Abstract: Accurate clinical diagnosis of patients presenting to primary care settings with acute sore throat remains challenging, often resulting in the over-prescribing of antibiotics. Using point-of-care tests (POCTs) to differentiate between respiratory infections is well-accepted, yet evidence on the application within primary care is sparse. We assessed the application of testing patients (n = 160) from three family practices with suspected Streptococcal infections using rapid molecular tests (ID NOW Strep A2, Abbott). In addition to comparing clinical evaluation and prescription rates with either usual care or testing, patients and staff completed a questionnaire about their experience of molecular POCT in primary care. The immediate availability of the result was important to patients (100%), and staff (≈90%) stated that molecular testing improved the quality of care. Interestingly, only 22.73% of patients with a Centor score > 2 tested positive for Strep A and, overall, less than 50% of Centor scores 3 and 4 tested positive for Strep A with the ID NOW testing platform. The addition of rapid molecular POCTs to clinical assessment resulted in a 55–65% reduction in immediate and deferred antibiotic prescriptions. The intervention was popular with patients and medical staff but was associated with increased cost and a longer appointment length. Full article

Additive manufacturing has advantages over other traditional manufacturing technologies for the fabrication of complex thin-walled parts. Previous correlation path strategies, when applied to laser metal deposition processes, suffer from contour deposition transboundary and surface “scar” type overstacking. Therefore, this paper proposes a hybrid path generation method for the laser metal deposition process. First, the topological logic of the STL model of the part is restored to reduce redundant calculations at the stage of obtaining the layered contour. Then, the path points are planned on the basis of the offset contours in a helical upward trend to form a globally continuous composite path in space considering the melt channel width. Finally, vectors that adaptively fit to the model surface are generated for the path points as tool orientations and they are optimized by smoothing the rotation angles. The results of experiments conducted on a multi-axis machine equipped with a laser metal deposition module show that the path generated by the proposed method is not only capable of thin-walled structures with overhanging and curved surface features but also improves the surface imperfections of the part due to sudden changes in the angle of rotation while ensuring the boundary dimensions. Full article

The advancement towards utilizing renewable energy sources is crucial for mitigating environmental issues such as air pollution and climate change. Offshore wind turbines, particularly floating offshore wind turbines (FOWTs), are developed to harness the stronger, steadier winds available over deep waters. Accurate metocean data forecasts, encompassing wind speed and wave height, are crucial for offshore wind farms’ optimal placement, operation, and maintenance and contribute significantly to FOWT’s efficiency, safety, and lifespan. This study examines the application of three machine learning (ML) models, including Facebook Prophet, Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX), and long short-term memory (LSTM), to forecast wind speeds and significant wave heights, using data from a buoy situated in the Pacific Ocean. The models are evaluated based on their ability to predict 1-, 3-, and 30-day future wind speed and wave height values, with performances assessed through Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics. Among the models, LSTM displayed superior performance, effectively capturing the complex temporal dependencies in the data. Incorporating exogenous variables, such as atmospheric conditions and gust speed, further refined the predictions.The study’s findings highlight the potential of machine learning (ML) models to enhance the integration and reliability of renewable energy sources through accurate metocean forecasting. Full article

The identification of co-regulated genes and their Transcription-Factor Binding Sites (TFBSs) are the key steps toward understanding transcription regulation. In addition to effective laboratory assays, various bi-clustering algorithms for the detection of the co-expressed genes have been developed. Bi-clustering methods are used to discover subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. By building two fuzzy partition matrices of the gene expression data with the Axiomatic Fuzzy Set (AFS) theory, this paper proposes a novel fuzzy bi-clustering algorithm for the identification of co-regulated genes. Specifically, the gene expression data are transformed into two fuzzy partition matrices via the sub-preference relations theory of AFS at first. One of the matrices considers the genes as the universe and the conditions as the concept, and the other one considers the genes as the concept and the conditions as the universe. The identification of the co-regulated genes (bi-clusters) is carried out on the two partition matrices at the same time. Then, a novel fuzzy-based similarity criterion is defined based on the partition matrices, and a cyclic optimization algorithm is designed to discover the significant bi-clusters at the expression level. The above procedures guarantee that the generated bi-clusters have more significant expression values than those extracted by the traditional bi-clustering methods. Finally, the performance of the proposed method is evaluated with the performance of the three well-known bi-clustering algorithms on publicly available real microarray datasets. The experimental results are in agreement with the theoretical analysis and show that the proposed algorithm can effectively detect the co-regulated genes without any prior knowledge of the gene expression data. Full article

Unbonded flexible risers consist of several helical and cylindrical layers, which can undergo large bending deformation and can be installed in different configurations to adapt to harsh marine environments; thus, they can be applied to transport oil and gas resources from ultra-deep waters (UDW). Due to their special geometric characteristics, they can ensure sufficient axial tensile stiffness while having small bending stiffness, which can undergo large deflection bending deformation. In recent years, the development of unbonded flexible risers has been moving in an intelligent, integrated direction. This paper presents a review of unbonded flexible risers. Firstly, the form and properties of each interlayer of an unbonded flexible riser are introduced, as well as the corresponding performance and configuration characteristics. In recent years, the development of unbonded flexible risers has been evolving, and the development of machine learning on unbonded flexible risers is discussed. Finally, with emphasis on exploring the design characteristics and working principles, three new types of unbonded flexible risers, an integrated production bundle, an unbonded flexible riser with an anti-H₂S layer, and an unbonded flexible riser with a composite armor layer, are presented. The research results show that: (1) the analytical methods of cross-sectional properties of unbonded flexible risers are solved based on ideal assumptions, and the computational accuracy needs to be improved. (2) Numerical methods have evolved from equivalent simplified models to models that account for detailed geometric properties. (3) Compared with ordinary steel risers, the unbonded flexible riser is more suitable for deep-sea resource development, and the structure of each layer can be designed according to the requirements of the actual environment. Full article

Abstract: In this study, we present a novel dual-loop robust trajectory tracking framework for autonomous underwater vehicles, with the objective of enhancing their performance in underwater searching tasks amidst oceanic disturbances. Initially, a real-world AUV experiment is conducted to validate the efficacy of a cross-rudder AUV configuration in maintaining sailing angle stability during the diving stage, which exhibits a strong capability for straight-line sailing. Building upon the experimental findings, we introduce a state-transform-model predictive guide law to compute the desired velocity for the dynamics loop. This guide law dynamically adjusts the controller across varying depths, thereby reducing model predictive control (MPC) computation while optimizing timing without compromising precision or convergence speed. Subsequently, we incorporate a sliding mode controller with a prescribed disturbance observer into the velocity control loop to concurrently enhance the robustness and convergence rate of the system. This innovative amalgamation of controllers significantly improves tracking precision and convergence rate, while also alleviating the computational burden—a pervasive challenge in AUV MPC control. Finally, various condition simulations are conducted to validate the robustness, effectiveness, and superiority of the proposed method. These simulations underscore the enhanced performance and reliability of our proposed trajectory tracking framework, highlighting its potential utility in real-world AUV applications. Full article

Amid the deepening imbalance in the supply and demand of allogeneic organs, xenotransplantation can be a practical alternative because it makes an unlimited supply of organs possible. However, to perform xenotransplantation on patients, the source animals to be used must be free from infectious agents. This requires the breeding of animals using assisted reproductive techniques, such as somatic cell nuclear transfer, embryo transfer, and cesarean section, without colostrum derived in designated pathogen-free (DPF) facilities. Most infectious agents can be removed from animals produced via these methods, but several viruses known to pass through the placenta are not easy to remove, even with these methods. Therefore, in this narrative review, we examine the characteristics of several viruses that are important to consider in xenotransplantation due to their ability to cross the placenta, and investigate how these viruses can be detected. This review is intended to help maintain DPF facilities by preventing animals infected with the virus from entering DPF facilities and to help select pigs suitable for xenotransplantation. Full article

Data are the core element of digital transformation. Data factor marketization (DFM) is critical in the process of digital transformation, which promotes urban green development. This paper analyzes the role of digitization in urban environmental sustainability. We investigate the effects of DFM on environmental pollution (EP) using a difference-in-differences approach and data from 283 cities in China from 2006 to 2019. The findings reveal that cities implementing DFM demonstrate an average reduction in EP of 2.67%. The mechanism behind DFM lowering EP involves fostering green innovation, increasing public awareness of environmental issues, attracting IT professionals, optimizing the industrial structure, and enhancing digital finance capabilities. Large cities, cities in the south, and those not primarily dependent on natural resources exhibit a more pronounced reduction in EP through DFM. Implementing policies related to digital infrastructure and enhancing the protection of urban intellectual property rights further amplifies the effect of DFM in reducing pollution. Additionally, this effect exhibits spatial spillover effects. This study contributes to the existing literature by (1) demonstrating DFM’s role in improving urban environmental quality in China through digital technology and market mechanisms, thereby aligning economic growth with ecological sustainability; (2) emphasizing the importance of public engagement in environmental stewardship through increased awareness and community participation in policymaking, as well as fostering social inclusion and ecological conservation; (3) emphasizing spatial spillover effects, the importance of inter-city collaboration in environmental policies, and advocating for comprehensive strategies to achieve broader environmental improvements across urban areas. Full article

Abstract: This study reports the synthesis of Ni-Co-SiC coatings onto Q235A steel substrates through magnetic-field-induced electrodeposition to improve the surface performances of the machine parts. The microstructure, topology, roughness, corrosion, and wear resistances of the coatings were investigated through X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), atomic force microscopy (AFM), hardness testing, electrochemical analysis, and friction wear testing, respectively. The Ni-Co-SiC coating deposited at 0.4 T (MS1) with a perpendicular magnetic direction showed the maximum SiC content and NiCo grain size (86.5 nm). The surface topology was also fine, dense, and smooth. In addition to that, the images obtained from the AFM characterization showed that the surface roughness of the MS1 coating was 76 nm, which was significantly lower compared to the roughness observed in Ni-Co-SiC coatings fabricated under the magnetic induction of 0 T (MS0) and magnetic field applied in a parallel direction to 0.4 T (MS2). The XRD results revealed that the preferential growth direction of the NiCo grains was changed from the (200) crystal plane to the (111) plane with the introduction of a perpendicular magnetic field. Moreover, MS2, MS1, and MS0 had thickness values of 25.3, 26.7, and 26.3 μm, respectively. Among all the coatings, MS1 showed the lowest friction coefficient and the highest hardness value (914.8 HV), suggesting enhanced wear resistance. Moreover, the MS1 coating revealed a maximum corrosion potential of −257 mV, and the lowest corrosion current of 0.487 μA/cm², suggesting its improved corrosion resistance. Full article

Type 2 diabetes mellitus (T2DM) management and glycemic control in underserved non-Hispanic Black adults presents with multifaceted challenges: balancing the optimal complexity of antihyperglycemic medications prescribed, limited medication access due to socioeconomic status, medication nonadherence, and high prevalence of cardiometabolic comorbidities. This single-center, cross-sectional, retrospective chart analysis evaluated the association of Medication Regimen Complexity (MRC) with cardiometabolic outcomes (glycemic, atherogenic cholesterol, and blood pressure control) among non-Hispanic Black adults with type 2 diabetes. Utilizing 470 independent patient electronic health records, MRC and other covariates were examined to determine their associations with cardiometabolic outcomes. Chi-square tests of independence and multiple logistic regression were performed to identify associations between MRC and cardiometabolic outcomes. Our findings indicate significant negative and positive associations between MRC and glycemic control and atherogenic cholesterol control, respectively. However, there were no associations between MRC and blood pressure control. As diabetes MRC was shown to be associated with poor glycemic control and improved atherogenic cholesterol control, there is a critical need to standardize interdisciplinary diabetes care to include pharmacists and to develop more insurance policy interventions that increase access to newer, efficacious diabetes medications for historically marginalized populations. Full article

Changes in gut microbiome composition have been implicated in the pathogenesis of graft-versus-host disease (GvHD) after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Our objective was to explore the microbial abundance in patients with GvHD after allo-HSCT. We conducted a single-center, prospective study in patients who underwent allo-HSCT and developed grade II or higher acute GvHD and/or moderate or severe chronic GvHD, to explore the microbial abundance of taxa at the phylum, family, genus, and species level, and we utilized alpha and beta diversity indices to further describe our findings. We collected fecal specimens at −2 to +2 (T1), +11 to +17 (T2), +25 to +30 (T3), +90 (T4), and +180 (T5) days to assess changes in gut microbiota, with day 0 being the day of allo-HSCT. We included 20 allo-HSCT recipients in the study. Compared with timepoint T1, at timepoint T4 we found a significant decrease in the abundance of Proteobacteria phylum (14.22% at T1 vs. 4.07% at T4, p = 0.01) and Enterobacteriaceae family (13.3% at T1 vs. <0.05% at T4, p < 0.05), as well as a significant increase in Enterococcus species (0.1% at T1 vs. 12.8% at T4, p < 0.05) in patients who developed acute GvHD. Regarding patients who developed chronic GvHD after allo-HSCT, there was a significant reduction in the abundance of Eurobactereaceae family (1.32% at T1 vs. 0.53% at T4, p < 0.05) and Roseruria genus (3.97% at T1 vs. 0.09% at T4, p < 0.05) at T4 compared with T1. Alpha and beta diversity analyses did not reveal a difference in the abundance of bacteria at the genus level in GvHD patients at T4 compared with T1. Our study reinforces results from previous studies regarding changes in gut microbiota in patients with acute GvHD and provides new data regarding the gut microbiome changes in chronic GvHD. Future studies will need to incorporate clinical parameters in their analyses to establish their association with specific changes in gut microbiota in patients with GvHD after allo-HSCT. Full article

The Reynolds-averaged Navier–Stokes (RANS)-based stochastic multiple mapping conditioning (MMC) approach has been used to study partially premixed jet flames of dimethyl ether (DME) introduced into a vitiated coflowing oxidizer stream. This study investigates DME flames with varying degrees of partial premixing within a fuel jet across different coflow temperatures, delving into the underlying flame structure and stabilization mechanisms. Employing a turbulence k-$\epsilon$ model with a customized set of constants, the MMC technique utilizes a mixture fraction as the primary scalar, mapped to the reference variable. Solving a set of ordinary differential equations for the evolution of Lagrangian stochastic particles’ position and composition, the molecular mixing of these particles is executed using the modified Curl’s model. The lift-off height (LOH) derived from RANS-MMC simulations are juxtaposed with experimental data for different degrees of partial premixing of fuel jets and various coflow temperatures. The RANS-MMC methodology adeptly captures LOH for pure DME jets but exhibits an underestimation of flame LOH for partially premixed jet scenarios. Notably, as the degree of premixing escalates, a conspicuous underprediction in LOH becomes apparent. Conditional scatter and contour plots of OH and CH₂O unveil that the propagation of partially premixed flames emerges as the dominant mechanism at high coflow temperatures, while autoignition governs flame stabilization at lower coflow temperatures in partially premixed flames. Additionally, for pure DME flames, autoignition remains the primary flame stabilization mechanism across all coflow temperature conditions. The study underscores the importance of considering the degree of premixing in partially premixed jet flames, as it significantly impacts flame stabilization mechanisms and LOH, thereby providing crucial insights into combustion dynamics for various practical applications. Full article

With the widespread adoption of battery technology in electric vehicles, there has been significant attention drawn to the increasing frequency of battery fire incidents. However, the jetting behavior and expansion force during the thermal runaway (TR) of batteries represent highly dynamic phenomena, which lack comprehensive quantitative description. This study addresses this gap by employing an enhanced experimental setup that synchronizes the video timing of cameras with a signal acquisition system, enabling the multidimensional quantification of signals, such as images, temperature, voltage, and pressure. It also provides a detailed description of the jetting behavior and expansion force characteristics over time for Li(Ni_0.8Co_0.1Mn_0.1)O₂ batteries undergoing thermal runaway in an open environment. The results from three experiments effectively identify key temporal features, including the timing of the initial jetting spark, maximum jetting velocity, jetting duration, explosion duration, and patterns of flame volume variation. This quantitative analytical approach proves effective across various battery types and conditions. The findings could offer scientific foundations and experimental strategies for parameter identification in fire prevention and thermal runaway model development. Full article

Abstract: Social innovation has emerged as a transformative force in businesses, particularly in the context of cultural and creative hotels. This study aims to explore the relationship between social innovation and value co-creation in cultural and creative hotels, compare the intrinsic characteristics of social innovation and value co-creation within these hotels, and investigate the key factors driving social innovation in this sector. Employing a qualitative research methodology based on the theory of social innovation, this paper examines the process of value co-creation and analyzes the three key drivers within the social innovation ecosystem: establishing interdependence and identity among organizations; enhancing cognitive and value exchanges between organizations; and generating consensus through the role transformation of participants. The findings suggest that the key drivers of the social innovation ecosystem not only enhance the innovation capabilities of businesses but also motivate them to collaboratively create mutually beneficial and symbiotic value. Full article

The Cosmonaut Sea is one of the least accessed regions in the Southern Ocean, and our knowledge about the fish biodiversity in the region is sparse. In this study, we provided a description of demersal fish diversity in the Cosmonaut Sea by analysing cytochrome oxidase I (COI) barcodes of 98 fish samples that were hauled by trawling during the 37th and 38th Chinese National Antarctic Research Expedition (CHINARE) cruises. Twenty-four species representing 19 genera and 11 families, namely, Artedidraconidae, Bathydraconidae, Bathylagidae, Channichthyidae, Liparidae, Macrouridae, Muraenolepididae, Myctophidae, Nototheniidae, Paralepididae and Zoarcidae, were discriminated and identified, which were largely identical to local fish occurrence records and the general pattern of demersal fish communities at high Antarctic shelf areas. The validity of a barcoding gap failed to be detected and confirmed across all species due to the indicative signals of two potential cryptic species. Nevertheless, DNA barcoding still demonstrated to be a very efficient and sound method for the discrimination and classification of Antarctic fishes. In the future, various sampling strategies that cover all geographic sections and depth strata of the Cosmonaut Sea are encouraged to enhance our understanding of local fish communities, within which DNA barcoding can play an important role in either molecular taxonomy or the establishment of a dedicated local reference database for eDNA metabarcoding analyses. Full article

Abstract: Short-rotation eucalyptus plantations play a key positive role in the forestry economy due to their fast-growing and high-yielding properties. However, some studies have suggested that eucalyptus plantations introductions may have negative impacts on biodiversity and ecosystems’ stability. In order to precisely and promptly determine the influence of eucalyptus plantations on soil characteristics and hydrological processes, based on the rotation change rules of eucalyptus plantations, this study combined the continuous change detection and classification and spectral mixture analysis (CCDC-SMA) algorithm and the random forest (RF) algorithm to map the distribution and stand age of short-rotation eucalyptus plantations in Hainan Island. First, the forest distribution map was used to mask out the rubber plantations, and forest disturbances were extracted through the CCDC-SMA algorithm to determine the potential short-rotation eucalyptus plantations distribution. Second, using CCDC-SMA algorithm fitting coefficients, field surveys, original spectral bands, vegetation indices, and digital elevation models (DEM) as inputs to the RF algorithm, short-rotation eucalyptus plantations distribution maps were created and evaluated based on Google Earth images. Finally, the stand age of the newly mapped short-rotation eucalyptus plantations was estimated based on the breakpoints of the CCDC-SMA algorithm. The results showed that the producer, user, and overall accuracies of the 2022 short-rotation eucalyptus plantations map were estimated at 0.95, 0.95, and 0.94, respectively, and the validation R² of the estimated stand ages was at 0.97. The eucalyptus plantations in Hainan Island had a total area of roughly 9.93 × 10⁴ ha in 2022. Danzhou City had the highest planting area of eucalyptus plantations, followed by Changjiang County, Chengmai County, and Lingao County. It was worth noting that the eucalyptus plantations were mostly located in places with low altitudes (<200 m) and flat slopes (<10°). Approximately 43.91% of eucalyptus plantations were located in the three major watersheds. In addition, the 1-year-old eucalyptus plantations accounted for the highest areal percentage of 30.58%. These datasets are valuable tools to aid sustainable production, ecological assessment, and conservation of eucalyptus plantations. Full article

Reclaimed landfill areas are excluded from various development options including construction, while contaminated zones around such places have no such restrictions. The successful reclamation of landfills means that the old landfill visually fits in well with its surroundings, but soil and water contamination problems remain valid. Former landfills were built without properly preparing the land, which resulted in the migration of contaminants in groundwater for a long period after these landfills were closed, further resulting in the limited use of such areas, at least for some purposes. Due to the development of cities, landfills formerly located in suburbs are becoming a part of these cities. In order to optimally and safely use these spaces, knowledge regarding the quality of the soil and water environment is necessary. This article presents methodological considerations regarding the use of carbon and sulfur isotope methods to assess groundwater contamination around former municipal waste landfills, especially reclaimed municipal landfills. It has been shown that natural groundwater is characterized by low values of both δ¹³C_DIC and δ³⁴S (δ¹³C_DIC from −20 to −10‰ and δ³⁴S at approximately −5‰), whereas leachate-contaminated groundwater is characterized by high values of both parameters (δ¹³C_DIC from −10 to + 5‰ and δ³⁴S from +5 to +20‰). The aim of this article is to demonstrate that carbon and sulfur isotope methods extended via SWOT analysis are universal and reliable methods for assessing the migration of pollutants, thus facilitating decisions regarding management. Full article

Delirium presents a significant clinical challenge, primarily due to its profound impact on patient outcomes and the limitations of the current diagnostic methods, which are largely subjective. During the COVID-19 pandemic, this challenge was intensified as the frequency of delirium assessments decreased in Intensive Care Units (ICUs), even as the prevalence of delirium among critically ill patients increased. The present study evaluated how the serum molecular fingerprint, as acquired by Fourier-Transform InfraRed (FTIR) spectroscopy, can enable the development of predictive models for delirium. A preliminary univariate analysis of serum FTIR spectra indicated significantly different bands between 26 ICU patients with delirium and 26 patients without, all of whom were admitted with COVID-19. However, these bands resulted in a poorly performing Naïve-Bayes predictive model. Considering the use of a Fast-Correlation-Based Filter for feature selection, it was possible to define a new set of spectral bands with a wider coverage of molecular functional groups. These bands ensured an excellent Naïve-Bayes predictive model, with an AUC, a sensitivity, and a specificity all exceeding 0.92. These spectral bands, acquired through a minimally invasive analysis and obtained rapidly, economically, and in a high-throughput mode, therefore offer significant potential for managing delirium in critically ill patients. Full article

A chitosan-based Cu²⁺ fluorescent probe was designed and synthesized independently using the C-2-amino group of chitosan with 1, 8-naphthalimide derivatives. A series of experiments were conducted to characterize the optical properties of the grafted probe. The fluorescence quenching effect was investigated based on the interactions between the probe and common metals. It was found that the proposed probe displayed selective interaction with Cu²⁺ over other metal ions and anions, reaching equilibrium within 5 min. Full article

Psoriasis vulgaris (PV) is a disease characterized by skin manifestations and systemic inflammation. There are no published studies to date on vitamin K status assessed by extrahepatic vitamin K-dependent proteins [e.g., osteocalcin (OC) and matrix Gla protein (MGP)] in patients with PV, even if vitamin K was found to promote wound contraction and decrease the healing time of the skin. Metabolic syndrome (MS), a comorbidity of PV, was found to influence vitamin K status, and vitamin D was found to be involved in the pathogenesis of PV. Therefore, our aim was to assess the status of vitamins K and D in subjects with PV. We enrolled 44 patients with PV and 44 age- and sex-matched subjects as a control group (CG), of which individuals with MS were designated the CG with MS subgroup. Furthermore, the PV patients were stratified into two subgroups: those with MS (n = 20) and those without MS (n = 24). In addition to the quantification of vitamin D and MGP in all subjects, the uncarboxylated OC/carboxylated OC (ucOC/cOC) ratio was also assessed as an inversely proportional marker of vitamin K status. We found an increased ucOC/cOC ratio in the PV group compared to CG but also a greater ucOC/cOC ratio in the PV with MS subgroup than in the CG with MS subgroup. MGP was decreased in the PV with MS subgroup compared to CG with MS subgroup. There was no difference in the vitamin D concentration between the groups. This is the first study to report decreased vitamin K status in patients with PV, independent of the presence of MS. Full article

The increase in Cervical Spondylosis cases and the expansion of the affected demographic to younger patients have escalated the demand for X-ray screening. Challenges include variability in imaging technology, differences in equipment specifications, and the diverse experience levels of clinicians, which collectively hinder diagnostic accuracy. In response, a deep learning approach utilizing a ResNet-34 convolutional neural network has been developed. This model, trained on a comprehensive dataset of 1235 cervical spine X-ray images representing a wide range of projection angles, aims to mitigate these issues by providing a robust tool for diagnosis. Validation of the model was performed on an independent set of 136 X-ray images, also varied in projection angles, to ensure its efficacy across diverse clinical scenarios. The model achieved a classification accuracy of 89.7%, significantly outperforming the traditional manual diagnostic approach, which has an accuracy of 68.3%. This advancement demonstrates the viability of deep learning models to not only complement but enhance the diagnostic capabilities of clinicians in identifying Cervical Spondylosis, offering a promising avenue for improving diagnostic accuracy and efficiency in clinical settings. Full article

This study aims to investigate the factors that influence millennials’ perceptions and preferences in regard to career choices within the state-owned energy sector in Indonesia. The research objective is to understand how to remain competitive in the current disruptive job market by examining a company’s recruitment and retention strategies, and analyzing data collected through econometric surveys. Factors significantly affecting the willingness to work at PLN include its past and present reputation, product societal impact, CSR efforts, and the individual’s gender, age, and job-seeking status, with positive views on PLN and its CSR activities encouraging the inclination to work there. Income expectations are influenced by similar aspects—PLN’s reputation, its product’s societal role, and CSR initiatives—alongside gender and education level, particularly for those with undergraduate or Master’s degrees. Notably, favorable perceptions of PLN and higher educational attainment are linked to increased salary expectations. The results from the survey indicate that a significant proportion of respondents, over 80%, expressed a desire to work at one of Indonesia’s state-owned energy companies (PLN), with a desired monthly salary of IDR 7,466,905. Furthermore, when compared to other state-owned energy companies in Indonesia, PLN holds a strong position, ranking second among this type of companies. This study provides valuable insights for energy companies in Indonesia, by understanding the career preferences of millennials and aligning their employer branding strategies accordingly, in order to remain competitive in the current job market. Full article