Opportunistic Use Research Articles

Deep Learning for Opportunistic Rain Estimation via Satellite Microwave Links

Accurate precipitation measurement is critical for managing flood and drought risks. Traditional meteorological tools, such as rain gauges and remote sensors, have limitations in resolution, coverage, and cost-effectiveness. Recently, the opportunistic use of microwave communication signals has been explored to improve precipitation estimation. While there is growing interest in using satellite-to-earth microwave link (SMLs) for machine learning-based precipitation estimation, direct rainfall estimation from raw signal-to-noise ratio (SNR) data via deep learning remains underexplored. This study investigates a range of machine learning (ML) approaches, including deep learning (DL) models and traditional methods like gradient boosting machine (GBM), for estimating rainfall rates from SNR data collected by interactive satellite receivers. We develop real-time models for rainfall detection and estimation using downlink SNR signals from satellites to user terminals. By leveraging a year-long dataset from multiple locations—including SNR measurements paired with disdrometer and rain-gauge data—we explore and evaluate various ML models. Our final models include ensemble approaches for both rainfall detection and cumulative rainfall estimation. The proposed models provide a reliable solution for estimating precipitation using Earth–satellite microwave links, potentially improving precipitation monitoring. Compared to the state-of-the-art power-law-based models applied to similar datasets reported in the literature, our ML models achieve a 46% reduction in the RMSE

Irritable bowel syndrome with diarrhea: is colonoscopy mandatory? Opportunistic use of colon mucosal organ culture to diagnose celiac disease.

Irritable bowel syndrome with diarrhea: is colonoscopy mandatory? Opportunistic use of colon mucosal organ culture to diagnose celiac disease.

Understanding how restoration reduces competition for habitat by combining theory, observation, and experiment.

Habitat selection theory enables inferences about species habitat choice across a range of observed population densities. However, it is relatively uncommon to use habitat selection theory in studies of habitat restoration efficacy to understand the effect of restoration on habitat competition. We combined observational density data and resource selection functions to analyze habitat correlations with both habitat selection theory and a mark-recapture experiment to show how habitat restoration can mitigate competition between species with similar habitat preferences. To restore degraded and channelized riverine habitat for juvenile Chinook salmon (Oncorhynchus tshawytscha) and steelhead trout (Oncorhynchus mykiss) engineered log jams (ELJs) have been installed to create pools to enhance growth and rearing. Application of habitat selection theory first showed that both species share a preference for ELJ-treated habitat over unrestored habitat. Linear models showed that steelhead are generalists with respect to depth in unrestored habitat, whereas both species' abundance varies along a depth gradient in ELJ-treated habitat. Selective versus opportunistic use of deep and shallow ELJ pools was density-dependent. We found a range of densities at which a "ghost of competition" exists, where Chinook are selective on deep ELJ-treated pools and steelhead are selective on shallow pools. A mark-recapture experiment confirmed that steelhead limit Chinook movement into unrestored habitat, but this competitive effect vanished in ELJ-treated habitat where selection occurred with respect to pool depth. The experiment, combined with theory, enabled (1) the identification of a mechanism allowing for shared preference of restored habitat and (2) the description of how restoration can mitigate competition.

Opportunistic use of lumbar computed tomography and magnetic resonance imaging for osteoporosis screening.

The purpose of this study is to determine whether the opportunistic use of computed tomography (CT) or magnetic resonance imaging (MRI) is effective for identifying spine surgical patients with OP. We retrospectively evaluated 109 lumbar spine surgery patients who received lumbar quantitative CT (QCT) and MRI. Using the area under the curve, the CT-based HU value and MRI-based VBQ score were calculated. Then, based on the QCT results, receiver operating characteristic (ROC) curves were constructed to determine the diagnostic performance of the HU value and VBQ score. The HU value was significantly lower in the OP group, and the VBQ score was significantly higher in the OP group. Using the area under the curve, the diagnostic performance of the HU value and VBQ score for OP were 0.959 and 0.880, respectively. The diagnostic threshold values determined with optimal sensitivity and specificity were an HU value of 97.06 and a VBQ score of 3.08. Opportunistic use of CT and MRI can simply distinguish patients with OP, which are expected to be potential alternatives to T-score for the osteoporosis screening.

A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery

Seasonal snow plays a crucial role in society and understanding trends in snow depth and mass is essential for making informed decisions about water resources and adaptation to climate change. However, quantifying snow depth in remote, mountainous areas with complex topography remains a significant challenge. The increasing availability of high-resolution synthetic aperture radar (SAR) observations from active microwave satellites has prompted opportunistic use of the data to retrieve snow depth remotely across large spatial and frequent temporal scales and at a high spatial resolution. Nevertheless, these novel SAR-based snow depth retrieval methods face their own set of limitations, including challenges for shallow snowpacks, high vegetation cover, and wet snow conditions. In response, here we introduce a machine learning approach to enhance SAR-based snow depth estimation over the European Alps. By integrating Sentinel-1 SAR imagery, optical snow cover observations, and topographic, forest cover and snow class information, our machine learning retrieval method more accurately estimates snow depth at independent in-situ measurement sites than current methods. Further, our method provides estimates at 100 m horizontal resolution and is capable of better capturing local-scale topography-driven snow depth variability. Through detailed feature importance analysis, we identify optimal conditions for SAR data utilization, thereby providing insight into future use of C-band SAR for snow depth retrieval.

Can Opportunistic Use of Computed Tomography Help Reveal the Association Between Hepatic Steatosis and Disease Severity in Hospitalized COVID-19 Patients?

To measure hepatic steatosis (HS) in hospitalized COVID-19 patients using unenhanced chest computed tomography (CT) imaging and to evaluate the relationship between disease severity and prognosis in adult patients.This retrospective study included 152 consecutive hospitalized COVID-19 patients with a positive reverse transcriptase polymerase chain reaction (RT-PCR) test. The COVID-19 Reporting and Data System (CO-RADS) and the chest CT score were evaluated. HS measurements were performed based on CT images using a single region of interest placed on the right liver lobe (segments V-VII). HS was defined as a liver attenuation value <40 Hounsfield units. Data were collected and compared with the patients' prognostic parameters.Of the 152 inpatients, 137 patients (90.1%) had a CT score ≥3 and 109 patients (71.7%) had a CO-RADS score ≥4, 43 (28.2%) had HS. All patients with HS (100%) and 94/109 (86.2%) patients without HS had a CT score ≥3. There was a statistically significant difference between the two groups in terms of chest CT score (p=0.006). There was no statistically significant difference between the two groups in terms of CO-RADS score (p=0.291). The median CRP levels were significantly increased in patients with HS compared to patients without HS (p=0.023). There was no significant difference in ICU hospitalization and mortality due to the presence of HS (p>0.05).The current study revealed significantly higher chest CT scores in COVID-19 patients with HS measured on CT compared to those without HS. Opportunistic use of CT images for the detection of HS can be considered as an adjunctive tool in the risk analysis of COVID-19 patients hospitalized due to COVID-19 pneumonia.The severity of COVID-19 disease is increased in hospitalized patients with hepatosteatosis compared to patients with a normal liver. Density measurements for the evaluation of HS using opportunistic CT applications can be considered as an adjunctive tool in the prognostic evaluation of hospitalized patients with COVID-19 pneumonia. · Parlak AE, Erdem Toslak İ, Turkoglu Selcuk N. Can Opportunistic Use of Computed Tomography Help Reveal the Association Between Hepatic Steatosis and Disease Severity in Hospitalized COVID-19 Patients?. Fortschr Röntgenstr 2024; DOI 10.1055/a-2369-8377.

Early Detection of Heart Failure with Autonomous AI-Based Model Using Chest Radiographs: A Multicenter Study.

The opportunistic use of radiological examinations for disease detection can potentially enable timely management. We assessed if an index created by an AI software to quantify chest radiography (CXR) findings associated with heart failure (HF) could distinguish between patients who would develop HF or not within a year of the examination. Our multicenter retrospective study included patients who underwent CXR without an HF diagnosis. We included 1117 patients (age 67.6 ± 13 years; m:f 487:630) that underwent CXR. A total of 413 patients had the CXR image taken within one year of their HF diagnosis. The rest (n = 704) were patients without an HF diagnosis after the examination date. All CXR images were processed with the model (qXR-HF, Qure.AI) to obtain information on cardiac silhouette, pleural effusion, and the index. We calculated the accuracy, sensitivity, specificity, and area under the curve (AUC) of the index to distinguish patients who developed HF within a year of the CXR and those who did not. We report an AUC of 0.798 (95%CI 0.77-0.82), accuracy of 0.73, sensitivity of 0.81, and specificity of 0.68 for the overall AI performance. AI AUCs by lead time to diagnosis (<3 months: 0.85; 4-6 months: 0.82; 7-9 months: 0.75; 10-12 months: 0.71), accuracy (0.68-0.72), and specificity (0.68) remained stable. Our results support the ongoing investigation efforts for opportunistic screening in radiology.

Opportunistic use of chest low-dose computed tomography (LDCT) imaging for low bone mineral density and osteoporosis screening: cutoff thresholds for the attenuation values of the lower thoracic and upper lumbar vertebrae.

Osteoporosis remains substantially underdiagnosed and undertreated worldwide. Chest low-dose computed tomography (LDCT) may provide a valuable and popular opportunity for osteoporosis screening. This study sought to evaluate the feasibility of the screening of low bone mineral density (BMD) and osteoporosis with mean attenuation values of the lower thoracic compared to upper lumbar vertebrae. The cutoff thresholds of the mean attenuation values in Hounsfield units (HU) were derived to facilitate implementation of opportunistic screening using chest LDCT. The participants aged 30 years or older who underwent chest LDCT and quantitative computed tomography (QCT) examinations from August 2018 to October 2020 in our hospital were consecutively included in this retrospective study. A region of interest (ROI) was placed in the trabecular bone of each vertebral body to measure the HU values. The correlations of mean HU values of lower thoracic (T11-T12) and upper lumbar (L1-L2) vertebrae with age and lumbar BMD obtained with QCT were performed using the Pearson correlation coefficient, respectively. The area under the curve (AUC) of the receiver operator characteristic (ROC) curve was generated to determine the cutoff thresholds for distinguishing low BMD from normal and osteoporosis from non-osteoporosis. A total of 1,112 participants were included in the final study cohort (743 men and 369 women, mean age 58.2±8.9 years; range, 32-88 years). The mean HU values of T11-T12 and L1-L2 were significantly different among 3 QCT-defined BMD categories of osteoporosis, osteopenia, and normal (P<0.001). The differences in HU values between T11-T12 and L1-L2 in each category of bone status were statistically significant (P<0.001). The mean HU values of T11-T12 (r=-0.453, P<0.001) and L1-L2 (r=-0.498, P<0.001) had negative correlations with age. Positive correlations were observed between the mean HU values of T11-T12 (r=0.872, P<0.001) and L1-L2 (r=0.899, P<0.001) with BMD. The optimal cutoff thresholds for distinguishing low BMD from normal were average T11-T12 ≤157 HU [AUC =0.941, 95% confidence interval (CI): 0.925-0.954, P<0.001] and L1-L2 ≤138 HU (AUC =0.950, 95% CI: 0.935-0.962, P<0.001), as well as distinguishing osteoporosis from non-osteoporosis were average T11-T12 ≤125 HU (AUC =0.960, 95% CI: 0.947-0.971, P<0.001) and L1-L2 ≤107 HU (AUC =0.961, 95% CI: 0.948-0.972, P<0.001). There was no significant difference between the AUC values of T11-T12 and L1-L2 for low BMD (P=0.07) and osteoporosis (P=0.92) screening. We have conducted a study on low BMD and osteoporosis screening using mean attenuation values of lower thoracic and upper lumbar vertebrae. Assessment of mean attenuation values of T11-T12 and L1-L2 can be used interchangeably for low BMD and osteoporosis screening using chest LDCT, and their cutoff thresholds were established.

When despotic leadership is more detrimental for employee job outcomes? Role of individual-level power distance orientation

PurposeUsing social exchange and power-dependence theories, this study advances the emergent debate on the dark side of leadership by examining the impact of despotic leadership style on employees’ supervisor-rated job performance, job satisfaction and turnover intention with individual-level power distance orientation as a moderator.Design/methodology/approachWe collected multisource and time-lagged survey data in two waves from 65 leaders and 300 subordinates as paired responses working in different banks, telecom and IT companies.FindingsIt was found that employees’ exposure to despotic leadership harms their job performance and job satisfaction and has a positive effect on their turnover intention. In line with our prediction, low power distance orientation employees react more strongly to despotic leadership while high power distance orientation mitigates its effects on employee job outcomes.Practical implicationsDespotic behavioral tendencies must be taken into consideration while selecting and appointing leaders. Organizational leaders should also refrain from opportunistic and exploitative use of their followers' efforts.Originality/valueThis study adds to the emerging literature on the dark side of leadership by examining the relationship of despotic leadership style with employee job outcomes. The unique contribution of this study is the examination of individual-level power distance orientation as the boundary condition of these relationships.

How Board Monitoring and Mandated Clawbacks Shape Managers’ Use of Discretion: Experimental Evidence

ABSTRACT We use an experimental setting to examine how an internal governance mechanism—board monitoring—moderates managers’ use of discretion in response to a regulatory governance mechanism—mandated clawbacks. Our study addresses a significant gap in the literature that has largely examined the effects of different governance mechanisms in isolation. We predict and find that mandated clawbacks increase managers’ tendency to use operational discretion (relative to accounting discretion) when board monitoring is weak, but not when board monitoring is strong. Our results have important policy implications by demonstrating that a firm’s internal environment may be more effective than rules in curtailing manager’s opportunistic use of discretion.

Mobile edge computing based cognitive network security analysis using multi agent machine learning techniques in B5G

The proliferation of wireless applications at an exponential rate has made spectrum problems worse. Saturation in the unlicensed frequency spectrum is rapidly increasing as a result of the increasing data rates required by new wireless devices. A proposed solution to this problem is cognitive radio, which allows for the opportunistic use of licenced spectrum in less crowded areas. Cognitive network-based security evaluations using mobile edge computing and a Beyond 5G' (B5G) machine learning (ML) model are the focus of this research. In this case, the security study was carried out using cognitive network data transfer and multi-agent reinforcement encoder neural network and mobile edge computing (MRENN-MEC), a multi-agent reinforcement encoder neural network with mobile edge computing. Scalability, quality of service, throughput, and forecast accuracy are some of the network properties that undergo experimental analysis.

Impression management in bilingual corporate reporting: An analysis of textual characteristics in Spanish and English

Globalization has made it more important for companies to communicate in different languages. However, translation in financial reporting is neglected in practice, despite offering managers the potential to increase/decrease opportunistic attitudes in different languages. In particular, opposing pressure and monitoring effects would suggest higher/lower incentives for impression management. This paper investigates whether there are different levels of impression management in different versions of bilingual (Spanish/English) annual reports produced by Spanish listed companies. Patterns in textual characteristics that may be used for impression management purposes are analyzed. Despite different incentives, the results show little difference in the opportunistic use (or lack thereof) of every textual variable analyzed between the different language versions. This implies that stakeholders may feel free to read the bilingual reports in the language in which they feel most comfortable, because the translation process does not seem to result in different levels of impression management.

Can artificial intelligence detect type 2 diabetes in women by evaluating the pectoral muscle on tomosynthesis: diagnostic study

ObjectivesThis retrospective single-center analysis aimed to evaluate whether artificial intelligence can detect type 2 diabetes mellitus by evaluating the pectoral muscle on digital breast tomosynthesis (DBT).Material methodAn analysis of 11,594 DBT images of 287 consecutive female patients (mean age 60, range 40–77 years) was conducted using convolutional neural networks (EfficientNetB5). The inclusion criterion was left-sided screening images with unsuspicious interpretation who also had a current glycosylated hemoglobin A1c (HBA1c) % value. The exclusion criteria were inadequate imaging, history of breast cancer, and/or diabetes mellitus. HbA1c values between 5.6 and 6.4% were categorized as prediabetic, and those with values ≥ 6.5% were categorized as diabetic. A recorded HbA1c ≤ 5.5% served as the control group. Each group was divided into 3 subgroups according to age. Images were subjected to pattern analysis parameters then cropped and resized in a format to contain only pectoral muscle. The dataset was split into 85% for training and 15% for testing the model’s performance. The accuracy rate and F1-score were selected as performance indicators.ResultsThe training process was concluded in the 15th epoch, each comprising 1000 steps, with an accuracy rate of 92% and a loss of only 0.22. The average specificity and sensitivity for all 3 groups were 95%. The F1-score was 0.95. AUC-ROC was 0.995. PPV was 94%, and NPV was 98%.ConclusionOur study presented a pioneering approach, applying deep learning for the detection of diabetes mellitus status in women using pectoral muscle images and was found to function with an accuracy rate of 92%.Critical relevance statementAI can differentiate pathological changes within pectoral muscle tissue by assessing radiological images and maybe a potential diagnostic tool for detecting diabetes mellitus and other diseases that affect muscle tissues.Key points• AI may have an opportunistic use as a screening exam for diabetes during digital breast tomosynthesis.• This technique allows for early and non-invasive detection of diabetes mellitus by AI.• AI may have broad applications in detecting pathological changes within muscle tissue.Graphical

Combing through museum collections. A “museomic” application of ZooMS

AbstractThis article presents the application of Zooarchaeology by Mass Spectrometry (ZooMS) to osseous Longobard artifacts from the collection of the “Musei Reali di Torino” (MRT; Torino, Italy). Like most archaeological items made of worked bone/antler in museum collections, the raw material of such specimens is usually attributed to deer, often without accurate taxonomic attribution. Therefore, the main aim of the present investigation was to shed light on taxonomical aspects using biomolecular approaches.We first examined the collagen preservation of the artifacts, then we compared three sampling methods (invasive, eraser‐based, and bag‐based), and we evaluated the quality of the collagen fingerprint obtained. Overall, we found a good, albeit not optimal, biomolecular preservation status, even in heavily restored objects coming from the 19th‐century collections.Out of 37 specimens analyzed through matrix‐assisted laser desorption ionization–time of flight–mass spectrometry (MALDI‐TOF‐MS) and nanoHPLC‐tandem MS, 31 yielded usable data. The results confirmed the widespread use of cervid as the osseous raw material for comb‐making in Longobard times in Piedmont, but we also found that bovine bones (Bos but also other taxa belonging to family Bovidae, such as caprines) and equid bones were exploited—demonstrating opportunistic use of animal resources. As far as the method is concerned, the ZooMS peptide markers useful to distinguish between bovids and cervids (m/z 1580 vsm/z 1550) (Buckley et al., 2009) are more frequently detected when analyzing bone chips, that is, with the invasive sampling method, rather than collagen extracts obtained using non‐invasive techniques. Nonetheless, the eraser method (eZooMS) seems to be a good trade‐off between invasivity and quality of the information obtained: eZooMS sampling does not leave visible marks on the object and therefore can contribute to facilitating the routine application of biomolecular methods in the daily practice of museum conservation laboratories. Indeed, an important outcome of the present study has been the establishment of a close collaboration between museum and biomolecular specialists.Taken together, our results suggest that the Longobards had a preference towards locally available resources, although this work did not highlight a clear association between raw material (deer, cattle, and other bovids) and object typology (in the case of combs) or function, except for buttons. The overall information obtained by this study confirms the potential of biomolecular approaches for reconstructing the biography of museum objects with a long and complex life and demonstrates the value of zooarchaeological study of museum collections.

Fully automated CT imaging biomarkers for opportunistic prediction of future hip fractures.

Assess automated CT imaging biomarkers in patients who went on to hip fracture, compared with controls. In this retrospective case-control study, 6926 total patients underwent initial abdominal CT over a 20-year interval at one institution. A total of 1308 patients (mean age at initial CT, 70.5 ± 12.0 years; 64.4% female) went on to hip fracture (mean time to fracture, 5.2 years); 5618 were controls (mean age 70.3 ± 12.0 years; 61.2% female; mean follow-up interval 7.6 years). Validated fully automated quantitative CT algorithms for trabecular bone attenuation (at L1), skeletal muscle attenuation (at L3), and subcutaneous adipose tissue area (SAT) (at L3) were applied to all scans. Hazard ratios (HRs) comparing highest to lowest risk quartiles and receiver operating characteristic (ROC) curve analysis including area under the curve (AUC) were derived. Hip fracture HRs (95% CI) were 3.18 (2.69-3.76) for low trabecular bone HU, 1.50 (1.28-1.75) for low muscle HU, and 2.18 (1.86-2.56) for low SAT. 10-year ROC AUC values for predicting hip fracture were 0.702, 0.603, and 0.603 for these CT-based biomarkers, respectively. Multivariate combinations of these biomarkers further improved predictive value; the 10-year ROC AUC combining bone/muscle/SAT was 0.733, while combining muscle/SAT was 0.686. Opportunistic use of automated CT bone, muscle, and fat measures can identify patients at higher risk for future hip fracture, regardless of the indication for CT imaging. CT data can be leveraged opportunistically for further patient evaluation, with early intervention as needed. These novel AI tools analyse CT data to determine a patient's future hip fracture risk.

Sources of bias in applying close-kin mark-recapture to terrestrial game species with different life histories.

Close-kin mark-recapture (CKMR) is a method analogous to traditional mark-recapture but without requiring recapture of individuals. Instead, multilocus genotypes (genetic marks) are used to identify related individuals in one or more sampling occasions, which enables the opportunistic use of samples from harvested wildlife. To apply the method accurately, it is important to build appropriate CKMR models that do not violate assumptions linked to the species' and population's biology and sampling methods. In this study, we evaluated the implications of fitting overly simplistic CKMR models to populations with complex reproductive success dynamics or selective sampling. We used forward-in-time, individual-based simulations to evaluate the accuracy and precision of CKMR abundance and survival estimates in species with different longevities, mating systems, and sampling strategies. Simulated populations approximated a range of life histories among game species of North America with lethal sampling to evaluate the potential of using harvested samples to estimate population size. Our simulations show that CKMR can yield nontrivial biases in both survival and abundance estimates, unless influential life history traits and selective sampling are explicitly accounted for in the modeling framework. The number of kin pairs observed in the sample, in combination with the type of kinship used in the model (parent-offspring pairs and/or half-sibling pairs), can affect the precision and/or accuracy of the estimates. CKMR is a promising method that will likely see an increasing number of applications in the field as costs of genetic analysis continue to decline. Our work highlights the importance of applying population-specific CKMR models that consider relevant demographic parameters, individual covariates, and the protocol through which individuals were sampled.

Reconsidering the patient: pauvres malades and malades pauvres in eighteenth-century medical contexts

Abstract When humoral medicine gave way to more scientific approaches in the nineteenth century, the physician’s gaze changed. Amongst the epistemological transformations were representations and perceptions of the poor patient: the pauvre malade—a pauper who happened to be sick and was to be taken care of by traditional medical charities—became a malade pauvre, a sick person who happened to be poor and was to be treated in new institutions. This article explores these changes but in settings beyond the clinic. In three case studies, we explore the practice of the Genevan physician Louis Odier, the management of the poor in the French spa of Saint-Amand and, finally, treatises about British spas. The agency of physicians, administrators and the poor themselves are analysed. Physicians played an ambiguous role. Some resisted innovation and refused to consider the poor as suffering from the same diseases as the affluent; others made opportunistic use of case histories, including those of the poor, to convince richer patients of the benefits of specific treatments, thereby reinforcing ‘class-blind’ ontological conceptions of disease. Administrators, for their part, tended to see the poor foremost as patients and put pressure on physicians to arrive at precise diagnoses.

Opportunistic Use of Computed Tomography to Determine Muscle-Adipose Ratio Reliably Predicts Wound Complications after Kocher-Langenbeck Surgical Exposure of the Acetabulum.

To determine whether muscle-to-adipose ratio (MAR) along the course of a Kocher-Langenbeck (KL) incision is more accurate at predicting post-operative wound complications following acetabuluar fixation than waist-hip ratio (WHR) or body mass index (BMI). Retrospective case series. Level-1 Trauma Center.Patients/Participants: Patients who sustained an acetabular fracture and had fixation through a KL approach. The calculation of the MAR. Presence of a surgical site infection or a wound healing complication. 193 patients were included in the study and mean follow-up was 17.4 months. Thirty (15.5%) patients developed a wound complication. Seventeen (8.8%) patients developed a superficial infection and thirteen (6.7%) developed a deep infection. Mean BMI for those who developed a wound complication was 35.9. The mean MAR was 0.67 for patients who developed a wound complication versus 0.75 for those who did not. ROC analysis showed an area under curve (AUC) for BMI to be .717 (95% CI, .577-.857, p = .006) and for MAR to be .680 (inverted, 95% CI .507-.854, p = .022). The AUC for WHR was not statistically significant. MAR is a significant predictor of post-operative wound complication in obese patients undergoing treatment of posterior wall acetabular fractures. The higher rate of wound complications in patients with a low MAR should be considered in the treatment of these patients and may be used to guide discussion regarding the risks of surgery, as well as the potential use of adjuncts to reduce wound healing complications. Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Analyzing the role of spectrum sensing for security enhancement in beyond 5G waveforms using energy detection

Spectrum sensing (SS) plays a pivotal role in bolstering the security of 5G waveforms. In this abstract, we explore how it contributes to safeguarding 5G networks. By continuously monitoring the radio environment, spectrum sensing allows for the early detection of unauthorized or malicious signals. Cognitive radio (CR) technology is a cutting-edge innovation that can alleviate the need for additional spectrum. It has the potential to influence the course of technology if applied effectively. A CR network allows secondary, unlicensed users to make opportunistic use of primary, licensed spectrum without negatively impacting primary, licensed transmission or customers. Spectrum sensing is the most crucial step in implementing opportunistic spectrum access. In the recent decade, many different spectrum sensing strategies have been proposed in the scientific literature. In this paper, the performance of energy detection-based cooperative spectrum sensing has been measured and visualized using MATLAB simulations.

RIS-Assisted Cooperative Time-Division Multiple Access.

Reconfigurable intelligent surface-aided communication systems have been intensively investigated to improve capacity, coverage, and energy efficiency via optimal controlling of phase shifts for passive reflecting elements. However, there are few studies on cooperative transmission incorporating RIS in TDMA systems, because RIS reflects all the incident signals, and it inadvertently leads to a boost in interference signals. In this paper, we propose RIS-assisted cooperative time-division multiple access, in which the required SINR of all users is guaranteed as much as possible by opportunistic use of RIS for cooperative transmission. The proposed scheme's primary function is that some time slots, i.e., cooperative time slots, serve a pair of users, i.e., a strong- and a weak-channel-conditioned user, using RIS. To support this functionality, we propose scheduling for non-cooperative and cooperative time slots, user pairing for scheduled cooperative time slots, and transmit beamforming vector design for the pair of UEs in each cooperative time slot. The simulation and numerical results demonstrate that the proposed scheme guarantees QoS for all UE as much as possible and minimizes the remaining required capacity indicating the amount of capacity that was not achieved compared with the required capacity.