bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterization of Volatilized Compounds in Conventional and Organic Vegetable Source Alternative Meat Curing Ingredients
Authors: Sheng Siyuan , Silva Erin , Ricke Steven C. , Claus James R
Published: January 2025

Analyzing volatile compounds is crucial in understanding the distinctive flavor and aroma of cured meat products. By examining these compounds, we can gain insights into the sensory characteristics of alternative ingredients and how they contribute to the overall flavor profile of cured meats. In this study, we characterized and quantified the volatile composition of commercially available, conventional, and organically produced pre-converted celery (Apium graveolens) and Swiss chard (Beta vulgaris subsp. maritima) juices and powders used in the meat curing industry. We optimized a method involving steam distillation with liquid-liquid phase extraction coupled with gas chromatog-raphy-tandem mass spectrometry (GC-MS/MS) to isolate and analyze volatile compounds from the vegetable powders and juices. The key volatile compounds identified in celery and Swiss chard were 3-butylisobenzofuran-1(3H)-one and 2-methoxy-4-vinylphenol, re-spectively. In both conventional and organic celery juice, the primary volatile compounds detected were senkyunolide, sedanolide, and limonene, in descending order of concentra-tion. This work lays the groundwork for sensory and volatile compound studies in alter-native meat curing, while also providing more comprehensive information for organic plant and meat producers, processors, and consumers.
Honey, Gellan Gum and Hyaluronic Acid as Therapeutic Approaches for Skin Regeneration
Authors: Sousa Patrícia , Moreira Alicia , Lopes Bruna , Sousa Ana Catarina , Coelho André , Rêma Alexandra , Balça Maria , Atayde Luís , Mendonça Carla , Silva Lucília P. da , Costa Cristiana , Marques Alexandra P. , Amorim Irina , Alvites Rui , Batista Filipa , Mata Filipa , Transmontano João , Maurício Ana Colette
Published: January 2025

Chronic wounds pose a significant health concern and their prevalence, stemming from different etiologies, is on the rise due to the global aging population, posing a substantial burden on healthcare systems in developed countries. This work aimed to develop and formulate new therapeutic options in the form of creams and dressings based on honey, gellan gum and hyaluronic acid, for preventing and treating chronic wounds across all stages. To address this, after the formulations were developed, in vitro cytocompatibility was determined. To confirm biocompatibility, a ovine wound model was used: full-thickness excisional wounds were treated with 3 formulations, namely gellan gum and honey sponges (GG-HNY), gellan gum, honey and hyaluronic acid sponges (GG-HA-HNY) and a honey-based cream (cream FB002). Daily assessments, including visual evaluation, and wound scoring, were conducted for 30 days. Following the study period, tissues were collected for histological analyses. The macroscopic examination revealed that all therapeutic groups facilitated lesion closure. Lesion size reduction, granulation tissue disappearance, and scar tissue development were consistent across all groups, with the group receiving cream demonstrating an advanced stage of healing. All groups achieved substantial wound closure by day 30, with no significant differences among them. Histopathological analysis following ISO standards revealed that GG-HA-HNY had the lowest ISO score, indicating minimal reactivity and inflammation, corroborated the in vitro cytocompatibility. Combining these insights with previous findings enhances our understanding of wound regeneration dynamics and contributes to refining therapeutic strategies for chronic wounds. The resultant formulations aim to outperform existing medical devices, delivering superior performance at a lower overall unit cost.
Biotherapeutic Potential of a Novel Probiotic Kluyveromyces marxianus Isolated from a Sourdough Starter Against Vaginal C. albicans Strains
Authors: Buonanno Annalisa , Imparato Marianna , Maione Angela , Carraturo Federica , Galdiero Emilia , Guida Marco , de Alteriis Elisabetta
Published: January 2025

There is an increasing interest in yeasts isolated from natural sources to be used as probi-otics. Saccharomyces-based probiotics have been proposed as a valid alternative to the con-ventional drug therapy for the prevention and treatment of vulvovaginal candidiasis, also considering the resistance of some Candida strains to many antifungals. Here we isolated from an artisanal sourdough a new yeast strain which was identified as Kluyveromyces marxianus and assessed its probiotic and safety properties, which resulted comparable at all to those exhibited by the commercial probiotic Saccharomyces boulardii. Then, we checked the antagonistic activity of the new isolate against some clinical fluconazole re-sistant C. albicans strains, showing its ability to inhibit filamentation, biofilm formation, and adhesion of C. albicans to vaginal epithelial A-431 cells. Also, K. marxianus reduced the cell damage provoked by C. albicans and the expression of SAP2 and SAP6 genes. On the whole, our results enlarge the spectrum of the beneficial properties of the food-grade yeast K. marxianus showing for the first time its biotherapeutic potential as anticandidal agent.
Optimizing Stem Length in Conversion Hip Arthroplasty: An Expanded Finite Element Analysis
Authors: Shimasaki Koshiro , Nishino Tomofumi , Yoshizawa Tomohiro , Watanabe Ryunosuke , Hirose Fumi , Yasunaga Shota , Mishima Hajime
Published: January 2025

Background/Objectives: Stress concentration around distal screw-removal holes confers a major risk for periprosthetic fractures following conversion total hip arthroplasty (cTHA) for intertrochanteric femoral fractures. Optimal stem-selection criteria and guidelines for cTHA can improve clinical outcomes. We determined the influence of cementless stem length on stress distribution around distal screw-removal holes. Methods: For finite element analysis, institutional data from preoperative CT scans of contralateral femurs of patients who underwent THA were used. To replicate the post-nail-removal state, we used 3D registration of Standard Triangulated Language data of the intramedullary nail as an unused material to simulate distal screw-removal holes, located 135 mm from the proximal end of the intramedullary nail. Cementless stems of 130, 140, 150, and 160 mm were individually registered using STL data, and cTHA models were constructed accordingly. Using simulations under load conditions representing normal walking and stair climbing, the mean and maximum equivalent stress values around the distal screw-removal holes were calculated. Repeated-measures ANOVA with Bonferroni correction was used for multiple comparisons. Results: Compared to the 130-mm stem, the 150-mm and 160-mm stems similarly reduced the maximum equivalent stress around distal screw-removal holes, although the 140-mm stem showed no significant difference with other stems. Conclusions: A ≥150-mm stem length reliably mitigates stress concentration around distal screw-removal holes post-cTHA, is the optimal choice for balancing effectiveness and risk of complications, and may contribute to improved long-term clinical outcomes. This study provides practical evidence for stem selection in cTHA and offers valuable insights for future treatment guidelines.
Femtosecond Laser Textured Surfaces for Radiative Cooling: Black Metals
Authors: Zheng Nan , Buividas Ričardas , Huang Hsin-Hui , Stonytė Dominyka , Palanisamy Suresh , Zhu De Ming , Katkus Tomas , Kretkowski Maciej , Nishijima Yoshiaki , Grineviciute Lina , Stoddart Paul R. , Juodkazis Saulius
Published: January 2025

There is a growing need for cost-effective methods for modifying the surfaces of a wide range of materials over large areas. Here, low reflectance R < 2% surfaces have been created over the near-to-mid IR spectral window of 2−20μm by ablating W, Al, and Cu with high average intensity 20-120 TW/cm^2 200 fs laser pulses at 1030 nm wavelength. The resulting chemical modification of the surfaces after laser ablation at ambient room conditions was assessed with X-ray photoelectron spectroscopy (XPS). The results show a consistent decrease in the metallic component together with an increase in metal oxides. Energy dispersive spectroscopy (EDS) showed a similar increase in oxygen content over a micrometer depth scale. The reduced refractive index of the metal oxides compared to the corresponding metals contributes to the reduction in infrared (IR) reflectance, together with the 3D hierarchically textured surface structure. IR-black metals show great potential for radiative cooling at elevated temperatures relevant to industrial and space applications.
Processing of Scene Grammar Inconsistencies in Children with Developmental Language Disorder—Insights from Implicit and Explicit Measures
Authors: Bahn Daniela , Türk Dilara Deniz , Tsenkova Nikol , Schwarzer Gudrun , Võ Melissa Le-Hoa , Kauschke Christina
Published: January 2025

Background/Objectives: Developmental Language Disorders (DLD) are often associated with co-occurring neurodevelopmental difficulties, including attentional or social-emotional problems. Another non-verbal domain, i.e. visual cognition and its relationship to DLD, is virtually unexplored. However, learning visuospatial regularities is crucial for navigating our daily environment. These regularities show certain similarities to the structure of language and there is preliminary evidence for a relationship between scene processing and language competence in preschoolers with and without DLD. The study compared implicit and explicit visuospatial knowledge of everyday indoor scenes in older children aged 6 to 10 years of both groups. Methods: We measured dwell times on semantic and syntactic object-scene inconsistencies via eye-tracking, the performance on an object-placement task, and their associations with children’s language, visual, and cognitive skills. Results: Visual attention towards object scene inconsistencies was highly comparable between groups, but children with DLD scored lower in a visual perception test and high language skills were associated with high visuo-cognitive performance in both tasks. In explicit scene grammar measure, this relationship only existed for children with DLD and disappeared when non-verbal cognitive performance was controlled. Conclusions: The acquisition of visual-spatial and linguistic knowledge is a multimodal process. Our study suggests the existence of mild problems in visuospatial processing, co-occurring with DLD, partly influenced by age and nonverbal cognitive ability. If scenes are perceived differently, this may also affect how words for objects and spatial relations are learned and may thus affect treatment.
An Intent Recognition Method for Aerial Swarm Based on Attention Pooling Mechanism
Authors: He Hui , Peng Zhihong , Shang Peiqiao , Wang Wenjie , Pei Xiaoshuai
Published: January 2025

In the realm of unmanned aerial vehicle (UAV) swarm intent recognition, conventional approaches predominantly focus on the attributes derived from singular targets at discrete instances. This trend leads to a significant limitation: the inability to effectively harness and capture the collective feature information of the entire swarm over temporal sequences. To address this gap, this study introduces a comprehensive end-to-end UAV swarm intent recognition approach. Initially, this method utilizes the distance threat coefficient and angular threat coefficient between UAVs to construct the graphical structural representation of the UAV swarm. Subsequently, an innovative deep learning framework, designated as attention-pool based on graph attention network and long short-term memory, which integrates a graph attention network, a novel graph pooling strategy, and a long short-term memory, is developed. This architecture can process the graphically structured data derived from the swarm modeling and accurately deduce the collective intent. Through experimental validation and analyses against existing methodologies, as well as ablation studies, it is evidenced that the model outperforms state-of-the-art methods in terms of accuracy of intent recognition.
Egocentric Behavior Analysis Based on Object Relationship Extraction with Graph Transfer Learning for Cognitive Rehabilitation Support
Authors: Besari Adnan Rachmat Anom , Ardilla Fernando , Saputra Azhar Aulia , Kurnianingsih  , Obo Takenori , Kubota Naoyuki
Published: January 2025

Recognizing human behavior is essential for early interventions in cognitive rehabilitation, particularly for older adults. Traditional methods often focus on improving third-person vision but overlook the importance of human visual attention during object interactions. This study introduces an egocentric behavior analysis (EBA) framework that uses transfer learning to analyze object relationships. Egocentric vision is used to extract features from hand movements, object detection, and visual attention. These features are then used to validate hand-object interactions (HOI) and describe human activities involving multiple objects. The proposed method employs graph attention networks (GATs) with transfer learning, achieving 97% accuracy in categorizing various activities while reducing computation time. These findings suggest that integrating the EBA with advanced machine learning methods could revolutionize cognitive rehabilitation by offering more personalized and efficient interventions. Future research can explore real-world applications of this approach, potentially improving the quality of life for older adults through better cognitive health monitoring.
Key Point Detection in Football Match Based on Convolutional Neural Network
Authors: Zhang Huan
Published: January 2025

Key point detection in football matches can provide effective support for understanding football videos and recognizing player movements. This study analyzes the position information of players in football matches using key point detection methods. A cascaded convolutional neural network for detection and regression is combined to design a key point detection model for the impact on football game commentary and player control rate. A first level key point detection network based on ResNet and a second level regression network based on heatmap are proposed by combining the fused stacked feature extraction and optimizing the original structure. The experiment confirmed that the model has an accuracy of 95.62% in detecting key points. At the same time, the coordinate error near the goal and other areas is relatively small. However, there is a significant coordinate error in the size exclusion zone. This research provides a certain reference for the analysis of future football matches.
Balancing Act: A Comparative Study of Routing Protocol Trade-Offs in MANETs
Authors: Kezai Mourad , Dembri Rafik , Boukhari Djamel Eddine
Published: January 2025

Mobile ad hoc networks (MANETs) are decentralized networks of mobile devices connected by wireless links, enabling various applications across domains. Research on MANETs often relies on simulations for efficient and replicable evaluations due to the absence of central administration and complex real-world scenarios. Effective simulation necessitates the integration of mobility models to orchestrate node movement. In this study, we explore existing mobility models, propose evaluation metrics, and conduct simulations to characterize, compare, and rank these models. Additionally, we analyze ad hoc routing protocols, focusing on packet delivery ratio, average packet arrival time, and total control packets. Notable protocols such as AODV, DSR, DSDV, and TORA are scrutinized, considering factors like node count and mobility, to provide comprehensive insights into their performance and efficacy.
Autonomous Teleoperated Robotic Arm Based on Imitation Learning Using Instance Segmentation and Haptics Information
Authors: Imai Kota , Takahashi Yasutake , Tsuichihara Satoki , Haruna Masaki
Published: January 2025

Teleoperated robots are attracting attention as a solution to the pressing labor shortage. To reduce the burden on the operators of teleoperated robots and improve manpower efficiency, research is underway to make these robots more autonomous. However, end-to-end imitation learning models that directly map camera images to actions are vulnerable to changes in image background and lighting conditions. To improve robustness against these changes, we modified the learning model to handle segmented images where only the arm and the object are preserved. The task success rate for the demonstration data and the environment with different backgrounds was 0.0% for the model with the raw image input and 66.0% for the proposed model with segmented image input, with the latter having achieved a significant improvement. However, the grasping force of this model was stronger than that during the demonstration. Accordingly, we added haptics information to the observation input of the model. Experimental results show that this can reduce the grasping force.
Improved Back-Propagation Neural Network Applied to Enterprise Employee Performance Appraisal and Evaluation
Authors: Zhang Yikai , Li Wei
Published: January 2025

Evaluating employee performance in enterprises is beneficial for improving talent competitiveness, operation level production, and overall enterprise performance. At present, many performance appraisal methods lack objectivity and fairness, which is not conducive to the long-term development of enterprises. Therefore, more research into scientific and effective performance appraisal methods is required. This study takes the grassroots employees of Enterprise A as an example, improving the existing performance appraisal evaluation indices to evaluate employee performance from three dimensions, including achievement, ability, and attitude, as determined by index weights using the analytic hierarchy process approach. An improved back-propagation neural network (BPNN) method is then designed to obtain performance appraisal and evaluation results. The error between the output of the improved BPNN method and expected output was small. Of the 20 extracted samples, the maximum and minimum error values were 0.05 and 0.01, respectively, and the average error was 0.03. The improved BPNN method evaluated only one out of 20 samples incorrectly, and the accuracy of the improved BPNN method was 96.21%, which is 19.88% and 10.75% higher than those of support vector machine and standard BPNN, respectively. The findings demonstrate that the improved BPNN method can be used in the appraisal and assessment of enterprise employee performance and has practical application value.
Spatiotemporal Interaction Based Dynamic Adversarial Adaptive Graph Neural Network for Air-Quality Prediction
Authors: Chen Xiaoxia , Wang Zhen , Xia Hanzhong , Dong Fangyan , Hirota Kaoru
Published: January 2025

Air quality issues have become a major environmental concern, with severe air pollution significantly reducing air quality and posing threats to human health. Accurate air quality prediction is crucial for preventing individuals from suffering the detrimental effects of severe air pollution. Recently, deep learning methods based on spatiotemporal graph neural networks (GNNs) have made considerable progress in modeling the temporal and spatial dependencies within air quality data by integrating GNNs with sequential models. Unfortunately, previous work often treats temporal and spatial dependencies as independent components, neglecting the intricate interactions between them. This oversight prevents the models from fully exploiting the complex spatiotemporal dependencies in the data, adversely affecting their predictive performance. To address these issues, we propose a general spatiotemporal interaction framework for air quality prediction. This framework models the bidirectional interactions between temporal and spatial dependencies in a data-driven manner. Furthermore, we designed a spatiotemporal feature extraction module and a dynamic adversarial adaptive graph learning module based on this framework. We introduce the Spatial-Temporal Interaction based Dynamic Adversarial Adaptive Graph Neural Network, capable of capturing the complex interactions between spatiotemporal dependencies and learning the dynamic spatial topology among sites by incorporating the competitive optimization concept of generative adversarial networks. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed method, outperforming existing baseline models.
Research on the Recognition of Piano-Playing Notes by a Music Transcription Algorithm
Authors: Guo Ruosi , Zhu Yongjian
Published: January 2025

With the deepening research on music works, music transcription algorithms have been increasingly studied. This study examined the recognition of piano-playing notes using a music-transcription algorithm. First, the characteristics of MelSpec, LogSpec, and the constant Q-transform (CQT) are briefly introduced. Then, a convolutional recurrent neural network (CRNN) transcription algorithm, which includes four convolutional blocks and one bidirectional long short-term memory (BiLSTM) structure, was designed. The recognition performance of this method was analyzed using the MAPS dataset. LogSpec was found to have the best recognition performance for piano-playing notes when used as an input feature. In the CRNN structure, the recognition performance for piano-playing notes was the best when four convolutional blocks were used. Compared with the convolutional neural network (CNN), BiLSTM, and CNN-hidden Markov model algorithms, the F1-values of the CRNN algorithm were 84.9%, 92.24%, and 79.27% for frames, notes, and offsets, respectively, achieving the best recognition results. The results verify that the CRNN transcription algorithm is effective for the recognition of piano-playing notes and can be applied in practice.
Cooperative Active Disturbance Rejection Control for Heavy-Haul Trains
Authors: Song Zongying , Li Shuo , Yu Xiaoquan , Yang Yingze , Wang Xingzhong
Published: January 2025

Cooperative control of multiple heavy-haul trains can improve the safety and efficiency of heavy-haul railway transportation. However, the influence of internal and external unknown disturbances for multiple heavy-haul trains is a serious obstacle, which will lead to imprecise train operation control. To address this issue, a cooperative active disturbance rejection control for heavy-haul trains is proposed. First, a multi-mass point longitudinal dynamic model of heavy-haul trains is established to meet the actual operation. Second, a cooperative active disturbance rejection controller is designed to estimate and compensate for the disturbance caused by the interaction between the trains and environment. Moreover, the extended state observer is leverage to estimate the nonlinear disturbance online, which enhances the resistance of multiple heavy-haul trains to nonlinear time-varying disturbance and suppresses the overshoots of train velocities and inter-train distance. Finally, the performance of the proposed method is verified in two different simulation scenarios: acceleration and deceleration conditions. The simulation results show that the proposed method reduces the maximum relative displacement by 38.9% and the velocity error by 54.5%.
False Data Injection Attack Detection for Virtual Coupling Systems of Heavy-Haul Trains: A Deep Learning Approach
Authors: Yu Xiaoquan , Li Wei , Li Shuo , Yang Yingze , Peng Jun
Published: January 2025

Cooperative control for virtual coupling systems of multiple heavy-haul trains can improve the safety and efficiency of heavy-haul railway transportation. However, the false data injection attack for the virtual coupling system is a serious obstacle, which will lead to imprecise train operation control. To address this issue, a deep learning-based false data injection attack (FDIA) detection for virtual coupling systems of heavy-haul trains is proposed. First, the cyber-physical model of the virtual coupling system is established. Second, a cooperative control law is designed for the virtual coupling system, and the effects of the FDIA on the virtual coupling system is analyzed. Then, the unsupervised autoencoder method is introduced to achieve the false data injection attack detection. The autoencoder network model is trained with normal operation data and tested with abnormal operation data. The performance of the proposed method is verified in four different simulation scenarios: normal case, velocity attack case, position attack case, and joint attack case. Simulation results show that the proposed method can effectively increase the detection accuracy and reduce the error rate with other supervised methods.
RANCANG BANGUN ALAT PERAJANG UBI KAYU DENGAN TRANSMISI PULI DAN SABUK PADA PUTARAN MOTOR LISTRIK 1400 RPM DAN DAYA MOTOR 0,5 HP
Authors: Anwar Irwan , Afdal Afdal , Wardianto Dedi
Published: January 2025

Ubi kayu merupakan salah satu komoditi pangan yang berkembang saat ini, para pelaku home industry pun berlomba untuk menghasilkan makanan berbahan dasar ubi yang beragam. Dalam proses pengelolaan nya masyarakat masih banyak menggunakan cara tradisional. Proses kerja dengan cara tradisional kurang memberikan hasil yang memuaskan karena produktifitasnya rendah,  oleh karena itu penulis mencoba mengembangkan suatu alat perajang ubi yang mampu menghasilkan produktifitas yang efektif dan efisien. Alat perajang ubi kayu dengan transmisi puli dan sabuk pada putaran motor listrik 1400 rpm dan daya motor 0,5 hp, mampu menghasil kan kapasitas pemotongan hingga 66 kg/jam. Alat perajang ubi ini mempunyai 2 mata pisau yang di baut pada piringan rumah mata pisau, dengan kecepatan sudut piringan 73 rad/s dan gaya yang di hasil kan oleh piringan sebesar 24,56 N. Putaran puli penggerak sebesar 700 rpm dengan perbandingan reduksi 2. Bahan puli yang di gunakan adalah FC30 dan tipe sabuk yang di gunakan adalah Tipe sabuk A, dengan diameter poros penggerak 12 mm dan poros yang di gerak kan 15 mm. Bearing yang di gunakan adalah bearing 6202. Alat perajang ubi ini mempunyai landasan potong dengan sisitem pegas tekan, yang dapat meminimalisir tingkat kecelakaan kerja.
PEMANFAATAN SERAT BAMBU SEBAGAI BAHAN TAMBAH CAMPURAN BETON
Authors: Mulyati Mulyati , Arman Arman , Aulia Farhan Muhammad , Alawiyah Nurfelia
Published: January 2025

Bambu merupakan tanaman penghasil serat yang dapat digunakan sebagai alternatif bahan tambah serat untuk campuran beton untuk mengatasi retak pada beton. Tujuan dari penelitian ini adalah untuk mengetahui nilai kuat tekan dan kuat tarik belah beton dari penggunaan serat bambu sebagai bahan tambah campuran beton.  Bambu yang digunakan adalah bambu petung dan bambu apus, ukuran serat panjang 20 mm dan diameter 1-2 mm, dengan persentase 1%, 2%, 3%, 4%, 5% dari berat semen.  Perencanaan campuran beton menggunakan SNI 7656-2012 untuk kuat tekan rencana 20 MPa pada umur 28 hari.  Hasil pengujian kuat tekan dan kuat tarik belah beton dari persentase penggunaan serta, untuk bambu petung diperoleh kuat tekan beton rata-rata berturut-turut sebesar 23,58 MPa, 24,69 MPa, 19,45 MPa, 17,17 MPa, 13,02 MPa, dan kuat tarik belah beton berturut-turut sebesar 2,05 MPa, 2,40 MPa, 2,06 MPa, 2,03 MPa, 2,02 MPa, sedangkan untuk bambu apus diperoleh kuat tekan beton rata-rata berturut-turut sebesar 22,56 MPa, 23,07 MPa, 15,37 MPa, 14,09 MPa, 14,07 MPa, dan kuat tarik belah beton rata-rata berturut-turut sebesar 2,15 MPa, 2,50 MPa, 2,47 MPa, 2,40 MPa, 2,30 MPa.  Penggunaan serat bambu sampai 2% pada campuran beton dapat meningkatkan kuat tekan beton, dan penggunaan serat bambu sampai 5% dapat meningkatkan kuat tarik belah beton.   Serat bambu petung menghasilkan kuat tekan beton yang lebih tinggi dari pada serat bambu apus, sedangkan serat bambu apus menghasilkan kuat tarik belah beton yang lebih tinggi dari pada serat bambu petung.
VARIASI KETINGGIAN SUMBER AIR TERHADAP TEKANAN DAN DEBIT AIR POMPA HIDRAM
Authors: Eka Putra Ismet , Wardianto Dedi , Pratama Agung
Published: January 2025

Pompa hidram merupakan salah satu pompa air yang hemat energi dan ramah lingkungan. Pompa hidram merupakan teknologi tepat guna dalam bidang pemompaan dengan menggunakan tenaga momentum air (water hammer) untuk menaikkan air, sehingga pompa hidram salah satu pompa air yang tidak menggunakan BBM dan listrik. Penelitian mengenai pompa hidram telah banyak dilakukan, akan tetapi masih banyak pula yang perlu dikaji sehingga pengetahuan tentang perencanaan pompa hidram lebih baik. Efektifitas kinerja dari pompa hidram dipengaruhi beberapa parameter, antara lain tinggi jatuh, diameter pipa, jenis pipa, karakteristik katup limbah, panjang pipa Inlet dan panjang pipa pada katup limbah. Sumber air berasal dari aliran sungai bendungan Unand, dengan variasi ketinggian sumber air yaitu 0,7m, 1,3m, dan 1,9m. dengan air dialirkan menggunakan pipa saluran air masuk yang berdiameter pipa 2 inchi dengan panjang pipa 8 meter, dan pada saluran air keluar yang berdiameter 1inchi dengan ketinggian 8 meter. Pengujian pertama dengan ketinggian sumber air 0,7m didapat tekanan air masuk 1,1 bar dan tekanan air keluar 1bar, dengan tinggi pipa air keluar 8 meter yang menghasilkan debit air keluar sebesar 0,037 (L/detik). Pada pengujian kedua dengan ketinggian sumber air 1,3m didapat tekanan air masuk 1,3 bar dan tekanan air keluar 1,2 bar, dengan tinggi pipa air keluar 8 meter yang menghasilkan debit air keluar sebesar 0,071 (L/detik). Dan pada pengujian ketiga dengan ketinggian sumber air 1,9m di dapat tekanan air masuk 1,4 bar dan tekanan air keluar 1,3 bar, dengan tinggi pipa air keluar 8 meter yang menghasilkan debit air keluar sebesar 0,091 (L/detik).
SISTEM TRACKING DAN MONITORING MOBIL RENTAL MENGGUNAKAN SENSOR GPS BERBASIS NODEMCU ESP32
Authors: Alfith Alfith , Effendi Asnal , Al Al , Maulana Rizki
Published: January 2025

Diperoleh kondisi di mana Modul GPS Neo6 dapat mendeteksi koordinat bumi dalam bentuk lintang dan bujur serta kecepatan pergerakan kendaraan ke koordinatnya. Aplikasi mengetahui di mana Anda berada berdasarkan koordinat, kecepatan, dan dampak mobil. Selain itu, aplikasi mengirimkan notifikasi peringatan kecepatan. Selain itu, aplikasi ini dapat mendeteksi nama pengguna dan kata sandi yang benar atau salah untuk melindungi formulir tampilan. Selain itu, untuk penelitian pada jalan lurus dan dengan kecepatan konstan, keakuratan peralatan akan diuji dengan mengumpulkan data kecepatan GPS dan data kecepatan sebenarnya jika terjadi perubahan kecepatan yang signifikan dalam jangka waktu yang singkat. Dengan empat posisi deteksi tabrakan kendaraan, pengujian sistem ini menghasilkan nilai kondisi yang tepat. Jika terjadi kecelakaan, itu akan segera dikirim ke aplikasi dan dikirim ke lokasi kecelakaan kendaraan yang digunakan, serta ke database firebase.
ANALISIS PUSHOVER GEDUNG BLOK B PASAR SAWAHLUNTO
Authors: Nugroho Fajar , Andrano Reza , Maizir Maizir
Published: January 2025

Indonesia is a country with a high risk of earthquakes. Earthquake vibrations can cause ground shifts, which, in turn, affect building structures. To assess the level of risk for building damage, it is necessary to conduct a collapse scheme analysis and seismic performance evaluation of buildings. In this study, the author examined one of the earthquake-prone areas, Sawahlunto City, located in West Sumatra Province, Indonesia. The focus of the research is Block B of the Sawahlunto Market, a building that serves as a hub for community economic activities. The analysis employed is pushover analysis. The research methods include data collection, load calculations, 3D modeling, and static pushover analysis. Structural modeling was carried out using SAP2000 software. The structural performance level was evaluated based on SNI 1726-2019 and ATC-40 standards, and the mechanism of structural collapse was analyzed using plastic hinge formation. The analysis results show that the capacity curves in the X and Y directions have almost identical shapes, indicating that the structural stiffness along both axes is nearly the same. The performance points in the X and Y directions classify the building structure under the immediate occupancy category. This indicates that the building is highly capable of withstanding earthquakes with no significant damage.
Una guerra para hablar de otra guerra: la censura franquista ante "Los que no fuimos a la guerra"
Authors: Acosta López Alejandro
Published: January 2025

La división ideológica entre aliadófilos y germanófilos a razón del estallido de la Primera Guerra Mundial fue uno de los episodios de polarización más significativos de la España del primer tercio del siglo XX. Los que no fuimos a la guerra, basada en la novela de Wenceslao Fernández Flórez, recordó al espectador esa discusión en la España del «Desarrollismo». El director, Julio Diamante Stihl, entendió que con su adaptación podía ganarse la permisividad de las autoridades y al mismo tiempo reflejar de manera crítica dos Españas enfrentadas. No obstante, pese a que del contenido de la película no se infería una crítica directa al régimen franquista ni se aludía explícitamente a la guerra civil, las autoridades censoras obstaculizaron y desmerecieron el film, posiblemente contemplando la militancia de Julio Diamante por encima de otras consideraciones artísticas.
Escritura epistolar y focalización interna en Nueve cartas a Berta (Basilio Martín Patino, 1966)
Authors: Pueo Juan Carlos
Published: January 2025

La película de Basilio Martín Patino Nueve cartas a Berta (1966) plantea una dialéctica compleja entre la focalización interna que proveen las cartas de su protagonista y la focalización externa promovida por las imágenes, que no se limitan a ilustrar el contenido de las cartas, sino que comentan, amplían e incluso contradicen aquello que expone la voz en off que da cuenta del contenido de las epístolas. La escritura de Lorenzo, protagonista de la cinta, es presentada como una forma problemática de exponer la asfixiante realidad española de los años sesenta, dentro de la corriente de renovación del lenguaje cinematográfico que supuso la aparición, al calor de otros movimientos como la Nouvelle Vaguefrancesa o el Free Cinema británico, del Nuevo Cine Español, atento sobre todo a la superación del neorrealismo de la década anterior y a la limitación que suponía hacer depender el relato de la perspectiva del personaje protagonista.
Dead Collections
Authors: Snyder Broussard Mary
Published: January 2025

Isaac Fellman’s Dead Collections: A Novel (2022) portrays a fictional archivist Sol, who experiences chronic illness in the form of vampirism. While he experiences many symptoms including cold skin and reliance on weekly blood transfusions, it is his life-threatening photophobia (aversion to sunlight) that becomes a serious impairment to commuting to and from work. While Sol and his vampirism are clearly works of fiction, the novel accurately depicts working in libraries with a chronic illness. This article compares and connects the fictional story in Dead Collections to the growing body of literature on library workers with disabilities and chronic illness, particularly articles that give voice to those with relevant lived experiences. Highlights of this discussion include the topics of non-apparent illnesses, disclosure, passing, reactions to disability among coworkers and supervisors, and the promises and disappointments of the Americans with Disabilities Act.
Detección de Parainfluenza tipo 3 (PIV3) en cuyes (Cavia porcellus) de sistema Familiar-Comercial en Cajabamba, 2023
Authors:   , Valera Sanchez Ingrid Sharon
Published: January 2025

La crianza familiar-comercial de cuyes (Cavia porcellus) es una actividad socio-económica importante para las familias rurales; por lo que un problema sanitario puede generar un impacto importante en su crianza. El presente estudio buscó detectar la presencia del virus de parainfluenza tipo 3 (PIV-3) a través de RT-PCR usando muestras de hisopados traqueales de cuyes en etapa de recría y reproductores de sistemas productivos familiar-comercial en la provincia de Cajabamba, departamento Cajamarca, 2023. Se aplicó el tipo de muestreo por conveniencia, abarcando 63 individuos como muestra considerando animales con signología respiratoria, como estornudos y mucosidad perinasal, o con antecedente y/o sospecha de presentación o enfermedad respiratoria transitoria. Los individuos pasaron por la etapa de faenamiento en el cual se realizó la contención física, aturdimiento e insensibilización, y posterior degüelle para realizar el proceso correcto de necropsia. La toma de muestra se recolectó con hisopo estéril por frotación en la zona objetivo, epitelio traqueal. Las muestras se analizaron en el laboratorio de Microbiología de la Facultad de Medicina Veterinaria de la Universidad Nacional Mayor de San Marcos. La técnica molecular, PCR transcripción inversa (RT-PCR), inició realizando la extracción de Ácido ribonucleico (ARN) viral de las muestras usando el kit comercial Mini Kit ARN/ADN vírico PureLink siguiendo las instrucciones del fabricante. Luego, el ARN viral se convirtió a ADN complementario (ADNc) utilizando el kit comercial Enzima Transcriptasa x100 determinaciones, donde al realizar la mezcla designada según fabricante, se procedió a colocar este preparado al termociclador. Por último, se amplificó 739 pares de bases del gen de la proteína M de parainfluenza obteniendo el producto para lectura en un sistema de electroforesis. De las muestras analizadas, no se obtuvo ningún positivo en la identificación del agente viral.
Implementing Symptom-Based Predictive Models for Early Diagnosis of Pediatric Respiratory Viral Infections
Authors: Soriano-Arandes Antoni , Andrés Cristina , Perramon-Malavez Aida , Creus-Costa Anna , Gatell Anna , Martín-Martín Ramona , Solà-Segura Elisabet , Riera-Bosch Maria Teresa , Fernández Eduard , Biosca Mireia , Capdevila Ramon , Sánchez Almudena , Soler Isabel , Chiné Maria , Sanz Lidia , Quezada Gabriela , Pérez Sandra , Canadell Dolors , Salvadó Olga , Ridao Marisa , Sau Imma , Rifà Mª Àngels , Macià Esperança , Burgaya-Subirana Sílvia , Vila Mònica , Vila Jorgina , Mejias Asunción , Anton Andrés , Soler-Palacin Pere , Prats Clara
Published: January 2025

Introduction: Respiratory viral infections, including SARS-CoV-2, respiratory syncytial virus (RSV), influenza, rhinovirus, and adenovirus, are major causes of acute respiratory illness (ARI) in children. Symptom-based predictive models are valuable tools for expediting diagnoses, particularly in primary care settings. This study assessed the effectiveness of machine learning-based models in estimating infection probabilities for these common pediatric respiratory viruses using symptom data.
Methods: Data were collected from 868 children with ARI symptoms evaluated across 14 primary care centers, members of COPEDICAT (Coronavirus Pediatria Catalunya), from October 2021 to October 2023. Random Forest and Boosting models with 10-fold cross-validation were used, applying SMOTE-NC to address class imbalance. Model performance was evaluated via area under the curve (AUC), sensitivity, specificity, and Shapley Additive exPlanations (SHAP) values for feature importance.
Results: The model performed best for RSV (AUC: 0.81, sensitivity: 0.64, specificity: 0.77) and effectively ruled out SARS-CoV-2 based on symptom absence, such as crackles and wheezing. Predictive performance was lower for non-enveloped viruses like rhinovirus and adenovirus, due to their nonspecific symptom profiles. SHAP analysis identified key symptoms patterns for each virus.
Conclusions: The study demonstrated that symptom-based predictive models effectively identify pediatric respiratory infections, with notable accuracy for RSV, SARS-CoV-2 and influenza.
Elevating RNA m5C methylation provides a promising strategy for crop productivity
Authors: Gu Xiaofeng , Li Xiulan , Li Cong , Wang Xiangyu , Yang Liwen , Guo Weijun , Mao Yichao , Liu Hanlin , Li Dongwei , Yan Shuangyong
Published: January 2025

RNA 5-methylcytidine (m5C) has been identified as a key epi-transcriptomic modification of mRNAs involved in regulating multiple post-transcriptional processes. Here, we found that knockout of the RNA m5C demethylase, OsNOP2, results in elevated m5C levels and positively influences numerous agronomic traits in rice. After verifying OsNOP2 RNA m5C demethylase function in vitro and in planta, we found that enhanced m5C levels arising from OsNOP2 knockout results in increased translation, particularly for transcripts involved in carbon assimilation and nitrogen metabolism. OsNOP2-KO boosts grain yield ~28% per plot in the Nipponbare genetic background in normal condition and maintains increased yield traits under both heat treatment and saline soil conditions. More importantly, knockout of OsNOP2 in the rice varieties Longgeng31 and Xiushui134, as well as its orthologs in wheat and tomato, also increases the RNA m5C level to enhance yield, supporting functional conservation of OsNOP2's regulatory impacts. Together, our findings unveil an RNA m5C elevating mechanism by OsNOP2 that epigenetically governs carbon assimilation and nitrogen utilization efficiency in plant, providing a potential strategy for genetic improvement in multiple crops.
Career Adaptability and Its Relation to Thriving at Work among Staff Nurses
Authors: Badran Fawzia M. , Mohamed Eman H.
Published: January 2025

Background
Career adaptability is vital in the nursing profession, especially given the rapidly changing healthcare environment. This adaptability nurtures a workplace where nurses can thrive excel, continuously develop new skills, and stay fully engaged in their roles.
Aim
The present research aimed to assess career adaptability and its relation to thriving at work among staff nurses.
Methods
A descriptive correlational design was included 105 staff nurses.
Results
In this research, the studied subjects reported that more than one fifth (21%) of studied nurses had a low level regarding total career adaptability. Also, more than half (55.2%) of them had moderate level regarding total career adaptability. While, (23.8%) of them had high level regarding total career adaptability. Furthermore, the minority (16.2%) of staff nurses had a low level regarding total thriving at work. Also, (65.7%) of them had moderate level regarding total thriving at work. While, only (18.1%) of them had high level regarding total thriving at work.
Conclusion
This research was concluded that career adaptability was positively correlated and significantly with thriving at work among nurses (r = .194**, p = 0.000).
Recommendation: The healthcare organizations should implement targeted interventions such as personalized career development programs, wellness initiatives to boost energy levels, and enhanced access to continuous learning opportunities.
Trial Registration Number [TRN] The study protocol was approved by the Research Ethics Committee of the Faculty of Nursing, Ain Shams University[code number: NUR 24.8.345].
An Updated Review of the SMA Clinical Trial Landscape in the United States: Findings from Analysis of Recruitment Targets on ClinicalTrials.gov and a Survey of SMA Clinical Trial Sites on Factors Affecting Site Capacity and Readiness
Authors: Sarr Fatou , Peterson Ilse , Glascock Jacqueline , Schroth Mary , Curry Mary
Published: January 2025

The approval of new disease-modifying treatments, diagnostic tools, and expansion of the drug pipeline has evolved clinical trial landscape for spinal muscular atrophy (SMA) - a rare neuromuscular disease. Nonetheless, significant unmet needs for the SMA population remain and requires continued evolution of the SMA clinical trial landscape. In 2023, Cure SMA developed a series of activities to asses the current SMA clinical trial landscape including the Cure SMA Capacity and Recruitment Survey. The purpose of the survey is to understand site capacity for SMA clinical trials in the US, identify factors that may limit site trial capacity and or readiness, and identify opportunities to increase site capacity. Twenty-four sites across the US with experience in conducting SMA clinical trials are represented in the results. Although existing clinical trial sites may have the capacity to take on additional SMA trials, many factors are inhibiting efficient trial management and study start-up. As recruitment needs for SMA clinical trials continue to evolve, action is needed to address the site needs – such as increased clinical staff support and bandwidth, streamlining training, and adequate funding to conduct trials - to further optimize trial site readiness and capacity. SMA clinical trial sites, sponsors, and contract research organizations will need to work together to optimize trial site readiness address challenges to capacity.
Development and validation of a nomogram for predicting necrotizing pneumonia in children with refractory Mycoplasma pneumoniae pneumonia
Authors: zhao lihua , Li Xiaoying , Cui Xiaojian , Xu Yongsheng , Zhang Tongqiang , Guo Wei , Ning Jing
Published: January 2025

Background
The early prediction of pulmonary necrosis in children with severe pneumonia improves patient prognosis and prevents complications. The aim of this study was to establish a linear model for predicting necrotizing pneumonia (NP) caused by Mycoplasma pneumoniae (MP) infection and to investigate the risk factors for lung necrosis in children with refractory Mycoplasma pneumoniae pneumonia (RMPP).
Methods
A total of 536 children with RMPP were enrolled, including 95 with NP and 441 with nonnecrotizing pneumonia (NNP). A prediction model was built on 375 cases and validated on 161 cases, which were divided by random sampling in R software. Multivariate logistic regression was performed to determine optimal predictors and to establish a nomogram for predicting NP. The performance of the nomogram was evaluated by the area under the characteristic curve (AUC), calibration ability and decision curve analysis (DCA).
Results
There were 315 (84.0%) NNP patients and 60 (16.0%) NP patients in the training group (n = 375) and 126 (78.3%) NNP patients and 35 NP patients (21.7%) in the validation group (n = 161). Multivariate logistic regression analysis identified 4 independent predictors that were used to construct a nomogram for predicting NP in children with RMPP, namely, fever duration (OR = 1.475; 95% CI 1.296–1.678; P < 0.001), WBC count (OR = 1.149; 95% CI 1.073–1.231; P < 0.001), IL-6 concentration (OR = 1.007; 95% CI 1.002–1.013; P = 0.007) and D-dimer concentration (OR = 1.361; 95% CI 1.121–1.652; P = 0.002). The area under the curve (AUC) of the nomogram was 0.899 (95% CI, 0.850–0.947) in the training set and 0.920 (95% CI, 0.874–0.966) in the validation set, indicating a good fit. The calibration plot and Hosmer‒Lemeshow test indicated that the predicted probability had good consistency with the actual probability in the training (P = 0.439) and validation (P = 0.526) groups. The DCA curve demonstrated a significantly better net fit in the model.
Conclusions
We developed and validated a nomogram model for predicting RMPP-associated NP in its early clinical stages using four risk factors. This four-risk factor model may assist physicians in predicting NP induced by RMPP.