bluuebunny/crossref_metadata_2025 huggingface dataset:
Refining tomato disease recognition: hyperparameter tuning on ResNet-101 architecture for precise leaf-based classification
Authors: Prasetyo Tegar Arifin , Gaol Tiurma Lumban , Sipahutar Nico Felix , Siahaan Tessalonika , Manik Trito Exaudi , Chandra Rudy
Published: March 2024

Tomatoes plants are widely recognized as versatile vegetables globally. This study aims to develop a high-precision web interface for classifying various leaf diseases in tomatoes. Utilizing a convolutional neural network (CNN) algorithm using the residual network-101 (ResNet-101) architecture, this tool aids farmers in accurately identifying leaf diseases in tomatoes, thereby reducing the risk of crop failure. The dataset comprises 6,800 images, categorized into four classes: early blight, spider mites two spotted, tomato yellow leaf curl virus, and healthy tomatoes, each containing 1,700 images. Hyperparameter tuning was conducted as part of an experiment aimed at enhancing the performance of the model. Experiments involved varying epoch values (10, 25, 30, 50, 60, 75, 100, and 110), a fixed batch size of 4, different learning rates (0.1, 0.01, 0.001, 0.0001), and num workers (4, 8, 16). The results demonstrated an accuracy of 99% with 100 epochs, a batch size of 4, a learning rate of 0.001, and 16 num workers. Consequently, this research contributes to a deeper understanding of disease management in tomato plants, ensuring optimal quality and quantity of the harvest.
A standard ranking algorithm for robust iris template protection
Authors: Hameed Yassir Mohammed Ali , Dziyauddin Rudzidatul Akmam , Kamaruddin Norshaliza , Ahmad Norulhusna
Published: March 2024

In iris biometric recognition systems, protecting the storage and transmission of iris templates is crucial, and template protection techniques are pivotal for ensuring their security. A prevalent approach involves using indexing methods as an effective algorithm for iris template protection, leveraging the index or rank of the extracted iris code to generate a secure iris template. Meantime, many privacy threats to biometric data have emerged, necessitating heightened protection measures. Specifically, protecting the privacy of iris data is imperative within the context of iris template protection during recognition processes. As stipulated by the international standard ISO/IEC 30136, effective iris template protection must concurrently meet the criteria of irreversibility, revocability, and unlinkability. Nevertheless, existing indexing methods on iris template protection faced the formidable challenge of simultaneously fulfilling these three privacy requirements while maintaining the efficacy of iris recognition. This paper introduces a standard ranking (standardR) algorithm, named standardR, designed to enhance the security of iris templates by transforming each iris template into an irreversible representation. The experimental results on the benchmarked Casia-Iris-interval dataset, along with two additional iris datasets MMU1 and UBRIS 1, demonstrate the efficacy of the proposed algorithm. The proposed standardR algorithm achieves an equal error rate (EER) of 0.1695% and an area under the curve of 0.93011% with the Casia-Iris-Interval dataset. Furthermore, the algorithm maintains efficient recognition with a reduced iris code length of 1280 bits, a time complexity of O(n log n), and satisfies the biometric template protection (BTP) requirements in irreversibility, unlinkability, and renewability.
Sanskrit to Hindi language translation using multimodal neural machine translation
Authors: Kammar Prashanth , Baraki Parashuram , Ganganayaka Sunil Kumar , Byranahalli Eraiah Manjunath Swamy , Arun Kumar Kolakaluri Lakshman
Published: March 2024

Machine translation (MT) is a subfield of computer features that focuses on the automatic translation from one natural language into another without any human involvement. Due to native people interacting in a variety of languages, there is a great need for translating information between languages to send and communicate thoughts. However, they disregard the significance of semantic data encoded in the text features. In this paper, multimodal neural machine translation (MNMT) is proposed for Sanskrit-Hindi translation. The main goal of the proposed method is to fully utilize semantic text features on NMT architecture and to minimize testing and training time. The MNMT is validated on two different NMT architectures: recurrent neural network (RNN) and self-attention network (SAN). The MNMT method’s efficacy is demonstrated by employing the dataset of Sanskrit-Hindi Corpora. Extensive experimental outcomes represent the proposed method’s enhancement over baselines on both architectures. The existing methods, namely, English-to-Indian MT system, Sanskrit-Hindi MT system, and hybrid MT system are used to justify the efficacy of the MNMT method. When compared to the above-mentioned existing methods, RA-RNN respectively achieves a superior BLEU and METEOR of 80.5% and 75.3%, while the RA-SAN respectively achieves a superior BLEU and METEOR of 78.2% and 77.1%.
Augmented reality for anatomy course for children with autism
Authors: Lazo-Amado Misael , Andrade-Arenas Laberiano
Published: March 2024

Autism spectrum disorder (ASD) that mainly affects social interaction and effective communication, showing problems in their learning, reflected in the lack of attention in schools, such as in anatomy classes. The main objective of the project is to develop a mobile application for children with autism to improve their learning in Anatomy and social interaction through augmented reality (AR). The methodology to be used is ADDIE which is in charge of analyzing the experience of the work team to find the ease of software development, proposing the efficient development with continuous improvement according to the evaluation to the specialists. The results show the evaluation of the specialists on the mobile application with AR where they will indicate their satisfaction with the application. In conclusion, it will be shown a mobile application with AR that offers a technological and eye-catching novelty for users in order to improve the educational development of children with autism spectrum disorder.
Text Classification on Customer Feedback: A Systematic Literatures Review
Authors: Sidek Zuleaizal , Syed Ahmad Sharifah Sakinah , Kumar Yogan Jaya , Ibrahim Teo Noor Hasimah
Published: March 2024

User feedback in text classification serves multiple purposes, including refining models, enhancing datasets, adapting to user preferences, identifying emerging topics, and evaluating system performance, all of which contribute to the creation of more effective and user-centric classification systems. Many text classification techniques, including data mining, machine learning, and deep learning approaches, have been employed in previous literature, each making significant contributions to the field. This paper aims to contribute by guiding researchers seeking commonly used classification techniques and evaluation metrics in text processing. Additionally, it identifies the classification technique that generates higher accuracy and works as a basis for researchers to synthesize studies within their respective fields. PRISMA methodology is adapted to systematically review 28 current literatures on text classification on user feedback. The results obtained are guided by four research questions; paper distribution year, dataset source and size; evaluation metric and model accuracy. The review has shown that support vector machines (SVM) are frequently employed and consistently achieve high levels of accuracy as high as 97.17% with various datasets used. The future direction of this work could explore models that integrate sentiment analysis and natural language understanding to more accurately capture nuanced user opinions and preferences.
A proposed model for enhancing e-bank transactions: an experimental comparative study
Authors: Alzyadat Wael , Shaheen Ameen , Al-Shaikh Ala’a , Alhroob Aysh , Al-Khasawneh Ziyad
Published: March 2024

In this paper, we introduce a novel approach to address the dynamic prediction of customer activity in electronic payment transactions for individual clients. Our approach is founded on customer online payment transaction records from registered UK-based online retailers between 01/12/2009 and 09/12/2011. These retailers primarily specialize in unique gift items for various occasions, catering to a wide range of clients, including wholesalers. We used classification analysis based on the correlation coefficient to measure and describe a customer's electronic payment capability based on the quality of products they purchase. Furthermore, we trained multi-layered models (linear model, deep learning, random forest, and support vector machines (SVM)) to capture the dynamics of e-bank transaction reinforcement for retail customers using machine learning. Real transaction data from a UK online retailer was employed in our study. The experimental results consistently demonstrated the effectiveness of our proposed strategy.
Cross-layer multipath routing approach and link quality indicator for QoS provisioning in mobile WMSN
Authors: Pochal Bharati S. , Agarkhed Jayashree , Patil Siddarama R.
Published: March 2024

With the recent advancement in mobile adhoc networks (MANET’s) and technology, applicability, and integration of wireless multimedia sensor networks (WMSN) in MANET’s has led to creation of smart distributed system for high-speed mobile multimedia streaming and real time multimedia traffic transmissions. In this paper, we propose cross-layer multipath routing approach with link quality indicator (CLMRLQI) to compute stable link between two nodes. CLMRLQI discovers stable multipath routes by considering cross-layer routing metrics such as energy and bandwidth to support quality of service (QoS). The simulation scenarios are carried on network simulation tool and QoS parameters such as throughput, PDR, delay, overhead and energy consumption are analysed.
An improve unsupervised discretization using optimization algorithms for classification problems
Authors: Mohamed Rozlini , Samsudin Noor Azah
Published: March 2024

This paper addresses the classification problem in machine learning, focusing on predicting class labels for datasets with continuous features. Recognizing the critical role of discretization in enhancing classification performance, the study integrates equal width binning (EWB) with two optimization algorithms: the bat algorithm (BA), referred to as EB, and the whale optimization algorithm (WOA), denoted as EW. The primary objective is to determine the optimal technique for predicting relevant class labels. The paper emphasizes the significance of discretization in data preprocessing, offering a comprehensive approach that combines discretization techniques with optimization algorithms. An investigative study was undertaken to assess the efficiency of EB and EW by evaluating their classification performance using Naive Bayes and K-nearest neighbor algorithms on four continuous datasets sourced from the UCI datasets. According to the experimental findings, the suggested EB has a major effect on the accuracy, recall, and F-measure of data classification. The classification performance using EB outperforms other existing approaches for all datasets.
Remarks on a stochastic geometric model for interference-limited cellular communications
Authors: Nassar Hamed , Taher Gehad Mohamed , El Hady El Sayed
Published: March 2024

A plethora of stochastic geometry (SG) models have been developed for cellular communications, especially in the context of internet of things (IoT) applica tions. A typical assumption in such models is that base stations (BS) are de ployed in the Euclidean plane as a spatial poisson point process (PPP) of some density λ, with each communicating equipment transmitting at some power p. The usual objective of these models is to characterize the cellular coverage prob ability in both the downlink (DL) and uplink (UL) directions. In this article we expose, in the form of four remarks, the peculiar behavior of a baseline stochas tic geometric model of an interference-limited cellular system. Specifically, we reveal that under some assumptions, the coverage probability in both the UL and DL directions for this system is independent of both λ and p, flagrantly contra dicting intuition. The aim of the article is by no means to invalidate the use of SG in modeling communications systems, but rather to point out that such modeling may not be adequate all the time.
A critical evaluation of DC microgrid implementation in Indonesia: opportunities and challenges
Authors: Halim Levin , Anugrah Pinto , Kurniawan Aditya , Karim Khairuddin
Published: March 2024

This study thoroughly investigates the potential of direct current (DC) microgrids to enhance electricity access in rural and remote areas of Indonesia that continue to face significant obstacles despite ongoing national electrification efforts. Utilizing a mixed-methods approach, this research comprehensively evaluates socio-economic and technical factors that influence the adoption of DC microgrids. The results indicate that DC microgrids offer significant potential for enhancing energy access, reliability, and sustainability, particularly when combined with renewable energy sources. This aligns with Indonesia’s move towards renewable energy. Nevertheless, the analysis identifies significant obstacles, such as the substantial initial investment, the requirement for complete regulatory frameworks, and the technological complexities that need to be conquered. In conclusion, DC microgrids present a promising solution for rural electrification. However, the implementation requires a strategy that emphasizes strategic investments, policy innovation, and capacity-building initiatives. This research significantly contributes to the study of sustainable energy by evaluating the criticality of integrating policies and technology for implementing DC microgrids as a key factor in achieving sustainable energy access in Indonesia.
Support vector regression-based state of charge estimation for batteries: cloud vs non-cloud
Authors: Youssef Mohamed Ben , Jarraya Imen , Zdiri Mohamed Ali , Salem Fatma Ben
Published: March 2024

Embracing the potential of cloud technology in the field of electric vehicle advancements, this paper explores the application of support vector regression (SVR) for accurate state of charge (SOC) estimation of lithium-ion batteries in various computational landscapes. This study aims to scrutinize and compare the performance of SOC estimation, with a specific focus on precision, computational efficiency, and execution speed. The investigation is conducted across diverse environments, including a traditional non-cloud setup and two cloud-based platforms-a standard cloud environment employing Amazon web services (AWS) EC2 servers and an enhanced configuration utilizing the MATLAB production server. The investigation not only emphasizes the effectiveness of cloud integration but also provides valuable insights into the strengths and weaknesses of the proposed methodology. The experimental results contribute to a nuanced understanding of the methodology’s performance, shedding light on its potential implications for advancing electric vehicle technologies. This study thus extends its significance beyond technical considerations, providing a broader perspective on its relevance to global electrification initiatives.
Multilevel inverter: harmonic analysis with and without filters for RL load using SPWM techniques
Authors: Nagarajappa Champa Patanegere , Deshpande Abhay Anandarao
Published: March 2024

Multilevel inverter (MLI) gains more attraction compared to conventional inverter as it generates a staircase output voltage that mimics sine waves of desired output voltage. Cascaded H-bridge (CHB) topology is taken into consideration owing to several benefits over conventional inverters. Various levels of CHB (3 and 5 level) inverters are compared on diverse parameters. In this paper level shift sinusoidal pulse width modulation technique is considered resulting in reduced lower order harmonic (LOH) distortion and improve the quality of output current and voltage depending on the load. Since, the LOH are too dangerous for power electronic circuits. An attempt to shift all the LOH above 50th order depending on the modulation technique with analogy for selecting an appropriate switching frequency is highlighted. The effect of change in switching frequency and modulation index (MI) on RMS output voltage, % voltage total harmonic distortion (VTHD), output power factor with different modulation techniques such as phase disposition pulse width modulation (PsD PWM), phase opposite disposition (POD PWM), alternative phase opposition disposition (APOD PWM) is portrayed in the paper. Further, to boost the performance a unique filter circuits with optimal design values of Inductance and capacitance driven with IEEE 519-2022 standards. The effectiveness in terms of with and without filter is verified and validated using MATLAB.
Development design of an IoT-based smart home monitoring system with security features
Authors: Fitriyan Rahmawati , Syafii Syafii
Published: March 2024

A smart home is a system that has been programmed and can work automatically by utilizing internet of things (IoT) technology, this system can control various electronic devices in the home. This paper presents a design for developing an IoT-based smart home monitoring system with the addition of security features. This research aims to design and develop a smart home monitoring system that uses the IoT which operates via the web and improves the security aspects of the system. This research includes the development of hardware and software that enables efficient and safe monitoring and control of various aspects of the home via smartphone or computer-based devices using resources from solar power plants. This system relies on the use of a Raspberry Pi as a microcontroller and several sensors. In this context has important value in maintaining user security, and privacy and supports the growing development of the smart home technology industry.
AGV maneuverability simulation and design based on pure pursuit algorithm with obstacle avoidance
Authors: Ketsayom Singhanart , Maneetham Dechrit , Crisnapati Padma Nyoman
Published: March 2024

This paper discusses the simulation of an automated guided vehicle (AGV) with the differential-drive mobile robot (DDMR) concept. Using this wheel configuration, the AGV can maneuver in tight workspaces. However, controlling a self-driving AGV with obstacle avoidance is not easy. Therefore, this paper proposes a control system to drive an AGV with several process stages. First, a kinematic model is formulated to represent the AGV with the concept of two wheels that can be controlled differentially. In the second stage, the pure pursuit control method is applied to the model so that the AGV can follow the waypoint coordinates determined and combine them with obstacle avoidance. Finally, the effectiveness of the control system was verified using simulation. The look-ahead parameter with a value of 0.2 meters shows optimal results so that pure pursuit control can reach all waypoint coordinates. Based on this simulation, the AGV prototype was then designed, assembled, and equipped with an internet of things-based obstacle avoidance system. While the simulation proves promising, the anticipated challenges identified in the AGV field test, such as GPS inaccuracies and signal obstructions, underscore the need for ongoing improvements in real-world applications.
Rhinitis phototherapy prototype with timer based on light energy
Authors: Loniza Erika , Junita Mita , Jusman Yessi , Mohd Kanafiah Siti Nurul Aqmariah , Chairunnisa Kurnia
Published: March 2024

The set of timers in using phototherapy is major problem which has to be resolved to get a good performance of rhinitis phototherapy. This research aims to develop a prototype of phototherapy for allergic rhinitis, incorporating a timer based on light energy. The prototype utilizes a laser diode as a visible light source, specifically with a wavelength of 650 nm. The recommended safe and effective dose of light energy ranges from 1 to 10 Joules, which has been converted into minutes. Measurement tests indicate an average wavelength of 652.40 nm for the right laser, with a measurement uncertainty of ±0.11, and 653.23 nm for the left laser, with a measurement uncertainty of ±0.05. The laser diode source has an average voltage of 1.91 volts and an average current of 1.89 milliamperes, with a measurement uncertainty of ±0.00 and ±0.01, respectively. Additionally, the average discrepancy in the timer is 0.082 minutes for the 10-minute setting and 0.082 minutes for the 20-minute setting. These results confirm the effectiveness and suitability of the developed tool for practical use. The proposed method was useful for rhinitis therapy by using light energy.
Mobilenet, inception ResNet and GoogleNet for epilepsy detection using spectrogram images
Authors: Edderbali Fatima , Harmouchi Mohammed , Essoukaki Elmaati
Published: March 2024

Epilepsy is considered the most common cerebral disorder, around 1% of the worldwide population suffer from it. Recently, detection of epilepsy has attracted more and more attention. It has become a hastily increasing problem that can worsen their conditions which necessitate a specific and crucial attention where the symptoms can be an impaired awareness or motor symptoms. Besides that, the difficult process of manual inspection of electroencephalography electroencephalogram (EEG). This paper proposes using transfer learning models to detect both normal and epileptic brain activity and auto-classify signals from the brain. The models considered for this study are GoogleNet, MobileNet, and inception residual neural network inception ResNet. These models were associated with seven different classifiers such as discriminant. These classifiers were tested, analyzed and compared with each other. The efficiency of models is comparatively evaluated through result using multiple metrics. We therefore attained an accuracy of 96.53%, a precision of 97.18%, a false positive rate of 2.78% and an F1-score of 96.50%. Finally, comparison of the suggested approach with existing research shows that the performance of epilepsy classification has been markedly enhanced.
IoT-enhanced infant incubator monitoring system with 1D-CNN temperature prediction model
Authors: Ady Aryanto I Komang Agus , Maneetham Dechrit , Crisnapati Padma Nyoman
Published: March 2024

This research aims to develop a monitoring system and temperature prediction model in neonatal premature infant incubators by applying the internet of things (IoT) concept and the 1-dimensional convolutional neural network (1D-CNN) method. The system is designed by integrating sensors, actuators, and microcontrollers connected through Wi-Fi network with message queue telemetry transport (MQTT) protocol. Sensor data in the incubator is stored in a database and displayed in real-time on a web application. The data in the database is also used for creating a temperature prediction model in the incubator. Test results indicate that the best model configuration consists of 5 neurons in the first layer, 20 neurons in the second layer, and a dense layer with 100. The evaluation of this model yields a high level of accuracy with an root mean square error (RMSE) of 0.200 °C, MSE of 0.004 °C, mean absolute error (MAE) of 0.152 °C, and mean absolute percentage error (MAPE) of 0.4%. Based on the error values obtained between the predicted and actual values from each evaluation technique in the model, it can be concluded that the range between the real and predicted values is approximately 0.2 °C. Overall, this research contributes to improving the quality of care for premature infants.
Enhancing lung lesion localization in CT-scans: a novel approach using FE_CXY and statistical analysis
Authors: Jafery Nurul Najiha , Sulaiman Siti Noraini , Osman Muhammad Khusairi , A. Karim Noor Khairiah , Abdullah Mohd Firdaus , Isa Iza Sazanita , Che Soh Zainal Hisham
Published: March 2024

Intelligence algorithm systems rely on a large dataset to effectively extract significant features that can recognize patterns for classification purposes and extensively utilized to assist the physicians in diagnosis of lung cancer. Extracting valuable features from the available dataset is crucial, especially in cases where additional real data may not be readily accessible. In this context, we propose a novel method called feature extraction based on centroid (FE_CXY) for lesion localization, utilizing a statistical approach. The approach begins with a segmentation process that employs image processing techniques to extract features of interest which is data centroid. This extracted data is then used to compute statistical measurements, revealing hidden patterns that contribute to distinguishing between lesion and non-lesion locations. The method’s efficiency is reflected in the development of robust models with improved performance in localizing lung lesions. The study’s statistical findings strongly indicate that FE_CXY plays a crucial role as an important feature for detecting lesion localization supported by a student’s t-test, which identifies a statistically significant difference in the patterns between lesion and non-lesion localization (p<0.05). By incorporating this method into lung cancer detection systems, we anticipate improved accuracy and efficacy, thereby benefiting early diagnosis and treatment planning.
Comparative analysis of coding schemes for effective wireless communication
Authors: Aljubouri Mohammed A. , Iskandarani Mahmoud Zaki
Published: March 2024

Communication systems have recently focused on sending information efficiently and effectively from one sending point to another across a communication channel in the shortest amount of time. The main objective of this work is to compare the high-range coding scheme types, such as low density parity check (LDPC), turbo, and convolution, to see which works better and is more efficient. to establish a coding system with quadrature amplitude modulation (QAM) modulation and an additive white gaussian noise (AWGN) noisy channel to find which is more reliable and resilient for encoding and decoding. Because of this, digital media has to be sent over wireless channels and through satellites, requiring a connected network all the time, which has become a major concern over time. Furthermore, the high amount of data and efficiency are the focus points. After running the simulation, it was found that 64 QAM with a rate of 0.455 and an efficiency of 2.731 has a bit error rate (BER) of 0.001 and a 7.08 dB energy per bit Eb/No, and the 256 QAM simulation revealed that it has a BER of 0.001 and 11.88 dB Eb/No with a rate of 0.736 and an efficiency of 5.891. Over the AWGN channel noise, the simulation built a standard orthogonal frequency division multiplexing (OFDM) system, which used MATLAB.
Design and implementation of duty cycle-based futuristic clustering technique in WSN
Authors: Tagare Trupti Shripad , Narendra Rajashree
Published: March 2024

In recent times, wireless sensor networks (WSNs) and their applications have exhibited a remarkable surge. These networks strive to devise and implement strategies that optimize network energy utilization, thereby extending their operational lifespan. An energy efficient network can be achieved using renewable source of energy and by controlling the duty cycle of nodes. The pivotal role of duty cycle in curtailing energy consumption in WSNs cannot be overstated. In this work, we introduce a novel duty cycle based futuristic clustering technique (DCBFCT) employing a nearest neighbor approach. This technique selectively induces sleep and awake modes in nodes, effectively minimizing the network’s overall energy consumption and, consequently, prolonging its lifespan. It calculates optimal node duty cycle values based on distance. Results demonstrate a substantial reduction in energy consumption, exhibiting an improved network lifetime. Empirical results presented in this study not only affirm the effectiveness of DCBFCT but also contribute valuable insights toward the development of sustainable and resilient WSNs in the era of burgeoning sensor network applications. The experimentation is conducted using the MATLAB/Simulink tool, considering diverse cases. The scalability and versatility of DCBFCT make it suitable for deployment in real-world applications, ranging from environmental monitoring to industrial automation.
Proactive ransomware prevention in pervasive IoMT via hybrid machine learning
Authors: Tariq Usman , Tariq Bilal
Published: March 2024

Advancements in information and communications technology (ICT) have fundamentally transformed computing, notably through the internet of things (IoT) and its healthcare-focused branch, the internet of medical things (IoMT). These technologies, while enhancing daily life, face significant security risks, including ransomware. To counter this, the authors present a scalable, hybrid machine learning framework that effectively identifies IoMT ransomware attacks, conserving the limited resources of IoMT devices. To assess the effectiveness of their proposed solution, the authors undertook an experiment using a state-of-the-art dataset. Their framework demonstrated superiority over conventional detection methods, achieving an impressive 87% accuracy rate. Building on this foundation, the framework integrates a multi-faceted feature extraction process that discerns between benign and malign actions, with a subsequent in-depth analysis via a neural network. This advanced analysis is pivotal in precisely detecting and terminating ransomware threats, offering a robust solution to secure the IoMT ecosystem.
Democratic Autocracy: a Populist Update to Fascism under Neoliberal Conditions
Authors: Tuğal Cihan
Published: March 2024

What are the social dynamics behind the rise and resilience of today’s authoritarian regimes? This paper seeks to answer this question by focusing on the longest lasting elected autocracy of our era, the AKP (Justice and Development Party) regime in Turkey. Building on the authoritarian neoliberalism literature’s criticism of the scholarship on competitive authoritarianism, I point out the seeds of authoritarianism in the pro-market reforms of the 1980s–2000s. However, both literatures fail to address the popular embrace of authoritarianism. In critical engagement with analyses of fascism, I develop the concept ‘democratic autocracy’: a parliamentarised, individualised, personalistic, and relatively more market-oriented, twenty-first century version of fascism. The democratic autocracy in Turkey organises and mobilises the people through parties, youth and aid associations, paramilitary organisations, and unions. However, both unresolved economic crises and democratic autocracy’s sociopolitical dynamics set limits to its resilience, as well as to the sustainability of its parliamentarism.
Moratorium As a Crucial Component of Corporate Restructuring Under Saudi Bankruptcy Law
Authors: Alfawzan Faisal
Published: March 2024

The moratorium or automatic stay is a fundamental component of corporate restructuring procedures. It provides the distressed company with a brief respite from its creditors by temporarily barring all collection efforts and all proceedings against the company and its assets. By temporarily barring such actions, the moratorium provides the company with time during which it can negotiate with its creditors and formulate a proper restructuring plan. The purpose of this article is to examine the efficacy of moratorium under the Saudi restructuring law in light of the UK and the US experiences. The article is principally concerned with two key issues: the scope of the moratorium and the circumstances under which relief from a moratorium may be granted.
Comparison of IWATE, Institut Mutualiste Montsouris, and Southampton Laparoscopic Liver Resection Difficulty Scoring Systems for Predicting Intra and Postoperative Outcomes in Robotic Hepatectomy
Authors: Sucandy Iswanto , Ross Sharona B. , Adorno Flores Jeilianis , Syblis Cameron , Pattilachan Tara Menon , Christodoulou Maria , Rosemurgy Alexander
Published: March 2024

Background

IWATE, Institut Mutualiste Montsouris (IMM), and Southampton are established difficulty scoring systems (DSS) for laparoscopic hepatectomy, yet none specifically address robotic hepatectomy. Our study evaluates these 3 DSS for predicting perioperative outcomes in robotic hepatectomy.

Methods

With IRB approval, we prospectively followed 359 consecutive patients undergoing robotic hepatectomies, assessing categorical metrics like conversions to open, intra/postoperative issues, Clavien-Dindo Score (≥III), 30 and 90-day mortality, and 30-day readmissions using Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) to determine efficacy in predicting their occurrence for each DSS. Continuous metrics such as operative duration, estimated blood loss (EBL), length of stay, and total cost were analyzed using Spearman’s correlation and regression. Predictive strength was significant with an AUC or correlation ≥.700 and P-value ≤.05.

Results

IMM had highest predictive accuracy for conversions to open (AUC = .705) and postoperative complications (AUC = .481). Southampton was most accurate in predicting Clavien Dindo ≥ III complications (AUC = .506). IWATE excelled in predicting 30-day mortality (AUC = .552), intraoperative issues (AUC = .798), In-hospital mortality (AUC = .450), 90-day mortality (AUC = .596), and readmissions (AUC = .572). Regression showed significant relationships between operative duration, EBL, and hospital cost with increasing scores for all DSS ( P ≤ .05).

Discussion

Statistical analysis of the 3 DSS indicates that each has specific strengths that can best predict intra- and/or postoperative outcomes. However, all showed inaccuracies and conflicting relationships with the variables, indicating lack of substantial hierarchy between DSS. Given these inconsistencies, a dedicated comprehensive DSS should be created for robotic hepatectomy.
Use of Thromboelastography in Assessment of Snake Bite Coagulopathy
Authors: Hammond Chandler , Ninad Nehal , Christie Dudley B.
Published: March 2024

Background: Monitoring for envenomation coagulopathy following snake bites has traditionally involved using serial coagulation studies.

Purpose: Thromboelastography (TEG), with its real-time assessment, could offer an alternative to standard of care. This study aims to evaluate the use of TEG with traditional coagulation labs in the assessment of snake bite coagulopathy.

Research Design: A retrospective review of patients who presented to a level 1 trauma center between 2014 and 2020 with snake bite envenomation was conducted, comparing traditional coagulation studies to TEG plus coagulation studies.

Results: The 12 patients who had studies within the stipulations of our review were tracked and subsequently progressed well clinically with no adverse effects and ultimately discharged home with an average hospital stay of 2.2 days.

Conclusions: Our data suggests that patients can be observed clinically without needing repeat coagulation studies if initial TEG and TCS are within normal limits.
Early Discharge after Enhanced Recovery Colectomy Does Not Increase Emergency Department Visits and Readmissions: A Single Institution Analysis
Authors: Baur Katherine , Sood Evan M. , Huebner Marianne , Ramm Carole , Kolli Nivya , Cleary Robert K.
Published: March 2024

Background

Same-day discharge after colorectal surgery in enhanced recovery pathways is increasing. This study aimed to determine if discharge on postoperative days (POD) one or two is associated with increased rates of emergency department (ED) visits and hospital readmissions after left and right colectomy.

Methods

Single institution retrospective analysis of prospective institutional colorectal surgery database between 07/01/2018 and 07/15/2022. Primary outcomes were ED visit and readmission rates for enhanced recovery open and minimally invasive right and left colectomy using logistic regressions models.

Results

820 patients met inclusion criteria. There were significant differences in discharge-day by diagnosis—58.5% of patients with Crohn’s disease were discharged on POD ≥4 and 21.6% with benign colon neoplasia were discharged on POD-0-1 ( P < .001). ED visits occurred in 12.9% of the study population and were not significantly different between discharge-day groups ( P = .096). Overall readmission rate was 8.5% and significantly different between discharge-day groups (0% POD-0 vs 8.3% POD-1 vs 5.8% POD-2 vs 6.9% POD-3 vs 12.9% POD ≥4, P = .041). Logistic regression showed that ED visits and readmissions for longer discharge-days (POD-2, POD-3, POD ≥4) were not significantly different than POD-0-1. Readmission diagnoses for the study population were higher for ileus (17.1%) and surgical site infection (SSI) type-III (22.9%) than for acute kidney injury (1.4%) and SSI type-I/II (1.4%).

Conclusion

Early discharge after left and right colectomy is not associated with increased rates of ED visits and readmissions. Same-day discharge may be feasible in selected enhanced recovery patients. Standardized post-discharge resources that safely allow same-day discharge require further investigation.
A Rare Ectopic Parathyroid Adenoma in the Prevertebral Space
Authors: Tran Alex , Molina-Vega Manual
Published: March 2024

Ectopic parathyroid adenomas are an uncommon etiology of primary hyperparathyroidism. We present a case of a patient admitted to the hospital with severe hypercalcemia and elevated parathyroid hormone levels, in whom imaging revealed two distinct parathyroid masses in the prevertebral space, representing a rare and atypical location for parathyroid tissue. This case highlights the importance of considering ectopic parathyroid adenomas as a potential cause of hyperparathyroidism and discusses the diagnostic challenges and management strategies associated with such cases.
Modified Graham Patch Repair of Small Bowel Anastomotic Leak
Authors: Alden Ashley , Bennett Robert D.
Published: March 2024

The modified Graham patch repair is a well-established technique for management of perforating foregut injuries, often learned by surgeons during general surgery training. There is, however, little to no data regarding the utilization of this technique for perforation of the distal midgut or in the re-operative field. We present two cases of midgut anastomotic complications successfully managed with modified graham patch repair at our institution. The first case is a 79-year-old female who underwent an emergent right hemicolectomy at an outside institution for management of an iatrogenic perforation during endoscopic polypectomy. Over the course of two years she underwent numerous abdominal operations, due to various complications, ultimately resulting in multiple resections and end ileostomy creation. She then had her ileostomy reversed by laparoscopic single incision (SILS) technique at our institution. This was also complicated by anastomotic leak. Intraoperatively, adequate mobilization of the anastomosis for resection was deemed not safe due to dense fibrosis and adhesions in the re-operative field; therefore, she underwent a SILS modified Graham patch repair of an ileocolic anastomotic defect with diverting loop ileostomy. Post-operatively, she had no radiographic evidence of leak from the repaired anastomosis, which facilitated successful loop ileostomy reversal five months later. Our second case is a 64-year-old male referred to our institution for management of his stage IV colon cancer. He underwent an open right hemicolectomy and hepatic metastectomy, which was complicated by anastomotic leak. The small defect was repaired via a SILS modified Graham patch technique. Five months postoperatively, he had neither radiographic nor endoscopic evidence of a leak; therefore, he successfully underwent ileostomy reversal without complication. We encourage further investigation and reporting of the role of the modified graham patch repair in management of midgut anastomotic complications, particularly when resection and re-anastomosis is unsafe due to a hostile re-operative field.
Discrepancies in Mass Shootings and Access to Trauma Care Across the United States, 2014-2018
Authors: Beiter Kaylin J. , Knowles Stacey M. , Tedesco Alexandra , Leonardi Claudia , Scharf Peter L. , Chapman Brett M. , Brown Tommy A. , Schoen Jonathan E. , Stuke Lance E. , Greiffenstein Patrick P. , Marr Alan B. , Hunt John P. , Smith Alison A.
Published: March 2024

Introduction

The United States has one of the highest rates of gun violence and mass shootings. Timely medical attention in such events is critical. The objective of this study was to assess geographic disparities in mass shootings and access to trauma centers.

Methods

Data for all Level I and II trauma centers were extracted from the American College of Surgeons and the Trauma Center Association of America registries. Mass shooting event data (4+ individuals shot at a single event) were taken from the Gun Violence Archive between 2014 and 2018.

Results

A total of 564 trauma centers and 1672 mass shootings were included. Ratios of the number of mass shootings vs trauma centers per state ranged from 0 to 11.0 mass shootings per trauma center. States with the greatest disparity (highest ratio) included Louisiana and New Mexico.

Conclusion

States in the southern regions of the US experience the greatest disparity due to a high burden of mass shootings with less access to trauma centers. Interventions are needed to increase access to trauma care and reduce mass shootings in these medically underserved areas.
A Prospective Competitive Effects Analysis of the AT&T/Time Warner Merger
Authors: Zimmerman Paul R. , Chang George , Ulrick Shawn W.
Published: March 2024

The vertical merger of AT&T and Time Warner combined one of the largest multiple video program distributors (MVPDs) in the United States with one of the largest providers of pay-TV programming. This study evaluates the potential competitive effects of the transaction by considering changes in the equity valuations of the respective upstream and downstream competitors to the merging parties when news of their proposed merger became public. Consistent with the government’s central theory of harm, it appears that financial markets expected the proposed transaction to result in Time Warner increasing its carriage fees to AT&T’s and DirecTV’s MVPD rivals. Market reactions to the announcement of AT&T’s commitment to enter into binding arbitration when negotiating future carriage fees for Tuner content provide further support for this inference. The results are difficult to rationalize in terms of the various efficiencies and synergies that AT&T claimed it would realize from the merger.
Research knowledge utilisation among Swedish health social workers: Influence of higher education, job tenure and workplace
Authors: Ståhl Daniel , Lundälv Jörgen
Published: March 2024

Health social workers, like social workers in general, have been found to utilise research knowledge to a low degree. No previous study has made comparisons based on the following: higher education, job tenure and workplace. This article is based on statistical comparisons between 1236 health social workers in Sweden working in various healthcare contexts. The central finding was that higher education was the factor with the largest impact. Workplace and especially job tenure were found to be less influential. An implication for practice is to raise the knowledge requirements in terms of research knowledge utilisation already from bachelor’s degree level.
Electrocatalytic recycling of indigo dye from waste denim fabric: an engineering approach toward efficient denim dye reuse
Authors: Wan Zhen , Wang Ziyan , Peng Lan , Yi Changhai
Published: March 2024

The denim fabric industry has experienced exponential growth and escalating concerns regarding sustainable production and recycling practices. The wide utilization of indigo dye in denim manufacturing presents significant challenges owing to its intricate chemical structure and inherent resistance to degradation. Improper disposal of waste denim fabric and the release of indigo dye impose severe ecological ramifications. In this study, we explore the productive application of electrochemistry to recycle indigo dye from waste denim fabric while repurposing the electrolyte for denim dyeing. Notably, the separation of indigo from waste denim textiles reaches 82.34%. It is calculated that 21.59 g of indigo can be recovered from 1 kg of denim waste. Recovered indigo can be reused for cotton yarn dyeing. In the experiment, indigo was recovered from 0.03 square meters of denim waste, and the K/ S value of the yarn was up to 2.997 after five dyeing cycles. The loss of fabric strength after recycling is as low as 13.32%, which has little effect on secondary fabric recycling. Our recycling method reduces the waste of 978.41 g of waste fabric and 21.59 g of indigo per kilo compared to conventional treatment. This research provides compelling scientific evidence supporting adopting a closed-loop system within denim production, underlining the potential for environmentally conscious and economically viable practices in the industry.
The investigation on dark dyeing properties of silk using Dioscorea cirrhosa Lour. tuber extracts with varied molecular weights
Authors: Lin Ke , Wang Qing , Huang Xin , Cao Changqing , Tan Boxin , Yang Xingzhuang , Li Shixiao , Meng Bo
Published: March 2024

This work reported the dark dyeing properties of Dioscorea cirrhosa Lour. tuber extracts with different molecular weights on silk. The study on component analysis indicates that the molecular weight of most condensed tannins and polyphenols in D. cirrhosa L. tuber extracts is higher than 1000. The kinetic study demonstrates that the adsorption processes of D. cirrhosa L. tuber extract components with molecular weights higher than 1000, and components with molecular weights lower than 1000 toward silk fabrics are fitted with the pseudo-second order model, and the survey of adsorption isotherms reveals that multiple interactions occurred between dyes and silk, encompassing hydrogen bonding and van der Waals forces. The dyeing research shows that the dyed silk achieves its darkest color when employing D. cirrhosa L. tuber extract components with molecular weights lower than 1000 with a Fe2+ mordant concentration of 4 g/L and a pH value of 5. The K/S and L* values of dyed silk are 9.05 and 30.77, respectively. Under this dyeing condition, the complex, Fe element content, and Fe2+ ratio on the silk dyed with D. cirrhosa L. tuber extract components with molecular weights lower than 1000 surface are higher than those of D. cirrhosa L. tuber extracts and D. cirrhosa L. tuber extract components with molecular weights higher than 1000. The present study indicates that condensed tannins in D. cirrhosa L. tuber extracts are not the sole active compounds involved in the silk dark dyeing process. Polyphenols with molecular weights below 1000 in D. cirrhosa L. tuber extracts are shown to influence significantly and, in some cases, dominate the silk dark dyeing process.