bluuebunny/crossref_metadata_2025 huggingface dataset:
On gravity unification in SL(2N,C) gauge theories
Authors: Chkareuli J. L.
Published: November 2024

The local SL(2N, C) symmetry is shown to provide, when appropriately constrained, a viable framework for a consistent unification of the known elementary forces, including gravity. Such a covariant constraint implies that an actual gauge field multiplet in the SL(2N, C) theory is ultimately determined by the associated tetrad fields which not only specify the geometric features of spacetime but also govern which local internal symmetries are permissible within it. As a consequence, upon the covariant removal of all “redundant” gauge field components, the entire theory only exhibits the effective $$SL(2,C)\times SU(N)$$

S
L
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2
,
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)
×
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U
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symmetry, comprising SL(2, C) gauge gravity on one hand and SU(N) grand unified theory on the other. Given that all states involved in the SL(2N, C) theories are additionally classified according to their spin values, many potential SU(N) GUTs, including the conventional SU(5) theory, appear to be irrelevant for standard spin 1/2 quarks and leptons. Meanwhile, applying the SL(2N, C) symmetry to the model of composite quarks and leptons with constituent chiral preons in its fundamental representations reveals, under certain natural conditions, that among all accompanying $$SU(N)_{L}\times SU(N)_{R}$$

S
U

(
N
)

L

×
S
U

(
N
)

R

chiral symmetries of preons and their composites only the $$SU(8)_{L}\times SU(8)_{R}$$

S
U

(
8
)

L

×
S
U

(
8
)

R

meets the anomaly matching condition ensuring masslessness of these composites at large distances. This, in turn, identifies SL(16, C) with the effective $$SL(2,C)\times SU(8)$$

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)
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symmetry, accommodating all three quark-lepton families, as the most likely candidate for hyperunification of the existing elementary forces.
Ionization loss spectra of high-energy protons in an oriented crystal at various incidence angles with respect to a crystalline plane
Authors: Trofymenko S. V. , Kyryllin I. V.
Published: November 2024

The ionization energy loss distributions (spectra) of high-energy protons in oriented silicon crystals are considered on the basis of computer simulation. Evolution of the spectra with the change of the angle $$\theta $$
θ
between the particle momentum and the crystal (110) plane is investigated. It is shown that both the most probable and the average energy loss change non-monotonically with the increase of $$\theta $$
θ
. While at small $$\theta $$
θ
these losses are lower than their values in a disoriented crystal, at larger $$\theta $$
θ
they can be higher than these values. The dependence of the most probable energy loss on $$\theta $$
θ
contains a discontinuity, which however might be challenging to register experimentally. Additionally, the influence of dechanneling and the incident beam divergence on the spectra of planar channeled protons is demonstrated.
Dark sector showers and hadronisation in Herwig 7
Authors: Kulkarni Suchita , Masouminia M. R. , Plätzer Simon , Stafford Dominic
Published: November 2024

We present a novel simulation of a strongly interacting dark sector also known as the Hidden Valley scenarios using angular ordered showers and the cluster hadronisation model in Herwig 7. We discuss the basics of this implementation and the scale hierarchies underpinning the simulation. With the help of a few benchmarks, we show the effect of variation of dark sector parameters on thrust and angularities within the dark sector, and study correlation functions, which can be helpful for understanding the angular structure of these events. Finally we comment on the uncertainties introduced due to lack of knowledge of hadronisation parameters within the dark sectors.
A novel approach to neutrino–hydrogen measurements
Authors: Duyang H. , Guo B. , Mishra S. R. , Petti R.
Published: November 2024

The limited statistics of the available (anti)neutrino–hydrogen (H) interactions has been a longstanding impediment for high-energy neutrino physics. We discuss a practical way to achieve accurate (anti)neutrino–hydrogen measurements, addressing the principal limitations of earlier experiments. Interactions on hydrogen are extracted by subtracting measurements on thin dedicated graphite (pure C) and polypropylene (CH2) targets within a highly segmented low-density detector. A kinematic selection is used to increase the purities to 80–95% before subtraction. A statistics of $${{\mathcal {O}}}(10^6)$$

O
(

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can be realistically achieved in modern neutrino beams for the various $$\nu ({\bar{\nu }})$$

ν
(

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¯

)

-H event topologies. The availability of such samples would allow a precise determination of neutrino and antineutrino fluxes, as well as to directly constrain nuclear effects from a comparison with corresponding measurements on heavy materials within the same detector. The (anti)neutrino fluxes and the nuclear smearing are typically the leading sources of systematic uncertainties in long-baseline oscillation experiments. (Anti)neutrino–hydrogen interactions also provide an ideal tool for a wide range of precision tests of fundamental interactions.
It's Time to Make Software Accessible
Authors: Branham Stacy M. , Wahid Shahtab
Published: November 2024

The articles that constitute this special issue on accessibility show that, regardless of whether you work on the back end, front end, design, or are part of an organization's leadership, there are steps you can take to make progress. Before we get to writing software and making policy, however, there is an even more fundamental concern: the widespread misconception of the nature of disability and assistive and accessible technology. If we are going to change the status quo, we must start there.
Racial Bias within Face Recognition: A Survey
Authors: Yucer Seyma , Tektas Furkan , Al Moubayed Noura , Breckon Toby
Published: November 2024

Facial recognition is one of the most academically studied and industrially developed areas within computer vision where we readily find associated applications deployed globally. This widespread adoption has uncovered significant performance variation across subjects of different racial profiles leading to focused research attention on racial bias within face recognition spanning both current causation and future potential solutions. In support, this study provides an extensive taxonomic review of research on racial bias within face recognition exploring every aspect and stage of the associated facial processing pipeline. Firstly, we discuss the problem definition of racial bias, starting with race definition, grouping strategies, and the societal implications of using race or race-related groupings. Secondly, we divide the common face recognition processing pipeline into four stages: image acquisition, face localisation, face representation, face verification and identification, and review the relevant corresponding literature associated with each stage. The overall aim is to provide comprehensive coverage of the racial bias problem with respect to each and every stage of the face recognition processing pipeline whilst also highlighting the potential pitfalls and limitations of contemporary mitigation strategies that need to be considered within future research endeavours or commercial applications alike.
ROSE: An IDE-Based Interactive Repair Framework for Debugging
Authors: Reiss Steven P. , Wei Xuan , Yuan Jiahao , Xin Qi
Published: November 2024

Debugging is costly. Automated program repair (APR) holds the promise of reducing its cost by automatically fixing errors. However, current techniques are not easily applicable in a realistic debugging scenario because they assume a high-quality test suite and frequent program re-execution, have low repair efficiency, and only handle a limited set of errors. To improve the practicality of APR for debugging, we propose ROSE, an interactive repair framework that is able to suggest quick and effective repairs of semantic errors while debugging in an Integrated Development Environment (IDE). ROSE allows an easy integration of existing APR patch generators and can do program repair without assuming the existence of a test suite and without requiring program re-execution. It works in conjunction with an IDE debugger and assumes a debugger stopping point where a problem symptom is observed. ROSE asks the developer to quickly describe the symptom. Then it uses the stopping point, the identified symptom, and the current environment to identify potentially faulty lines, uses a variety of APR techniques to suggest repairs at those lines, and validates those repairs without re-executing the program. Finally, it presents the results so the developer can examine, select, and make the appropriate repair. ROSE uses novel approaches to achieve effective fault localization and patch validation without a test suite or program re-execution. For fault localization, ROSE builds on a fast abstract-interpretation-based flow analysis to compute a static backward slice approximating the real dynamic slice while taking into account the symptom and the current execution. For patch validation without re-running the program, ROSE generates simulated traces based on a live-programming system for both the original and repaired executions and compares the traces with respect to the problem symptoms to infer patch correctness. We implemented a prototype of ROSE that works in an Eclipse-based IDE and evaluated its potency and utility with an effectiveness study and a user study. We found that ROSE's fault localization and validation are highly effective and a ROSE-based tool using existing APR patch generators generated correct repair suggestions for many errors in only seconds. Moreover, the user study demonstrated that ROSE was helpful for debugging and developers liked to use it.
SpectAcle: Fault Localisation of AI-Enabled CPS by Exploiting Sequences of DNN Controller Inferences
Authors: Lyu Deyun , Zhang Zhenya , Arcaini Paolo , Zhang Xiao-Yi , Ishikawa Fuyuki , Zhao Jianjun
Published: November 2024

Cyber-Physical Systems (CPSs) are increasingly adoptingdeep neural networks (DNNs)as controllers, giving birth toAI-enabled CPSs. Despite their advantages, many concerns arise about the safety of DNN controllers. Numerous efforts have been made to detect system executions that violate safety specifications; however, once a violation is detected, to fix the issue, it is necessary to localise the parameters of the DNN controller responsible for the wrong decisions leading to the violation. This is particularly challenging, as it requires to consider a sequence of control decisions, rather than a single one, preceding the violation. To tackle this problem, we proposeSpectAcle, that can localise the faulty parameters in DNN controllers.SpectAcleconsiders the DNN inferences preceding the specification violation and usesforward impactto determine the DNN parameters that are more relevant to the DNN outputs. Then, it identifies which of these parameters are responsible for the specification violation, by adapting classic suspiciousness metrics. Moreover, we propose two versions ofSpectAcle, that consider differently the timestamps that precede the specification violation. We experimentally evaluate the effectiveness ofSpectAcleon 6067 faulty benchmarks, spanning over different application domains. The results show thatSpectAclecan detect most of the faults.
Bounded Verification of Atomicity Violations for Interrupt-Driven Programs via Lazy Sequentialization
Authors: Zhang Yuan , Qu Lei , Wu Yifei , Wu Leihuan , Yu Tingting , Chen Rui , Kong Weiqiang
Published: November 2024

Detecting atomicity violations effectively in interrupt-driven programs is difficult due to the asymmetric concurrency interleaving of interrupts. Current approaches face two main challenges: (1) A large number of false positives are generated by efficient static analysis techniques. (2) Loops with large or unknown bounds in these programs limit the scalability of the bounded verification techniques. To address these challenges, we present NIChecker, a new bounded verification tool designed to detect atomicity violations in interrupt-driven programs. The key ideas are: (1) Transforming an interrupt-driven program into a bounded sequential C program through lazy sequentialization technique. This sequential program accurately models interrupt masking and nested interrupt execution. (2) Combining a refined loop abstraction technique with our sequentialization to enhance the efficiency of detecting programs with intractable loops. (3) Integrating slicing and an interleaving path reduction technique known as preemption point reduction in NIChecker to shrink the explored state space. We prove the bounded correctness of our translation and discuss the impact of our optimizations. We evaluate NIChecker on 31 academic benchmark programs and 18 real-world interrupt-driven programs. Our results show that NIChecker achieves better precision, a lower false positive rate, and a significant verification speed-up than related state-of-the-art tools.
Quantitative Correction of Ohmic Effects on Square Wave Voltammetry for High-Concentration Soluble-Soluble Redox Reactions in Molten Salts
Authors: Shaheen Nora A. , Guo Jicheng , Hoyt Nathaniel C.
Published: November 2024

Molten salts are of particular interest for a variety of industrial applications. As such, accurate characterization of species’ concentrations within the molten salt media is critical to ensure a well-controlled unit operation. Although electroanalytical tools are properly suited for precise in situ monitoring in these systems, uncompensated ohmic resistance R
Ω can lead to erroneous, technique-dependent results. Using numerical simulations applied to a model system, this work will first illustrate then quantify the extent to which R
Ω attenuates square wave voltammograms. This approach allows for post-experiment correction that leads to converging results across voltammetric techniques and facilitates accurate predictions of species’ concentrations.
Promising Anti-Microbial from Functionalized Magnesium Nickel Doped Cobalt Nano-Ferrites Structural and Optical Studies
Authors: Naik Priyanka , Gounhalli S. G.
Published: November 2024

Using low temperature solution combustion method, the magnesium-nickel doped cobalt nanoferrites of formula MgxNixCo1–2xFe2O4 (x values 0.0, 0.1, 0.2.) have been synthesized. The anticancer assessment was done on the nano ferrites using MTT assay on triple negative breast cancer MDAMB-231 cell line. The anti-inflammatory studies were to measure inhibition % of anti-inflammatory at different concentrations. The structural characterizations analyzed using XRD, FTIR, FE-SEM and TGA-DTA. The XRD results confirms that the samples have cubic spinel structures. The crystallite size is confirmed and it lies between 30 nm–36 nm. The FT-IR confirms the presence of a metal ion peak around 559 cm−1 to 587 cm−1. The formation of metal phase oxide in the TGA curve is the cause of the last stage of weight loss at temperatures between 600 and 800 °C. The SEM gives the aggregated grain boundaries with irregular distribution of particles with grain size around 27.5 nm. The optical spectral features confirmed as cut-off varied between 503 nm–534 nm; Indirect band gap varied between 1.94 eV–1.73 eV; direct band gap varied between 2.45–2.12 eV. The application-oriented studies of the present investigation led to the bio-related applications in various fields.
The extraction of neural strategies from the surface EMG: 2004–2024
Authors: Farina Dario , Merletti Roberto , Enoka Roger M.
Published: November 2024

This review follows two previous papers [Farina et al. Appl Physiol (1985) 96: 1486–1495, 2004; Farina et al. J Appl Physiol (1985) 117: 1215–1230, 2014] in which we reflected on the use of surface electromyography (EMG) in the study of the neural control of movement. This series of papers began with an analysis of the indirect approaches of EMG processing to infer the neural control strategies and then closely followed the progress in EMG technology. In this third paper, we focus on three main areas: surface EMG modeling; surface EMG processing, with an emphasis on decomposition; and interfacing applications of surface EMG recordings. We highlight the latest advances in EMG models that allow fast generation of simulated signals from realistic volume conductors, with applications ranging from validation of algorithms to identification of nonmeasurable parameters by inverse modeling. Surface EMG decomposition is currently an established state-of-the-art tool for physiological investigations of motor units. It is now possible to identify large samples of motor units, to track motor units over multiple sessions, to partially compensate for the nonstationarities in dynamic contractions, and to decompose signals in real time. The latter achievement has facilitated advances in myocontrol, by using the online decoded neural drive as a control signal, such as in the interfacing of prostheses. Looking back over the 20 yr since our first review, we conclude that the recording and analysis of surface EMG signals have seen breakthrough advances in this period. Although challenges in its application and interpretation remain, surface EMG is now a solid and unique tool for the study of the neural control of movement.
Brain Morphometry Differences Between Children With Autism Spectrum Disorder and Healthy Individuals in a Middle Eastern Population: A Cross‐Sectional Retrospective Study
Authors: Majrashi Naif A. , Mohtasib Rafat , Masawi Ahmed , Almujally Abdullah , Alyami Ali S. , Hendi Ali , Ageeli Wael , Madkhali Yahia , Alwadani Bandar , Refaee Turkey
Published: November 2024

This study is aimed at investigating brain morphometry differences between children with ASD and healthy controls (HCs) in Saudi Arabia and exploring the association between IQ levels and brain volumes within the ASD group. Participants (N = 29, 31.1% females and 68.9% males) ranging in age from 6 to 17 years were scanned by MRI at the King Faisal Specialist Hospital and Research Center (KFSHRC). Brain volumes were analyzed while correcting for age, sex, and total intracranial volume (TICV). The analysis revealed that individuals with ASD exhibited larger volumes compared to HCs in the left caudate (p < 0.001), right caudate (p < 0.001), total caudate (p < 0.001), and total hippocampus (p = 0.014). These findings provide evidence for anatomical brain abnormalities in individuals with ASD and highlight the heterogeneity of these differences across brain regions. Furthermore, the analysis revealed that higher IQ levels were negatively associated with the volumes of the left thalamus, left pallidum, left accumbens area, right thalamus, right hippocampus, and total thalamus but positively correlated with the third lateral ventricle volume in HCs, p < 0.05. These results suggest a meaningful relationship between cognitive abilities, as measured by IQ, and variations in brain volumes in HCs. The study significantly contributes to the understanding of the neurobiology of ASD in a Middle Eastern population and emphasizes the importance of considering cognitive functioning in relation to brain morphology in ASD research and clinical practice.
An Effective Islanding Detection Method for Distributed Generation Integrated Power Systems Using Gabor Transform Technique and Artificial Intelligence Techniques
Authors: Ghunnam Mohannad , Shareef Hussain , M. Omer Zahi , Khamis Aziah , Eslami Mahdiyeh
Published: November 2024

Incorporating distributed generation (DG) technology into modern power systems heralds a multitude of technological, economic, and environmental advantages. These encompass enhanced reliability, reduction in power losses, heightened efficiency, low initial investment requirements, abundant availability, and a minimized environmental footprint. However, the rapid detection and disconnection of DG units during islanding events are paramount to circumvent safety hazards and potential equipment damage. Although passive techniques are predominantly utilized for islanding protection due to their minimal systemic interference, their susceptibility to substantial nondetection zones (NDZ) instigates a transition toward more innovative techniques. Addressing this crucial need, the study employs a novel amalgamation of signal processing methodologies and a suite of intelligent classifiers to augment the detection of islanding events in power systems. Concealed features from a variety of signals are extracted by the methodology and utilized as robust inputs to the intelligent classifiers. This empowers these classifiers to make a reliable distinction between islanding events and other types of disturbances. The breadth of the study is expanded to evaluate a range of sophisticated models including gradient boosting, decision tree–based models, and multilayer perceptrons (MLPs). These models are thoroughly tested on a radial distribution system integrated with two DG units and subjected to rigorous simulations and comparative analysis using the DIgSILENT Power Factory software. The findings underscore the efficacy of the proposed method, showcasing a significant improvement over conventional techniques in terms of efficiency and resilience. Above all, the methodology exhibits an exceptional ability to discriminate between islanding events and other system disturbances. Specifically, the islanding detection method demonstrated a detection time of less than one cycle (20 ms), ensuring rapid response. Moreover, the proposed algorithm demonstrated the capability to detect all ranges of power mismatch, including zero‐power mismatch, thereby eliminating the NDZ. These results illuminate the benefits of integrating signal processing methodologies with intelligent classifiers, marking a significant stride forward in the realm of islanding event detection.
Insights Into Lithium‐Ion Battery Cell Temperature and State of Charge Using Dynamic Electrochemical Impedance Spectroscopy
Authors: Knott L. M. , Long E. , Garner C. P. , Fly A. , Reid B. , Atkins A.
Published: November 2024

Understanding and accurately determining battery cell properties is crucial for assessing battery capabilities. Electrochemical impedance spectroscopy (EIS) is commonly employed to evaluate these properties, typically under controlled laboratory conditions with steady‐state measurements. Traditional steady‐state EIS (SSEIS) requires the battery to be at rest to ensure a linear response. However, real‐world applications, such as electric vehicles (EVs), expose batteries to varying states of charge (SOC) and temperature fluctuations, often occurring simultaneously. This study investigates the impact of SOC and temperature on EIS in terms of battery properties and impedance. Initially, SSEIS results were compared with dynamic EIS (DEIS) outcomes after a full charge under changing temperatures. Subsequently, DEIS was analysed using combined SOC and temperature variations during active charging. The study employed a commercial 450 mAh lithium‐ion (Li‐ion) cobalt oxide (LCO) graphite pouch cell, subject to a 1C constant current (CC)–constant voltage (CCCV) charge for SSEIS and CC charge for DEIS, with SOC ranging from 50% to 100% and cell temperatures from 10 to 35°C. The research developed models to interpolate battery impedance data, demonstrating accurate impedance predictions across operating conditions. Findings revealed significant differences between dynamic data and steady‐state results, with DEIS more accurately reflecting real‐use scenarios where the battery is not at equilibrium and exhibits concentration gradients. These models have potential applications in battery management systems (BMSs) for EVs, enabling health assessments by predicting resistance and capacitance changes, thereby ensuring battery cells’ longevity and optimal performance.
The Mechanical Properties of Reinforced Concrete Beam–Column Joints Under the High‐Temperature Effect of Fire
Authors: Zhou Mingrong , Wang Yue
Published: November 2024

In order to study the deformation behavior of reinforced concrete frame beam–column joints under high‐temperature fire and the variation of their mutual constraint with temperature, structural tests were conducted on the entire process of temperature rise and fall of reinforced concrete frame beams under high‐temperature coupling load. Innovatively, based on structural testing, the Vulcan program was used to conduct numerical analysis of the fire response of the moment–angle relationship between beam–column nodes under constant load when the frame beam is subjected to high temperature alone or when both beams and columns are subjected to high temperature simultaneously. The results show that the rotational deformation and restrained bending moment of the beam–column joints in reinforced concrete frames increase first and then decrease with the increase of fire temperature, and the peak rotation angle of the beam–column joints has a lag effect compared to the peak bending moment. As the temperature continues to rise, the beam end angle begins to decrease after reaching its peak value, and the peak value of the beam end angle appears later than the constrained bending moment. Based on the thermal mechanical coupling test, a fire response calculation model for reinforced concrete beam–column joints was established using the Vulcan program; when the beam is subjected to high temperature alone and when the beam–column is subjected to high temperature simultaneously, the rotation angle shows an increasing trend within 80 min and 120 min, respectively, and the structural bearing capacity of the latter decreases more severely than the former. The bending moment and rotation angle of beams under high‐temperature conditions alone and beams–columns under high‐temperature conditions exhibit synchronous changes with the development of fire temperature within 60 min before fire exposure. After the fire exposure time is extended, the deformation of the rotation angle continues to increase, while the bending moment begins to decrease until it stabilizes. The structural response of beams and columns subjected to high temperature simultaneously is stronger than that of beams subjected to high temperature alone, and their peak moment appears earlier. The deformation and constraint force of beam–column joints during high‐temperature fire are nonsynchronous.
Experimental Study on the Frost Heave Characteristics of Silt Clay Modified With Hydrophobic Nano‐ZnO
Authors: Zhang Yaning , Wang Laifa , Wang Xinyan , Ming Feng , Wang Zhanxu
Published: November 2024

Silty clay is widely distributed in seasonally frozen zones in China and frequently engages in engineering projects. Nevertheless, it exhibits significant frost susceptibility and generates substantial related freezing damage. To address this problem, this study investigates the impact of nano‐zinc oxide (ZnO) on silty clay’s frost heave characteristic. We conducted tests on silty clay with varying nano‐ZnO contents, assessing plasticity limit, liquid limit, frost heave, and uniaxial compressive strength. The findings reveal that: (1) the addition of nano‐ZnO can decrease the free water content, and result in both the plastic limit and the liquid limit increase, and further accelerate the freezing process, which is helpful to mitigate the frost heave caused by water migration; (2) the frost heave ratio decreases with increasing nano‐ZnO content within the tested range, 4.0% addition of nano‐ZnO can significantly reduce frost heave by 66.96%, and transform the silty clay from extreme frost heave to frost heave; (3) with the nano‐ZnO content increases, the uniaxial compressive strengths of the specimen initially increases (0%–3.0%) and subsequently decreases (4.0%), and the brittleness also becomes more pronounced. According to the results of mechanical and frost heave tests, the optimal content of nano‐ZnO is ascertained to be 3.0%. The results of this study provide a promising solution to mitigate the frost heave of silty clay, particularly in regions with limited coarse‐grained soil.
Innovative Automation in Injera Production: Design and Performance of a Relay‐Based Control System
Authors: Gebeyehu Mebratu Assefa , Kebede Getnet Ayele
Published: November 2024

This study presents an innovative approach for automating the traditional injera production process, a staple food in Ethiopia, Eritrea, Sudan, and the Horn of Africa. Existing semiautomatic and manual methods suffer from inefficiencies, inconsistent product quality, and significant health risks owing to prolonged exposure to high temperatures and smoke. These limitations necessitate the development of more effective solutions. The proposed method involves the design and implementation of a fully automated injera‐making machine that integrates a relay‐based control system, microcontroller‐driven temperature regulation, and automated dough‐handling mechanisms. This novel design ensures consistent baking conditions, minimizes manual intervention, and enhances baker safety. The experimental results demonstrate that the machine can produce 20 injera per hour, reflecting a 33.33% increase in efficiency compared to traditional methods. In addition, the system reduces the energy consumption through precise temperature control to address sustainability concerns. This research offers a comprehensive solution to the challenges of traditional injera production, significantly improving both the production process and well‐being of those involved. Future work will focus on optimizing the design for mass production and exploring alternative energy sources to enhance sustainability further.
Pembrolizumab plus pharmakologisches Ascorbat zur Behandlung des Leiomyosarkoms
Authors: Rieth John M. , Belzer Alex C. , Walhof Mackenzie L. , Milhem Mohammed M.
Published: November 2024

Einleitung: Das Leiomyosarkom (LMS) ist ein maligner Tumor mit Differenzierung der glatten Muskulatur. Das metastasierte LMS ist mit einer schlechten Prognose und einer begrenzten Wirksamkeit der systemischen Therapie assoziiert. Neue Behandlungsmethoden für diese Krebsart werden dringend benötigt. Falldarstellung: Wir berichten über den erstmaligen Einsatz von Pembrolizumab plus pharmakologischem Ascorbat bei 3 Patientinnen mit metastasiertem LMS. In allen Fällen wurde ein anhaltendes objektives Ansprechen und eine deutlich bessere Krankheitskontrolle als mit Chemotherapie oder anderen immuntherapeutischen Ansätzen beobachtet. Drei Patientinnen mit metastasiertem LMS, jeweils einer mit uterinem, vaskulärem und Weichteil-LMS, wurden mit Pembrolizumab plus pharmakologischem Ascorbat behandelt. Die Patientinnen mit uterinem LMS erhielt die Kombinationstherapie bei der Vorstellung und zeigte nach 12 Monaten ein anhaltendes Ansprechen. Die Patientin mit metastasiertem LMS der Vena cava inferior erhielt die Kombinationstherapie bei der Vorstellung und zeigte ein anhaltendes Ansprechen über 12 Monate, bis neue Metastasen gefunden wurden. Bei der Patientin mit Weichteil-LMS kam es unter der Monotherapie mit Pembrolizumab zu einer Krankheitsprogression, bevor Ascorbat hinzugefügt wurde, woraufhin sie ein anhaltendes Ansprechen über 17 Monate zeigte. Es wurden keine behandlungsbedingten unerwünschten Ereignisse berichtet.
Design type-2 fuzzy for superconducting magnetic energy storage to enhance frequency transient response
Authors: Abdillah Muhammad , Bagus Laksono Arie , Indriani Pertiwi Nita , Aryo Nugroho Teguh , Setiadi Herlambang , Araminta Jasmine Senit , Evanda Putra Naufal
Published: November 2024

Renewable energy has become a new trend in power systems. Renewable-based power plants such as wind power systems and photovoltaics. This paper proposed a novel method for inertia emulation based on superconducting magnetic energy storage (SMES). To get better inertia support for the system, a type-2 fuzzy controller is used as the SMES controller. An area power system is used as the test system to investigate the performance of type-2 fuzzy controller on SMES. Time domain simulation is carried out to show the efficacy of the proposed method. From the simulation results, it is found that the proposed controller can reduce the overshoot of frequency by up to 20% compared to the type-1 fuzzy controller. It is also hoped that the proposed method can be used as a reference of the Industrial people.
Progression of polymeric nanostructured fibres for pharmaceutical applications
Authors: Abu Owida Hamza , I. Al-Nabulsi Jamal , M. Turab Nidal , Al-Ayyad Muhammad , Alazaidah Raed , Alshdaifat Nawaf
Published: November 2024

Electrospinning has emerged as a simple and cost-effective technique for producing polymer nanofibers, offering a versatile approach for creating nanostructured fibers from a wide range of polymer materials. The pharmaceutical field has particularly welcomed the advent of electrospun nanofibers, as they hold immense potential for revolutionizing drug delivery systems. The recent surge of interest in electrospun nanofibers can be attributed to their unique characteristics, including elasticity and biocompatibility, which make them highly suitable for various biomedical applications. By incorporating functional ingredients into blends of nanostructured fibers, the capabilities and reliability of drug delivery devices have been significantly enhanced. This review aims to provide a comprehensive summary of recent research endeavors focusing on the concept of nanofibrous mesh and its multifaceted applications. With an emphasis on the simplicity of fabrication and the virtually limitless combinations of materials achievable through this approach, nanofibrous meshes hold the promise of transforming specific treatment modalities. By streamlining the delivery of therapeutic agents and offering enhanced control over drug release kinetics, nanofibrous meshes may herald a new era in targeted and personalized medicine.
Fuzzy logic method for making push notifications on monitoring system of IoT-based electric truck charging
Authors: Al Madani Kurniawan Aqsha , Khaula Amifia Lora , Iskandar Riansyah Moch. , Furizal Furizal , Suwarno Iswanto , Ma’arif Alfian , Maghfiroh Hari
Published: November 2024

To minimize the negligence when charging electric vehicles, it is deemed important to have an internet of things (IoT) based monitoring system using a notification feature. The monitoring system of electric vehicle battery charging used a voltage divider and temperature sensor (DS18B20) installed on the Arduino Mega 2560 microcontroller with the addition of an ESP8266 Wi-Fi module for sending microcontroller data into the Blynk platform. A notification feature was added as the reminder that the battery has been overcharging or overheating. This study applied the Mamdani fuzzy logic method to determine the conditions when notifications must appear. The results of the application of the Mamdani fuzzy logic method were able to determine the conditions for push notifications to appear using the parameters as desired; by so doing, it is possible to create a battery monitoring system with accurate push notification feature to prevent the battery from being overcharged and overheated.
Modeling 6(10)-35 kV electrical network for fault location via negative correlation
Authors: Saken Koyshybaevich Sheryazov , Anastasia Igorevna Uspanova , Titko Jelena , Igor Vladimirovich Koshkin , Arman Bolatbekovich Utegulov
Published: November 2024

In order to maintain the technical leadership of the economic sector in any nation, there is currently a greater focus on guaranteeing the fail-safe operation of electrical networks and electrical equipment. This paper presents a model for evaluating the fault location procedure based on computer simulation in MATLAB/Simulink of complex 6(10)-35 kV power line systems. The proposed algorithm for preprocessing electrical network signals in normal and emergency modes uses a negative statistical correlation of all possible electrical parameters, while the resulting percentage errors when estimating the location of the fault are within acceptable limits. Algorithms and significant parameters have been determined for effectively carrying out the procedure for searching for the location of a fault through the use of modeling programs, namely: zero-sequence voltage, negative-sequence voltage, initial current value. and the positive sequence voltage is the transition resistance at the accident site. An assessment of the results of preliminary modeling may indicate that devices for finding the location of a fault in the 6(10)-35 kV electrical network will be able to use information obtained about the object using the developed methodology, adjust calculation algorithms and take into account the operating modes of the electrical network.
Real-time IoT security framework for detecting a person with a weapon using Raspberry Pi, Google Vertex AI, and AWS
Authors: Schutte Storm , Uddin Jia
Published: November 2024

Realtime crime scene detection is a vital issue for ensuring security in various environments. Building on recent advancements in machine learning algorithms, this paper presents an IoT framework for real-time weapon and face detection. By deploying a convolutional neural network (CNN) architecture in Vertex AI and utilizing the portable camera module of a Raspberry Pi, to detect whether a person is carrying a weapon. This is achieved by pre-processing, which we resize and annotate the images. Then, train and validate the CNN model with the annotated label dataset. The trained model is saved in Google Cloud’s Vertex AI portal. Then we tested the model by uploading live images from a camera as well as a few video clips, to a Django application in amazon web hosting services (AWS) to Vertex AI. The model exhibited an accuracy of 97.2% along with a F1 score of 0.97. In addition, the model outperforms the other state-of-the-art models by less trainable parameters and higher accuracy.
Human-centered design approach for enhancing supply chain management systems in SMEs: insights from Malaysia
Authors: Ahmad Sabrina , Zainudin Nurhamizah , Ermahani A. Jalil Intan , Hefri Ariyanto Hepy , Ahmad Mazida
Published: November 2024

A reliable supply chain management (SCM) system is crucial to small and medium enterprises (SMEs) to meet the increasing demands of supply chain operations. However, the misalignment between the functional SCM system and the complex, dynamic, and diverse needs of the supply chain stakeholders is paramount. This paper presents an effort to adopt human-centered design (HCD) in the process of identifying requirements for a SCM system, aimed at providing valuable support to SMEs. The HCD places a strong emphasis on shaping design choices in alignment with users' tasks, needs, and preferences, instead of requiring users to adapt their behaviors to fit the system. The survey method is employed to get the SMEs' perspectives on the potential benefits of incorporating HCD into the requirements of the SCM system. The findings showed that a minimum of 80% of the respondents agreed that HCD brings numerous benefits to the development of SCM systems for SMEs in Malaysia.
SeeAround: an offline mobile live support system for the visually impaired
Authors: Sebban Othmane , Azough Ahmed , Lamrini Mohamed
Published: November 2024

The inability of blind or partially-sighted people to understand visual content and real-life situations reduces their standard of living, especially in a world mainly tailored for sighted individuals. Despite the progress made by certain devices to assist them in using touch, sound, or other senses, these solutions often fall short of bridging the comprehension gap. Our work proposes an intuitive, user-friendly mobile-based framework named "SeeAround" that is capable of automatically providing real-time audio descriptions of the user's immediate visual surroundings. Our solution addresses this challenge by leveraging key point detection, image captioning, text-to-speech (TTS), optical character recognition (OCR), and translation algorithms to offer comprehensive support for visually impaired individuals. Our system architecture relies on convolutional neural networks (CNNs) such as Inception-V3, Inception-V4, and ResNet152-V2 to extract detailed features from images and employs a multi-gated recurrent unit (GRU) decoder to generate word-by-word natural language descriptions. Our framework was integrated into mobile applications and optimized with TensorFlow lite pre-trained models for easy integration on the Android platform.
Identification and validation of factors affecting the success of smart village services
Authors: Hadian Nur , Purwanto Purwanto , Wibowo Adi
Published: November 2024

This study aims to explore and validate the factors that influence the performance and effectiveness of smart village services. Smart villages have become a focus for improving the quality of life of rural communities in the era of digital technology. However, there is a lack of methods to measure and evaluate the effectiveness of smart villages. We propose a holistic framework to measure and evaluate the effectiveness of smart services in smart villages. In this study, factors that influence the success of smart village effectiveness are identified. How effective the smart village services are can be understood using the information system success model approach by DeLone and McLean. This framework is expected to provide a better understanding of the effectiveness of smart village services so that people are willing to adopt the smart village service concept. In addition, this model can also be used as decision-making support for stakeholders and is expected to improve the quality of life of rural communities in a sustainable manner.
Detection and prevention of Man-in-The-Middle attack in cloud computing using Openstack
Authors: Tissir Najat , Aboutabit Noureddine , El Kafhali Said
Published: November 2024

This paper proposes a new technique designed to prevent and detect address resolution protocol (ARP) spoofing attacks in general, and specifically Man-in-the-Middle (MitM) attacks, within the context of cloud computing. The solution focuses on establishing appropriate flow filtering rules based on parameters such as 'time feature' and internet control message protocol '(ICMP) protocol'. The tests were conducted using the Openstack platform. One of the key benefits of this proposed approach is the improved performance in effectively detecting a significant number of malicious packets. We implemented this solution on the Openstack platform and conducted evaluations to demonstrate its efficacy. The results confirm that our method achieves superior performance in detecting MitM attacks, with a packet detection ratio (PDR) of 60.4%. Moving forward, this work will contribute to protecting cloud environments from a large number of MitM attacks.
Enhancing costumer churn prediction with stacking ensemble and stratified k-fold
Authors: Rofik Rofik , Unjung Jumanto , Prasetiyo Budi
Published: November 2024

In the era of rapid technological advancement, the telecommunications industry undergoes significant changes. Factors such as the speed of technological change, high customer expectations, and changing preferences are the main obstacles that affect the dynamics of telecommunications companies. One major issue faced is the high customer churn rate, adversely impacting company revenue and profitability. Previous studies indicate that customer churn prediction remains complex in the telecommunications industry, with opportunities to optimize algorithm selection and prediction model construction methods. This research aims to improve the accuracy of customer churn prediction by employing a complex model that utilizes stacking ensemble learning techniques. The proposed model combines 6 base algorithms: extreme gradient boosting (XGBoost), random forest, light gradient boosting machine (LightGBM), support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN), with XGBoost as the meta-learner model. The research process involves preprocessing, class data balance with synthetic minority oversampling technique (SMOTE), training using stratified k-fold, and model evaluation. The model is tested using the Telecom Churn dataset. The evaluation results show that the constructed stacking model achieves 98% accuracy, 98.74% recall, 98.03% precision, and 98.38% F1 score. This study demonstrates that optimizing the stacking ensemble model with SMOTE and stratified k-fold enhances customer churn prediction accuracy.
Continual learning on audio scene classification using representative data and memory replay GANs
Authors: Daqiqil ID Ibnu , Abe Masanobu , Hara Sunao
Published: November 2024

This paper proposes a methodology aimed at resolving catastropic forgetting problem by choosing a limited portion of the historical dataset to act as a representative memory. This method harness the capabilities of generative adversarial networks (GANs) to create samples that expand upon the representative memory. The main advantage of this method is that it not only prevents catastrophic forgetting but also improves backward transfer and has a relatively stable and small size. The experimental results show that combining real representative data with artificially generated data from GANs, yielded better outcomes and helped counteract the negative effects of catastrophic forgetting more effectively than solely relying on GAN-generated data. This mixed approach creates a richer training environment, aiding in the retention of previous knowledge. Additionally, when comparing different methods for selecting data as the proportion of GAN-generated data increases, the low probability and mean cluster methods performed the best. These methods exhibit resilience and consistency by selecting more informative samples, thus improving overall performance.
Object detection in video surveillance using MobileNetV2 on resource-constrained low-power edge devices
Authors: Lokhande Harshad , R. Ganorkar Sanjay
Published: November 2024

Edge-based video surveillance systems encounter significant obstacles in object detection due to the limited computational power and energy efficiency of edge devices, which are required to deliver real-time processing capabilities. Traditional object detection models are excessively resource-hungry for these environments, making optimization techniques absolutely essential. This study robustly explores the implementation of quantized transfer learning utilizing SSD MobileNet V2 with 8-bit quantization to significantly elevate the performance of object detection on resource-constrained edge devices. Experimental results decisively indicate that the Raspberry Pi 5 and Nvidia Jetson Orin Nano surpass other devices, achieving total latencies of 5 ms and 85 ms, respectively, affirming their exceptional suitability for real-time applications. The quantized int8 model secures an impressive accuracy of 80.65% while dramatically lowering memory consumption and latency when compared to the unoptimized int32 model. Furthermore, the model demonstrates outstanding performance on a masked-unmasked dataset with an F1 score of 0.92 for masked detection. These findings underscore the transformative potential of quantization in enhancing both inference speed and resource efficiency in edge-based surveillance systems. Future research will boldly investigate advanced hybrid quantization strategies and architectural enhancements to achieve an optimal balance of efficiency and accuracy, alongside scalability across a broader spectrum of edge devices and datasets.