bluuebunny/crossref_metadata_2025 huggingface dataset:
The Potential of Single‐Chain Variable Fragment Antibody: Role in Future Therapeutic and Diagnostic Biologics
Authors: Gezehagn Kussia Getachew , Tessema Tesfaye Sisay
Published: August 2024

The advancement of genetic engineering has revolutionized the field of immunology by allowing the utilization of intrinsic antibody structures. One of the biologics that are being produced by recombinant antibody technology is single‐chain fragments variable (scFv). Genes of variable regions, the heavy and light chains that are genetically linked into a single transcript by a short flexible linker peptide, are used to generate this fragment from cellular and synthetic libraries. The specificity and affinity of these molecules are comparable to those of parental antibodies. Fusion with marker proteins and other potent molecules improves their stability, circulation half‐life, activity, and efficient purification. Besides, this review comprises construction protocols, therapeutics, and diagnostic applications of scFv, as well as related challenges. Nonetheless, there are still issues with efficacy, stability, safety, intracellular administration, and production costs that need to be addressed.
Short‐ and Midterm Outcomes of Off‐ and On‐Pump Coronary Artery Bypass in Patients with a Mean Age of 65 or More: Systematic Review and Meta‐Analysis
Authors: Trotman Jake E. , Eboka Toluwalase F. , Smart Neil A. , King Nicola
Published: August 2024

Background. Advancing age is a nonmodifiable risk factor for the development of coronary artery disease. Furthermore, patients >65 years old are considered at high risk for coronary artery bypass grafting (CABG). The aim of this study was to investigate whether there were any differences in clinical outcomes for patients with a mean age ≥65 undergoing CABG on or off pump. Methods. Systematic searches were conducted in EMBASE, PubMed, Web of Science, and Cochrane Central Registry of Controlled Trials (CENTRAL). The key search terms used were “cardiopulmonary bypass” OR “On pump” AND “off pump” OR “beating heart” AND “coronary artery bypass grafting” OR “CABG” AND “age.” This was followed by a meta‐analysis assessing the primary outcomes mortality, myocardial infarction, renal failure, and stroke in the short—(30 days) and midterm (12–44 months) and repeat revascularisation at midterm follow up. Secondary outcomes investigated included postoperative atrial fibrillation, number of units of blood transfused, ventilation time, length of intensive care unit stay, and length of hospital stay. Results. 14 studies involving 10,260 participants, 5,141 of whom had on‐pump CABG and 5,119 of whom had off‐pump CABG were identified. There was a significantly greater need for repeat revascularisation in the off‐pump group (risk ratio 1.47, 95% confidence interval 1.07 to 2.01, I2 = 0%, p = 0.02) at midterm follow up. The off‐pump group also had a shorter hospital stay. All other comparisons were insignificant. Conclusion. A number of different factors contribute to whether the increased need for repeat revascularisation for off‐pump patients is truly clinically significant. This requires further investigation in meta‐analysis based on longer‐term trials in patients with a mean age ≥65. Otherwise, the similarity in clinical outcomes for patients in this age group suggests the choice to carry out CABG on or off pump should continue to be at the surgeon’s discretion.
Unlocking the Maze: Exploring Nested Ecosystem of Mobility as a Service through Systematic Literature Review
Authors: Saleem Muhammad Abid , Yasmin Fouzia , Ismail Hina , Low David , Afzal Hanan
Published: August 2024

Technological advancements in the transportation sector have enabled new mobility solutions. Mobility as a Service (MaaS) is one such example that represents the integration of information technology‐enabled apps with transport modes to provide door‐to‐door and affordable transport options to substitute private cars. Research in transportation is growing in focus on MaaS, and so are commercial MaaS products in various developed countries across the world. This study employs the systematic quantitative literature review approach to select scientific research articles on MaaS published to date and proposes a nested ecosystem framework involving actors, infrastructure, value, and customers. The ecosystem framework presented in this review provides valuable guidance to both transport sector academics and practitioners, highlighting the challenges involved in the successful deployment of MaaS schemes. In the end, this review provides future research directions to expand knowledge on MaaS to answer questions in the wake of fast‐growing transport technology and global mobility patterns.
Impact of Donor Coronary Artery Disease on Recipient Outcomes in Heart Transplantation
Authors: Shah Aakash , Robinson Justin , Goerlich Corbin E. , Pasrija Chetan , Griffith Bartley P.
Published: August 2024

Background. Efforts to expand the heart donor pool have included the use of extended criteria donors (e.g., advanced age or comorbid conditions). Another potential avenue is donor hearts with pre‐existing coronary artery disease (CAD). Methods. A retrospective cohort study was performed using the Organ Procurement and Transplantation Network database between 10/1/1987 and 12/3/2020. Postoperative complications, graft failure, and survival were examined between recipients of donor hearts with abnormal (CAD donor) versus normal (non‐CAD donor) coronary angiogram. Analysis was performed in unmatched cohorts as well as after propensity score matching. Results. A total of 12,230 heart transplant recipients were identified, of which 940 (7.6%) had an abnormal donor coronary angiogram. There were no differences between CAD and non‐CAD donor groups in acute rejection, stroke, or dialysis prior to discharge or treatment for rejection <1 year. However, the CAD donor group had a greater pacemaker incidence (5.2% vs. 3.6%, P = 0.02). After matching, 785 patients were in each group and there were no differences in perioperative outcomes or treatment for rejection <1 year. There was no difference in 10‐year freedom from graft failure (53.1% vs. 54.6%, log rank = 1.03, and P = 0.31) or cumulative survival (54.7% vs. 55.8%, log rank = 0.63, and P = 0.43) between the matched CAD and non‐CAD donor groups. Conclusion. The presence of CAD may not be a contraindication to transplantation. Carefully selected donor hearts with CAD may have equivalent postoperative and long‐term outcomes to donor hearts without CAD. Further study in this area may expand the pool of donors.
Enhancing Efficiency of Luminescent Solar Concentrators through Laser Grooving Techniques
Authors: Mayoof Matrood Haider , Ahmadi-Kandjani Sohrab , Asgari Asghar
Published: August 2024

This study presents an innovative approach to mitigate the cost of solar devices by employing luminescent solar concentrators (LSCs) that act as waveguides to direct sunlight toward photovoltaic (PV) cells. LSCs, while effective, face challenges such as escape cones and reabsorption losses during light concentration. To address these issues, we investigate the application of a CO2 laser grooving technique to create microstructure grooves with varying characteristics (depths and spacing) on polymeric waveguide sheets. Our findings reveal a significant improvement in optical properties within the 500–600 nm range, aligning well with the emission spectrum of luminophores in the polymer matrix plate and the solar cell. Current–voltage (I–V) measurements of silicon solar cells attached to the LSC edge exhibit enhanced performance postlaser grooving. Additionally, we explore the characteristics of the solar cell attached to the scribed LSCs under different incident light angles. The champion LSC device, built with a groove depth of 492 nm and a spacing of 0.2 mm, achieved a power conversion efficiency of 25.47%, an open‐circuit voltage of 326.8 mV, a short‐circuit current density of 153.28 mA/cm2, and a fill factor of 50.8% under 100 mW/cm2 illumination (AM 1.5 G). These results are in good agreement with the predictions. Experimental and predicted results indicate that the introduction of extensive microstructure grooves not only minimizes optical losses from the escape cone but also enhances the optical and electrical properties of LSC systems. Scribed LSC devices emerge as a practical choice for integration into zero‐ or net‐energy buildings.
An Efficient Approach for Identifying Potential Bus Passenger Demand Based on Multisource Data
Authors: Li Lianghua , Xue Shouqiang , Xiao Yun
Published: August 2024

Big data provide massive samples and resources for exploring the operating rules of public transportation. This article proposes a method that combines multiple data sources to identify potential bus passenger flows, aiming to address the issue of insufficient identification accuracy with a single data source. First, the spatially weighted K‐means algorithm and improved DBSCAN algorithm are designed to partition traffic zones and residents’ travel flow OD is extracted based on mobile phone signaling data. Second, using bus IC card data and vehicle trajectory data, a method for identifying bus passenger boarding and alighting stops based on spatiotemporal clustering is proposed and the bus passenger flow OD for each traffic zone is calculated. By comparing the resident travel flow OD with the bus passenger flow OD, we set a threshold for the potential bus passenger demand proportion. Finally, the analysis is conducted using actual data from a city in central China. The city is divided into 43 traffic zones, with the maximum bus passenger flow proportion between zones being 14.9%, the minimum being 5.0%, and the average being 7.2%. The initial threshold for the potential bus passenger demand proportion is thus set to 7.2%, and a sensitivity analysis is conducted by gradually decreasing the threshold in increments of 0.5% to 6.7%, 6.2%, 5.7%, and 5.2%. The corresponding potential bus passenger demand OD pairs between traffic zones are identified as 419, 358, 245, 151, and 51. Urban managers should focus on the 51 pairs with relatively large potential flows to gradually optimize and balance the development of the bus network based on actual conditions. The method proposed provides important theoretical and practical support for effectively optimizing urban bus networks. However, there are limited indicators for identifying potential passenger flows; in the future, more multidimensional indicators will be taken into consideration.
The Change of Original‐ and Generic‐Targeted Antitumor Drugs under the National Centralized Drug Procurement Policy in Hohhot: An Interrupted Time‐Series Analysis
Authors: Wu Rihan , Gong Zhaotang , Wang Weisong , Zhu Benben , Guleng Siri
Published: August 2024

Background. This study aims to assess and explore the different effects of national centralized drug procurement (NCDP) on three targeted antitumor drugs’ use in clinical practice. Materials and Methods. Clinical drug volume data were collected covering 18 months before, during, and after the seventh round of the NCDP in Inner Mongolia. The interrupted time‐series method was employed to estimate the net effect of policy impact. Results. The volume of generic afatinib (30 mg) increased by 25.78 DDDs, the expenditures decreased by 3641.14 yuan (p = 0.001), and the DDDc decreased by 124.35 yuan (p < 0.001). The volume of generic afatinib (40 mg) increased by 65.19 DDDs (p < 0.001), the expenditures increased by 1304.93 yuan (p < 0.001), and the DDDc decreased by 120.2 yuan (p < 0.001). The volume of generic sunitinib increased by 75.79 DDDs (p < 0.001), the expenditures decreased by 15869.78 yuan (p < 0.001), and the DDDc decreased by 243.28 yuan. There was no significant change trend in volume, expenditures, and DDDc of the three original‐targeted drugs after NCDP intervention. Conclusions. After the policy intervention, generic afatinib (40 mg) successfully aligned with the objectives of NCDP by reducing drug costs and enhancing patient affordability; however, the desired outcomes were not achieved for generic afatinib (30 mg) and generic sunitinib. This discrepancy may be attributed to the inherent clinical efficacy and safety profiles of these drugs. Therefore, in implementing NCDP, it is necessary to enhance the clinical efficacy and safety of generic‐targeted antitumor drugs while considering economic efficiency.
Method on Efficient Operation of Multiple Models for Vision‐Based In‐Flight Risky Behavior Recognition in UAM Safety and Security
Authors: Kim Byeonghun , Noh Byeongjoon , Song Kyowon
Published: August 2024

The rapid development of urban air mobility (UAM) has emphasized the need for in‐flight control and passenger safety management. Recently, with the significant spread of technology in the field of computer vision, research has been conducted to manage passenger safety and security with vision‐based approaches. Previous research predominantly focuses on single‐task vision models, which limits their ability to comprehensively recognize various situations. In addition, conventional vision‐based deep learning models require substantial computational power, potentially reducing the operational sustainability of UAMs with limited electrical resources. In this study, we propose a novel cabin surveillance framework for passenger safety and security. The proposed method achieves high accuracy by using a single model optimized for a specific task and ensures maximum computational efficiency through a scheduler that executes the appropriate models based on the situation. It can effectively perform roles such as detecting prohibited items and recognition of dangerous/abnormal behavior. Moreover, it simplifies the management of the involved models by adding new models or updating the existing ones, and it provides a sustainable system by reducing energy consumption. Through comprehensive experiments on various benchmarks, we validated the effectiveness of each model and verified the practicality of the proposed framework in terms of time complexity and resource usage through practical tests.
In Vitro Antibacterial and Antioxidant Activities, Pharmacokinetics, and In Silico Molecular Docking Study of Phytochemicals from the Roots of Ziziphus spina-christi
Authors: Gebrehiwot Hadush , Ensermu Urgessa , Dekebo Aman , Endale Milkyas , Nefo Duke Tariku
Published: August 2024

Ziziphus spina-christi (Rhamnaceae family) is a medicinal plant traditionally used to treat dandruff, wounds, hair loss, diarrhea, mastitis, abdominal pain, and gastrointestinal complications. To support this, the present work aims to study the in vitro antibacterial and antioxidant activities of compound isolates from the roots of Ziziphus spina-christi along with their in silico computational analyses. Compounds were isolated on silica gel column chromatography and an agar disc diffusion and DPPH radical scavenging assays were employed to study the antibacterial and antioxidant activities, respectively. The ADME and toxicity properties of the compounds were evaluated using SwissADME and ProTox‐II online Web tools, respectively. Conversely, the in silico molecular docking studies were attained via a Biovia Discovery Studio Visualizer 2021 in combination with the AutoDock Vina software. The silica gel chromatographic separation of the combined CH2Cl2 : CH3OH (1 : 1) and CH3OH root extracts afforded trimethyl trilinolein (1), stearic acid (2), 13‐hydroxyoctadeca‐9, 11‐dienoic acid (3), β‐sitosteryl‐3β‐glucopyranoside‐6′‐O‐palmitate (4), and stigmasterol (5). Notably, the in vitro antibacterial study revealed the extract and β‐sitosteryl‐3β‐glucopyranoside‐6′‐O‐palmitate (4) with the highest inhibitory activities (15.25 ± 0.35 and 14.25 ± 0.35 mm, respectively) against E. coli compared to ciprofloxacin (21.00 ± 0.35 mm) at 2 mg/mL. The CH2Cl2 : CH3OH (1 : 1) extract (IC50 : 1.51 µg/mL) and β‐sitosteryl‐3β‐glucopyranoside‐6′‐O‐palmitate (4) (IC50 : 5.41 µg/mL) also exhibited auspicious DPPH scavenging activities, followed by stigmasterol (5) (IC50 : 6.88 µg/mL) compared to the ascorbic acid standard (IC50 : 0.46 µg/mL). The molecular docking analyses unveiled the highest binding affinity by β‐sitosteryl‐3β‐glucopyranoside‐6′‐O‐palmitate (4) (−8.0 kcal/mol) against P. aeruginosa PqsA relative to the ciprofloxacin standard (−8.2 kcal/mol). Furthermore, the organ toxicity predictions showed that all the compounds exhibit no hepatotoxicity and cytotoxicity effects and stigmasterol (5) affords drug‐likeness protocols. Overall, the combined experimental and computational investigations of this study support the traditional uses of Ziziphus spina-christi for antibacterial and natural antioxidant applications.
KHSRP Stabilizes m6A-Modified Transcripts to Activate FAK Signaling and Promote Pancreatic Ductal Adenocarcinoma Progression
Authors: Xu Zilan , Zhou Yifan , Liu Shaoqiu , Zhao Hongzhe , Chen Ziming , Li Rui , Li Mei , Huang Xudong , Deng Shuang , Zeng Lingxing , Zhao Sihan , Zhang Shaoping , He Xiaowei , Liu Ji , Xue Chunling , Bai Ruihong , Zhuang Lisha , Zhou Quanbo , Chen Rufu , Lin Dongxin , Zheng Jian , Zhang Jialiang
Published: August 2024

N 6-Methyladenosine (m6A) is the most prevalent RNA modification and is associated with various biological processes. Proteins that function as readers and writers of m6A modifications have been shown to play critical roles in human malignancies. Here, we identified KH-type splicing regulatory protein (KHSRP) as an m6A binding protein that contributes to the progression of pancreatic ductal adenocarcinoma (PDAC). High KHSRP levels were detected in PDAC and predicted poor patient survival. KHSRP deficiency suppressed PDAC growth and metastasis in vivo. Mechanistically, KHSRP recognized and stabilized FAK pathway mRNAs, including MET, ITGAV, and ITGB1, in an m6A-dependent manner, which led to activation of downstream FAK signaling that promoted PDAC progression. Targeting KHSRP with a PROTAC showed promising tumor suppressive effects in mouse models, leading to prolonged survival. Together, these findings indicate that KHSRP mediates FAK pathway activation in an m6A-dependent manner to support PDAC growth and metastasis, highlighting the potential of KHSRP as a therapeutic target in pancreatic cancer.

Significance: KHSRP is a m6A-binding protein that stabilizes expression of FAK pathway mRNAs and that can be targeted to suppress FAK signaling and curb pancreatic ductal adenocarcinoma progression.
HER2 Antibody–Drug Conjugates Are Active against Desmoplastic Small Round Cell Tumor
Authors: Zhang Tom , Febres-Aldana Christopher A. , Liu Zebing , Dix Jenna-Marie , Cheng Ryan , Dematteo Raymond G. , Lui Allan J.W. , Khodos Inna , Gili Leo , Mattar Marissa S. , Lisanti Jeanine , Kwong Charlene , Linkov Irina , Tipping Murray J. , de Stanchina Elisa , Odintsov Igor , Ladanyi Marc , Somwar Romel
Published: August 2024

Purpose:

Desmoplastic small round cell tumor (DSRCT) is a rare but highly aggressive soft tissue sarcoma that arises in the abdominopelvic cavity of young males. Since the discovery of EWSR1::WT1 fusion as the driver of DSRCT, no actionable genomic alterations have been identified, limiting disease management to a combination of surgery, chemotherapy, and radiation, with very poor outcomes. Herein, we evaluated ERBB2/HER2 expression in DSRCT as a therapeutic target.

Experimental Design:

ERBB2/HER2 expression was assessed in clinical samples and patient-derived xenografts (PDX) using RNA sequencing, RT-qPCR, and a newly developed HER2 IHC assay (clone 29D8). Responses to HER2 antibody–drug conjugates (ADC)—trastuzumab deruxtecan (T-DXd) and trastuzumab emtansine—were evaluated in DSRCT PDX, cell line, and organoid models. Drug internalization was demonstrated by live microscopy. Apoptosis was evaluated by Western blotting and caspase activity assays.

Results:

ERBB2/HER2 was detectable in DSRCT samples from patients and PDXs, with higher sensitivity RNA assays and improved IHC detectability using clone 29D8. Treatment of ERBB2/HER2-expressing DSRCT PDX, cell line, and organoid models with T-DXd or trastuzumab emtansine resulted in tumor regression. This therapeutic response was long-lasting in T-DXd–treated xenografts and was mediated by rapid HER2 ADC complex internalization and cytotoxicity, triggering p53-mediated apoptosis and growth arrest. Xenograft regression was associated with bystander payload effects triggering global tumor niche responses proportional to HER2 status.

Conclusions:

ERBB2/HER2 is a therapeutic target in DSRCT. HER2 ADCs may represent novel options for managing this exceptionally aggressive sarcoma, possibly fulfilling an urgent and historically unmet need for more effective clinical therapy.
Fasting in Science and Clinics: A Report on Proceedings from the International Scientific Symposium and Conference on Fasting in Berlin (June 2023)
Authors: Breinlinger Carolin , Meiß Sara , Hanslian Etienne , Jordan Stefan , Boschmann Michael , Khokhar Anika Rajput , Michalsen Andreas , Koppold Daniela A.
Published: August 2024

Background: A fasting conference and scientific symposium on fasting were held in Berlin in June 2023. Researchers and clinicians from around the world shared new findings, clinical insights, and work in progress during a 3-day program. Summary: Different fasting regimens, including prolonged, short-term, intermittent fasting, and time-restricted eating were discussed for preventive and therapeutic settings. Experimental and clinical findings shared ranged from biochemical and cellular fasting responses to fasting-mimicking agents, the role of the gut microbiome, and immunological effects. Clinically, a special focus was placed upon metabolic, autoimmune, neurodegenerative, and oncological diseases. The discussion also covered how modern technologies, practical adaptations to traditional protocols, and a supportive network of specialized physicians can assist in the practical application of fasting, among other subjects. Key Messages: Dose-response relationships, gender aspects, and the subjective experience of fasting seem promising for future research, while further investigation of religious fasting may offer deeper insights into motivational and health aspects.
Feasibility of Assessment of Habitual Salt Intake Using a 24-h Urinary Salt Excretion Self-Measuring Device
Authors: Morikawa Nozomi , Yamasue Kotaro , Tochikubo Osamu
Published: August 2024

Introduction: To assess habitual salt intake, tools are needed to measure 24-h urinary salt excretion repeatedly. We developed and validated a new portable salt monitor, which measures salt excreted per urination and sums the values to provide an accurate estimate of urinary salt excretion over 24 h. Methods: A previously developed salt monitor was improved with respect to the capacity, volume sensors, and equation for urinary sodium chloride concentration estimation. In 20 healthy Japanese female volunteers, 24-h urinary salt excretion was measured using the salt monitor and a conventional 24-h urine collection method on eight nonconsecutive days. Results: In a total of 157 days, there were no fixed or proportional errors between the methods. The mean salt intake over 8 days was 8.5 ± 2.0 g/day for the 24-h urine collection and 8.3 ± 2.3 g/day for the salt monitor, showing a strong correlation (r = 0.912, p < 0.001). At a cut-off value of 6 g, the salt monitor was able to completely classify individuals by habitual salt intake. Conclusion: The validity of the new salt monitor was confirmed. The device can be considered an alternative to the traditional 24-h urine collection for repeated surveys and self-management of daily salt intake.
Development and assessment of solar radiation forecasting models based on operational data
Authors: Suwarno Suwarno , Cahyadi Catra Indra , Sukarwoto Sukarwoto , Napitupulu Janter
Published: August 2024

Operational forecasting of solar radiation is critical for better decision-making by solar energy system operators, due to the variability of energy resources and demand. Although the numerical weather forecasting (NWP) model can predict solar radiation variables, there are often significant errors, especially in direct normal irradiation (DNI), which are influenced by the type and concentration of aerosols and clouds. This paper presents an artificial neural network (ANN) based method to generate operational DNI forecasts using weather and aerosol forecast data from the European Center for medium-range weather forecasts (ECMWF) and Copernicus atmospheric monitoring service (CAMS) respectively. The ANN model is designed to predict weather and aerosol variables at a certain time as input, while other models use the DNI forecast improvement period before the instant forecast. The model was developed using North Sumatra location observations and obtained DNI forecasting results every 10 minutes on the first day with DNI forecasting compared to the initial forecasting which was scaled down with the R2, mean absolute error (MAE), and relative mean square error (RMSE) models were 0.6753, 151.2, and 210.2 W/m2, so that and provides good agreement with experimental data.
New droop-based control of parallel voltage source inverters in isolated microgrid
Authors: Sanni Timilehin F. , Awelewa Ayokunle A. , Adoghe Anthony U. , Balogun Adeola , Somefun Tobi
Published: August 2024

Microgrids, featuring distributed generators like solar energy and hybrid energy storage systems, represent a significant step in addressing challenges related to the greenhouse effect and outdated transmission infrastructures. The operation and control of islanded microgrids, particularly in terms of grid voltage and frequency, rely on the synchronization of multiple parallel inverters connected to the distributed generators. However, to determine the necessary grid parameters for effective control, the presence of circulating currents from unbalanced grid voltages arises as a challenge. This situation necessitates the development of a new approach to achieve phase angle locking for grid synchronization, with the aim of maintaining the voltage within acceptable limits in islanded microgrids. This objective is realized through the creation of a microgrid network model, design of an adaptive filter, utilizing the double second-order generalized integrator–phase-locked loop (DSOGI-PLL), for dynamic voltage transformation. The design is evaluated by simulation using MATLAB/Simulink. The primary goal is to investigate the DSOGI-PLL-based droop control and compare its performance with the conventional synchronous reference frame–phase-locked loop (SRF-PLL) control approach. Notably, the DSOGI-PLL successfully eliminates the ripples in phase angle estimation, consequently enhancing the quality of voltage output in the microgrid.
Optimizing electric vehicle charging station placement integrates distributed generations and network reconfiguration
Authors: Bukit Ferry Rahmat Astianta , Zulkarnain Hendra , Kusuma Choirul Purnama
Published: August 2024

The surge in adoption of electric vehicles (EVs) within the transportation sector can be attributed to the growing interest in sustainable transportation initiatives. It is imperative to position electric vehicle charging stations (EVCS) strategically and distribute generations (DGs) to mitigate the effects of electric vehicle loads. This research employs the whale optimization algorithm (WOA) to optimize the placement of EVCS and DGs alongside network reconfiguration. The backward-forward sweep (BFS) power flow technique is utilized to compute load flow under varying load conditions. The primary objective of this investigation is to minimize power losses and enhance the voltage profile within the system. The proposed approach was tested on IEEE-33 and 69 bus systems and compared with particle swarm optimization (PSO) and genetic algorithm (GA) techniques. The simulation outcomes affirm the effectiveness of whale optimization algorithm in determining that integrating 3 EVCS with 3 DGs yields optimal outcomes following network reconfiguration, resulting in a 56.22% decrease in power losses for the IEEE-33 bus system and a 76.13% reduction for the IEEE-69 bus system. The simulation results indicate that the proposed approach enhances system performance across all metrics, showcasing the superior performance of WOA compared to PSO and GA in accomplishing set objectives.
Wind-powered water pumping system for corn plantations under the food estate program on Sumba Island, Indonesia
Authors: Aziz Amiral , Rostyono Didik , Zaky Toha , Hesty Nurry Widya , Ifanda Ifanda , Fauziah Khotimatul , Prasetyo Ridwan Budi , Wijayanto Rudi Purwo , Witjakso Ario , Syawitri Taurista Perdana , Mayasari Agustina Putri
Published: August 2024

The Food and Agriculture Organization (FAO) released a communiqué in March 2020 cautioning about the possibility of a worldwide food emergency due to coronavirus disease (COVID-19). As a response to the food shortages brought on by the COVID-19 outbreak, the authorities of Indonesia initiated a nationwide program aimed at improving the country's food supply known as the food estate (FE), which was later incorporated into national strategic programs. The climate and availability of surface water sources in this region make establishing an FE area in the Central Sumba Regency difficult. Sumba, on the other hand, possesses wind energy resources that can be transformed into electrical energy and used to pump underground water for agricultural purposes. A wind-powered water pump (WPW) is being developed in this study to provide water for maize plantations in the FE region in Central Sumba District, Indonesia. The study on the levelized cost of energy (LCOE) for water pumping indicates that the wind-powered system is more economically viable than the diesel-powered alternative. The LCOE for a WPW pumping system is 6,994 IDR/kWh, whereas the LCOE for a diesel-powered system is 16,667 IDR/kWh. The overall net present value of WPW and diesel-powered systems is 708,667,200 IDR and 2,158,349,000 IDR, respectively. This study contributes significantly to informed decision-making for enhancing the performance viability of the wind water pumping system for the food estate program in Indonesia.
Mechatronic system to classify plastic and metal bottles using capacitive and inductive sensors
Authors: Melo Napoly , Gonzales Abigail Sanchez , Paiva-Peredo Ernesto
Published: August 2024

The problem addressed in this article focuses on the management of plastic waste, which has experienced a significant increase in recent years, posing challenges in its management and recycling. In addition, the concentration of microplastics in water and their impact on health and the food chain is highlighted. The proposed solution consists of developing a mechatronic system for sorting plastic and metal bottles using capacitive and inductive sensors, respectively. The system demonstrated efficiency in tests, achieving 100% sorting for plastic and metal bottles. The need for bottles to be properly positioned for optimal performance was identified. This work highlights the importance of automation in mechatronic systems and the effectiveness of capacitive and inductive sensors in sorting materials.
Sliding mode control for the speed loop combined with adaptive coefficients for urban trains’ load variations of Nhon – Hanoi Station Metro line
Authors: Anh An Thi Hoai Thu , Cuong Tran Hung , Dinh Ha Van
Published: August 2024

Electric trains are becoming increasingly popular due to their environmental protection and ability to transport a large number of passengers. Alongside this trend, traction motors for electric trains have become diverse thanks to the rapid development of power electronics. Among them, the permanent magnet synchronous motor (PMSM) stands out with advantages such as high efficiency, high torque-to-current ratio, and compactness compared to other motors of the same power, making it the top choice. However, PMSM motors are nonlinear objects, so the nonlinear control technique of sliding mode control has been applied to the speed loop in this paper. Additionally, electric trains' inertial torque and load torque vary due to changes in the number of passengers during peak and off-peak hours and weather conditions. Therefore, this paper introduces two adaptive coefficients to account for these variations. Simulation results show that the sliding mode control technique for the speed loop circuit provides a faster and more accurate speed response. Meanwhile, the two parameters also adapt to the inertial and load torque variations. This ensures the safety and efficiency of the electric train system, contributing to the advantages of this mode of transportation.
Single phase robustness variable structure load frequency controller for multi-region interconnected power systems with communication delays
Authors: Nguyen Phan-Thanh , Nguyen Cong-Trang
Published: August 2024

This paper proposes an estimator-based single phase robustness variable structure load frequency controller (SPRVSLFC) for the multi-region interconnected power systems (MRIPS) with communication delays. The key attainments of this research consist of two missions: i) a global stability of the power systems is guaranteed by removing the reaching phase in traditional variable structure control (TVSC) technique; and ii) a novel output feedback load frequency controller is established based on the estimator tool and output information only. Initially, a single-phase switching function is constructed to disregard the reaching phase in TVSC. Then, an unmeasurable state variable of the MRIPS is estimated by using the proposed estimator tool. Next, a new SPRVSLFC for the MRIPS is suggested based on the support of the estimator tool and output data only. Furthermore, a sufficient constraint is constructed by retaining the linear matrix inequality (LMI) procedure for ensuring the robust stability of motion dynamics in sliding mode. Finally, the performance of interconnected power plant under changed multi-constraints is imitated with the novel control technique to validate the practicability of the plant.
Fuzzy integral tracking control of an activated sludge process
Authors: Bekaik Mounir , Bouras Hichem , Hamana Ahmed Sami
Published: August 2024

This paper addresses the issue of tracking the output of an activated sludge process using fuzzy integral control. First, the dynamics of the nonlinear process are modeled with a dynamic state space fuzzy model integrating the effect of external disturbances, and then an additional integral state of the output tracking error is introduced to obtain an augmented Takagi-Sugeno (TS) fuzzy model. The TS fuzzy model is able to describe the dynamics of complex nonlinear systems with an excellent degree of accuracy. It is formulated by fuzzy if-then rules which can give local linear representation of the overall nonlinear system. Second, the design of the fuzzy integral control is performed, in which the state feedback gains are obtained by solving linear matrix inequalities (LMI). The objective is to ensure trajectory tracking of an activated sludge process (ASP) by controlling two key variables: the substrate concentration and the level of dissolved oxygen. To assess the performance of the proposed control strategy, a comparative analysis is carried out with a gain scheduling PI (GS-PI) controller. Simulation results are provided to illustrate the effectiveness of the proposed approach. Where, the fuzzy integral control reduces the high energy consumption in water treatment plants.
Fuzzy-proportional-integral-derivative-based controller for stable control of unmanned aerial vehicles with external payloads
Authors: Tiep Do Khac , Tien Nguyen Van
Published: August 2024

In the paper, a proportional derivative (PD) controller and a fuzzy system tuning gains from proportional integral derivative controller are applied to stabilize an unmanned aerial vehicle (UAV), to control the attitude. Inputs of fuzzy logical controller consist of the speed required for the distance between the current position of quadcopter and the defined reference point and differences between orientation angles and variance in differences. Outputs of fuzzy logical controller consist of the proportional integral derivative coefficients which make pitch, roll, yaw and height values. The fuzzy-PD control algorithm is real-time applied to the quadcopter in MATLAB/Simulink environment. Based on data from experimental studies, although both classical proportional integral derivative controller and fuzzy-PD controller have accomplished to track a defined trajectory with the quadcopter.
A novel approach to simplified and secure message cryptography using chaotic logistic maps and index keys
Authors: Al-Ofeishat Hussein Ahmad , Alkasassbeh Jawdat S. , Alzyoud Khalaf Y. , Al-Taweel Farouq M. , Alrawashdeh Hisham , Al-Rawashdeh Ayman Y.
Published: August 2024

This paper proposes a novel method of message cryptography aiming to provide a simple, secure, and highly efficient approach to encryption and decryption. Unlike existing methods that rely on complex logical operations, our method utilizes simple rearrangement operations, reducing computational complexity while ensuring robust security. It employs a sophisticated, high-entropy private key designed to withstand hacking attempts. This key generates two chaotic keys using chaotic logistic map models, which are sorted to form two index keys essential for rearranging message blocks and characters during encryption and decryption. The process is facilitated by two simple operations, Get_index and Get_min, based on the index keys. These operations achieve streamlined procedures without compromising security. The method's speed is evaluated across various message lengths, demonstrating significant improvements in encryption time and throughput. The comparative analysis highlights the superior efficiency of this method compared to existing methods. Rigorous testing confirms that the proposed method meets stringent quality and sensitivity requirements, ensuring robust cryptographic standards. This innovative approach offers a promising solution for secure message encryption and decryption, combining simplicity, security and efficiency to meet the evolving needs of secure communication systems.
The contribution of digitized electroencephalogram in the clinical and therapeutic monitoring of substance uses disorders
Authors: Mengad Aziz , Ertel Merouane , Chakkouch Meryem , Ouaamr Ahmed , Elomari Fatima
Published: August 2024

The approaches used in clinical and therapeutic monitoring in addictology are multiple and generally based on subjective instruments such as interviews or observations. However, the lack of frankness can be an obstacle, as the patients monitored may not be entirely honest in their responses, and improvement in symptoms does not always mean continued abstinence. This article proposes a new objective method for monitoring the clinical and therapeutic evolution of addicted patients based on the study of electro-physiological changes collected by digitized electroencephalogram (EEG). The study is a case-control study of 30 hospitalized addicts who met the diagnostic and statistical manual of mental disorders (DSM-V) criteria for substance use disorders substance use disorders (SUD) and who underwent a standard digitized EEG at the end of their hospitalization. A control group of 30 healthy individuals was also included. This research shows a dominance of rapid-frequency beta 2 and hypovolted alpha 2 rhythms in cases with a clear sensitivity to activating maneuvers occasioned by hyperpnoea (HPN) and intermittent light stimulation (ISL) giving either a significant slowing of electrogenesis, bi-occipital entrainment, or an oculo-clonic response signifying a need for further care. However, the major challenge in understanding the EEG signal is that it is not always specific to SUD and suggests the need to consider the trans-diagnostic framework.
Dual soft decoding of linear block codes using memetic algorithm
Authors: Sliman Rajaa , Azouaoui Ahmed
Published: August 2024

In this article we will approach the soft-decision decoding for the linear block codes, is a kind of decoding algorithms used to decode data to form better original estimated received message, it is considered as a NP-hard problem. In this article we present a new decoder using memetic algorithm such metaheuristic technic operates on the dual code rather than the code itself that aims to find the error caused when sending a codeword calculated from a message of k bits of information, the resulting codeword contains n bits, including the redundancy bits, the efficiency of an error-correcting code is equivalent to the ratio k/n, the rate is belong the interval [0,1]. Hence a good code is the one that ensures a certain error correcting capability at minimum ratio. The results proved that this approach using a combination of genetic algorithm and local search algorithm provides a sufficiently good solution to an optimization problem; the new decoder is applied on linear codes where the structure is given by a parity check matrix.
A novel received signal strength indicator method for modeling Massive MIMO beamforming via multi-task deep learning
Authors: Ramadan Ibrahim El-Metwally , AbdElHalim Eman , Saleh Ahmed Ibrahim , Mostafa Hossam El-Din Salah
Published: August 2024

To achieve the best performance in terms of accuracy and complexity of massive multiple-input multiple-output (Massive MIMO) in wireless communication systems, hybrid beamforming (HBF) is a promising technique that provides high data rate multiplexing gains and enhances the spectral efficiency (SE) of the system. In this paper, a novel received signal strength indicator (RSSI) method is proposed to design an HBF for Massive MIMO BF via multitasking deep learning (DL) that minimizes the reliance on the channel state information (CSI) feedback. The trade-off between the enhancement SE of the system and the deep neural networks (DNNs) performance is optimized, and the results reveal that the proposed novel DL techniques achieve predicted spectral efficiencies with accuracy of 99.23% and 95.64% for Deep-HBF and Deep-AFP, respectively. The processing times for Deep-HBF and Deep-AFP are 709.2914 sec and 1425.864 sec, respectively. Notably, Deep-AFP exhibits a higher range of computational complexity compared to Deep-HBF. It is worth mentioning that the proposed techniques utilize the same DNN architecture.
Prediction of student performance at polytechnic using machine learning approach
Authors: Hutajulu Kristina , Wulandhari Lili Ayu
Published: August 2024

Educational data mining (EDM) is a strategic technique for exploring data in educational environments to gain a deeper understanding of education. One of the goals of EDM is to predict things related to students in the future which can be done using a machine learning approach. In this paper, a regression model is developed to predict student performance in the first semester and the waiting period for graduate employment using machine learning approach based on informatics management (MI) and non-informatics management (non-MI) student data. Four regression models are compared for predicting student performance in the first semester and waiting period for graduate employment, including support vector regression (SVR), random forest regression (RFR), AdaBoost regression (ABR), and XGBoost regression. Based on the experiment, prediction of students' performance in the first semester, the highest R2 result produced by SVR model by value of 0.58 for MI and by RFR by value of 0.34 for non-MI. While, waiting period for graduate employment prediction, the highest R2 result produced by AdaBoost regression by value of 0.44 for MI and SVR by value of 0.39 for non-MI.
A semantic-based approach for domain specific language development
Authors: Negm Eman , Salah Akram , Makady Soha
Published: August 2024

A domain specific language (DSL) ties the business and technical models, by letting technical developers write programs with the business domain properties. Yet, DSLs are not used due to the cost of developing them. Such cost stems from the needed expertise within both the domain knowledge and language development technicalities for any DSL engineer who would design such a language. This paper proposes a semantic-based DSL development approach that utilizes an ontology as a formal way for domain representation. The domain ontology is semi-automatically transformed into a DSL. Then, an ontology reasoning algorithm provides reasoning services on the DSL structure and the programs developed using such DSL by application developers. Such reasoning services can automatically detect flaws in the DSL design like possible inconsistency or the presence of unsatisfiable or redundant classes thus serving the DSL engineer. The reasoning services can also discover inconsistency or redundant classes in programs built using the designed DSL, thus serving the application developer. The proposed approach was implemented within a language workbench using projectional-editing and was evaluated on two different ontologies from varied domains. The results show correct transformation of the input ontology, valid instantiation of designed application, and efficient reasoning services.
Integrating numerical methods and machine learning to optimize agricultural land use
Authors: Tynykulova Assemgul , Mukhanova Ayagoz , Mukhomedyarova Ainagul , Alimova Zhanar , Tasbolatuly Nurbolat , Smailova Ulmeken , Kaldarova Mira , Tynykulov Marat
Published: August 2024

In the current context, optimizing the utilization of agricultural land resources is increasingly vital for production intensification. This study presents a methodological approach employing numerical methods and machine learning algorithms to analyze and forecast land use optimality. The objective is to develop effective models and tools facilitating rational and sustainable agricultural land resource management, ultimately enhancing productivity and economic efficiency. The research employs data dimensionality reduction techniques such as principal component analysis and factor analysis (FA) to extract key factors from multidimensional land data. The simplex method is utilized to optimize resource allocation among crops while considering constraints. Machine learning algorithms including extreme gradient boosting (XGBoost), support vector machine (SVM), and light gradient boosting machine (LightGBM) are employed to predict optimal land use and yield with high accuracy and efficiency. Analysis reveals significant differences in model performance, with LightGBM achieving the highest accuracy of 99.98%, followed by XGBoost at 95.99%, and SVM at 43.65%. These findings underscore the importance of selecting appropriate algorithms for agronomic data tasks. The study's outcomes offer valuable insights for formulating agricultural practice recommendations and land management strategies, integrable into decision support systems for the agricultural sector, thereby enhancing productivity and production efficiency.
Development of an algorithm for identifying the autism spectrum based on features using deep learning methods
Authors: Amirbay Aizat , Mukhanova Ayagoz , Baigabylov Nurlan , Kudabekov Medet , Mukhambetova Kuralay , Baigusheva Kanagat , Baibulova Makbal , Ospanova Tleugaisha
Published: August 2024

The presented scientific work describes the results of the development and evaluation of two deep learning algorithms: long short-term memory with a convolutional neural network (LSTM+CNN) and long short-term memory with an autoencoder (LSTM+AE), designed for the diagnosis of autism spectrum disorders. The study focuses on the use of eye tracking technology to collect data on participants' eye movements while interacting with animated objects. These data were saved in NumPy array format (.npy) for ease of later analysis. The algorithms were evaluated in terms of their accuracy, generalization ability, and training time, which was confirmed by experts. The main goal of the study is to improve the diagnosis of autism, making it more accurate and effective. The convolutional neural network long short-term memory and autoencoder-long short-term memory models have shown promise as tools for achieving this goal, with the autoencoder model standing out for its ability to identify internal relationships in data. The article also discusses potential clinical applications of these algorithms and directions for future research.