects of e-government are evident from an organisational point of view, individuals can either accept or decline e-government solutions and governments aim to increase citizen acceptance of their e-government initiatives. There is a wide range of literature with so-called technology acceptance models to study population acceptance of e-government. However, these models have some shortcomings regarding the planned implementation of tangible systems in the public sector (ex-ante acceptance). We, therefore, propose a revised model of information system acceptance in the public sector, the GISAM, and apply it to the case of the 2021 ballot proposal on electronic identity (e-ID) introduction in Switzerland. Using the VOX dataset allows us to examine the survey data of 2,092 Swiss citizens, covering their voting decisions and reasons for them, and several socio-demographic and socio-economic variables. Our findings show that a broad range of factors influences voter acceptance of e-ID. Contrary to traditional technology acceptance models, the results of this study suggest that individual attitudes towards digitisation play a significant role in technology adoption in the e-ID context. Future research should investigate the case of ex-ante technology acceptance more deeply.

BackgroundWhile sepsis, an exaggerated response to infection, can affect people of all age groups, it is more prevalent in middle-aged and older adults. Older adults suffer worse short-term and long-term outcomes than younger patients. Older sepsis survivors are commonly discharged to long-term acute care facilities, where they often die within 1 year. Those who return home from the hospital lose the momentum of physical function improvement after early inpatient rehabilitation, and often face exacerbation of comorbidities and decline in physical function. Additionally, patients who are discharged home often live at distant locations and are not able to commute to rehabilitation centers due to their poor health status. Therefore, remotely delivered exercise interventions tailored to this population hold promise to improve physical function safely and effectively after sepsis. However, this type of intervention has yet to be tested in this population. ObjectiveThis study aims to assess the safety, feasibility, and ease of recruitment and retention of participants for a remotely delivered physical activity intervention for improving physical function in middle-aged and older sepsis survivors. MethodsThe proposed intervention will be delivered through a digital health platform that comprises a patient-facing mobile app and a 12-week physical activity program specifically designed for middle-aged and older sepsis survivors with poor health status who may face challenges participating in traditional out-patient or community-based exercise interventions. This study is ongoing and plans to enroll 40 sepsis survivors aged 55 years and older who will be randomized to either a remotely delivered exercise intervention group or a control group (electronic health diary). Both groups will use a tablet containing the Health in Motion app (Blue Marble Health). The intervention group will receive a clinician-designed personalized avatar-guided home exercise program and reminders while the control group will self-report daily activities using the in-app health diary feature. ResultsThis study is the first to use a home-based, remotely monitored 12-week exercise program to improve physical function in sepsis survivors. This study will evaluate the safety, feasibility, and efficacy, providing the necessary knowledge to design and calculate power for future larger trials. ConclusionsThis study will provide important information for planning a future randomized clinical trial to test the efficacy of a remotely delivered exercise intervention in this high-risk population. Trial RegistrationClinicalTrials.gov NCT05568511; https://clinicaltrials.gov/study/NCT05568511 International Registered Report Identifier (IRRID)DERR1-10.2196/60270

Background: Patients with genetic skeletal dysplasias often require lower extremity total joint arthroplasty (TJA) due to early joint degeneration; however, little data exists regarding the outcomes of TJA in this population. Our purpose was to review the literature to determine the complication rates, revision rates, implant survivorship, and patient-reported outcomes of total knee arthroplasty and total hip arthroplasty (THA) in those with genetic skeletal dysplasias. Methods: A systematic literature review of online databases (PubMed and Google Scholar) was conducted. Studies that reported the outcomes of THA or total knee arthroplasty in patients with genetically confirmed skeletal dysplasias were included. Case reports and studies that defined dysplasia based on height alone were excluded. Fourteen studies met the criteria for data extraction and analysis. Results: Our review yielded a sample of 596 skeletal dysplasia patients with a median follow-up of 6.01 years (1.7-15.9). Mean age was 54.04 years, and mean body mass index was 29.1 kg/m2. Cementless fixation was utilized in 65.7% of THAs, while all knees were cemented. Hip implant survivorship was 79% at 10 years and 56% at 20 years. Knee implant survivorship was 92% at 10 years and 46% at 20 years. Hip and knee revisions were 15.3% and 13.5%, respectively. The most common indication was aseptic loosening and polyethylene wear. Patient-reported outcomes improved across all domains. Conclusions: The literature regarding lower extremity TJA in those with genetic skeletal dysplasias demonstrates appropriate 10-year implant survivorship and improvement in patient-reported outcomes across all survey domains.

Increasing wildfire and declining snowpacks in mountain regions threaten water availability. We combine satellite-based fire detections with snow seasonality classifications to examine fire activity in California's seasonal and ephemeral snow zones. We find a nearly tenfold increase in fire activity during 2020–2021 versus 2001–2019. Accumulation season broadband snow albedo declined 25%–71% at two burned sites (2021 and 2022) according to in-situ data relative to un-burned conditions, with greater declines associated with increased burn severity. By enhancing snowpack susceptibility to melt, both decreased snow albedo and canopy drove midwinter melt during a multi-week dry spell in 2022. Despite similar meteorological conditions in December–February 2013 and 2022–linked to persistent high pressure weather regimes–minimal melt occurred in 2013. Post-fire snowpack differences are confirmed with satellite measurements. With growing geographical overlap between wildfire and snow, our findings suggest California's snowpack is increasingly vulnerable to the compounding effects of dry spells and wildfire.

The need to be competent in neuromodulation is and should be a prerequisite prior to completing a fellowship in interventional pain medicine. Unfortunately, many programs lack acceptable candidates for these advanced therapies, and fellows may not receive adequate exposure to neuromodulation procedures. The American Society of Pain and Neuroscience (ASPN) desires to create a consensus of experts to set a minimum standard of competence for neurostimulation procedures, including spinal cord stimulation (SCS), dorsal root ganglion stimulation (DRG-S), and peripheral nerve stimulation (PNS). The executive board of ASPN accepted nominations for colleagues with excellence in the subject matter of neuromodulation and physician education. This diverse group used peer-reviewed literature and, based on grading of evidence and expert opinion, developed critical consensus guides for training that all accredited fellowship programs should adopt. For each consensus point, transparency and recusal were used to eliminate bias, and an author was nominated for evidence grading oversight and bias control. Pain Education and Knowledge (PEAK) Consensus Guidelines for Neuromodulation sets a standard for neuromodulation training in pain fellowship training programs. The consensus panel has determined several recommendations to improve care in the United States for patients undergoing neuromodulation. As neuromodulation training in the United States has evolved dramatically, these therapies have become ubiquitous in pain medicine. Unfortunately, fellowship programs and the Accreditation Council for Graduate Medical Education (ACGME) pain program requirements have not progressed training to match the demands of modern advancements. PEAK sets a new standard for fellowship training and presents thirteen practice areas vital for physician competence in neuromodulation.

Abstract Water years (WY) 2017 and 2023 were anomalously wet for California, each alleviating multiyear drought. In both cases, this was unexpected given La Niña conditions, with most seasonal forecasts favoring drier‐than‐normal winters. We analyze over seven decades of precipitation and snow records along with mid‐tropospheric circulation to identify recurring weather patterns driving California precipitation and Sierra Nevada snowpack. Tropical forcing by ENSO causes subtle but important differences in these wet weather patterns, which largely drives the canonical seasonal ENSO‐precipitation relationship. However, the seasonal frequency of these weather patterns is not strongly modulated by ENSO and remains a primary source of uncertainty for seasonal forecasting. Seasonal frequency of ENSO‐independent weather patterns was a major cause of anomalous precipitation in WY2017, record‐setting snow in WY2023, and differences in precipitation outcome during recent El Niño winters 1983, 1998, and 2016. Improved understanding of recurrent atmospheric weather patterns could help to improve seasonal precipitation forecasts.

Summary: Canonical splice site variants (CSSVs) are often presumed to cause loss-of-function (LoF) and are assigned very strong evidence of pathogenicity (according to American College of Medical Genetics/Association for Molecular Pathology criterion PVS1). The exact nature and predictability of splicing effects of unselected rare CSSVs in blood-expressed genes are poorly understood. We identified 168 rare CSSVs in blood-expressed genes in 112 individuals using genome sequencing, and studied their impact on splicing using RNA sequencing (RNA-seq). There was no evidence of a frameshift, nor of reduced expression consistent with nonsense-mediated decay, for 25.6% of CSSVs: 17.9% had wildtype splicing only and normal junction depths, 3.6% resulted in cryptic splice site usage and in-frame insertions or deletions, 3.6% resulted in full exon skipping (in frame), and 0.6% resulted in full intron inclusion (in frame). Blind to these RNA-seq data, we attempted to predict the precise impact of CSSVs by applying in silico tools and the ClinGen Sequence Variant Interpretation Working Group 2018 guidelines for applying PVS1 criterion. The predicted impact on splicing using (1) SpliceAI, (2) MaxEntScan, and (3) AutoPVS1, an automatic classification tool for PVS1 interpretation of null variants that utilizes Ensembl Variant Effect Predictor and MaxEntScan, was concordant with RNA-seq analyses for 65%, 63%, and 61% of CSSVs, respectively. In summary, approximately one in four rare CSSVs did not show evidence for LoF based on analysis of RNA-seq data. Predictions from in silico methods were often discordant with findings from RNA-seq. More caution may be warranted in applying PVS1-level evidence to CSSVs in the absence of functional data.

Atopic dermatitis is a chronic inflammatory skin disease that may progress to erythroderma in severe cases. Biologic agents such as dupilumab have recently become the mainstay of systemic treatment for moderate-to-severe cases, yet many patients remain refractory to therapy. Here, we present a case of erythrodermic atopic dermatitis, resistant to prednisone and dupilumab, with remarkably rapid achievement of remission following treatment with upadacitinib, an oral selective Janus kinase 1 inhibitor.

Vygotsky (1978) describes play as having three main components, one being the ability for a child to create an imaginary situation, the second taking on and acting out roles, and the third, following a set of rules that were determined by the roles children took on during play during social or group settings. Hence, supporting much needed social skills and processes that foster a positive social development. The ambiguities of play, specifically the intricate functions between what play entails and the aligned developmental outcomes of play, makes defining play challenging. Research has revealed that children who are in isolated environments, with reduced physical contact among peers of their own age, tend to have lower levels of academic achievements, and are more susceptible to long term psychological stress as they get older (Ammermueller, 2012; Lacey, Kumari & Bartley, 2014). Specifically, the trauma of isolation affects both the social and cognitive domains of development among preschoolers. Isolation, also takes a toll on the type of play children can engage in. The lack of play during a pandemic can prevent children from feeling a sense of joy and familiarity. This article describes how play is not just a mechanism for supporting academic achievement in young children, but also a form of supporting emotional survival during a crisis.

The accurate estimation of Li-ion battery (LIB) states such as State of Charge (SOC), State of Health (SOH), and State of Power (SOP) plays a pivotal role in the efficient operation of Electric Vehicles (EVs). These parameters can impact the battery’s health, driving range, and overall vehicle performance. Transformer-based artificial neural networks have shown impressive results in natural language processing (NLP) and estimation problems of many other domains. This paper presents an intensive study on the capabilities of various Transformer-based models in estimating the SOC and SOH of LIBs, the SOP is obtained based on the estimated SOC. This paper provides the following key original contributions: 1) the application of the Informer and Reformer variants of the Transformer model for the first time for SOH estimation of LIBs in EVs, 2) studying the effect of inverted embedding of iTransformers, a modified architecture of the transformers, on SOC and SOH estimation, inversion is performed on the Informer and Reformer as well; 3) applying a simple feature extraction method using partial discharge cycles for SOH estimation with Transformer-based models; 4) a new robust method is proposed for SOC estimation based on a 2-Encoder-Transformer with a one-dimensional convolutional neural network (1D-CNN) architecture; 5) the various architectures are trained, validated and tested on two real-world datasets comprising various driving scenarios and battery conditions. Comparative analysis with various deep learning architectures show impressive accuracy for estimating the SOC and SOH, leading to better SOP calculation.

Social media platforms are among the most widely used means of communication. However, some individuals exploit these platforms for nefarious purposes, with “cyberbullying” being particularly prevalent. Cyberbullying, which involves using electronic means to harass or harm others, is especially common among young people. Consequently, this study aims to propose a model for detecting cyberbullying using a deep learning algorithm. Three datasets from Twitter, Instagram, and Facebook were utilized to predict instances of bullying using the Long Short-Term Memory (LSTM) method. The results obtained revealed the development of an effective model for detecting cyberbullying, addressing challenges faced by previous cyberbullying detection techniques. The model achieved accuracies of approximately 96.64%, 94.49%, and 91.26% for the Twitter, Instagram, and Facebook datasets, respectively.

Abstract Addressing the uncertainty and variability in the quality of 3D printed metals can further the wide spread use of this technology. Process mapping for new alloys is crucial for determining optimal process parameters that consistently produce acceptable printing quality. Process mapping is typically performed by conventional methods and is used for the design of experiments and ex situ characterization of printed parts. On the other hand, in situ approaches are limited because their observable features are limited and they require complex high-cost setups to obtain temperature measurements to boost accuracy. Our method relaxes these limitations by incorporating the temporal features of molten metal dynamics during laser-metal interactions using video vision transformers and high-speed imaging. Our approach can be used in existing commercial machines and can provide in situ process maps for efficient defect and variability quantification. The generalizability of the approach is demonstrated by performing cross-dataset evaluations on alloys with different compositions and intrinsic thermofluid properties.

Abstract Enhancing IoT security is a corner stone for building trust in its technology and driving its growth. Limited resources and diversified nature of IoT devices make them vulnerable to attacks. Botnet attacks compromise the IoT systems and can pose significant security challenges. Numerous investigations have utilized machine learning and deep learning techniques to identify botnet attacks in IoT. However, achieving high detection accuracy with reasonable computational requirements is still a challenging research considering the particularity of IoT. This paper aims to analytically study the performance of the tree based machine learning in detecting botnet attacks for IoT ecosystems. Through an empirical study performed on a public botnet dataset of IoT environment, basic decision tree algorithm in addition to ensemble learning of different bagging and boosting algorithms are compared. The comparison covers two perspectives: IoT botnet detection capability and computational performance. Results demonstrated that the significant potential for the tree based ML algorithms in detecting network intrusions in IoT environments. The RF algorithm achieved the best performance for multi-class classification with accuracy rate of 0.999991. It achieved also the highest results in all other measures.

Cities and transit agencies, including the Los Angeles County Metropolitan Transportation Authority (LA Metro), are interested in shifting travel from solo driving to more sustainable modes, including transit, biking, walking, and other non-auto modes. As LA Metro and other agencies collect more data about their system and its users, there are many opportunities to apply that data to productively improve the system and drive policy. In this project, our primary goal was to identify a set of specific indicators Metro and other agencies could use to measure their success in the creation and provision of a high-quality non-auto mobility system for both existing users reliant on these modes as well as future users who shift from driving.

Metal additive manufacturing is associated with thermal cycles of high rates of heating, melting, cooling, and solidification. Some areas within the build experience thermal cycles that depend on the paths of the energy source. Additionally, geometrical features, such as thin walls and overhangs, can lead to heat accumulation, potentially affecting the microstructure, fatigue life, and induced residual stresses that may lead to dimensional distortion and cracking. The identification of significant heat accumulation can be used for part quality monitoring to inform the design process, enhance the quality of printed parts, and optimize the process parameters. This study aims to efficiently identify heat accumulation with affordable in-situ infrared imaging for further characterization and mitigation to enhance the quality of printed parts. A computational framework employing machine learning is developed to identify zones of local heat accumulation in real time. The effectiveness of this approach is demonstrated by experiments conducted on a build with a wide variety of geometrical features. In addition, characterization and detailed analyses of detected local heat accumulation zones are provided.

Abstract Diabetic foot ulcers (DFUs) are a serious and prevalent complication of diabetes. Current diagnostic options are limited to macroscopic wound analysis such as wound size, depth, and infection. Molecular diagnostics promise to improve DFU diagnosis, staging, and assessment of treatment response. Here, we developed a rapid and easy‐to‐use fluorescent pH‐sensing bandage for wound diagnostics. In a fluorescent dye screen, we identified pyranine as the lead compound due to its suitable pH‐sensing properties in the clinically relevant pH range of 6–9. To minimize the release of this dye into the wound bed, we screened a library of ionic microparticles and found a strong adhesion of the anionic dye to a cationic polymeric microparticle. These dye‐loaded microparticles showed a strong fluorescence response in the clinically relevant pH range of 6–9 and a dye release below 1% after 1 day in biological media. The dye‐loaded microparticles were subsequently encapsulated in a calcium alginate hydrogel to minimize the interaction of the microparticles with the wound tissue. This pH‐sensing diagnostic wound dressing was tested on full thickness dorsal wounds of mice, and a linear fluorescence response (R2 = 0.9909) to clinically relevant pH values was observed. These findings encourage further development of this pH‐sensing system for molecular diagnostics in DFUs.

Abstract The Internet-of-Things (IoT) environment has revolutionized the quality of living standards by enabling seamless connectivity and automation. However, the widespread adoption of IoT has also brought forth significant security challenges for manufacturers and consumers alike. Detecting network intrusions in IoT networks using machine learning techniques shows promising potential. However, selecting an appropriate machine learning algorithm for intrusion detection poses a considerable challenge. Improper algorithm selection can lead to reduced detection accuracy, increased risk of network infection, and compromised network security. This article provides a comparative evaluation to six state-of-the-art boosting-based algorithms for detecting intrusions in IoT. The methodology overview involves benchmarking the performance of the selected boosting-based algorithms in multi-class classification. The evaluation includes a comprehensive classification performance analysis includes accuracy, precision, detection rate, F1 score, as well as a temporal performance analysis includes training and testing times.