Success Rate Research Articles

TomFi: Small Object Tracking Using Commodity WiFi

Rodent infestation has always been one of the most severe threats to humans, which to solve consumes massive manpower and resources. People usually use traps or poisons to treat rat infestation. Such passive countermeasures are inefficient. Damage often occurs when the rats are finally trapped or killed, not to mention the potential risk to injure humans or cause pollution. In this article, we propose a WiFi-based active small object tracking system named TomFi . The core components of TomFi include several deep learning techniques that bridge rat locations/motions and WiFi signal variations (represented by the channel state information). TomFi first employs a detection model to detect the appearance of the rat and then localize it based on a two-branch localization model. Through the design of the two-branch localization model, to our best knowledge, we are the first to solve the information loss and distortion problem. We conducted extensive experiments in both the laboratory environment and real-world kitchen scenarios. The results show that TomFi can achieve a detection success rate of 99%+ and a centimeter-level localization accuracy in real time.

Point Cloud Attacks in Graph Spectral Domain: When 3D Geometry Meets Graph Signal Processing.

With the increasing attention in various 3D safety-critical applications, point cloud learning models have been shown to be vulnerable to adversarial attacks. Although existing 3D attack methods achieve high success rates, they delve into the data space with point-wise perturbation, which may neglect the geometric characteristics. Instead, we propose point cloud attacks from a new perspective-the graph spectral domain attack, aiming to perturb graph transform coefficients in the spectral domain that correspond to varying certain geometric structures. Specifically, leveraging on graph signal processing, we first adaptively transform the coordinates of points onto the spectral domain via graph Fourier transform (GFT) for compact representation. Then, we analyze the influence of different spectral bands on the geometric structure, based on which we propose to perturb the GFT coefficients via a learnable graph spectral filter. Considering the low-frequency components mainly contribute to the rough shape of the 3D object, we further introduce a low-frequency constraint to limit perturbations within imperceptible high-frequency components. Finally, the adversarial point cloud is generated by transforming the perturbed spectral representation back to the data domain via the inverse GFT. Experimental results demonstrate the effectiveness of the proposed attack in terms of both the imperceptibility and attack success rates.

A Contemporary Review on the Critical Role of Nonsteroidal Anti-inflammatory Agents in Colorectal Cancer Therapy.

Nonsteroidal Anti-Inflammatory Drugs (NSAIDs) are widely recognized as effective pain relievers and function by inhibiting the cyclooxygenase enzyme (COXs). Moreover, they have been found to participate in various cellular processes through different signaling pathways, such as WNT, MAPK, NF-KB, and PI3K/AKT/mTOR. This makes them potential candidates for chemoprevention of several malignancies, particularly colorectal cancer (CRC). However, the use of NSAIDs in cancer prevention and treatment is a complex issue due to their adverse effects and gastrointestinal toxicity. Therefore, it is crucial to explore combination therapies that can minimize side effects while maximizing synergistic effects with other agents and to evaluate the success rate of such approaches in both pre-clinical and clinical studies. In this review, we aim to provide an overview of the effects of NSAIDs in the prevention and treatment of CRC. We will focus on elucidating the possible mechanisms of action of these drugs, the signaling pathways involved in CRC, and the potential synergistic effects when combined with other therapeutic agents.

Clinical, Radiographic, and Histologic Outcomes of Regenerative Endodontic Treatment in Human Immature Teeth Using Different Biological Scaffolds: A Systematic Review and Meta-analysis.

Biological scaffolds such as blood clot (BC), platelet-rich plasma (PRP), platelet- rich fibrin (PRF), and platelet pellet (PP) are used in regenerative endodontic treatments (RETs). To systematically and quantitatively evaluate clinical, radiographic, and histologic outcomes of RET studies using different biological scaffolds. MEDLINE, Scopus, Cochrane library, and Embase were searched to identify studies on RET procedures with any scaffold type performed on immature non-vital human teeth, employing any type of biological scaffold. Clinical, radiographic, and histologic outcomes were extracted. Cochrane collaboration risk of bias tool and Newcastle-Ottawa scale were used for quality assessment. Random and fixed model meta-analysis was carried out with 95% confidence interval. Thirty-two studies were included in the qualitative analysis from the primarily retrieved 1895 studies. Only one study had high risk of bias and 71.8% of the studies had high quality. None of the studies reported any histologic findings. Thirty studies were included in meta-analysis. Clinical success rate of RET using either BC, PRP, or PRF was >99%. Furthermore, 32%, 23%, and 27% of BC, PRP, and PRF cases regained vitality, respectively. Periapical healing was seen in 67%, 75%, and 100% of BC, PRP, and PRF cases, respectively. There was no statistical difference between BC, PRP, or PRF regarding clinical success or any radiographic outcomes. There was no significant difference between BC, PRP, and PRF in terms of clinical and radiographic outcomes. When it is difficult or dangerous to induce bleeding in root canals, PRP and PRF may be employed instead.

Comparison of Surgical and Colonoscopy Tissue to Establish Colorectal Patient-derived Organoids.

Patient-derived organoids (PDOs) are ex vivo models that retain the functions and characteristics of individualized source tissues, including a simulated tumor microenvironment. However, the potential impact of undiscovered differences between tissue sources on PDO growth and progression remains unclear. This study aimed to compare the growth and condition of PDO models originating from surgical resection and colonoscopy and to provide practical insights for PDO studies. Tissue samples and relevant patient clinical information were collected to establish organoid models. PDOs were derived from both surgical and colonoscopy tissues. The growth of the organoids, including their state, size, and success rate of establishment, was recorded and analyzed. The activity of the organoids at the end stage of growth was detected using calcein-AM fluorescence staining. The results showed that the early growth phase of 2/3 colonoscopy-derived organoids was faster compared to surgical PDOs, with a growth difference observed within 11-13 days of establishment. However, colonoscopy-derived organoids exhibited a diminished growth trend after this time. There were no significant differences observed in the terminal area and quantity between the two types of tissue-derived organoids. Immunofluorescence assays of the PDOs revealed that the surgical PDOs possessed a denser cell mass with relatively higher viability than colonoscopy-derived PDOs. In the establishment of colorectal patient-derived organoids, surgically derived organoids require a slightly longer establishment period, while colonoscopy-derived organoids should be passaged prior to growth inhibition to preserve organoid viability.

Success rates and failures of fixed and removable space maintainers after the premature loss of primary molars.

The evidence base for the use of space maintainers is relatively sparce despite being used for decades after the premature loss of primary molars. This study aims to increase the dental evidence base via investigating retrospectively the success rates of prefabricated fixed and removable space maintainers inserted from 2019 to 2021 and followed up until February 2023 at a specialized university clinic and to identify reasons for any reported minor and major failure. The authors hypothesized that there is no significant difference in failure rates between fixed and removable space maintainers inserted after the premature loss of a single primary molar per quadrant. Patients' digital records were searched yielding 645 space maintainers. After the application of inclusion criteria, 157 (67%) fixed prefabricated space maintainers in 112 children and 77 (33%) removable space maintainers in 61 children were analyzed for an average of 18.4 ± 9.5 months. Kaplan-Meier survival analysis with Mantel-Cox statistics showed an overall cumulative survival time of 31.6 months (SE = 1.15, 95% CI = 29.4 to 33.9). Major failure occurred significantly more in removable maintainers (n = 40/67, 59.7%), mostly due to loss of the appliance, compared to fixed space maintainers (n = 27/67, 40.3%; P < .001). The present study indicates that space maintainers were mainly placed in young children with high caries experience, where treatment was mostly possible using advanced behavior management. Fixed space maintainers had a significantly lower failure rate than their removable counterpart. However, both require continual repairs, preservation, or even replacement till the eruption of the permanent tooth.

Return to Sports in Patients Aged 50 Years or Younger After Robotic-Assisted Patellofemoral Arthroplasty: A 10-Year Experience Reporting High Clinical Benefits and High Patient Satisfaction With Return to an Active Lifestyle

Background: Limited data are available on return to sports and patient psychometric ratings of success after patellofemoral arthroplasty (PFA) in younger patients with high expectations to return to an active lifestyle. Purpose/Hypothesis: The purpose of this article was to determine the role of PFA and its success in meeting patient expectations regarding the return to low-impact recreational sports and an active lifestyle in younger, active patients. It was hypothesized that PFA would allow younger patients to return to low-impact sports and an active lifestyle and achieve high patient psychometric ratings. Study Design: Case series; Level of evidence, 4. Methods: In this 10-year prospective study (2009-2018), robotic-assisted PFA was performed on 44 patients (32 women and 12 men; n = 51 consecutive knees), with a mean age of 37.2 years (range, 21-50 years). The follow-up rate was 98%, with a mean of 5.3 years (range, 2-9.3 years). Primary clinical outcomes were as follows: the validated Cincinnati Knee Rating System sports activity and symptom rating scales, patient psychometric ratings of the substantial clinical benefit (SCB), and the Patient Acceptable Symptom State (PASS). Secondary outcomes were the Cincinnati Knee Rating System occupational rating, visual analog pain scale, and the 12-Item Short Form Health Survey. Survivorship was defined by conversion to total knee replacement (TKR). Results: Before PFA, 78% of patients (35/45 knees) were symptomatic and unable to perform recreational sports, with only 20% of patients (9/45 knees) performing some low-impact sports. After PFA, 80% of patients (36/45 knees) were able to perform low-impact sports, and 7% (3/45 knees) performed jumping-pivoting sports (P < .001). The SCB scored by the patient showed 87% of knees as good, very good, or normal. On the PASS analysis, 89% of patients (95% CI, 76%-96%) were “pleased,” and 93% (95% CI, 82%-99%) would undergo surgery again. There were clinically relevant improvements in symptoms of pain, swelling, and giving way (P = .0001). Preoperatively, 91% of knees had moderate to severe pain with activities of daily living, and only 11% of knees had pain at the follow-up. Five of the 50 knees (10%) underwent TKR conversion with one patient lost to follow-up. Conclusion: PFA resulted in a high return of patients to low-impact sports with high SCB and PASS psychometric ratings. The robotic-assisted 3-dimensional preoperative planning allowed precise intraoperative trochlear implant alignment in knees with severe trochlear dysplasia. PFA is recommended as an alternative treatment in younger patients with end-stage symptomatic patellofemoral arthritis. Registration: NCT02738476 (ClinicalTrials.gov identifier).

Machine and Deep Learning Implementations for Heritage Building Information Modelling: A Critical Review of Theoretical and Applied Research

Research domain and Problem: HBIM modelling from point cloud data has become a crucial research topic in the last decade since it is potentially considered as the central data model paving the way for the digital heritage practice beyond digitization. Reality Capture technologies such as terrestrial laser scanning, drone-mounted LiDAR sensors and photogrammetry enable the reality capture with a sub-millimetre accurate point cloud file that can be used as a reference file for Heritage Building Information Modelling (HBIM). However, HBIM modelling from the point cloud data of heritage buildings is mainly manual, error-prone, and time-consuming. Furthermore, image processing techniques are insufficient for classification and segmentation of point cloud data to speed up and enhance the current workflow for HBIM modelling. Due to the challenges and bottlenecks in the scan-to-HBIM process, which is commonly criticized as complex with its bespoke requirements, semantic segmentation of point clouds is gaining popularity in the literature. Research Aim and Methodology: Therefore, this paper aims to provide a thorough critical review of Machine Learning and Deep Learning methods for point cloud segmentation, classification, and BIM geometry automation for cultural heritage case study applications. Research findings: This paper files the challenges of HBIM practice and the opportunities for semantic point cloud segmentation found across academic literature in the last decade. Beyond definitions and basic occurrence statistics, this paper discusses the success rates and implementation challenges of machine and deep learning classification methods. Research value and contribution: This paper provides a holistic review of point cloud segmentation and its potential for further development and application in the Cultural Heritage sector. The critical analysis provides insight into the current state-of-the-art methods and advises on their suitability for HBIM projects. The review has identified highly original threads of research, which hold the potential to significantly influence practice and further applied research.

Enhancing the transferability of adversarial attacks with diversified input strategies

Abstract Adversarial examples are often used to test and evaluate the security and robustness of image classification models. Though adversarial attacks under white-box setting can achieve a high attack success rate, due to overfitting, the success rate of black-box attacks is relatively low. To this end, this paper proposes diversified input strategies to improve the transferability of adversarial examples. In this method, various transformation methods are applied to randomly transform the original image multiple times, thereby generating a batch of transformed images. Then, in the process of back-propagation, the loss function gradient of the transformed images is calculated, and a weighted average of the obtained gradient values is performed to generate adversarial perturbation, which is iteratively added to the original image to generate adversarial examples. Meanwhile, by increasing the variety of data augmentation transformation types and the number of input images, the proposed method effectively alleviates overfitting and improves the transferability of adversarial examples. Extensive experiments on the ImageNet dataset indicate that the proposed method can perform black-box attacks better than benchmark methods, with an average of 97.2% success rate attacking multiple models simultaneously.

Optimized wavelet and feature set of EEG signal for Parkinson disease classification

Parkinson disease (PD) is a type of neurodegenerative disorder that affects the motor movement of the patient. But each technique has its own advantages or disadvantages. In gene, speech and handwriting data model, the feature extraction and reduction is an important step for efficient classification. These two steps require proper attention for selection and also require high processing time as compared to other data model like images. Because in image modality, the deep learning algorithm can be applied that can perform all process and automate the classification. As compared to these domains, the signal produces better and best results. Because the electroencephalogram (EEG) signal are taken from the brain using electrodes and it helps to observe the brain signals effectively and immediately as compared to the other data modals. Hence, in this paper, the wavelet transform will be used to decompose the signals and statistical features will be extracted from the transformed signal. Here, the satin bower bird optimization will be used for both type of wavelet selection and feature reduction process for final classification. The reduced feature set will be classified using Ensemble Neural Network type including InceptionV3, DenseNet, MobileNet, Xception, and NasNet) recently proposed for medical image classification. The whole process will be realized using MATLAB R2021a software and its performance will be evaluated in terms of Accuracy and is compared against Automated Tunable Q-wavelet transform performance. The proposed ensemble method, employing EEG signal processing and neural networks, achieved a 97% success rate in discriminating PD datasets, surpassing Convolutional Neural Network (CNN) and Machine Learning (ML) classifications (88% –92%). Utilizing MATLAB R2021a, its superiority over Q-wavelet transform was evident, signifying improved PD dataset discrimination.

Determination of the time location points and investigation of the body sensor network using RATDMA and PATDMA

Wireless body sensor networks have gained significant importance across diverse fields, including environmental monitoring, healthcare, and sports. This research is concentrated on sports applications, specifically exploring the viability of a wireless body area network tailored for high-performing athletes. The paper is divided into three sections. First, the design of the node location that is used for real-time monitoring of a sportsperson in which the node position, such as the human thigh, foot, arm, wrist, and chest, was estimated and the best position was selected. Second, the accuracy of an application when related to the other schemes such as TDMA with ZigBee and RA-TDMA &amp; PA-TDMA was done. The reliability using RA-TDMA performed well and showed approximately 98% reliability. Finally, the features of wireless communiqués that affect the presentation of the network for RA-TDMA were estimated, such as delay and jitter. These findings collectively contribute to advancing the understanding of optimizing wireless body sensor networks for sports applications, with notable achievements including the identification of the arm as the optimal sensor placement, achieving a 98% success rate, and surpassing alternative techniques in network performance parameters like packet delivery rate.

Improved ant colony optimization algorithms for multi-objective investment decision model based on intelligent fuzzy clustering algorithm

With the continuous changes and development of financial markets, it has brought many difficulties to investment decision-making. For the multi-objective investment decision-making problem, the improved Ant colony optimization algorithms was used to improve the effectiveness and efficiency of the multi-objective investment decision-making. Therefore, based on intelligent Fuzzy clustering algorithm and Ant colony optimization algorithms, this paper studied a new multi-objective investment decision model, and proved the advantages of this method through comparative analysis of experiments. The experimental results showed that the improved Ant colony optimization algorithms has significantly reduced the system’s construction costs, operating costs and financial costs, all of which were controlled below 41%. Compared with the traditional Ant colony optimization algorithms, this method had lower values in policy risk, technical risk and market risk, and can effectively control risks. Meanwhile, the environmental, economic, and social benefits of this method were all above 58%, and the average absolute return rate and success rate in this experiment were 21.5450% and 69.4083%, respectively. Therefore, from the above point of view, the multi-objective investment decision model based on intelligent Fuzzy clustering algorithm and the improved Ant colony optimization algorithms can effectively help decision-makers to find the best investment decision-making scheme, and can improve the accuracy and stability of decision-making. This research can provide reference significance for other matters in the field of investment decision-making.

Building Research Capacity and Promoting Collaboration Through Grant Writing: Lessons From Ukrainian Universities

&lt;p style="text-align: justify;"&gt;This study examines the state of grant writing practices in Ukrainian universities and proposes recommendations to enhance their capacity to secure external funding for research and development. Through a mixed-methods approach involving surveys and semi-structured interviews, data was gathered from researchers and faculty members. The study identifies challenges faced by Ukrainian universities in obtaining external funding and highlights important themes for improving grant-writing practices. These themes include promoting collaboration, providing resources and infrastructure, implementing language support policies, raising awareness of funding opportunities, offering institutional support, fostering a research culture, establishing internal grant review processes, leveraging international collaborations, and emphasising evaluation and learning. By addressing these themes, Ukrainian universities can improve their grant-writing practices, increase proposal competitiveness, and enhance success rates in securing external funding. This study provides valuable insights, complements existing literature, and offers practical recommendations for grant writing in Ukrainian universities.&lt;/p&gt;

Predictors of Success of Endonasal Septal Perforation Repair: A 10-Year Experience.

Background: There is no consensus on optimal repair technique for nasal septal perforations (NSPs). Objective: To measure success rate and evaluate predictors of success for NSP repair. Methods: Medical records of patients who underwent NSP repair from 2010 to 2020 were reviewed. Included patients had at least 60 days of postsurgical follow-up. Surgical technique involves an endonasal approach; subperichondrial dissection with local flap mobilization; and multilayer closure using cartilage interposition graft, fascia graft, and mucoperichondrial flaps. A chi-squared test or Fisher exact test was used for statistical analysis. Results: Eighty-one repairs were performed with a closure rate of 86%. The median patient age was 46 years (range 13-77); 34.6% of perforations were ≥2 cm. Conchal (77.8%), rib (7.4%), or septal (7.4%) cartilage was used as graft material. A complication rate of 8.6% was reported. Perforation size or graft material had no impact on successful closure rate. Of patients with failed repairs, 55% had perioperative complications or conditions associated with poor healing. Conclusion: An endonasal approach for NSP repair showed a high success rate across diverse presentations; however, NSP repair was significantly more likely to be successful in patients without perioperative complications or pre-existing conditions associated with poor wound healing.

Retrospective comparison of first-line treatments for odontogenic sinusitis based on duration of symptoms.

To investigate the clinical efficacy of dental treatment and endoscopic sinus surgery (ESS), each primary/combined treatment modality, in patients with odontogenic sinusitis (ODS), according to its phase, acute or chronic. We retrospectively reviewed clinical data on 172 patients diagnosed with ODS. They were divided into two groups: acute (≤ 3 months; 90 patients) and chronic (> 3 months; 82 patients) ODS. The success rate and time to resolution of each primary/combined treatment modality were compared between the two groups. In both ODS groups, the success rate was highest with combined ESS and dental therapy, followed by ESS alone and dental therapy alone. ESS outperformed dental therapy (96.6% vs 65.5% for acute ODS, p = 0.011; 80.6% vs 56.5% for chronic ODS, p = 0.046) and led to quicker resolution of symptoms for acute ODS than dental therapy (0.9 vs 1.7 months, p = 0.012). In the comparison between ESS alone and combined therapy, no significant difference was observed for acute ODS, whereas combined therapy demonstrated a superior success rate for chronic ODS (100% vs 80.6%, p = 0.046). In our study, the clinical utility of dental treatment and/or ESS depended on the morbidity period of ODS. For chronic ODS, combined ESS and dental treatment seems to be an effective first-line treatment.

Evaluation of the Validity of Endoscopic Transpapillary Gallbladder Drainage for Acute Cholecystitis Based on the Tokyo Guidelines 2018.

We evaluated the validity of endoscopic transpapillary gallbladder drainage (ETGBD) as a bridging therapy prior to elective Lap-C for the patients with acute cholecystitis (AC). The Tokyo Guidelines 2018 recommend early laparoscopic cholecystectomy (Lap-C) for patients with AC, however, some patients require the preoperative drainage because of inadequate for early Lap-C du to background and comorbidities. We performed a retrospective cohort analysis using data from our hospital records from 2018-2021. In total, 71 cases of 61 patients with AC underwent ETGBD. The technical success rate was 85.9%. Patients in the failure group had more complicated branching of the cystic duct. The length of time until feeding was started and until WBC levels normalized, and the length of hospital stay were significantly shorter in the success group. The median waiting period for surgery was 39 days in the ETGBD success cases. The median operating time, amount of bleeding, and length of postoperative hospital stay were 134 min, 83.2g, and 4 days, respectively. In patients who underwent Lap-C, the waiting period for surgery and the operating time were similar between the ETGBD success and failure groups. However, the temporary discharge period after drainage and the length of postoperative hospital stay were significantly longer in the patients with ETGBD failure. Our study revealed that ETGBD has equivalent efficacy prior to elective Lap-C despite some challenges that lower its success rate. Preoperativ ETGBD can improve patient quality of life by eliminating the need for a drainage tube.

1239 Trends in Geospatial Availability of Neuro-oncology Clinical Trials in the United States Over Two Decades

INTRODUCTION: Advances in brain tumors treatments rely on successful neuro-oncology (NO) clinical trials. To date, data on NO trials completion in the US is scarce. Understanding these factors could help improve access to and the design of future clinical trials. METHODS: NO clinical trials registered in the US were analyzed (2000-2019). Univariate and multivariate analysis were used to compare success rates among trials. Geospatial analysis was performed to determine the trial density and failure rates across different states. Trial density was estimated by the number of trials/state population. RESULTS: A total of 2,610 neuro-oncology clinical trials were registered in the US during the study period, of which 1,257 met a primary endpoint – completion or trial failure. North Dakota had the highest trial density (6.03 trials/100,000 residents), followed by South Dakota (5.83) and several Northeastern states (3.90-5.60). The change in trial failure rates did not correlate with trial density by state (p = 0.777). The states with the greatest increase in trial failure rates were Nevada (23.1%), West Virginia, Alaska, and Idaho (all three at 20.0%). Florida showed a trend towards a statistically significant increase in failure rates to 10.9% (p = 0.09). CONCLUSIONS: The results of this study highlight the variations in success rates and geographic distribution of neuro-oncology clinical trials in the United States. Factors beyond trial density may be critical to trial success. The significant geographic variations in trial density and failure rates across states emphasize the need for targeted efforts to improve access participation to trials in certain regions. Further studies aimed at identifying the factors contributing to the increase in trial failure rates are needed to develop strategies to improve trial enrollment.

Stent Insertion for Inoperable Hilar Cholangiocarcinoma: Comparison Between Unilateral Radioactive Stent and Bilateral Normal Stent.

To comparatively analyze the clinical efficacy and safety of unilateral radioactive stent (RS) insertion versus bilateral normal stent (NS) insertion in patients with inoperable hilar cholangiocarcinoma (HC). Patients with inoperable HC were treated in our hospital from January 2016 to December 2020. The treatment approach included the insertion of either unilateral RS or bilateral NS, evaluating the efficacy and safety of therapy in 2 distinct groups. A total of 58 individuals experienced the insertion of a unilateral RS, whereas 57 patients underwent the insertion of bilateral NS. No statistically significant difference between the unilateral RS and bilateral NS groups was seen in the technical success rates (98.3% vs 94.7%, P = 0.598) and clinical success rates (98.2% vs 100%, P = 0.514). While there is no statistically significant difference in the rates of stent restenosis (19.3% vs 9.3%, P = 0.132) between the two groups, the unilateral RS group demonstrated substantially longer stent patency (202 vs 119 d, P = 0.016) and overall survival (229 vs 122 d, P = 0.004) compared with the bilateral NS group. Moreover, 8 patients (14.0%) in the unilateral RS group and 14 patients (25.9%) in the bilateral NS group had postoperative complications with no significant difference ( P = 0.116). When inserting stents for inoperable HC, both unilateral RS and bilateral NS insertion procedures have demonstrated favorable therapeutic efficacy. Nevertheless, inserting a unilateral RS provided a longer duration of stent patency and overall survival than implantation of bilateral NS.

The "Hitch-Stitch" for Preventing High Tracheal Stent Migration: Expanding Indications and Extended Experience.

Airway stenting is a standard treatment option for tracheo-bronchial obstruction and other conditions like tracheo-esophageal fistula (TEF). To prevent stent migration, a percutaneous fixation strategy called the "hitch-stitch" was described in 2016 as an efficient technique. We describe an extended experience of the "hitch-stitch" for silicone stents in high tracheal stenting situations and expanding indications to metal stents. Seventy-four percutaneous stitches were placed in 54 patients (36 males and 18 females). The mean age of the patients was 42 (±19) years. The indication for stenting was tracheal stenosis in 63/74 (85%) cases and TEF in 11/74 (15%) cases. Silicone stents were used in 56/74 (75.5%) cases while self-expanding metal stents (SEMS) were used in 18/74 (24.5%) cases. The majority of the stents (62/74, 85%) were high tracheal stents. The "hitch-stitch" as an anti-migration strategy had a high success rate, with distal stent migration in only 2 cases (2.7%) as the stitch had given way-these were repositioned and hitched with double stitches. Minor complications included delayed skin healing (2.8%) and suture site infection (1.4%), which was successfully managed with oral antibiotics. Stitch removal was easy with no complications. In this large series of percutaneous "hitch-stitch" as a stent migration prevention strategy, long-term data shows it is safe and effective in an expanded cohort of silicone stents. Its extended utility is the successful application to metal stents, especially in TEF, where the consequences can be deleterious. It is simple to do, with minimal extra requirements and not associated with any significant complications.

Novel odor sensing intelligence and surveillance capabilities in controller-responder robots

The rise in global travel has led to an increased need for heightened security measures at airports. Despite the best efforts of airport security officers, in the past year, hundreds of kilograms of illegal drugs and thousands of agricultural invasive species have found their way into the country, posing a severe threat to public safety and the environment. Moreover, human threats pose a significant risk to civil aviation, reinforcing the need for advanced security technology. In response to these challenges, NOSI (Novel Odor Sensing Intelligence) and ROSI (Reconnaissance Operations Security Intelligence), intelligence surveillance systems consisting of semi-autonomous controller-responder robots, were developed as a proof of concept to supplement the efforts of security and K-9 (police dogs) operators at airports. NOSI is equipped with multi-channel gas sensors for odor detection, enabling it to identify illegal drugs and invasive species in the baggage handling process, while ROSI is equipped with computer vision to identify individuals already in the government’s database of persons of interest. These coordinated robots also provide travelers with important information pertaining to their journey and allow them to trigger emergency alerts. The robots were tested in a custom-designed test bed that replicated both the behind-the-scenes baggage handling and front-office customer service operations of an airport, thus simulating a realistic airport-like setting. Based on design criteria, NOSI and ROSI demonstrated success rates of 73.4 percent and 69.8 percent, respectively. Improvements in areas of robot stability, sensor accuracy, and feature expansion were documented for further development. In conclusion, the NOSI and ROSI framework can enhance the efficiency and accuracy of airport infrastructure monitoring and supplement the capabilities of human and K9 operators. Overall, this approach can potentially revolutionize operations in various infrastructures and represents the future of human-robot collaboration.