Successful Application Research Articles

A Study of Talent Acquisition Lifecycle – A Review on Pyxidia Techlab

The research paper explores into the use of digital tools in talent acquisition, focusing on sourcing, screening, and shortlisting applicants. In today's digital age, recruitment has adopted a variety of digital tools, including video job descriptions (JDs) and resumes. These technologies make it possible to examine candidates efficiently, focusing on communication skills, personality, and cultural fit before inviting them to interviews. Keyword utilization speeds up applicant selection based on qualifications and experience. Following the initial screening, AI-driven questionnaires assess candidates' job-related skills and knowledge. This ensures that only the most competent candidates make it to HR and stakeholder interviews. These in-person or virtual interviews test interpersonal skills and problem-solving ability. Following completion, successful applicants go through onboarding, which includes team introductions and resource allocation for effective role performance. Talent acquisition has been altered by digital technologies like as video JDs, resumes, AI questions, and virtual interviews. This saves time and resources while also increasing productivity, employee satisfaction, and organizational success. Keywords— Talent Acquisition, Methodology, Stages, Challenges, E-Recruitment.

Application of artificial intelligence for the classification of the clinical outcome and therapy in patients with viral infections: The case of COVID-19.

With the end of the coronavirus disease 2019 (COVID-19) pandemic, it becomes intriguing to observe the impact of innovative digital technologies on the diagnosis and management of diseases, in order to improve clinical outcomes for patients. The research aims to enhance diagnostics, prediction, and personalized treatment for patients across three classes of clinical severity (mild, moderate, and severe). What sets this study apart is its innovative approach, wherein classification extends beyond mere disease presence, encompassing the classification of disease severity. This novel perspective lays the foundation for a crucial decision support system during patient triage. An artificial neural network, as a deep learning technique, enabled the development of a complex model based on the analysis of data collected during the process of diagnosing and treating 1000 patients at the Tešanj General Hospital, Bosnia and Herzegovina. The final model achieved a classification accuracy of 82.4% on the validation data set, which testifies to the successful application of the artificial neural network in the classification of clinical outcomes and therapy in patients infected with viral infections. The results obtained show that expert systems are valuable tools for decision support in healthcare in communities with limited resources and increased demands. The research has the potential to improve patient care for future epidemics and pandemics.

P24. Plastic Surgery Foundation (PSF) Funding Trends and Publication Metrics for PSF Grant Awardees: A Retrospective 10-year Analysis on Successful Applicants

P24. Plastic Surgery Foundation (PSF) Funding Trends and Publication Metrics for PSF Grant Awardees: A Retrospective 10-year Analysis on Successful Applicants

Lethal and sublethal effects of Citrus aurantium and Citrus sinensis essential oils and their major component limonene on Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae)

Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) is an insect pest with great socio-economic importance in different agricultural regions worldwide. This species is highly adaptive and polyphagous, feeding on wild and cultivated host plants. Successive pesticide applications, which negatively affect the environment, are mainly used for managing H. armigera. Studies have investigated alternative methods with low environmental and socioeconomic impacts, including using essential oils. Essential oils are secondary plant-derived metabolites with insecticidal activity in different insect pest populations. Thus, the objective of this study was to evaluate the insecticidal activity of essential oils of Citrus aurantium (Linnaeus) (Rutaceae) and Citrus sinensis (Linnaeus) (Rutaceae) as well as their major component limonene on the biology and reproduction of H. armigera. The essential oils were characterized using gas chromatography-mass spectrometry. Further, their toxicity and the effects of their lethal and sublethal concentrations in larvae and on biological and reproductive parameters in adults were evaluated. All treatments exhibited insecticidal activity and lethal and sub-lethal effects on the biology and reproduction of H. armigera. Therefore, these essential oils could be used to control H. armigera.

Long‐term successive biochar application increases plant lignin and microbial necromass accumulation but decreases their contributions to soil organic carbon in rice–wheat cropping system

AbstractBiochar application is widely recognized as an effective approach for increasing soil organic carbon (SOC) and mitigating climate change in agroecosystems. However, the effects of biochar application on net accumulations and relative contributions of different SOC sources remain unclear. Here, we explored the effects of biochar application on plant‐derived (PDC) and microbial necromass C (MNC) in a 10‐year experimental rice–wheat rotation field receiving four different intensities of biochar application (0, 2.25, 11.5, and 22.5 t ha−1 for each crop season), using phospholipid fatty acids (PLFAs), lignin phenols and amino sugars as biomarkers of microbial biomass, PDC and MNC, respectively. Our results showed that biochar application increased SOC content and stock by 32.6%–203% and 26.4%–145%, respectively. Higher biochar application (11.5 and 22.5 t ha−1) increased soil pH, total nitrogen (TN), total phosphorus (TP), SOC/TN, and root biomass. In addition, higher biochar application enhanced bacterial, fungal, and total microbial biomass. Plant lignin phenols and MNC contents significantly increased, whereas their contributions to SOC significantly decreased with the increase in biochar application rates due to the disproportionate increase in PDC and MNC, and SOC. Fungal necromass had a greater contribution to SOC than bacterial necromass. The fungal/bacterial necromass decreased from 2.56 to 2.26 with increasing biochar application rates, because of the higher abundances of bacteria than that of fungi as indicated by PLFAs under higher biochar application rates. Random forest analyses revealed that pH, TP, and SOC/TN were the main factors controlling plant lignin and MNC accumulation. Structural equation modeling revealed that biochar application increased lignin phenols by stimulating root biomass, whereas enhanced MNC accumulation was primarily from increased microbial biomass and lignin phenols. Overall, our findings suggest that biochar application increases the accumulation of the two SOC sources but decreases their contributions to SOC in paddy soils.

Modified calibration strategies and parameter regionalization potential for streamflow estimation using a hydrological model

ABSTRACT Calibration and regionalization are two key tasks in hydrological modelling, and the availability of increased data and computing resources has enhanced our ability to complete these tasks. This paper addresses both tasks, i.e. (i) it proposes novel model structure-based calibration strategies with the goal to improve estimation of high streamflows, and (ii) it explores the association of soil hydraulic properties with optimal parameters to estimate streamflows in ungauged catchments. Both tasks are demonstrated by employing a conceptual hydrological model, Sacramento Soil Moisture Accounting (SAC-SMA), on multiple catchments in the Narmada River Basin. The initial calibrated model exhibited good model performance, with high flows being underestimated. Seven calibration strategies via revising parameter space are explored, and improvement in high flow performance is observed at nine out of 12 catchments. Estimated streamflows based on soil hydraulic parameters demonstrate regionalization potential. The results indicate successful application of the proposed methods which are transferable to other basins.

Bacterial resistance to antimicrobial photodynamic therapy: A critical update

Bacterial antibiotic resistance is one of the most significant challenges for public health. The increase in bacterial resistance, mainly due to microorganisms harmful to health, and the need to search for alternative treatments to contain infections that cannot be treated by conventional antibiotic therapy has been aroused. An alternative widely studied in recent decades is antimicrobial photodynamic therapy (aPDT), a treatment that can eliminate microorganisms through oxidative stress. Although this therapy has shown satisfactory results in infection control, it is still controversial in the scientific community whether bacteria manage to develop resistance after successive applications of aPDT. Thus, this work provides an overview of the articles that performed successive aPDT applications in models using bacteria published since 2010, focusing on sublethal dose cycles, highlighting the main PSs tested, and addressing the possible mechanisms for developing tolerance or resistance to aPDT, such as efflux pumps, biofilm formation, OxyR and SoxRS systems, catalase and superoxide dismutase enzymes and quorum sensing.

A comprehensive review of advances in hepatocyte microencapsulation: selecting materials and preserving cell viability.

Liver failure represents a critical medical condition with a traditionally grim prognosis, where treatment options have been notably limited. Historically, liver transplantation has stood as the sole definitive cure, yet the stark disparity between the limited availability of liver donations and the high demand for such organs has significantly hampered its feasibility. This discrepancy has necessitated the exploration of hepatocyte transplantation as a temporary, supportive intervention. In light of this, our review delves into the burgeoning field of hepatocyte transplantation, with a focus on the latest advancements in maintaining hepatocyte function, co-microencapsulation techniques, xenogeneic hepatocyte transplantation, and the selection of materials for microencapsulation. Our examination of hepatocyte microencapsulation research highlights that, to date, most studies have been conducted in vitro or using liver failure mouse models, with a notable paucity of experiments on larger mammals. The functionality of microencapsulated hepatocytes is primarily inferred through indirect measures such as urea and albumin production and the rate of ammonia clearance. Furthermore, research on the mechanisms underlying hepatocyte co-microencapsulation remains limited, and the practicality of xenogeneic hepatocyte transplantation requires further validation. The potential of hepatocyte microencapsulation extends beyond the current scope of application, suggesting a promising horizon for liver failure treatment modalities. Innovations in encapsulation materials and techniques aim to enhance cell viability and function, indicating a need for comprehensive studies that bridge the gap between small-scale laboratory success and clinical applicability. Moreover, the integration of bioengineering and regenerative medicine offers novel pathways to refine hepatocyte transplantation, potentially overcoming the challenges of immune rejection and ensuring the long-term functionality of transplanted cells. In conclusion, while hepatocyte microencapsulation and transplantation herald a new era in liver failure therapy, significant strides must be made to translate these experimental approaches into viable clinical solutions. Future research should aim to expand the experimental models to include larger mammals, thereby providing a clearer understanding of the clinical potential of these therapies. Additionally, a deeper exploration into the mechanisms of cell survival and function within microcapsules, alongside the development of innovative encapsulation materials, will be critical in advancing the field and offering new hope to patients with liver failure.

Boundary-Aware Abstractive Summarization with Entity-Augmented Attention for Enhancing Faithfulness

With the successful application of deep learning, document summarization systems can produce more readable results. However, abstractive summarization still suffers from unfaithful outputs and factual errors, especially in named entities. Current approaches tend to employ external knowledge to improve model performance while neglecting the boundary information and the semantics of the entities. In this article, we propose an entity-augmented method (EAM) to encourage the model to make full use of the entity boundary information and pay more attention to the critical entities. Experimental results on three Chinese and English summarization datasets show that our method outperforms several strong baselines and achieves state-of-the-art performance on the CLTS dataset. Our method can also improve the faithfulness of the summary and generalize well to different pre-trained language models. Moreover, we propose a method to evaluate the integrity of generated entities. Besides, we adapt the data augmentation method in the FactCC model according to the difference between Chinese and English in grammar and train a new evaluation model for factual consistency evaluation in Chinese summarization.

ELMP-Net: The successive application of a randomized local transform for texture classification

This work proposes a method for texture classification based on the successive application of a local transform presented here for the first time. Such transform comprises two steps: (1) We built a two-layer mapping relating each pixel with its neighborhood, with the weights in the first layer randomly assigned; (2) We use the parameters learned by such mapping to transform the original image. Finally, we extract local descriptors at different stages of the successive application of this transform to compose the texture descriptors. The performance of our method is verified in the classification of benchmark texture databases and compared with state-of-the-art approaches. We also present an application for plant species identification. The results confirm our expectation that a model that is not based on the classical learning-based approach can still be competitive in texture analysis.

A profile of two different applicant test batteries with regards to sex and age in successful law enforcement applicants.

Law enforcement (LE) applicant assessments and initial academy training vary greatly across the United States of America (USA), with 820+ academies operating across 50 different states. Rising obesity and declining physical fitness underscore the need for benchmarks of applicant physical performance. Create a "point-in-time" descriptive profile of the performance of LE applicants and determine any differences between males, females, and age using two applicant test batteries (ATBs). Archival data from one large USA LE academy were analysed. Applicants completed one of two ATBs; ATB1 (n = 1674): 68.8-meter agility run (AR), 60-s push-ups, 60-s sit-ups, 60-s arm ergometer revolutions, and 2.4 km run; ATB2 (n = 355): AR, 60-s push-ups, 60-s sit-ups, and a multistage fitness test. Data were coded for sex and age. Independent samples t-tests compared the sexes. A one-way ANOVA with Bonferroni post hoc analysis compared age groups (18- 24, 25- 29, 30- 34, 35- 39, 40 + years). Males outperformed females in all fitness assessments across both ATBs (p≤0.02). The 18- 24 age group demonstrated faster run times in the AR and 2.4 km for ATB1 compared to all groups. (p≤0.03). In ATB2, the 18- 24 age group was faster in the AR compared to all other groups except the 25- 29 age group (p≤0.026). Regardless of ATB used, females and older applicants generally would benefit from specific fitness training to better prepare for academy. Older applicants may experience greater challenges in running tasks, especially those involving sprinting, which could also be impacted by qualities important for running/sprinting (i.e., maximal strength and power).

Rapamycin rescues loss of function in blood-brain barrier-interacting Tregs.

In autoimmunity, FOXP3+ Tregs skew toward a proinflammatory, nonsuppressive phenotype and are, therefore, unable to control the exaggerated autoimmune response. This largely affects the success of autologous Treg therapy, which is currently under investigation for autoimmune diseases, including multiple sclerosis (MS). There is a need to ensure in vivo Treg stability before successful application of Treg therapy. Using genetic fate-mapping mice, we demonstrate that inflammatory, cytokine-expressing exFOXP3 T cells accumulate in the CNS during experimental autoimmune encephalomyelitis. In a human in vitro model, we discovered that interaction with inflamed blood-brain barrier endothelial cells (BBB-ECs) induces loss of function by Tregs. Transcriptome and cytokine analysis revealed that in vitro migrated Tregs have disrupted regenerative potential and a proinflammatory Th1/17 signature, and they upregulate the mTORC1 signaling pathway. In vitro treatment of migrated human Tregs with the clinically approved mTORC1 inhibitor rapamycin restored suppression. Finally, flow cytometric analysis indicated an enrichment of inflammatory, less-suppressive CD49d+ Tregs in the cerebrospinal fluid of people with MS. In summary, interaction with BBB-ECs is sufficient to affect Treg function, and transmigration triggers an additive proinflammatory phenotype switch. These insights help improve the efficacy of autologous Treg therapy of MS.

The Effect of Clopidogrel Treatment on Osseointegration of Titanium Implants: A Histomorphometric Study in Rabbits.

To evaluate the effect of continuous perioperative clopidogrel treatment on the osseointegration of titanium implants. A total of 32 New Zealand rabbits were randomly divided between two groups: a clopidogrel group (n = 16) and a control group (n = 16). For 1 week prior to the surgical placement of a titanium implant in their medial femoral condyle, rabbits in the clopidogrel group received 3 mg/kg of clopidogrel daily, and the control group received only the vehicle. This treatment was continued for another 6 weeks postoperatively. At 6 weeks, the rabbits were euthanized and postmortem histologic and histomorphometric evaluation of the implants was performed. The surgical procedures and postoperative period were uneventful and well tolerated by all animals without any surgical wound dehiscence, signs of infection, or other complication. No implant failure was observed in any of the groups. Histomorphometric analysis showed that bone-to-implant contact (BIC) was 48.77% for the clopidogrel group and 34.65% for the control group, with statistically significant difference between them (P < .001). Moreover, clopidogrel group had significantly greater bone tissue density (40.52% vs 28.74%, respectively; P <.001) and mean trabecular thickness (284.7 μm vs 180.7 μm, respectively; P < .001) in proximity to the implant surface than the control group, while the mean trabecular number had no difference between groups (1.56 vs 1.60, respectively; P = .961). The present study showed that continuous clopidogrel treatment does not negatively affect osseointegration, but rather promotes it in terms of BIC and bone density around the titanium implants. Further studies on the effect of the P2Y12 receptor and its antagonists on peri-implant bone homeostasis may provide useful information or applications for long-term success of dental implant therapy.

Automatic Context-Aware Inference of Engagement in HMI: A Survey

Engagement is the process by which participants establish, maintain, and end their perceived connection. Automatic engagement inference is one of the tasks required to develop successful human-centered HMI applications. Engagement is a multi-faceted multimodal construct requiring high accuracy in interpretating contextual, verbal and non-verbal cues, making the development of an intelligent automated engagement inference system challenging. Existing surveys concentrate on specific application settings, and a comprehensive survey covering the different engagement facets, definition and inference across various contexts is lacking. Moreover, despite the importance of context-aware modeling, the literature lacks a systematic context-aware overview on the topic. This paper presents a comprehensive survey on previous work in engagement for HMI, entailing interdisciplinary definition, engagement components, publicly available datasets, ground truth assessment, and commonly used features and methods, serving as a guide for the development of future HMI interfaces with reliable context-aware engagement inference capability. An in-depth review across embodied and disembodied interaction modes, and an emphasis on the interaction context of which engagement is studied sets apart this survey from existing ones. Our findings suggest four important directions for future research: (1) context-aware computational modeling, (2) temporal dynamics, (3) personalised computing, and (4) bias and fairness of engagement inference systems.

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.

Divergent Component of Motion Planning and Adaptive Repetitive Control for Wearable Walking Exoskeletons.

Wearable walking exoskeletons show great potentials in helping patients with neuro musculoskeletal stroke. Key to the successful applications is the design of effective walking trajectories that enable smooth walking for exoskeletons. This work proposes a walking planning method based on the divergent component of motion to obtain a stable joint angle trajectory. Since periodic and nonperiodic disturbances are ubiquitous in the repeating walking motion of an exoskeleton system, a major challenge in the walking control of wearable exoskeleton is the joint angle drift problem, that is, the joint angle motion trajectories are not necessarily periodic due to the presence of disturbance. To address this challenge, this work develops an adaptive repetitive control strategy to guarantee that the motion trajectories of joint angle are repetitive. In particular, by treating the disturbance as system uncertainties, an adaptive controller is designed to compensate for the uncertainties based on an integral-type Lyapunov function. A fully saturated learning approach is then developed to achieve asymptotic tracking of repetitive walking trajectories. Extensive experiments are carried out to demonstrate the effectiveness of the tracking performance.

Unlocking Amides: A General Method for the Self-Immolative Release of Amide-Containing Molecules.

The controlled liberation of molecules from a constructed framework is a subject of profound interest across various chemical fields. It allows for the masking of a molecule's properties and precise deployment upon a single controllable release event. While numerous methodologies have been developed for amines, alcohols, and thiols, approaches for utilising amides as payload-release handles are still in their early stages of development, despite the prevalence of amides in therapeutic compounds and materials. Herein, is presented a comprehensive strategy for the controlled and selective release of a diverse range of amides with stable linkers. The versatility of this approach is demonstrated by its successful application in the targeted release of various amide-containing drugs in their natural form via the use of commonly used trigger motifs, such as dipeptides or glycosides. As a proof of concept, the FDA-approved antibiotic linezolid has been successfully converted into a prodrug form and released selectively only in the presence of the trigger event. Significantly, in its prodrug state, no activity against Mycobacterium tuberculosis was exhibited. Linezolid's full potential was achieved only upon controlled release, where an equipotent efficacy to the free linezolid control was demonstrated, thus emphasising the immense potential of this method.

Microstructure evolution and mechanical properties of new Co-free maraging steel produced by wire arc additive manufacturing

In this work, P-MIG is used as additive process, a new low-cost Co-free maraging steel wire is used as raw material to additive manufacturing block component, and the microstructure and mechanical properties of the component is investigated. The microstructure of the component is composed of martensite and austenite, and the closer to the bottom, the more serious the elemental segregation and the more austenite content. From the top to the bottom the high-angle grain boundaries increase and the grain size decrease, the difference of the heat dissipation condition and the thermal cycle are the reasons for this phenomenon. The texture of austenite is mainly Cube texture, and the texture of martensite is Rotated Cube texture and Goss texture. The tensile strength of XD, YD, ZD three directions is 1172 ± 15 MPa, 1021 ± 7 MPa, 1139 ± 16 MPa, respectively, impact toughness is 64.7 ± 1.6 J/cm2, 51.1 ± 5.6 J/cm2, 51.4 ± 3.1 J/cm2, respectively, TRIP effect occurs in the process of tensile, the main mode of fracture in tensile and impact is toughness fracture. The microhardness decreases gradually from bottom to top, which is due to the short-lived aging strengthening by thermal cycle, the change of grain size and GND density. The results of the work reveal the successful application of the new low-cost Co-free maraging steel wire in WAAM and provide an important reference for the adjustment of additive process, subsequent heat treatment and the adjustment of wire composition.

Selection of clusters based on internal indices in multi-clustering collaborative filtering recommender system

The successful application of a multi-clusteringbased neighborhood approach to recommender systems has led to increased recommendation accuracy and the elimination of divergence related to differences in clustering methods traditionally used. The Multi-Clustering Collaborative Filtering algorithm was developed to achieve this, as described in the author’s previous papers. However, utilizing multiple clusters poses challenges regarding memory consumption and scalability. Not all partitionings are equally advantageous, making selecting clusters for the recommender system’s input crucial without compromising recommendation accuracy. This article presents a solution for selecting clustering schemes based on internal indices evaluation. This method can be employed for preparing input data in collaborative filtering recommender systems. The study’s results confirm the positive impact of scheme selection on the overall recommendation performance, as it typically improves after the selection process. Furthermore, a smaller number of clustering schemes used as input for the recommender system enhances scalability and reduces memory consumption. The findings are compared with baseline recommenders’ outcomes to validate the effectiveness of the proposed approach.

Outage probability of cognitive radio-NOMA assisted unmanned aerial vehicles network

Unmanned aerial vehicles have been applied and play a critical role in radio surveillance because of their flexibility, mobility, and likely line-of-sight to ground destinations. Here, UAVs provide LoS links to users in the communication shadow of obstructive buildings. This paper investigates a cognitive radio network system model between unmanned aerial vehicles to transfer information from a licensed primary device to unlicensed users in a secondary network. In which PN users rely on non-orthogonal multiple access to prevent interference with each other. The primary objective of this study is to examine the outage probability of the secondary users with perfect and imperfect successive interference cancellation applications. Finally, we derive the users’ exact closed-form expressions and use Monte Carlo simulations to validate the analytical results.