bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing earthquake damage prediction using particle swarm optimization-based feature selection
Authors: Anisa Sri Winarsih Nurul , Anggi Pramunendar Ricardus , Fajar Shidik Guruh , Widjajanto Budi , Syaifur Rohman Muhammad , Oka Ratmana Danny
Published: November 2024

Earthquakes have destroyed the economy and killed many people in many countries. Emergency response actions immediately after an earthquake significantly reduce economic losses and save lives, so accurate earthquake damage predictions are needed. This research looks at how machine learning (ML) techniques are used to predict damage from earthquakes. The ML algorithms used are k-nearest neighbors (KNN), decision tree (DT), random forest (RF), and Naïve Bayes (NB). Feature selection is necessary, it needs to select the most relevant features from big data. One of the most commonly used algorithms to optimize ML is particle swarm optimization (PSO). PSO is also suitable for feature selection. This research compares various of PSO. Based on research, the RF algorithm with Phasor PSO has the highest fitness score. This process succeeded in reducing features from 38 features to 14 features. Based on the process after feature selection, it was found that the KNN, DT, and RF algorithms had improved. RF obtained the best accuracy, namely 72.989%. The processing time in DT, RF, and NB is faster than before. In conclusion, the ML algorithm can be combined with PSO feature selection to create a classification model that provides better performance than without feature selection.
Analyzing 5G performance: investigating altitude-induced variations
Authors: Daengsi Therdpong , Sriamorntrakul Pakkasit , Chatchalermpun Surachai , Phanrattanachai Kritphon
Published: November 2024

Since the launch of fifth generation (5G) services in Thailand in 2020, there have been continuous improvements in 5G coverage. Currently, 5G coverage extends to most areas throughout the country. However, coverage issues persist not only in rural areas but also in high-rise buildings in urban areas. Consequently, a study was conducted within such buildings. This paper assesses the performance of 5G at different altitude test points. The chosen location for the field tests was a high-rise building within a crowded public hospital, which receives numerous patients every weekday, in the major urban area of Bangkok. Two smartphones from the same manufacturer, both supporting 5G technology and equipped with the Speedtest application, were employed as tools for this study. Tests were carried out on the third and twenty-fourth floors of the high-rise building for data collection. The primary finding of this study reveals that download speeds exhibited a significant decrease with increasing altitude of the test points, as evidenced by statistical analysis (p-values0.001). This implies an issue with altitude-induced variations, indicating a need for the improvement of indoor 5G coverage in high-rise buildings.
Hybrid approach for tweets similarity classification founded on case based reasoning and machine learning techniques
Authors: Bensassi Ismail , Kouissi Mohamed , Ndama Oussama , En-Naimi El Mokhtar , Zouhair Abdelhamid
Published: November 2024

Twitter sentiment analysis becomes a popular research subject in the last decade. It aims to extract sentiments of users through their public opinion about a given topic. This article proposes a hybrid approach for Twitter sentiment analysis founded on dynamic case based reasoning (DCBR), multinomial logistic regression machine learning algorithm and multi-agent system. Our approach proposes a method to find similar tweets based on content similarity measure using the scientific measurement of keyword weight term frequency-inverse document frequency (TF-IDF). This approach includes gathering and pre-processing tweets, getting score and polarity of tweets, the use of multinomial logistic regression machine learning algorithm to classify our tweets into various classes, using the feature extraction method to extract useful features and then the K-nearest neighbors (KNN) algorithm to make it easier to find similar tweets to our tweet target case. This approach is adaptive and generic and able to track users' tweet to predict their behavior and sentiments in critical situations and delivering personalized content. The current study focuses on Covid-19 tweets, and a public Twitter dataset is used for this purpose.
Optimization of perovskite solar cell with MoS2-based HTM layer using hybrid L27 Taguchi-GRA based genetic algorithm
Authors: Ezwan Kaharudin Khairil , Salehuddin Fauziyah , Ahmad Jalaludin Nabilah , Suhaila Mohd Zain Anis , Arith Faiz , Aisah Mat Junos Siti , Ahmad Ibrahim
Published: November 2024

This article proposes an optimization method to predictively model the perovskite solar cell with molybdenum disulfide (MoS2) based inorganic hole transport material (HTM) for improved fill factor (FF) and power conversion efficiency (PCE) by finding the most optimum thickness and donor/acceptor concentration for each layer via a hybrid L27 Taguchi grey relational analysis (GRA) based genetic algorithm (GA). Numerical simulation of the device is carried out by employing one-dimensional solar cell capacitance simulator (SCAPS-1D) while the optimization procedures are developed based on combination of multiple methods; L27 Taguchi orthogonal array, GRA, multiple linear regression (MLR), and GA. The results of post-optimization reveal that the most optimum layer parameters for improved FF and PCE are predicted as follows; SnO2F thickness (0.855 μm), SnO2F donor concentration (9.206×1018 cm-3), TiO2 thickness (0.011 μm), TiO2 donor concentration (9.306×1016 cm-3), CH3NH3PbI3 thickness (0.897 μm), CH3NH3PbI3 donor concentration (0.906×1013 cm-3), MoS2 thickness (0.154 μm), and MoS2 acceptor concentration (9.373×1017 cm-3). Both FF and PCE of the device are improved by ~1.1% and ~12.6% compared to the pre-optimization.
Chronic disease prediction chatbot using deep learning and machine learning algorithms
Authors: Sia Mandy , Ng Kok-Why , Haw Su-Cheng , Jayaram Jayapradha
Published: November 2024

Ever since the rise of human civilization, more and more diseases have been discovered with the rapid growth of medical knowledge. This sheer volume of information makes it hard for humans to memorize or even utilize it efficiently. Thus, machine learning emerged as a powerful tool for complex calculations by offering a solution to this challenge. This paper intends to use deep learning and machine learning algorithms to develop a predictive model that can recognize potential diseases based on symptoms. The model is then seamlessly integrated into a text-based disease prediction assistant chatbot that serves as a communication platform between the users and the system. The algorithms researched for the disease prediction models are k-nearest neighbours (KNN), support vector machines (SVM), random forest, and neural networks. After that, a chatbot application is created by integrating long short-term memory (LSTM), natural language toolkit (NLTK) libraries, and Telegram. As a result, the SVM models demonstrated excellent performance by achieving an accuracy of 92.24%, closely followed by random forest with 92.23%, KNN with 91.57%, and artificial neural network (ANN) with 91.52% accuracy. In short, this paper presents a potential solution for a more accurate disease prediction tool by implementing the best disease prediction model with the chatbot models together.
Predicting graduation in Moroccan open-access bachelors: early indicators and re-enrollment data
Authors: Oqaidi Khalid , Aouhassi Sarah , Mansouri Khalifa
Published: November 2024

The primary aim of higher education institutions is the successful graduation of their students. This study explores open-access higher education in Morocco, introducing a predictive model for assessing the probability of students achieving a science bachelor's degree. We analyzed data from 2012 to 2022, initially encompassing 45,573 student entries, and narrowed it down to 14,054 records after data cleaning. Focusing on early academic indicators from enrollment onwards-excluding current program performance—we used popular machine learning classifiers to examine the predictive capacity for student graduation and early dropout. Our comparison included analyses with and without re-enrollment data. Upon analyzing various machine learning algorithms, we attained accuracies between 79% and 86%, identifying random forest (RF) as the superior model for predicting outcomes both with and without incorporating re-enrollment data. This analysis was grounded on initial indicators observed during enrollment and throughout subsequent years, deliberately excluding current academic performance metrics from consideration.
Hyperparameter tuning for deep learning model used in multimodal emotion recognition data
Authors: Widardo Fernandi , Chowanda Andry
Published: November 2024

This study attempts to address overfitting, a frequent problem with multimodal emotion identification models. This study proposes model optimization using various hyperparameter approaches, such as dropout layer, l2 kernel regularization, batch normalization, and learning rate schedule, and discovers which approach yields the most impact for optimizing the model from overfitting. For the emotion dataset, this research utilizes the interactive emotional dyadic motion capture (IEMOCAP) dataset and uses the motion capture and speech audio data modality. The models used in this experiment are convolutional neural network (CNN) for the motion capture data and CNN-bidirectional long short-term memory (CNN-BiLSTM) for the audio data. This study also applied a smaller model batch size in the experiment to accommodate the limited computing resources. The result of the experiment is that the optimization using hyperparameter tuning raises the validation accuracy to 73.67% and the f1-score to 73% on audio and motion capture data, respectively, from the base model of this research and can competitively compete with another research model result. It is hoped that the optimization experiment results in this study can be useful for future emotion recognition research, especially for those who have encountered overfitting problems.
A simplified approach to establishing the impact of software source code changes on requirements specifications
Authors: Mugambi Muthengi Fredrick , Muchangi Mugo David , Makau Mutua Stephen , Mueni Musyoka Faith
Published: November 2024

This paper proposes an improved approach to establishing the impact of source code changes in software features. An association of the methods affected by the changes made and functional requirements of the software reveals the likely impact of the changes made in the software. Changes in source code are exhibited in the operational behaviour of the software. Functional requirements and source code artefacts play a key role in assessing the impact of the changes made. In this study, we investigated the possibility of establishing the association between the changed methods and the functional requirements. The study found out that changes made in the methods can be mapped to the functional requirements that the methods are implementing. The motivation in this endeavour was to assess the impacted software requirements which translate to the likely software features affected by the changes made in the software. With the intent of the software users being seen in the requirements statements and the method naming by the developers, a mapping of the two software artefacts would help developers find out the impacted software features when assessing the overall effect of the committed changes.
Enhancing detection of zero-day phishing email attacks in the Indonesian language using deep learning algorithms
Authors: Roesmiatun Purnamadewi Yasinta , Zahra Amalia
Published: November 2024

Email phishing is a manipulative technique aimed at compromising information security and user privacy. To overcome the limitations of traditional detection methods, such as blacklists, this research proposes a phishing detection model that leverages natural language processing (NLP) and deep learning technologies to analyze Indonesian email headers. The primary objective is to more efficiently detect zero-day phishing attacks by focusing on the unique linguistic and cultural context of the Indonesian language. This enables the development of models capable of recognizing phishing attack patterns that differ from those in other language contexts. Four models are tested, combining Indonesian bidirectional encoder representation of transformers (IndoBERT) and FastText feature extraction techniques with convolutional neural network (CNN) and long short-term memory (LSTM) deep learning algorithms. The results indicate that the combination of FastText and CNN achieved the highest performance in accuracy, precision, and F1-score metrics, each at 98.4375%. Meanwhile, the FastText model with LSTM showed the best performance in recall, with a score of 98.9583%. The research suggests exploring deeper into email content or integrating analysis between headers and email content in future studies to further improve accuracy and effectiveness in phishing email detection.
Utilizing virtual reality for real-time emotion recognition with artificial intelligence: a systematic literature review
Authors: Aji Purnomo Fendi , Arifin Fatchul , Dwi Surjono Herman
Published: November 2024

Efficiency and optimization in virtual reality (VR) technology is an urgent need, especially in the context of optimizing algorithms to recognize user emotions while using VR. Efficient VR technology can improve user experience and enable more immersive and responsive interactions. This study adopts the preferred reporting items for systematic reviews and meta-analyses (PRISMA) (2020) method to identify and analyze gaps in the existing literature, focusing on the optimization of electroencephalogram (EEG) signal classification algorithms to recognize VR users' emotions. The literature search was conducted through the Scopus database, with article selection based on the type of emotion classified, the classification method used, the limitations of the research, and the results obtained. Of the 1478 articles found, 74 articles passed the initial selection stage, and the final stage 13 articles were selected for further analysis. The selected articles provide important insights into the development of EEG classification algorithms for VR users, especially in multi-user settings. The findings identify potential and opportunities in the development of more efficient and accurate EEG signal classification algorithms for VR users. By focusing on emotion classification in a multi-user VR environment, this research contributes to improving the efficiency of VR technology and supporting a better and more responsive user experience.
Cluster-based routing protocols through optimal cluster head selection for mobile ad hoc network
Authors: Alayu Melkamu Yenework , Purushothaman Raguraman , Sujatha Madugula , Kumar Napa Komal , Zeleke Mekonen Mareye , Admassu Assegie Tsehay , Olalekan Salau Ayodeji
Published: November 2024

Mobile ad hoc networks (MANETs) operate without fixed infrastructure, with mobile nodes acting as both hosts and routers. These networks face challenges due to node mobility and limited resources, causing frequent changes in topology and instability. Clustering is essential to manage this issue. Significant research has been devoted to optimal clustering algorithms to improve cluster-based routing protocols (CBRP), such as the weighted clustering algorithm (WCA), optimal stable clustering algorithm (OSCA), lowest ID (LID) clustering algorithm, and highest connectivity clustering (HCC) algorithm. However, these protocols suffer from high re-clustering frequency and do not adequately account for energy efficiency, leading to network instability and reduced longevity. This work aims to improve the CBRP to create a more stable and long-lasting network. During cluster head (CH) selection, nodes with high residual energy or degree centrality are chosen as CH and backup cluster head (BCH). This approach eliminates the need for re-clustering, as the BCH can seamlessly replace a failing CH, ensuring continuous cluster maintenance. The proposed modified cluster-based routing protocol (MCBRP) evaluated network simulator 2 (ns2) demonstrates that MCBRP is more energy-efficient, selecting optimal CH and balancing the load to enhance network stability and longevity.
Development and experimental study of an intelligent water quality monitoring system based on the internet of things
Authors: Amirgaliyev Beibut , Kozbakova Ainur , Kozbakova Ainur , Omarova Perizat , Merembayev Timur , Omarova Perizat , Amirzhan Kanat , Merembayev Timur , Amirzhan Kanat
Published: November 2024

The goal of this work is to create an intelligent internet of things (IoT)-based water quality monitoring system that will effectively monitor and analyze water parameters, collect real-time data, and provide critical information for decision-making in water management and environmental issues. Provide data transfer over wireless networks such as Wi-Fi or Bluetooth. The scientific novelty of the project lies in the development of an innovative system that combines modern IoT technologies and machine learning methods to provide comprehensive and accurate water quality monitoring, which is a significant contribution to water management and environmental safety. Five sensors are connected to Arduino-Mega 2560, ESP-32-E in a discrete manner to determine water parameters. The extracted sensor data is transferred to a desktop application developed on the Blynk App platform and compared with World Health Organization (WHO) standard values. Based on the measurement results, the proposed system can successfully analyze water parameters using the fast forest binary classifier to determine whether the tested water sample is potable or not. An intuitive user interface has been created that will allow users to monitor and analyze water quality data in real time. Provide the ability to create graphs, charts, and reports for visual presentation of data.
Redefining Cardiac Antibody-Mediated Rejection With Donor-Specific Antibodies and Graft Dysfunction
Authors: Goldberg Jason F. , Tian Xin , Bon Ann , Xu Yifei , Gerhard Eleanor , Brower Ruth , Jang Moon Kyoo , Kong Hyesik , Andargie Temesgen E. , Park Woojin , Najjar Samer S. , Tchoukina Inna , Shah Keyur B. , Hsu Steven , Rodrigo Maria E. , Marboe Charles , Berry Gerald J. , Valantine Hannah A. , Shah Palak , Agbor-Enoh Sean
Published: November 2024

BACKGROUND:

Heart transplant recipients with donor-specific antibodies (DSAs) have an increased risk for antibody-mediated rejection. However, many patients with graft dysfunction and DSA do not have evidence of antibody-mediated rejection by endomyocardial biopsy (EMB).

METHODS:

Participants from this prospective, multicenter study underwent serial EMB, echocardiogram, DSA, and donor-derived cell-free DNA evaluations. Outcomes were defined as pAMR+ (pAMR≥1) or DSA+/left ventricle (LV) dysfunction (DSA presence+LVEF drop ≥10% to an LVEF≤50%). Cox regression evaluated the association between antibody-mediated rejection categories and death or sustained (for 3 months) reduction of LVEF to <50%.

RESULTS:

Two hundred sixteen patients (29% women, 39% Black race, median age 55 [interquartile range, 47–62] years) had 1488 EMB, 2792 DSA, 1821 echocardiograms, and 1190 donor-derived cell-free DNA evaluations. DSAs were present in 86 patients (40%). Fourteen patients had isolated pAMR+ episodes and 8 patients had isolated DSA+/LV dysfunction episodes; 2 patients had pAMR+ and then subsequently DSA+/LV dysfunction with pAMR+. Median %dd-cfDNA was significantly higher at diagnosis of pAMR+ (0.63% [interquartile range, 0.23–2.0];
P
=0.0002), or DSA+/LV dysfunction (0.40% [interquartile range, 0.36–1.24];
P
<0.0001), compared with patients without these outcomes (0.01% [interquartile range, 0.0001–0.10]). Both pAMR+ and DSA+/LV dysfunction were associated with long-term clinical outcome of death (n=18) or prolonged LV dysfunction (n=10): pAMR+ (hazard ratio, 2.8 [95% CI, 1.03–7.4];
P
=0.043); DSA+/LV dysfunction (hazard ratio, 26.2 [95% CI, 9.6–71.3];
P
<0.001); composite of both definitions (hazard ratio, 6.5 [95% CI, 2.9–14.3];
P
<0.001). Patients who developed pAMR+ or DSA+/LV dysfunction within the first 6 months of transplant were more likely to die within 3 years posttransplant (hazard ratio, 3.9 [95% CI, 1.03–14.6];
P
=0.031).

CONCLUSIONS:

Expanding the characterization of antibody-mediated rejection to include patients with DSA and concurrent allograft dysfunction identified DSA+ patients at risk for death and prolonged LV dysfunction.
Loss of the Endothelial Glycocalyx Component EMCN Leads to Glomerular Impairment
Authors: Hu Zhengping , Cano Issahy , Lei Fengyang , Liu Jie , Bossardi Ramos Ramon , Gordon Harper , Paschalis Eleftherios I. , Saint-Geniez Magali , Ng Yin Shan Eric , D’Amore Patricia A.
Published: November 2024

BACKGROUND:

EMCN (endomucin), an endothelial-specific glycocalyx component, was found to be highly expressed by the endothelium of the renal glomerulus. We reported an anti-inflammatory role of EMCN and its involvement in the regulation of VEGF (vascular endothelial growth factor) activity through modulating VEGFR2 (VEGF receptor 2) endocytosis. The goal of this study is to investigate the phenotypic and functional effects of EMCN deficiency using the first global EMCN knockout mouse model.

METHODS:

Global EMCN knockout mice were generated by crossing EMCN-floxed mice with ROSA26-Cre mice. Flow cytometry was used to analyze infiltrating myeloid cells in the kidneys. The ultrastructure of the glomerular filtration barrier was examined by transmission electron microscopy, whereas urinary albumin, creatinine, and total protein levels were analyzed from freshly collected urine samples. Expression and localization of EMCN, EGFP (enhanced green fluorescent protein), CD45 (cluster of differentiation 45), CD31, CD34, podocin, and albumin were examined by immunohistochemistry. Mice were weighed regularly, and their systemic blood pressure was measured using a noninvasive tail-cuff system. Glomerular endothelial cells and podocytes were isolated by fluorescence-activated cell sorting for RNA sequencing. Transcriptional profiles were analyzed to identify differentially expressed genes in both endothelium and podocytes, followed by gene ontology analysis. Protein levels of EMCN, albumin, and podocin were quantified by Western blot.

RESULTS:

The EMCN
−/−
mice exhibited increased infiltration of CD45
+
cells, with an increased proportion of Ly6G
high
Ly6C
high
myeloid cells and higher VCAM-1 (vascular cell adhesion molecule 1) expression. EMCN
−/−
mice displayed albuminuria with increased albumin in the Bowman’s space compared with the EMCN
+/+
littermates. Glomeruli in EMCN
−/−
mice revealed fused and effaced podocyte foot processes and disorganized endothelial fenestrations. We found no significant difference in blood pressure between EMCN knockout mice and their wild-type littermates. RNA sequencing of glomerular endothelial cells revealed downregulation of cell-cell adhesion and MAPK (mitogen-activated protein kinase)/ERK (extracellular signal–regulated kinase) pathways, along with glycocalyx and extracellular matrix remodeling. In podocytes, we observed reduced VEGF signaling and alterations in cytoskeletal organization. Notably, there was a significant decrease in both mRNA and protein levels of podocin, a key component of the slit diaphragm.

CONCLUSION:

Our study demonstrates a critical role of the endothelial marker EMCN in supporting normal glomerular filtration barrier structure and function by maintaining glomerular endothelial tight junction and homeostasis and podocyte function through endothelial-podocyte crosstalk.
Neuroprotective Potential of Nitroglycerin in Ischemic Stroke: Insights into Neural Glucose Metabolism and Endoplasmic Reticulum Stress Inhibition
Authors: Jiang Shangqian , Ding Yuchuan , Wang Hongrui , Kim Enoch , Geng Xiaokun
Published: November 2024

Background

Glyceryl trinitrate (GTN), also known as nitroglycerin, is predominantly recognized as a vasodilator for ischemic heart disease, and its potential neuroprotective properties in acute ischemic stroke remain under exploration. We sought to discover the therapeutic advantages and mechanisms of post‐recanalization GTN administration in acute ischemic stroke.

Methods and Results

A total of 118 male Sprague–Dawley rats were divided into groups: sham, transient/permanent middle cerebral artery occlusion (MCAO) with or without GTN treatment, and transient/permanent MCAO treated with both GTN and KT5823, an inhibitor of PKG. Acute ischemic stroke was induced by transient MCAO for 2 hours followed by 6 or 24 hours of reperfusion and permanent MCAO (28‐hour MCAO without reperfusion). The study assessed infarct volumes, neurological deficits, glucose metabolism metrics, NO, and cGMP levels via ELISA. mRNA and protein expression of key molecules of hyperglycolysis, gluconeogenesis, endoplasmic reticulum stress as well as signaling molecules (PKG, AMPK) were conducted via reverse transcription polymerase chain reaction and Western blotting, and cell death was assessed with TUNEL and ELISA. GTN significantly reduced cerebral infarct volumes, neurological deficits, and cell death only after transient MCAO. GTN led to a significant reduction in the expression of NO and cGMP levels, key glucose metabolism, endoplasmic reticulum stress‐related genes and proteins, and phosphorylated AMPK while boosting PKG expression, in transient MCAO but not permanent MCAO. The GTN‐induced reduction in glucose metabolites, lactate, and reactive oxygen species was exclusive to transient MCAO groups. Coadministration of GTN and PKG inhibitors reversed the observed GTN benefits.

Conclusions

GTN induced neuroprotection in transient MCAO by improving glucose metabolism and potentially controlling endoplasmic reticulum stress through the NO‐cGMP‐PKG signaling cascade to inhibit AMPK phosphorylation.
Surfing beta burst waveforms to improve motor imagery-based BCI
Authors: Papadopoulos Sotirios , Darmet Ludovic , Szul Maciej J. , Congedo Marco , Bonaiuto James J. , Mattout Jérémie
Published: November 2024

Our understanding of motor-related, macroscale brain processes has been significantly shaped by the description of the event-related desynchronization (ERD) and synchronization (ERS) phenomena in the mu and beta frequency bands prior to, during, and following movement. The demonstration of reproducible, spatially- and band-limited signal power changes has, consequently, attracted the interest of non-invasive brain-computer interface (BCI) research for a long time. BCIs often rely on motor imagery (MI) experimental paradigms that are expected to generate brain signal modulations analogous to movement-related ERD and ERS. However, a number of recent neuroscience studies has questioned the nature of these phenomena. Beta band activity has been shown to occur, on a single-trial level, in short, transient, and heterogeneous events termed bursts rather than sustained oscillations. In a previous study, we established that an analysis of hand MI binary classification tasks based on beta bursts can be superior to beta power in terms of classification score. In this article, we elaborate on this idea, proposing a signal processing algorithm that is comparable to- and compatible with state-of-the-art techniques. Our pipeline filters brain recordings by convolving them with kernels extracted from beta bursts and then applies spatial filtering before classification. This data-driven filtering allowed for a simple and efficient analysis of signals from multiple sensors, thus being suitable for online applications. By adopting a time-resolved decoding approach, we explored MI dynamics and showed the specificity of the new classification features. In accordance with previous results, beta bursts improved classification performance compared to beta band power, while often increasing information transfer rate compared to state-of-the-art approaches.
The Unintended Impact of R&D Tax Credits on Innovative Search
Authors: Balsmeier Benjamin , Fleming Lee , Stiebale Joel , Veihl Maria
Published: November 2024

Research and development tax credits often aim to increase investment in experimentation, hoping that firms invent fundamentally new technologies that in turn generate positive spillovers. Since most policies require that companies make profits in order to claim credits, they might also shift investments towards less risky refinement and exploitation. Following the availability of credits, we demonstrate that firms do not experiment but deepen invention in areas of extant expertise. We observe stronger shifts for firms operating in uncertain markets where search failures are more likely to reduce credit eligibility.
A Critical Examination of the Theoretical Foundations of Wasaṭī Minority Jurisprudence
Authors: al-Hudawi Mohamed Jabir Ali , Poljarević Emin , Ahmad Khalil
Published: November 2024

This study critically examines the wasaṭī approach within minority jurisprudence, leveraging insights from prominent Muslim scholars like Yūsuf al-Qaraḍāwī and Ṭāhā Jābir al-ʿAlwānī. The wasaṭī methodology emerges as a balanced pathway in addressing the religio-legal challenges faced by minority Muslim communities, by navigating between adaptationist and rejectionist viewpoints. By employing the method of revivalist ijtihād, wasaṭī scholars have issued many pioneering legal opinions that aim to integrate Muslim minorities into their respective host societies. However, this study also pinpoints certain limitations in this process. Its narrow concentration on Western contexts has drawn criticisms from traditionalist scholars for overlooking the longstanding experiences of Muslim minorities elsewhere. The study argues for a more inclusive analytical framework to better understand the jurisprudential needs of diverse Muslim minority populations globally. It highlights the necessity of extending research beyond Western contexts.
A revision of the genus Renealmia (Zingiberaceae) in Tropical Africa. I. New species from Central Africa (Cameroon, Rio Muni, Gabon, Democratic Republic of the Congo), and notes on the identity of Renealmia africana, R. congolana, and R. dewevrei
Authors: FISCHER EBERHARD , DHETCHUVI JEAN-BAPTISTE M.M.
Published: November 2024

Ten new species of Renealmia from tropical Africa are described, among them one from Cameroon (R. tenuispica), one from Rio Muni (Equatorial Guinea), Gabon and D.R. Congo (R. ewangoi), four from Gabon (R. longicarpa, R. longituba, R. tchimbeleana, R. tenuis), and four from D.R. Congo (R. botsimana, R. longispicata, R. montana, R. vermoeseniana). For each new species a preliminary conservation assessment is provided. The synonymy of Renealmia africana and R. congolana is clarified, and R. dewevrei is reinstated as a distinct species. From the three syntypes of Renealmia africana a lectotype is chosen.
Six new species of Diacyclops (Copepoda: Cyclopoida) from Australia
Authors: KARANOVIC TOMISLAV
Published: November 2024

Nine species and one subspecies of the freshwater copepod genus Diacyclops Kiefer, 1927 have been reported so far from Australia. Two of them are nearly cosmopolitan and frequently found in disturbed surface waters, one is endemic to eastern Australia and found predominantly in springs, while all others are endemic to the Pilbara region of Western Australia and found only in subterranean waters. Here I describe another six endemics: one from a spring in South Australia (D. leijsi sp. nov.), one from two caves in the south-western corner of Western Australia (D. eberhardi sp. nov.), one from several bores in the Pilbara region of Western Australia (D. ballaballensis sp. nov.), and three from several bores in New South Wales (D. hancocki sp. nov., D. tomlinsonae sp. nov., and D. huntervalleyensis sp. nov.). All six new species are easily distinguishable from each other by a unique shape and armature of the caudal rami and a different armature formula of the swimming legs. All, except one pair, also have a different armature formula of the antenna. Interestingly, all nine Western Australian endemics have a 12-segmented antennula (and other characters of the alticola-group), while the four endemics from eastern Australia have this appendage further reduced. Because of this, and previous molecular work that suggested a polyphyly of the Western Australian species, I performed a cladistic analysis of all 16 Australian taxa based on 21 morphological characters. As with many groups that are dominated by subterranean species, the resulting cladograms suggest numerous homoplasies. The group of species from eastern Australia is certainly polyphyletic, while the Western Australian group appears to be paraphyletic. A key to all Australian species is also included.
Desert Bugs (Hemiptera: Heteroptera: Thaumastellidae): New records and review of the distribution and habitat of this relict group
Authors: KMENT PETR , JACOBS DAWID H. , CARAPEZZA ATTILIO , DECKERT JÜRGEN , RIDER DAVID A. , KÓBOR PÉTER
Published: November 2024

The known distribution and habitat information of the three described species of the genus Thaumastella Horváth, 1896 (Hemiptera: Heteroptera: Pentatomomorpha: Pentatomoidea: Thaumastellidae) are reviewed. In addition a fourth, undescribed species is reported from South Africa (Western Cape). Thaumastella aradoides Horváth, 1896, is recorded for the first time from Egypt, Oman, Saudi Arabia, United Arab Emirates, and the states of Khartoum and Sennar in Sudan. Additional records of Thaumastella namaquensis Schaefer & Wilcox, 1971, from Namibia (Hardap, ||Kharas) and South Africa (Northern Cape), as well as information on habitat of Thaumastella namaquensis and Thaumastella elizabethae Jacobs, 1989, are provided. Photographs of the holotypes of the three already described species and their labels are also presented. The distribution of all four species is mapped.
Lucanus qizhihaoi, a new stag beetle from Wuyishan National Park of Fujian, China (Coleoptera: Lucanidae: Lucaninae)
Authors: LIN XIN , SU RONG-XIANG , XIN FEI-YI , LIAO ZHI-YU , LU HUANG-HUI , LIU XIAN-ZHOU , SONG HAI-TIAN
Published: November 2024

A new stag beetle species is described from Wuyishan National Park of Fujian, China, Lucanus qizhihaoi Lin, Su, Xin & Song, new species, which represents the easternmost distribution record of the L. boileaui group, previously known only from southwestern China and northern Indochina Peninsula. Evidences are found on morphological characters, the mitochondrial marker COI and significant geographic isolation to clearly distinguished the new species from its closest relative, L. boileaui Planet, 1897.
A survey of Brignoliolus Ovchinnikov, 1999 stat. rev. (Araneae, Agelenidae) with the description of a new species from Israel
Authors: ZONSTEIN SERGEI , MARUSIK YURI M.
Published: November 2024

Brignoliolus Ovchinnikov, 1999 stat. rev. originally described as a subgenus of Coelotes Blackwall, 1841 is rediagnosed, redescribed and elevated to the genus rank. This genus is distinguished from Coelotes by a lighter general colouration and a longer and thinner apical segment of the posterior lateral spinnerets, as well as by the distinctive shape of the epigyne with long teeth located anteriorly of a trapezoidal or sandglass fovea. Brignoliolus is considered to encompass nine species, including B. ovchinnikovi sp. nov. from Israel, described herein. The new species most resembles B. vignai and B. caudata but differs from them by the shape and spination of cymbial tip and relative length of epigynal fovea and teeth. All species are discussed, diagnosed and their correct distributional data is provided.
Eight new combinations are established: B. arganoi (Brignoli, 1978) comb. nov.; B. caudatus (de Blauwe, 1973) comb. nov.; B. charitonovi (Spassky, 1939) comb. nov.; B. coenobita (Brignoli, 1978) comb. nov.; B. juglandicola (Ovchinnikov, 1984) comb. nov.; B. nenilini (Ovchinnikov, 1999) comb. nov.; B. turkestanicus (Ovchinnikov, 1999) comb. nov.; B. vignai (Brignoli, 1978) comb. nov..
How Does Temperature Change Affect Residents’ Health? A Multidimensional Health Perspective
Authors: Yin Bibo , Chen Yilin , Xu Xiaocang
Published: November 2024

Frequent heatwaves, the heat island effect, and numerous extreme weather events due to climate change and urbanization pose significant risks to public health. We constructed a system of indicators to measure four dimensions of health: physiological, psychological, social, and behavioral. Meanwhile, fixed-effect models were used to analyze data from the three waves (2013–18) across 113 cities in the China Health and Retirement Longitudinal Study (CHARLS). The findings displayed that both low and high temperatures negatively affect physiological, psychological, social, and behavioral health. Low temperatures had a greater potential to damage human physiological health and incur more medical expenses compared to high temperatures. The elderly were more vulnerable to physiological damage from extreme low temperatures. Excessive alcohol consumption and poor working environments intensified the negative influence of extreme temperatures. Geographical climate variations led to distinct health adaptability to heat and cold among residents of the south and north. In the mechanism test, household income was an effective mediating channel in this effect, which was further certified in rural households. Although installing air conditioning conferred psychological benefits in mitigating the adverse effects of extreme temperatures, its overuse induced physiological disorders. This study broadens health measurement and provides empirical evidence on the health effects of temperature extremes.
The Hidden “Ear-Way”: A Cohort Analysis of Otologic Manifestations in Aspirin Exacerbated Respiratory Disease
Authors: Pellizzari Richard Antonio , Wei Julia , Saba Elias , Shah Swapnil , Greene LaBryson , Tran Quynh-Lam , Debbaneh Peter , Rivero Alexander
Published: November 2024

Objectives:

This study aims to report otologic manifestations in a cohort of patients with aspirin exacerbated respiratory disease (AERD) to determine if severity of sinonasal inflammation is associated with presence of otologic sequalae (OS).

Methods:

All AERD patients treated at a tertiary care center between 2009 and 2016 were included in analysis. Demographics, history of hearing loss, Lund-Mackay (LMK) scores, number of previous sinus procedures, CT findings, and pure tone averages (PTA) were compared between patients with and without OS using chi-square test, Fisher’s exact test, and Wilcoxon rank sum test.

Results:

Of 255 AERD patients, 58.4% were female with a mean age of 48.9 (SD: 13.4) years. The majority (52.2%) had otologic manifestations, most commonly: otitis media requiring antibiotics (n = 89, 34.9%), peripheral vertigo (n = 59, 23.1%), and middle ear effusion (n = 44, 17.3%). A total of 74 patients (29.0%) had hearing loss. PTA ranged from 13.3 to 61.7 dB for the cohort, with no significant difference between those with and without OS. There was no significant difference in LMK in both groups. Patients with OS had a significantly greater number of sinus procedures than those without OS (median = 2.4 and 1.8 respectively; P = .01).

Conclusions:

Otologic manifestations are common in patients with AERD. While there was an increased number of sinonasal surgeries performed in the patients with OS, there was no correlation between sinonasal inflammation, and the presence of OS as measured by LMK score or PTA. Otologic signs and symptoms should be considered in patients with AERD to help mitigate patient morbidity.

Level of Evidence:

III.
Restoration of Oral Competence With Local Advancement Flap After Free-Flap Reconstruction
Authors: DeSisto Nicole G. , Verducci Chloe , Pittman Amy L. , Yang Shiayin F.
Published: November 2024

Objective:

Surgical management of head and neck cancer (HNC) can result in significant functional deficits. Large tumors of the oral cavity may require extensive resection and the need for microvascular free-flap reconstruction involving the use of adynamic tissue. This may result in oral incompetence, drooling, and unsatisfactory esthetic outcomes. Maintenance of the orbicularis oris and function of the oral sphincter are important objectives in the reconstruction of large defects. When this cannot be accomplished, secondary local flaps can be employed to restore the orbicularis oris.

Methods:

We present 2 cases of oral incompetence following microvascular free- flap reconstruction for large resections of HNC.

Results:

All cases were managed with debulking of flap, modified Karapandzic flaps, and advancement of composite skin, muscle, and buccal mucosa to restore the function of the oral sphincter and improve oral competence. The surgical techniques demonstrated in our cases resulted in improvement in both functional and esthetic outcomes.

Conclusion:

Restoring continuity of the orbicularis oris muscle is a critical functional and esthetic consideration in microvascular free flap reconstruction where the lip is involved or disrupted. However, when this cannot be achieved, restoration of the oral sphincter and oral commissure can be achieved with a secondary reconstructive procedure.
Problems of Legal Assessment of Integrated Environmental Risks of the Silk Road Economic Belt
Authors: Fu Ting , Altayeva Kamilya , Ma Yingjie
Published: November 2024

The Silk Road Economic Belt has played a key role in shaping international trade relations since ancient times until today. In recent years, the particularities of the country’s involvement in the Silk Road Initiative have been exacerbated by environmental concerns and the emergence of legislative disagreements in the aspect of environmental risk management. It is for this reason that the issue of the legal assessment of the integrated environmental risks of the Silk Road Economic Belt remains relevant. Therefore, the purpose of this paper is to investigate and establish the problems of legal assessment of the integrated environmental risks of the Silk Road Economic Belt, using Kazakhstan as an example, and to identify measures to address them. It has been determined that environmental protection in the Silk Road Economic Belt dimension is not legally backed up, and that all provisions, conventions and so on are voluntary in nature. Using the integrative method, it is the compliance with all national environmental legislation, the implementation of the Aarhus Convention provisions, the construction of a specialised information resource in Kazakhstan with current information on Silk Road projects, the development and introduction of legislative changes towards increasing the rates of environmental risk insurance in the country, have been established as the main measures to address the legal assessment of the integrated environmental risks of the Silk Road Economic Belt in Kazakhstan. Thus, the practical significance of the study is that the measures identified to address the problems of legal assessment of the integrated environmental risks of the Silk Road Economic Belt can be used by the Kazakh authorities in the aspect of the ‘One Belt, One Road’ initiative.
Book review: Yan Wang. Pension Policy and Governmentality in China: Manufacturing Public Compliance
Authors: Pandit Priyanka
Published: November 2024

Yan Wang. Pension Policy and Governmentality in China: Manufacturing Public Compliance. LSE Press, 2022, 259 pp. ISBN (Paperback): 978-1-909890-88-6.
Fair Across the Board? Relating Teacher Commitment to Teachers’ Perceptions of Principal Versus Assistant Principal Leadership
Authors: Nelson Jennifer L. , Ahn Joonkil , Hegtvedt Karen A.
Published: November 2024

Purpose: Little is known about how teachers view the leadership of assistant principals in comparison to that of principals, especially in relationship to teachers’ work outcomes. We examine whether a gap exists between teachers’ perceptions of fairness from principals and assistant principals, and whether this gap is associated with teachers’ commitment to their school. Research Methods: We employ mixed methods with a converging evidence model to understand the relationship between teachers’ perception gaps and commitment outcomes. We analyze interview data from 98 teachers across five high schools in one metropolitan area in the U.S. South to describe these gaps, and analyze survey data from 354 teachers from these same schools using structural equation modeling. Findings: Our qualitative analysis uses a typology to show examples of typical work scenarios where a teacher perceives a gap or no gap in fairness. Results from our quantitative data analysis suggest teachers express more commitment when they assess as fair (i.e., unbiased) the performance feedback from assistant principals rather than head principals. Yet, teacher commitment hinges on greater considerate interpersonal treatment from principals than from assistant principals. Overall, gaps in administrator fairness are associated with lower average teacher commitment. Implications for Research and Practice: Our investigation advances understandings of school leadership by clarifying role distinctions between principals and assistant principals that go beyond task types and considering expectations teachers hold of different school administrators. These perception differences matter as they appear associated with levels of teacher commitment.
Differential Item Functioning Effect Size Use for Validity Information
Authors: Finch W. Holmes , Hidalgo Montesinos Maria Dolores , French Brian F. , Hernandez Finch Maria
Published: November 2024

There has been an emphasis on effect sizes for differential item functioning (DIF) with the purpose to understand the magnitude of the differences that are detected through statistical significance testing. Several different effect sizes have been suggested that correspond to the method used for analysis, as have different guidelines for interpretation. The purpose of this simulation study was to compare the performance of the DIF effect size measures described for quantifying and comparing the amount of DIF in two assessments. Several factors were manipulated that were thought to influence the effect sizes or are known to influence DIF detection. This study asked the following two questions. First, do the effect sizes accurately capture aggregate DIF across items? Second, do effect sizes accurately identify which assessment has the least amount of DIF? We highlight effect sizes that had support for performing well across several simulated conditions. We also apply these effect sizes to a real data set to provide an example. Results of the study revealed that the log odds ratio of fixed effects (Ln [Formula: see text]) and the variance of the Mantel–Haenszel log odds ratio ([Formula: see text]) were most accurate for identifying which test contains more DIF. We point to future directions with this work to aid the continued focus on effect sizes to understand DIF magnitude.