Explore the vast range of titles available.

Introduction: Appendiceal neuroendocrine neoplasms (aNENs) are the most common malignant appendiceal neoplasms. Localized aNENs are typically managed with an appendectomy; however, right colectomy may be necessary in patients with a high risk of nodal disease. However, the role of right hemicolectomy and the optimal surveillance strategy, particularly for tumors between 1 and 2 cm, remains controversial. Material and Methods: This retrospective, observational study evaluated patients diagnosed with aNENs between January 1995 and July 2015 at two tertiary centers in Ireland and Italy. Data were extracted from a prospectively maintained registry and included clinical, pathological, and therapeutic variables, as well as follow-up outcomes. Results: Forty-three patients (41.8% male; median age 27.5 years) were included, with a median follow-up of 49 months. The median tumor size was 6.4 mm (range: 0.6–40 mm). The majority were G1 tumors (58%), and staging distribution was predominantly Stage I (60%). While no significant differences in demographics or tumor features were observed between centers, completion right hemicolectomies were more frequent in the Irish cohort (p = 0.04). Follow-up practices varied, with more intensive imaging and biochemical monitoring observed in the Italian cohort. Overall prognosis was excellent, with a single case of recurrence during the study period. Conclusions: Most aNENs are effectively managed with appendectomy alone, and routine follow-up may be unnecessary in the absence of adverse pathological features. Accurate risk stratification, driven by comprehensive histopathological assessment, is critical for optimizing management and surveillance strategies.

Geographic routing is a routing paradigm, which uses geographic coordinates of network nodes to determine routes. Greedy routing, the simplest form of geographic routing forwards a packet to the closest neighbor towards the destination. A greedy embedding is a embedding of a graph on a geometric space such that greedy routing always guarantees delivery. A Schnyder drawing is a classical way to draw a planar graph. In this manuscript, we show that every Schnyder drawing is a greedy embedding, based on a generalized definition of greedy routing.

Geographic routing is an appealing routing strategy that uses the location information of the nodes to route the data. This technique uses only local information of the communication graph topology and does not require computational effort to build routing table or equivalent data structures. A particularly efficient implementation of this paradigm is greedy routing, where along the data path the nodes forward the data to a neighboring node that is closer to the destination. The decreasing distance to the destination implies the success of the routing scheme. A related problem is to consider an abstract graph and decide whether there exists an embedding of the graph in a metric space, called a greedy embedding, such that greedy routing guarantees the delivery of the data. In the present paper, we use a metric-free definition of greedy path and we show that greedy routing is successful on planar triangulations without considering the existence of greedy embedding. Our algorithm rely entirely on the combinatorial description of the graph structure and the coordinate system requires O(log(n)) bits where n is the number of nodes in the graph. Previous works on greedy routing make use of the embedding to route the data. In particular, in our framework, it is known that there exists an embedding of planar triangulations such that greedy routing guarantees the delivery of data. The result presented in this article leads to the question whether the success of (any) greedy routing strategy is always coupled with the existence of a greedy embedding?

Deployment of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) has been considered to be a feasible solution to provide network coverage in scenarios where the conventional terrestrial network is overloaded or inaccessible due to an emergency situation. This article studies the problem of optimal placement of the UAVs as ABSs to enable network connectivity for the users in such a scenario. The main contributions of this work include a less complex approach to optimally position the UAVs and to assign user equipment (UE) to each ABS, such that the total spectral efficiency (TSE) of the network is maximized, while maintaining a minimum QoS requirement for the UEs. The main advantage of the proposed approach is that it only requires the knowledge of UE and ABS locations and statistical channel state information. The optimal 2-dimensional (2D) positions of the ABSs and the UE assignments are found using K-means clustering and a stable marriage approach, considering the characteristics of the air-to-ground propagation channels, the impact of co-channel interference from other ABSs, and the energy constraints of the ABSs. Two approaches are proposed to find the optimal altitudes of the ABSs, using search space constrained exhaustive search and particle swarm optimization (PSO). The numerical results show that the PSO-based approach results in higher TSE compared to the exhaustive search-based approach in dense networks, consuming similar amount of energy for ABS movements. Both approaches lead up to approximately 8-fold energy savings compared to ABS placement using naive exhaustive search.

Background Urinary tract infections (UTIs) are the second most common infection, affecting 150 million people each year worldwide. Enterobacteriaceae species expressing extended-spectrum β-lactamases (ESBLs) are on the rise across the globe and are becoming a severe problem in the therapeutic management of clinical cases of urinary tract infection. Knowledge of the prevalence and antibiogram profile of such isolates is essential to develop an appropriate treatment methodology. This study aimed to investigate the prevalence of Enterobacteriaceae isolates exhibiting ESBL and their selective oral antibiogram profile at the district general hospital, Polonnaruwa. Results A total of 4386 urine specimens received to the Microbiology Laboratory during the study period. Among them, 1081 (24.6%) showed positive results for urine culture while 200/1081 specimens showed ESBL isolates. Out of the selected 200 specimen’s majority (67.5%) of samples received from the In-Patient Department. There were 200 patients and reported that 115 (57.5%) were females and 85 (42.5%) were males. The majority (51%) of the patients belong to the age group of 55–74 years. Among the ESBLs positive specimens, the majority 74.5% ( n  = 149) identified organisms were E. coli followed by Klebsiella spp.17.5% ( n  = 35), Enterobacteriaceae 7% ( n  = 14) and only1% ( n  = 2) isolate of Proteus spp. Mecillinam (87.92%) and Nitrofurantoin (83.2%) showed higher effectiveness against E. coli . Nitrofurantoin showed the highest effectiveness against Klebsiella spp. (40%), other Enterobacteriaceae spp. (100%). Proteus spp. showed 100% effectiveness and resistance respectively against Ciprofloxacin, Cotrimoxazole and Nitrofurantoin. Conclusion The most predominant ESBLs producing uro-pathogen was the E. coli in the study setting and E. coli had higher sensitivity rate against Mecillinam. Among currently used oral antibiotics Nitrofurantoin was the best choice for UTIs caused by ESBL producers.

A novel content caching strategy is proposed for a cache enabled device-to-device (D2D) network where the user devices are allowed to communicate using millimeter wave (mmWave) D2D links (> 6 GHz) as well as conventional sub 6 GHz cellular links. The proposed content placement strategy maximizes the successful content delivery probability of a line of sight D2D link. Furthermore, a heuristic algorithm is proposed for efficient content delivery. The overall scheme improves the successful traffic offloading gain of the network compared to conventional cache-hit maximizing content placement and delivery strategies. Significant energy efficiency improvements can also be achieved in ultra-dense networks.

The paper considers physical layer security (PLS) of an untrusted unmanned aerial vehicle (UAV) network, where a multitude of UAVs communicate in full-duplex (FD) mode. A source-based jamming (SBJ) scheme is exploited for secure communication without utilizing any external jammers. Firstly, the optimal power allocation between the confidential signal and the jamming signal is derived to maximize the secrecy rate of each link between the source and the destination. Then, the best UAV selection scheme is proposed to maximize the overall secrecy rate of the network. The corresponding secrecy outage probability (SOP) and the average secrecy rate (ASR) of the network are analyzed based on the proposed UAV selection and the optimal power allocation schemes. Asymptotic results are also obtained to derive the achievable diversity order. Results are validated through numerical evaluations while providing useful network design insights such as locations and altitudes of the UAVs.

This paper proposes a spectrum selection scheme and a transmit power minimization scheme for a device-to-device (D2D) network cross-laid with a cloud radio access network (CRAN). The D2D communications are allowed as an overlay to the CRAN as well as in the unlicensed industrial, scientific and medical radio (ISM) band. A link distance based scheme is proposed and closed-form approximations are derived for the link distance thresholds to select the operating band of the D2D users. Furthermore, analytical expressions are derived to calculate the minimum required transmit power to achieve a guaranteed level of quality of service in each operating band. The results demonstrate that the proposed scheme achieves nearly 50% power saving compared to a monolithic (purely overlay or purely ISM band) D2D network.

Privacy issues related to video camera feeds have led to a growing need for suitable alternatives that provide functionalities such as user authentication, activity classification and tracking in a noninvasive manner. Existing infrastructure makes Wi-Fi a possible candidate, yet, utilizing traditional signal processing methods to extract information necessary to fully characterize an event by sensing weak ambient Wi-Fi signals is deemed to be challenging. This paper introduces a novel end to-end deep learning framework that simultaneously predicts the identity, activity and the location of a user to create user profiles similar to the information provided through a video camera. The system is fully autonomous and requires zero user intervention unlike systems that require user-initiated initialization, or a user held transmitting device to facilitate the prediction. The system can also predict the trajectory of the user by predicting the location of a user over consecutive time steps. The performance of the system is evaluated through experiments.