bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the WHO standards to assess quality of care for children with acute respiratory infections: findings of a baseline multicentre assessment (CHOICE) in Italy
Authors: Lazzerini Marzia , Dagnelut Massimo , Dalena Paolo , Sforzi Idanna , Toniutti Maristella , Felici Enrico , Bressan Silvia , Trobia Gian Luca , Martelossi Stefano , Lubrano Riccardo , Fasoli Silvia , Marchetti Federico , Iuorio Andrea , Grisaffi Chiara , Galiazzo Silvia , Patanè Francesca , Stefani Chiara , Casciana Maria Luisa , Troisi Angela , Barbi Egidio
Published: August 2024

Background

Experience is lacking on the implementation of the WHO standards for improving the quality of care (QOC) for children at facility level. We describe the use of 10 prioritised WHO standard-based quality measures to assess provision of care for children with acute respiratory infections (ARI) in Italy.

Methods

In a multicentre observational study across 11 emergency departments with different characteristics, we collected 10 WHO standard-based quality measures related to case management of children with ARI and no emergency/priority signs. Univariate and multivariate analyses were conducted.

Results

Data from 3145 children were collected. Major differences in QOC across facilities were observed: documentation of saturation level and respiratory rate varied from 34.3% to 100% and from 10.7% to 62.7%, respectively (p<0.001); antibiotic prescription rates ranged from 22.6% to 80.0% (p<0.001), with significant differences in the pattern of prescribed antibiotic; hospitalisations rates ranged between 2.3% and 30.6% (p<0.001). When corrected for children’s individual sociodemographic and clinical characteristics, the variable more consistently associated with each analysed outcome was the individual facility where the child was managed. Higher rates of antibiotics prescription (+33.1%, p<0.001) and hospitalisation (+24.7%, p<0.001) were observed for facilities in Southern Italy, while university centres were associated with lower hospitalisation rates (−13.1%, p<0.001), independently from children’s characteristics.

Conclusions

The use of 10 WHO standard-based measures can help quickly assess QOC for children with ARI. There is an urgent need to invest more in implementation research to identify sustainable and effective interventions to ensure that all children receive high QOC.
Analysis of emergency resuscitative thoracotomy in the combat setting
Authors: Fisher Andrew David , April M D , Gurney J M , Shackelford S A , Luppens C , Schauer S G
Published: August 2024

Introduction

Emergency resuscitative thoracotomy (ERT) is a resource-intensive procedure that can deplete a combat surgical team’s supply and divert attention from casualties with more survivable injuries. An understanding of survival after ERT in the combat trauma population will inform surgical decision-making.

Methods

We requested all encounters from 2007 to 2023 from the Department of Defense Trauma Registry (DoDTR). We analysed any documented thoracotomy in the emergency department and excluded any case for which it was not possible to distinguish ERT from operating room thoracotomy. The primary outcome was 24-hour mortality.

Results

There were 48 301 casualties within the original dataset. Of those, 154 (0.3%) received ERT, with 114 non-survivors and 40 survivors at 24 hours. There were 26 (17%) survivors at 30 days. The majority were performed in role 3. The US military made up the largest proportion among the non-survivors and survivors. Explosives predominated in both groups (61% and 65%). Median Composite Injury Severity Scores were lower among the non-survivors (19 vs 33). Non-survivors had a lower proportion of serious head injuries (13% vs 40%) and thorax injuries (32% vs 58%). Median RBC consumption was lower among non-survivors (10 units vs 19 units), as was plasma (6 vs 16) and platelets (0 vs 3). The most frequent interventions and surgical procedures were exploratory thoracotomy (n=140), chest thoracostomy (n=137), open cardiac massage (n=131) and closed cardiac massage (n=121).

Conclusion

ERT in this group of combat casualties resulted in 26% survival at 24 hours. Although this proportion is higher than that reported in civilian data, more rigorous prospective studies would need to be conducted or improvement in the DoDTR data capture methods would need to be implemented to determine the utility of ERT in combat populations.
Successful management of pre-existing psoriatic arthritis through targeting the IL-23/IL-17 axis in cancer patients receiving immune checkpoint inhibitor therapy: a case series
Authors: Li Yuanteng Jeff , Msaouel Pavlos , Campbell Matthew , Hwu Patrick , Diab Adi , Kim Sang T.
Published: August 2024

Background

Immune checkpoint inhibitors (ICIs) have significantly improved outcomes for patients with cancer. However, these therapies are associated with adverse events including de novo immune-related adverse events or flare of pre-exiting autoimmune disorders. Up to 80% of patients with cancer and pre-existing psoriasis (PsO) or psoriatic arthritis (PsA) experience PsO/PsA flare after initiating ICIs. Targeting the interleukin (IL)-17/IL-23 axis is a mainstream of the PsO/PsA treatment. However, whether this treatment can effectively control PsO/PsA with ICI exposure while preserving anti-tumour efficacy remains unknown.

Case reports

We report three patients with PsA and cancer, who received ICIs for their cancer treatment. All patients were male. Two patients had clear cell renal cell carcinoma, and one had melanoma. Two patients received anti-PD-1 antibody monotherapy, while one patient received combined anti-CTLA-4 and PD-1 antibody therapy. One patient had been receiving anti-IL-17A antibody (secukinumab), while the other two patients started anti-IL-17A antibody (ixekizumab) and anti-IL-23 antibody (guselkumab) after their PsA flared up during ICI treatment. Of note, with the anti-IL-17A or anti-IL-23 antibody treatment, their PsA remained in remission, and they well tolerated the ICI therapy. Importantly, all three patients showed persistent tumour responses to ICI therapy, including two complete remissions and one stable disease, respectively.

Conclusions

These three cases suggest that targeting the IL-17/23 axis may be an effective and safe approach for patients with cancer and pre-existing PsA being considered for ICI therapy.
Entropy and radiative heat transfer analysis in water-based nanofluid flow with Catteneo–Christov heat flux
Authors: Sharma K. , Jindal R. , Goyal V. , Vijay N. , Duraihem Faisal Z.
Published: August 2024

This paper aims to investigate the thermal behavior of water-based nanofluid flow over a rotating surface, focusing on understanding the effects of different types of nanoparticles on thermal efficiency, considering Catteneo–Christov heat flux and variable viscosity effects. By considering four distinct nanoparticles — silicon dioxide, titanium dioxide, copper oxide and zinc oxide — this study aims to provide insights into how nanoparticle addition influences heat production, thermal boundary layer thickness and overall thermal performance. The study employs computational methods by utilizing the BVP Midrich algorithm for the solution procedure. The computational approach allows for a detailed investigation of the thermal behavior of nanofluid flows across a rotating surface under varying conditions. The study concludes that adding nanoparticles in the base liquid increases heat production in the system, resulting in enhanced thermal boundary layer thickness. The comparative analysis shows that different nanoparticle types exhibit varying effects on thermal efficiency, suggesting that careful selection of nanoparticles can optimize heat transport and thermal management processes. Moreover, it seems there’s a noteworthy downfall in the thermal profile concerning the relaxation time parameter, whereas a converse trend is observed for Biot number.
Bifurcation and Stability of a Haptotaxis Mathematical Model for Complex MAP
Authors: Chen Hong-Bing
Published: August 2024

This paper studies the bifurcation and stability of a parabolic–ordinary-parabolic system to a model with three reactive species: molecular Anchors A>, Nanoparticulates P and Matrix constituents M (MAP). Global asymptotic stability is studied for both the ODE system and the reaction–diffusion system. The results show that the positive equilibrium is globally asymptotically stable in both the ODE and the corresponding reaction–diffusion systems. Furthermore, by selecting the haptotaxis coefficient as a bifurcation factor, we analyze the existence, direction, and stability of the Hopf bifurcation. The results also reveal that haptotaxis is crucial in determining the stability and bifurcation behavior of the model, suggesting that it has a destabilizing effect and can undergo Hopf bifurcation. Moreover, numerical simulations are presented to verify and illustrate the theoretical results.
Bifurcations and Chaos in a Cantilever Beam Vibration Model with Small Damping and Periodic Forced Terms
Authors: Dai Yanfei , Li Jibin
Published: August 2024

For the cantilever beam vibration model with small damping and forced terms, under some special parameter conditions, the corresponding unperturbed system can be described by a planar dynamical system. In this paper, by using the dynamical system method to analyze the unperturbed differential system and find bifurcations from the corresponding phase portraits, the dynamical behavior can be derived. Exact explicit homoclinic and heteroclinic solutions, and periodic solution families can be found. For the periodic perturbation system, the chaotic behavior of system is revealed. Numerical simulations are carried out to illustrate the theoretical results.
Photodynamic activity of cationic ball-type zinc phthalocyanine in cell culture experiments with MCF-7 and MG63 cancer cells
Authors: Soylu Sinan , Bağda Efkan , Altun Ahmet , Bağda Esra , Kurt Atilla , Erden Fuat , Yabaş Ebru
Published: August 2024

Effective binding of cationic ball-type zinc phthalocyanine (BT-Pc) to G-quadruplex (G4) DNA molecules has already been demonstrated previously. This motivated us to investigate the PDT performance of BT-Pc in cell culture studies. Accordingly, the present work involves the application of BT-Pc to breast (MCF-7) and osteosarcoma (MG63) cancer cells, and also to healthy fibroblast cells (L929). Cell lines (CL) were irradiated at 640 nm wavelength, and the photodynamic activity was evaluated. The results show that cytotoxicity was significantly stronger in MCF-7 cells than in both L929 and MG63 cells (p < 0.05) in the absence of IPL irradiation. Also, we report that the application of l IPL provided significant photodynamic activity in MCF-7 and MG63 cells (p < 0.05). These results further suggest that BT-Pc might become a promising photosensitizer for future clinical trials.
Influence of Granular Medium Parameters on Poncelet Drag Coefficient
Authors: Sun Miao , Chi Runqiang , Zhang Hongyu , Wang Yufu , Xiao Junxiao , Cao Wuxiong , Pang Baojun , Jiang Shengyuan
Published: August 2024

The Poncelet equation can effectively characterize the resistance of a granular medium to a projectile. However, the determination of the drag coefficient remains challenging. This study aims to explore the potential effects of granular medium parameters on the drag coefficient and to identify key granular medium parameters. Horizontal ballistic tests and quasi-static penetrometer tests were conducted on dry silica sand of four different grain sizes (0.3–0.6 mm, 0.6–2.0 mm, 2.0–4.0 mm and 4.0–8.0 mm). During the ballistic tests, the bulk density of the sand targets was kept consistent, and the entering velocities and the penetration depth of the projectiles were recorded. The bearing strength of the sand targets with different relative densities (30%, 50% and 70%) was measured in the quasi-static tests. Furthermore, two methods were employed to determine the drag coefficients, namely, (1) single-parameter fitting (SF), where the static resistance term was defined as the average bearing strength and the drag coefficients were fitted using the ballistic test data, and (2) two-parameter fitting (TF), where the optimal drag coefficients and bearing strengths were derived from ballistic data. The results showed that the TF method was susceptible to the size effect, differing significantly from the SF method when the projectile diameter was less than 21 times the grain size. The relative density was identified by the SF method as a critical factor affecting the drag coefficient of the granular medium.
Heterostructured Nanocomposites of NiCo–S@Ni(OH)2 on Ni Foam for High-Performance Supercapacitors
Authors: Mo Junxiang , Li Jun , Li Jing , Li Yashan
Published: August 2024

The development of composite materials is a key area of research for enhancing the electrochemical performance of electrode materials. In this study, NiCo–S nanostructured hybrid electrode materials were prepared on nickel foam (NF) by using binary metal–organic frameworks (MOFs) as the sacrificial template through a two-step hydrothermal method. Ni–MOF was then deposited on the surface of NiCo–S/NF through the hydrothermal method and subsequently converted into Ni(OH)2 through a subsequent alkalization treatment. The duration of the Ni–MOF hydrothermal process was varied as a parameter. NiCo–S@Ni–MOF/NF was prepared through hydrothermal reaction for 6[Formula: see text]h, 9[Formula: see text]h and 12[Formula: see text]h, followed by heating in a 6[Formula: see text]M KOH solution at 75[Formula: see text]C for 6[Formula: see text]h in a water bath. The electrode materials with the best morphology and electrochemical properties were obtained when the hydrothermal reaction time was 6[Formula: see text]h. The optimized electrode had a specific capacity of 2533.2[Formula: see text]F[Formula: see text]g[Formula: see text] at 1[Formula: see text]A[Formula: see text]g[Formula: see text] and a capacity retention rate of 60.8% at 10[Formula: see text]A[Formula: see text]g[Formula: see text], which indicated good rate performance. Additionally, the electrode material demonstrated excellent cycling stability, with a capacity retention rate of 95.3% after 5000 cycles at 50[Formula: see text]mV[Formula: see text]s[Formula: see text]. The energy density of the hybrid supercapacitor device assembled from this electrode material was 33.5[Formula: see text]Wh[Formula: see text]kg[Formula: see text] when the power density was 849.89[Formula: see text]W[Formula: see text]kg[Formula: see text] at a current density of 1[Formula: see text]A[Formula: see text]g[Formula: see text].
Delineation of Shoreline and Associated Land Use/Land Cover Changes along the Coast of Chattogram, Bangladesh Based on Remote Sensing and GIS Techniques
Authors: MASUD Ibrahim , UDDIN Mohammad Muslem , LOODH Rupak
Published: August 2024

This study aimed at delineating the shoreline by using the Digital Shoreline Analysis System (DSAS 5.0) tool and detected the changes of land use/land cover (LULC) by Google Earth Engine (GEE) platform. The shoreline is divided into two zones, whereas Zone I covered 87.12[Formula: see text]km and Zone II possessed 168.05[Formula: see text]km. According to End Point Rate (EPR), the mean shoreline change rate of Zone I is 3.55[Formula: see text]m/year and Zone II is [Formula: see text]6.84[Formula: see text]m/year. Likewise, based on Linear Regression Rate (LRR), the mean shoreline change rate of Zone I is 5.46[Formula: see text]m/year and Zone II is [Formula: see text]4.71[Formula: see text]m/year, respectively. Apart from that, the Net Shoreline Movement (NSM) recorded in Zone I is 109.42[Formula: see text]m as well as Zone II is [Formula: see text]213.25[Formula: see text]m, which also revealed how much the shoreline has changed during the last 32 years. This study also used the Kalman filter model to forecast the shoreline positions for 20 years. The most destructive signal is that more than 70% of the coastline is vulnerable due to erosion, whereas 6% is highly vulnerable. By contrast, the results of LULC changes demonstrated the increasing trend of water bodies, built up, and agricultural land while vegetation along with bare land is reduced continuously. The overall accuracy is recorded above 88%, and the kappa co-efficient is found above 0.87 for all three years. The outcome of this study will provide fruitful insight into coastal land use management and adaptation measures against the ongoing along with future threats of shoreline changes to coastal ecosystems and livelihoods.
On the Importance of the Execution Schedule for Bayesian Inference
Authors: Wijnings Patrick W. A. , Roa-Villescas Martin , Stuijk Sander , de Vries Bert , Corporaal Henk
Published: August 2024

Bayesian inference is a probabilistic approach to the problem of drawing conclusions from observed data. Its main challenge is computational, which the Bayesian community tends to address through approximation techniques. However, these techniques come with their own set of challenges, including approximation errors, the difficulty of assessing these errors, and the inherited NP-hardness of the inference problem. Concurrently, in an effort to keep up with Moore’s law, the computer engineering community has developed an increasing number of programming techniques for today’s heterogeneous hardware. These techniques aim to optimize the
execution schedule
, which refers to the order and mapping of computations on the available execution units. In this work, we advocate for the utilization of these techniques to avoid a common pitfall known as
premature approximation
. Notably, these techniques have the potential to significantly enhance performance, thereby reducing the need for approximation and thus mitigating the challenges that accompany it. We first demonstrate how optimization of the storage strategy, i.e., when and where intermediate results are stored, allows for a trade-off between
runtime
and
peak memory usage
. We then investigate various techniques that aim to automatically generate efficient execution schedules. Finally, we focus on a specific, runtime-efficient execution schedule identified through design space exploration and compare its performance with that of two established solvers for probabilistic inference. The results show that our optimized implementation achieves speedups ranging from

\(2\times\)

to

\(316\times\)

for the UAI 2014 Promedus benchmark problems compared to the reference solvers. The ideas and methods presented in our study are examined within the framework of exact inference for discrete random variables. However, they are effectively applicable to scenarios involving continuous variables.
Tuning Deposition Conditions for VN Thin Films Electrodes for Microsupercapacitors: Influence of the Thickness
Authors: Lebreton Allan , Barbé Jérémy , Lethien Christophe , Coleman Jonathan N. , Brousse Thierry
Published: August 2024

Vanadium nitride is a highly promising material for micro-pseudocapacitors when used as a bifunctional thin film, i.e. an electrode material and a current collector, owing to its remarkable electrical and electrochemical properties. However, the specific limitations associated with high-rate cycling remain unclear. In this study, we evaluate how the characteristic time associated with charge/discharge of vanadium nitride films is modified with the film thicknesses using electrochemical impedance spectroscopy and cyclic voltammetry measurements coupled to a semi-empirical model commonly utilized to assess the high-rate behaviour of battery electrodes. Both methodologies are in good agreement and revealed that rate capability of this bi-functional material is limited by the VN electrical conductivity. To confirm this finding, VN thin films were sputtered on platinum current collectors, leading to a six-fold reduction in the characteristic time associated with charge/discharge of the current collectors/electrode material. This underscores the importance of using current collectors even for highly conductive electrode materials.
“I Never Thought I’d See the Day That I Would Be Doing Things on Zoom”: Reimagining Day Care for Older People and Unpaid Carers in the Context of COVID‐19
Authors: Cameron Ailsa M. , Bennett Laura , Willis Paul , Thorn Joanna , Patsios Demi , West Karen , Green Ruth , Hankins Simon
Published: August 2024

This paper considers the role of collective forms of day care for older people and their carers during the enforced closure of services in 2020 due to COVID‐19. The study provides a unique opportunity to examine how the sector adapted to these unprecedented times. The paper draws on qualitative data collected from 8 case studies conducted in England, during which 120 interviews were held with older people, their carers, staff, and managers of services and local stakeholders. The findings demonstrate how day centres reimagined their services to meet the needs of older people and their carers in new and imaginative ways. They offer insights into the potential role day care centres could play in the new place‐based partnerships envisaged in recent legislation. The study provides an opportunity to consider the importance of day care services in the light of their enforced closure, providing a more nuanced understanding of day care provision. The impact of COVID‐19 on the social care sector meant that many day care centres were not fully operational and consequently the samples may not reflect the experiences of all centres.
Carbapenem‐Resistant Burkholderia cepacia Complex Isolates Carrying blaNDM−1 and blaNDM−5 in Ventilator‐Associated Pneumonia Patients and Contaminated Ventilator Tubing
Authors: Saeed Muhammad , Rasheed Farhan , Rasool Muhammad Hidayat , Hayat Sumreen , Khurshid Mohsin
Published: August 2024

Ventilator‐associated pneumonia (VAP) represents an important nosocomial infection, frequently encountered in intensive care unit (ICU) settings which results in prolonged hospitals stays. The nosocomial infections caused by Burkholderia cepacia complex (BCC) bacteria pose a significant challenge in healthcare settings owing to their intrinsic resistance to many antibiotics. This study investigates the antimicrobial susceptibility patterns and mechanisms of carbapenem resistance among BCC bacteria from VAP patients and the ventilator tubing. The blood and respiratory specimens from patients diagnosed with VAP were collected. In addition, the ventilators were also screened for the presence of BCC bacteria. The susceptibility profiling of BCC isolates was performed against the various antimicrobial agents, and screening for acquired beta‐lactamase enzymes was conducted by polymerase chain reaction. Out of the total 134 patients with BCC‐associated VAP, B. cepacia, Burkholderia multivorans, and Burkholderia cenocepacia was 68.7% (n = 92), 18.7% (n = 25), and 12.7% (n = 17). Overall, the BCC isolates showed varying susceptibility to different antibiotics: 76.9% were susceptible to chloramphenicol, 76.1% to minocycline, 69.4% to meropenem, 60.4% to ceftazidime, 51.5% to trimethoprim‐sulfamethoxazole, and 50% to levofloxacin. Resistance to ceftazidime (51/92, 55.4%) and meropenem (36/92, 39.1%) was exclusively observed in B. cepacia isolates, and all isolates of B. multivorans and B. cenocepacia were found to be susceptible to both beta‐lactam drugs. Among the 134 clinical isolates, 15 were found to harbor the blaNDM variants, that is, blaNDM−1 and blaNDM−5. All carbapenem‐resistant isolates from the ventilator tubing were identified as B. cepacia and were found to harbor either the blaNDM−1 or the blaNDM−5 variants. The observed increase in resistance and the emergence of acquired beta‐lactamases among BCC isolates highlight a concerning trend that could potentially lead to serious outbreaks.
Exploring Acceptance Factors for Welfare Technology among Nurses in Non‐Clinical Care for Older Adults: A Scoping Review
Authors: Hvalič-Touzery Simona , Šetinc Mojca , Dolničar Vesna
Published: August 2024

Welfare technologies are becoming a necessity for health and long‐term care, but little is known about their acceptance by nurses working with older adults in institutional, home, or community setting. The aim of this five‐stage scoping review was to address this knowledge gap by identifying factors influencing nurses’ acceptance of welfare technologies, both before and after using them. A search of seven bibliographic databases and grey literature was conducted in December 2022, and based on defined selection criteria, 27 sources published between 2007 and 2022 were then considered. The data were examined with ATLAS.ti 9 using content‐based analysis, and five groups of acceptance factors were identified: individual, organisational, patient, technological, and social influence. Post‐use acceptance factors were found to be more nuanced than pre‐use factors. While the pre‐use factors included mainly attitudes, needs, outcome expectations, and ethical concerns, the post‐use factors more prominently featured experience, usefulness, technical issues, device characteristics, effort expectancy, and organisational factors. Nevertheless, patient‐centeredness was reflected in both. This study contributes to a better understanding of the factors affecting nurses’ acceptance of welfare technologies in the care of older adults in non‐clinical settings, and it underscores the patient’s pivotal role in all aspects of nursing practice. By offering insights into both pre‐use and post‐use factors, this study gives an enriched perspective on the acceptance of welfare technologies in nursing care and provides guidance for future research and practice.
Electrification of the Thermal Load in an Industrial Hall by Replacing Conventional Energy with Solar Energy
Authors: Felseghi Raluca-Andreea , Pleșa Angela , Popa Alexandru-Silviu
Published: August 2024

In the context of the European Energy Directives for the period 2020–2035 and the perspectives for 2050, the next generations of energy systems and associated technologies for the power supply of buildings will have a significant impact on pollution and greenhouse gas emissions mitigation, generating clean electricity with financial efforts in decrease. Until recently, the processes of burning conventional fossil fuels represented the main source of thermal energy, but along with technological development, new solutions have been implemented for providing thermal energy that works based on renewable energies, and the hypothesis of heating electrification based on alternative resources is feasible. The present research article addresses the topic of Electrification of the Thermal Load by Replacing Conventional Energy with Solar Energy within a case study with practical applicability to an existing industrial production hall. This study aims to assess, within a comparative analysis, the feasibility of replacing conventional energy fossil resources with solar energy resources, with the objective of decarbonization through electrification of the heating in industrial hall workspaces. In the case study, by implementing the solar generator that has the role of taking over the building thermal load, carbon dioxide emissions are reduced by 90% compared to the heat generation using conventional fossil energy, and the costs can be reduced by around of 70% compared to the current costs charged by economic operators in the domain.
Table 1 from Loss of STK11 Suppresses Lipid Metabolism and Attenuates KRAS-Induced Immunogenicity in Patients with Non–Small Cell Lung Cancer
Authors: Principe Daniel R. , Pasquinelli Mary M. , Nguyen Ryan H. , Munshi Hidayatullah G. , Hulbert Alicia , Aissa Alexandre F. , Weinberg Frank
Published: August 2024

Clinical and demographic characteristics of the patient cohort
Figure S4 from Loss of STK11 Suppresses Lipid Metabolism and Attenuates KRAS-Induced Immunogenicity in Patients with Non–Small Cell Lung Cancer
Authors: Principe Daniel R. , Pasquinelli Mary M. , Nguyen Ryan H. , Munshi Hidayatullah G. , Hulbert Alicia , Aissa Alexandre F. , Weinberg Frank
Published: August 2024

Figure S4. KRAS-induced CD8+ T-cell infiltration is unmodified by EGFR or BRAF status, but lost in LRP1B co-mutated tumors
Supplementary Figure 4 from KRAS Promotes GLI2-Dependent Transcription during Pancreatic Carcinogenesis
Authors: Sigafoos Ashley N. , Tolosa Ezequiel J. , Carr Ryan M. , Fernandez-Barrena Maite G. , Almada Luciana L. , Pease David R. , Hogenson Tara L. , Raja Arul Glancis L. , Mousavi Fatemeh , Sen Sandhya , Vera Renzo E. , Marks David L. , Flores Luis F. , LaRue-Nolan Kayla C. , Wu Chen , Bamlet William R. , Vrabel Anne M. , Sicotte Hugues , Schenk Erin L. , Smyrk Thomas C. , Zhang Lizhi , Rabe Kari G. , Oberg Ann L. , Zaphiropoulos Peter G. , Chevet Eric , Graham Rondell P. , Hagen Catherine E. , di Magliano Marina P. , Elsawa Sherine F. , Pin Christopher L. , Mao Junhao , McWilliams Robert R. , Fernandez-Zapico Martin E.
Published: August 2024

Supplementary Figure 4 shows the validation of Kras signaling activation and chronic pancreatitis phenotypic examples in KC and KCRG mice.
A Drug Discovery Pipeline for MAPK/ERK Pathway Inhibitors in Caenorhabditis elegans
Authors: Gorgoń Szymon , Billing Ola , Eriksson Anna U. , Hemmingsson Oskar
Published: August 2024

Oncogenic signaling through the MAPK/ERK pathway drives tumor progression in many cancers. Although targeted MAPK/ERK pathway inhibitors improve survival in selected patients, most tumors are resistant. New drugs could be identified in small-animal models that, unlike in vitro models, can address oral uptake, compound bioavailability, and toxicity. This requires pharmacologic conformity between human and model MAPK/ERK pathways and available phenotypic assays. In this study, we test if the conserved MAPK/ERK pathway in Caenorhabditis elegans could serve as a model for pharmacological inhibition and develop in vivo pipelines for high-throughput compound screens. Using fluorescence-based image analysis of vulva development as a readout for MAPK/ERK activity, we obtained excellent assay Z-scores for the MEK inhibitors trametinib (Z = 0.95), mirdametinib (Z = 0.93), and AZD8330 (Z = 0.87), as well as the ERK inhibitor temuterkib (Z = 0.86). The throughput was 800 wells per hour, with an average seed density of 25.5 animals per well. Readouts included drug efficacy, toxicity, and pathway specificity, which was tested against pathway activating upstream (lin-15)- and downstream (lin-1) mutants. To validate the model in a high-throughput setting, we screened a blinded library of 433 anticancer compounds and identified four MEK inhibitors among seven positive hits. Our results highlight a high degree of pharmacological conformity between C. elegans and human MAPK/ERK pathways, and the presented high-throughput pipeline may discover and characterize novel inhibitors in vivo.

Significance:

Many tumors depend on MAPK/ERK signaling to sustain growth, avoid cell death, and metastasize. We show that specific and clinically relevant MAPK/ERK signaling inhibitors can be discovered in vivo with a high-throughput screening pipeline in small animals.
Factors affecting MOOC and LMS acceptance in basic training of newcomer civil servants in Indonesia
Authors: Cahyawan Robby , Djunaedi Achmad , Subarsono Agustinus , Susilastuti Dewi Haryani
Published: August 2024

Pelatihan dasar calon pegawai negeri sipil (Latsar CPNS) is basic training for newcomer civil servants that must be followed during the pre-service period. The coronavirus disease 2019 (COVID-19) outbreak has caused face-to-face learning in the classroom to be canceled, so the learning process in the training organization must be replaced with a learning process using massive open online course (MOOC) and learning management system (LMS) with a distance learning system. This study used a modified form of the unified theory of acceptance and use of technology (UTAUT) model framework. The core factors in the UTAUT framework called facilitating conditions, will be divided into two factors. The two factors are the availability of infrastructure and devices, and internet capability (IC). The respondents of this study are newly recruited civil servants at the Ministry of Transportation of the Republic of Indonesia with 400 respondents used in the analysis process. We found that performance expectation (PE), effort expectation (EE), social influence (SI), and self-efficacy (SE) affect student behavior (SB). In addition, SI and IC affect SE. Meanwhile, the relationship between infrastructure and device availability (IDA) with SE has an insignificant result. In improving Latsar CPNS services, training organizations should pay attention to several factors that can influence SB.
An innovative machine learning optimization-based data fusion strategy for distributed wireless sensor networks
Authors: Sollapure Naganna Shankar , Govindaswamy Poornima
Published: August 2024

Self-sufficient sensors scattered over different regions of the world comprise distributed wireless sensor networks (DWSNs), which track a range of environmental and physical factors such as pressure, temperature, vibration, sound, motion, and pollution. The use of data fusion becomes essential for combining information from various sensors and system performance. In this study, we suggested the multi-class support vector machine (SDF-MCSVM) with synthetic minority over-sampling techniques (SMOTE) data fusion for wireless sensor network (WSN) performance. The dataset includes 1,334 instances of hourly averaged answers for 12 variables from an AIR quality chemical multisensor device. To create a balanced dataset, the unbalanced data was first pre-processed using the SMOTE. The grey wolf optimization (GWO) approach is then used to reduce features in an effort to improve the efficacy and efficiency of feature selection procedures. This method is applied to classify the fused feature vectors into multiple categories at once to improve classification performance in WSNs and address unbalance datasets. The result shows the proposed method reaches high precision, accuracy, F1-score, recall, and specificity. The computational complexity and processing time were decreased in the study by using the proposed method. This is great potential for accurate and timely data fusion in dispersed WSNs with the successful integration of data fusion technologies.
An improved student’s facial emotions recognition method using transfer learning
Authors: Rajae Amimi , Amina Radgui , El Hassane Ibn El Haj
Published: August 2024

Instructors endeavour to encourage active participation and interaction among learners. However, in settings with a large number of students, such as universities or online platforms, obtaining real-time feedback and evaluating teaching methodology presents a significant challenge. In this paper, we introduce a student engagement recognition system based on a hybrid method using handcrafted features and transfer learning. The research is conducted on two databases for emotion detection based on facial cues (FER13) benchmarked dataset and our database. We use the local binary patterns (LBP) method combined with pre-trained MobileNet model for feature extraction and classification. The proposed system adeptly discerns students’ facial expressions and categorizes their engagement states as either ‘engaged’ or ‘disengaged’. We determine the most effective model by evaluating and comparing several deep learning models, including Inception-V3, VGG16, EfficientNet, ResNet, and DenseNet. Experimental results underscore the efficacy of our approach, revealing a remarkable accuracy, surpassing benchmarks set by state-of-the-art models.
Improving imbalanced class intrusion detection in IoT with ensemble learning and ADASYN-MLP approach
Authors: Soni Soni , Remli Muhammad Akmal , Mohd Daud Kauthar , Al Amien Januar
Published: August 2024

The exponential growth of the internet of things (IoT) has revolutionized daily activities, but it also brings forth significant vulnerabilities. intrusion detection systems (IDS) are pivotal in efficiently detecting and identifying suspicious activities within IoT networks, safeguarding them from potential threats. It proposes a ensemble approach aimed at enhancing model performance in such scenarios. Recognizing the unique challenges posed by imbalanced class distribution, the research employs three sampling techniques LightGBM adaptive synthetic sampling (ADASYN) with multilayer perceptron (MLP), XGBoost ADASYN with MLP, and LightGBM ADASyn with XGBoost to address class imbalance effectively. Evaluation confusion matrix performance metrics underscores the efficacy of ensemble models, particularly LightGBM ADASYN with MLP, XGBoost ADASYN with MLP, and LightGBM ADASYN with XGBoost, in mitigating imbalanced class issues. The LightGBM ADASYN with MLP model stands out with 99.997% accuracy, showcasing exceptional precision and recall, demonstrating its proficiency in intrusion detection within minimal false positives negatives. Despite computational demands, integrating XGBoost within ensemble frameworks yields robust intrusion detection results, highlighting a balanced trade-off between accuracy, precision, and recall. This research offers valuable insights into the strengths with different ensemble models, significantly contributing to the advancement of accurate and reliable IDS in realm of IoT.
Exploring the potential of DistilBERT architecture for automatic essay scoring task
Authors: Ikiss Soumia , Daoudi Najima , Abourezq Manar , Bellafkih Mostafa
Published: August 2024

Automatic assessment of writing essays, or the process of using computers to evaluate and assign grades to written text, is very needed in the education system as an alternative to reduce human burden and time consumption, especially for large-scale tests. This task has received more attention in the last few years, being one of the major uses for natural language processing (NLP). Traditional automatic scoring systems typically rely on handcrafted features, whereas recent studies have used deep neural networks. Since the advent of transformers, pre-trained language models have performed well in many downstream tasks. We utilize the Kaggle benchmarking automated student assessment prize dataset to fine-tune the pre-trained DistilBERT in three different scenarios, and we compare results with the existing neural network-based approaches to achieve improved performance in the automatic essay scoring task. We utilize quadratic weighted Kappa (QWK) as the main metric to evaluate the performance of our proposed method. Results show that fine-tuning DistilBERT gives good results, especially with the scenario of training all parameters, which achieve 0.90 of QWK and outperform neural network models.
Enhancing network security using unsupervised learning approach to combat zero-day attack
Authors: Perumal Rajakumar , Karuppiah Tamilarasi , Panneerselvam Uppiliraja , Annamalai Venkatesan , Kaliyaperumal Prabu
Published: August 2024

Machine learning (ML) and advanced neural network methodologies like deep learning (DL) techniques have been increasingly utilized in developing intrusion detection systems (IDS). However, the growing quantity and diversity of cyber-attacks pose a significant challenge for IDS solutions reliant on historical attack signatures. This highlights the industry's need for resilient IDSs that can identify zero-day attacks. Current studies focusing on outlier-based zero-day detection are hindered by elevated false-negative rates, thereby constraining their practical efficacy. This paper suggests utilizing an autoencoder (AE) approach for zero-day attack detection, aiming to achieve high recall while minimizing false negatives. Evaluation is conducted using well-established IDS datasets, CICIDS2017 and CSECICIDS2018. The model's efficacy is demonstrated by contrasting its performance with that of a one-class support vector machine (OCSVM). The research underscores the OCSVM's capability in distinguishing zero-day attacks from normal behavior. Leveraging the encoding-decoding capabilities of AEs, the proposed model exhibits promising results in detecting complex zero-day attacks, achieving accuracies ranging from 93% to 99% across datasets. Finally, the paper discusses the balance between recall and fallout, offering valuable insights into model performance.
Hybrid logistic regression support vector model to enhance prediction of bipolar disorder
Authors: Agnihotri Nisha , Prasad Sanjeev Kumar
Published: August 2024

Bipolar disorder has become one of the major mental health issues due to stressed life around the world. This is the major reason for suicides these days as these people are unable to convey their feeling and emotions to others. This proposed research shows the logistic regression and support vector machine hybrid model to predict bipolar disorder in patients is to develop an accurate and reliable model that can effectively predict the presence of bipolar disorder in patients based on their clinical and demographic data. The purpose is to make a framework that can help healthcare professionals diagnose bipolar disorder early, thereby enabling timely and appropriate treatment to be provided. The model should take into account various patient-specific features, such as age, gender, family history, medication use, and other medical conditions, in addition to relevant clinical and demographic variables. The aim is to create a model that can accurately classify patients with bipolar disorder and non-bipolar disorder patients while minimizing false-positive and false-negative classifications. The work shows improvement in evaluation detection in performance with hybrid logistic support vector regression (LSVR) to detect disorder and protect them to avoid worse situation.
Auto digitization of aerial images to map generation from UAV feed
Authors: Kannan Raju Jagadeesh , Yadav Karunesh Pratap , Sreedevi Balasubramanian , Chelliah Jehan , Muthumarilakshmi Surulivelu , Jeyapriya Jeyaprakash , Murugan Subbiah
Published: August 2024

Nowadays the rapid growth of unmanned aerial vehicles (UAVs) bridges the space between worldly and airborne photogrammetry as well as allow flexible acquisition of great solution images. In the case of natural disasters such as floods, tsunamis, earthquakes, and cyclones, their effects are most often felt in the micro-spaces and urban environments. Therefore, rescuers have to go around to get to the victims. This paper presents an auto digitization of aerial images to map generation from UAV feed at night time. In case of a power outage and an absence of alternative light sources, rescue operations are also slowed due to the darkness caused by the lack of electricity and the inability to light additional sources. In other words, to save lives, we need to know about all essential large-scale feature spaces in the dark so that we can use this information in times of disaster. The research proposed a soft framework for crisis mapping to aid in mapping the state of the aerial landscape in disaster-stricken areas, allowing strategic rescue operations to be more effectively planned.
Comparative efficiency analysis of RF power amplifiers with fixed bias and envelope tracking bias
Authors: Babu Ambily , Shivaleelavathi Bangalore Gangadharaiah , Yatnalli Veeramma
Published: August 2024

RF power amplifier (RF PA) finds its application in almost all the areas of electronics, mobile communication being identified as a major area. The paper performs a comparative efficiency analysis of RF power amplifiers operating with a fixed bias and an envelope tracking bias. Simulations are performed using Keysight advanced design system (ADS) tool. A class a RF PA operating at a 12 dB gain is fixed for the work. 16 QAM LTE signal operating at 5 MHz input frequency, with a peak to average power ratio (PAPR) of 6.0 dB is used as input signal. An envelope simulation at 2.5 GHz is performed on the RF power amplifier. Simulation result shows an improvement of 12% in power added efficiency (PAE) at 6 dB back-off and 6.422% in mean PAE while using envelope tracking power amplifiers, compared to RF PA with fixed supply. Envelope tracking power amplifiers reduced AM/AM distortions also by a factor of 0.248. The results obtained are much better than that obtained using a conventional RF PA with fixed bias. RF PA being the most power dissipative block in a mobile handset, improving its efficiency contributes directly to a great improvement in the battery lifetime of mobile phones. The major challenges faced by envelope tracking PA (ETPA) designers in achieving this efficiency improvement is also delineated in the paper.
Industrial process optimization through advanced HMI systems: exploring the integration of IoT and AI
Authors: Arce Santillan Dora Yvonne , Nolasco Sandoval Luis Alfredo , Martinez Santillán Albert Isaac , Avalos Yataco Percy Jesús , Presentación Quispe Higmmer Santiago , Hercilla Huapaya Nelson Rene
Published: August 2024

Facing the challenge of improving efficiency and stability in industrial processes, this study examines the impact of implementing advanced human-machine interface (HMI) systems, complemented by the internet of things (IoT) and artificial intelligence (AI). The integration of a PLC and HMI-controlled system has resulted in a 22.85% increase in efficiency, stabilizing production and reducing process variability. Tools such as PLCSIM and TIA PORTAL were crucial for validating control logic and programming. Additionally, the study explores the potential of AI and IoT to amplify these benefits, suggesting a significant advancement in automation that could transform operational efficiency and quality in related industries. These findings provide a relevant framework for companies looking to integrate emerging technologies into their operations, promoting continuous improvement and more informed management.
Inset-fed microstrip patch antenna optimization for 2.4 GHz using surrogate model assisted differential evolution machine learning algorithm
Authors: Alaba Magnoudewa , Onyango Konditi Dominic Bernard , Oduol Vitalice Kalecha
Published: August 2024

In this work, we have used the surrogate model assisted differential evolution (SADEA) to model a one and two-element inset-fed patch antenna array to optimize its parameters for efficiency and usability. The microstrip patch antennas operates in a frequency band of 2.4 GHz. The optimization process focused on fine-tuning the patch length, patch width, and notch width to enhance key performance metrics directivity, return loss, and bandwidth. The design is made in CST software with an FR-4 substrate and simulated in the ADE1.0 software a MATLAB toolbox. Significant enhancements were achieved including a directivity gain of 3.04 dB, and 5.58 dB a return loss of -19 dB, -16 dB, and an expanded impedance bandwidth from 0.0798 GHz, 0.0588 GHz to 0.0951 GHz, 0.0824 GHz respectively. The antenna was constructed and then measured. The findings showed that the measurements and the fabrication process closely matched, especially in terms of return loss.
Teaching Araling Panlipunan: A Study of Challenges and Coping Mecha-nisms among Non-Social Studies Major Educators
Authors: David Rowena May T.
Published: August 2024

This research focused on the challenges and coping mechanisms of Non-Social Studies major teachers assigned to teach Araling Panlipunan in Bulacan, Philippines. This, therefore, makes the pressing call for comprehensive content knowledge, effective assessment strategies, collaborative approaches to teaching, and continuous professional development in order to fill the gap in expertise in teaching Philippine history, culture, and society. Key findings revealed that teachers belonging to the Non-Social Studies major often experience problems in their teaching brought about by insufficient subject-specific knowledge, which consequently affects their confidence and accuracy in content delivery. In this regard, it is proposed in the study a program on "Capacitating the Competence of the Non-Social Studies Major Teachers." This program seeks to enhance teaching competencies with focused interventions that equip teachers with necessary competencies and knowledge. These recommendations include a vertical professional learning community, teaching aids bank, and focused seminars and training programs. The study reinforces continuous learning and institutional support as ways to educational excellence and calls for future research into th impact of advanced degrees on teaching effectiveness.