bluuebunny/crossref_metadata_2025 huggingface dataset:
Waste incinerator monitoring system based on remote communication with android interface
Authors: Sarosa Moechammad , Wirayoga Septriandi , Putri Ratna Ika , Adhisuwignjo Supriatna
Published: December 2024

Raya Ngijo Housing, one of the areas in Karangploso in Malang District has a temporary waste management team that organises the collection of waste from residents and sends it to the landfill. The process of collecting waste from residents is usually at the temporary disposal site (TPS) in the form of moving waste from residential cleaning vehicles and accommodated at the TPS until collection by the Malang District environmental service container for disposal to the transferred to landfills (TPA). Problems often occur when the container collection process is delayed for various reasons, so that the amount of rubbish in the TPS is excessive. One of the solutions made by the cleaning team is to burn excess waste and can be burned using a furnace. However, the combustion carried out cannot be ensured perfect combustion which is feared by the environmental service. Therefore, a remote communication-based furnace monitoring system and android application were made to ensure the perfection of the combustion process so that it could be monitored by the cleaning team. Parts per million (PPM) carbon dioxide (CO2) levels of combustion smoke and combustion temperature are also monitored and controlled in accordance with the safe standards set by the environmental agency
An internet of things-driven smart key system with real-time alerts: innovations in hotel security
Authors: Jaya Putra , Fikri Ryan , Samala Agariadne Dwinggo , Sanjaya Dimas
Published: December 2024

This paper presents an innovative smart key system designed to enhance the safety and convenience of hotel guests. The system employs an iterative, agile approach encompassing the phases of requirement analysis, design, implementation, and testing. Key components of the input circuitry include limit switches, RFID-RC522 and SW420 vibration sensors, which collectively gather data. This data is processed using an Arduino Uno microcontroller and integrated with internet of things (IoT) technology. On the output side, the system incorporates a solenoid lock and is capable of promptly notifying users via Telegram in response to unauthorized access attempts. Importantly, the system can distinguish between vibrations caused by unauthorized entry and those from legitimate usage. Rigorous testing validates its efficacy, issuing Telegram alerts promptly when detecting security breaches. This technological advancement significantly enhances hotel room security, providing an intelligent real-time solution. The fusion of IoT, Arduino microcontroller, and precise sensor configuration underscores the system's reliability, setting new benchmarks for security in the hospitality sector. The comprehensive approach detailed in this paper offers valuable insights applicable to a wide range of security applications.
Implementation of flexible axis photovoltaic system based on internet of things
Authors: Firdaus Aji Akbar , Daud Muhamad Zalani , Rajendran Parvathy , Solihin Mahmud Iwan , Wang Li , Azmita Mimi , Arof Hamzah
Published: December 2024

Electricity is a crucial aspect in human life. With population growth, ongoing regional development, and continuous construction activities, the demand for electricity and fuel in Indonesia is increasing. The substantial power consumption leads to larger financial expenditures for the community. Additionally, the use of electricity, as it has been traditionally employed, has negative environmental impacts. Solutions are needed to address these issues, and one effort involves the use of renewable energy, such as the development of solar power plants (PLTS). PLTS, also known as solar cells, is preferred as it can be used for various relevant purposes in different locations, particularly in offices, factories, residential areas, and others. However, the use of static, single-axis, and dual-axis solar panels still has drawbacks, such as suboptimal sunlight intensity and high motor power consumption. Therefore, a flexible-axis solar panel tracking system has been developed to follow the direction of sunlight, ensuring optimal power efficiency, and significant electricity generation. The flexible-axis tracker system results in a 34.13% increase in power efficiency.
Design of agrivoltaic system with internet of things control for chili fruit classification using the neural network method
Authors: Wanayumini Wanayumini , Satria Habib , Rosnelly Rika
Published: December 2024

Agriculture is a leading sector in the economy as well as the most dominant provider of employment for the Indonesian people. The fertile soil factor allows various types of fruit to be grown, including chilies. However, complex problems make chili farmers have limitations in implementing conventional farming systems. Therefore, the development of an agrivoltaic system with internet of things (IoT) integrated sensors on chili plants can help farmers more easily control, add vitamins, fertilizers, and provide plant nutrients that can be done automatically periodically based on a real-time clock schedule. This system also operates using photovoltaic (PV) as a pumping machine for water circulation. Other technologies such as mini smart cameras are also being developed to monitor and take pictures of chilies which will later be converted using the graphical user interface (GUI) application for segmentation. The method used in this chili fruit classification uses an artificial neural network in classifying ripe, raw, and rotten chilies. The classification results obtained an R value of 0.9, which means it is close to a value of 1 in the suitability of the chili image. Therefore, farmers will find it easier to sort the chilies that will be harvested.
A study of IoT based real-time monitoring of photovoltaic power plant
Authors: Ouederni Ramia , Bouaziz Bechir , Bacha Faouzi
Published: December 2024

Global electricity demand has increased in the last few years. This need is growing all the time as energy consumption increases using conventional energy, which will soon be phased out. So, we had to look at alternative energies, namely renewable energies. The largest and most efficient of these is solar energy, and to make the most of this energy with the greatest efficiency, the performance of these solar panels needs to be directly monitored. This study presents an independent monitoring system based on the internet of things (IoT) to measure essential factors (terminal voltage, load current, energy consumption, humidity, temperature, and light intensity). These values are realistic and accurate, based on the sensors used to measure the aforementioned factors and then using the Node MCU ESP8266 to transmit the analyzed data to the circuit. The Thingspeak platform was then employed to display, analyze, and store these results in real time.
Video surveillance system based on artificial vision and fog computing for the detection of lethal weapons
Authors: Yauri Ricardo , Monterrey José
Published: December 2024

Citizen insecurity in underdeveloped third world countries is aggravated by poor management of arms control and illegal trafficking, which requires information technology solutions in intelligent video surveillance systems for the detection of lethal weapons. The literature review highlights the need for an intelligent video surveillance system to combat high crime, using fog computing, which processes data in real time for the detection of weapons and other crimes. Furthermore, at an international level, solutions based on artificial intelligence and deep learning are being implemented for object recognition and weapons detection. Therefore, this paper describes the design of an intelligent video surveillance system based on artificial vision, fog and edge computing to detect lethal weapons in domestic environments, performing weapon classification and data transmission to police centers. The intelligent video surveillance system allows detecting lethal weapons and operates in three stages: an edge node with a Raspberry Pi 4; a detection algorithm based on a convolutional neural network with YOLOv5; and streaming tagged images to a security unit via WhatsApp. The main conclusion is that the system achieved a precision greater than 0.85 and a quick and efficient response in sending alerts, representing a scalable and effective solution against home burglary.
Finite element analysis method as an alternative for furniture prototyping process and product testing
Authors: Kristianto Fesa Putra , Amarta Zain , Hutasoit Nicolas , Fariz Nuthqy , Herinda Fania Putri
Published: December 2024

In the current furniture industry, making furniture goes through many steps. There are ordering materials, designing, building a prototype, and testing samples. This process is considered quite complex, requiring significant costs, and lengthy production time. The application of finite element analysis (FEA) can be a solution to simulate the furniture manufacturing process. Objective of this research was to determine FEA could substitute making and test prototype furniture thereby saving costs and time. This method utilizes ANSYS 18.1 software for more accurate and rapid calculations, incorporating load variables of 400 N, 600 N, 800 N, and 1,000 N, along with gravitational acceleration of 10 \frac{m}{s^2}. The research evaluates the difference (expressed as a percentage) between the results obtained from simulations and those obtained directly from experiments, considering maximum equivalent stress, maximum principal stress, and total deformation values. The final step involves comparing the simulation with direct testing in terms of cost and time. The research results show an average error factor of 5% across all aspect. In terms of cost, the method can save 1,807 USD and reduce production time by up to one month. From these findings, it can be concluded that the process of prototyping and sample testing can be replaced using the finite element method.
Multimodal recognition with deep learning: audio, image, and text
Authors: Gummula Ravi , Arumugam Vinothkumar , Aranganathan Abilasha
Published: December 2024

Emotion detection is essential in many domains including affective computing, psychological assessment, and human computer interaction (HCI). It contrasts the study of emotion detection across text, image, and speech modalities to evaluate state-of-the-art approaches in each area and identify their benefits and shortcomings. We looked at present methods, datasets, and evaluation criteria by conducting a comprehensive literature review. In order to conduct our study, we collect data, clean it up, identify its characteristics and then use deep learning (DL) models. In our experiments we performed text-based emotion identification using long short-term memory (LSTM), term frequency-inverse document frequency (TF-IDF) vectorizer, and image-based emotion recognition using a convolutional neural network (CNN) algorithm. Contributing to the body of knowledge in emotion recognition, our study's results provide light on the inner workings of different modalities. Experimental findings validate the efficacy of the proposed method while also highlighting areas for improvement.
Analysing feature selection: impacts towards forecasting electricity power consumption
Authors: Malik Azman Ab , Tao Lyu , Allias Noormadinah , Hamzah Irni Hamiza
Published: December 2024

This study focuses on the development of electrical power forecasting based on electricity usage in Wuzhou, China. To develop a forecasting model, the important features need to be identified. Therefore, this study investigates the performance of the feature selection method, focusing on the mutual information as a filter and random forest as a wrapper-based feature selection. From the experiment, six features have been chosen, whereby both feature selection methods chose almost identical features. Later, the selected features are trained and tested with common machine learning models, namely random forest regressor, support vector regression (SVR), k-nearest neighbor (KNN) regressor, and extreme gradient boosting (XGBoost) regressor. The performances of the feature selections tested on each of the models are measured in terms of mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R²). Findings from the experiment revealed that XGBoost outperform the other machine learning models with RMSE 0.9566 and R² indicated with 0.2561. However, SVR outperformed XGBoost and other model by obtaining MAE 0.6028. It can be concluded that the performance of filter-based outperformed the embedded feature selection.
Development of internet of vehicles and recurrent neural network enabled intelligent transportation system for smart cities
Authors: Surve Jyoti , Bangare Manoj L. , Bangare Sunil L. , Pol Urmila R. , Mali Manisha , Meenakshi Meenakshi , Alsalmani Abdullah , Morsi Sami A.
Published: December 2024

The number of deaths has increased as a direct result of the increased frequency of traffic accidents, congestion, and other risk factors. Developing countries have prioritised the development of intelligent transport systems in order to reduce pollution, traffic congestion, and wasted time. This article describes an intelligent transport system that leverages the internet of vehicles (IoV) and deep learning to forecast traffic congestion. Data is acquired using a car’s global positioning system (GPS), road and vehicle sensors, traffic cameras, and traffic speed, density, and flow. All acquired data is stored in one location on a cloud server. The cloud server also stores historical traffic, road, and vehicle data. Using particle swarm optimisation, features are improved. The optimised dataset is used to train and test recurrent neural networks (RNNs), support vector machines (SVMs), and multi layer perceptrons (MLPs). A deep learning algorithm can predict traffic congestion and make recommendations to drivers on how fast to travel and which route to take. The experimental effort employs the performance measurement system (PeMS) traffic dataset. RNN has achieved accuracy of 95.1%.
Algorithm-driven development of a simulation tool for industrial manipulator stability analysis
Authors: Mustary Shabnom , Kashem Mohammod Abul , Chowdhury Mohammad Asaduzzaman , Uddin Jia
Published: December 2024

Industrial manipulators are essential to many manufacturing processes because they increase efficiency and productivity dramatically. However, maintaining operational safety and averting potential risks in industrial environments requires that these manipulators be stable. The development and implementation of an entirely algorithm-driven novel simulation tool intended to assess industrial manipulators’ stability in-depth are presented in this research. The suggested tool combines sophisticated mathematical models with the material properties of the manipulator, such as deflection, stiffness, and damping. To analyses the dynamic behaviour of manipulators under various operating situations, a hypothetical simulation technique to assess the stability of robot manipulators combined with material properties is taken into consideration. The simulation tool offers vital insights into the stability characteristics of manipulators, allowing engineers and designers to enhance their performance and guarantee operational safety. The simulation tool’s usefulness is showcased through case studies and comparative evaluations, emphasizing its capacity to improve the design and implementation of industrial manipulators in practical situations. In summary, this research enhances the field of industrial automation by offering a strong framework for assessing and upgrading the stability of manipulator systems. This, in turn, improves productivity and safety in industrial settings.
Comparative analysis of feature descriptors and classifiers for real-time object detection
Authors: Nandeshwar Vikas J. , Bhatlawande Sarvadnya , Solanke Anjali , Sathe Harsh , Satao Shivanand , Satpute Safalya , Saste Atharva
Published: December 2024

Detecting objects within complex environments, such as urban settings, holds significant importance across various applications, including driver assistance systems, traffic monitoring, and obstacle detection systems. Particularly crucial for these applications is the accurate differentiation between cars and roads. This study introduces a novel approach that leverages traditional feature descriptors and classifiers for real-time object detection. It conducts an exhaustive comparative analysis of feature descriptors and classifiers to identify the most effective model for real-time object detection. Handcrafted features of images are extracted using algorithms such as scale invariant feature transform (SIFT), oriented fast and brief (ORB), fast retina key-point (FREAK), and local binary pattern (LBP). Seven classifiers are employed, including support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), decision tree (DT), logistic regression (LR), Naive Bayes, and extreme gradient boosting (XGBoost). The performance of the 28 generated combinations of feature descriptors and classifiers is evaluated based on the parameters of accuracy, precision, F1 score, and recall. The model utilizing LBP and XGBoost achieves the highest accuracy, reaching 83.59%. The system architecture comprises a camera, a high-speed computing unit, a display, and an audio subsystem, with the algorithm implemented on a Raspberry Pi 4B (8 GB).
Police Use of Social Media in Community Policing in Malawi
Authors: Mutupha Joseph Fredrick , Gama Limbani Chrispin
Published: December 2024

Social media use in police work has taken an unprecedented turn with many police departments leveraging on its opportunities. The purpose of this study focussed on police’s use of social media in community engagement in Malawi. This qualitative case study employed focus group discussions with 44 Community Policing Officers and six in-depth interviews with one Regional Community Policing Coordinator and five Community Policing Coordinators that were sampled purposively. Much as there is high presence of social media among the police, there lacks a serious exploitation of this technology in the Malawi police service as its use is basically grounded on individual practices for mere communication and interaction. The police are challenged in using social media due to among others technological infrastructure barriers and resistant to change among police officers. Informed by the Diffusion of Innovations theory, the study established the laxity within the Malawi Police Service in adopting social media at institutional level. The study recommends that the Malawi Police Service should establish regional social media pages and invest in more robust technological infrastructures.
Cytotaxonomic study of the fern genus Lepisorus (Polypodiaceae) from China
Authors: LIANG ZHEN-LONG , ZHAO JING , RU JIAN , CHEN CHUN-FA , GAO XIN-FEN , ZHANG LI-BING
Published: December 2024

A cytotaxonomic study is conducted for Lepisorus from eastern, southern, and southwestern China, involving 120 samples of about 26 species in seven sections. The chromosome numbers of 10 species are reported for the first time. The basic chromosome numbers, x = 19 and x = 22 discovered in L. cespitosus and L. lineariformis, respectively, are found to be new base numbers in Lepisorus. The taxonomic implications of the chromosome information of some species and the evolution of chromosomes in the genus are discussed.
Dendrobium moniliforme var. hongbinii (Orchidaceae: Epidendroideae), a new variety from Sichuan, China
Authors: ZHENG BAO-QIANG , YANG HONG-BIN , WANG QUN , CHEN XING-LIANG
Published: December 2024

A new variety of Orchidaceae, Dendrobium moniliforme var. hongbinii, collected from Sichuan Province, China, is described and illustrated based on morphological characteristics and molecular biological analysis. It belongs to Dendrobium and is similar to D. moniliforme, from which it differs by the length of mentum is more than half of the bud, the angle is obtuse, calyx sac ends acuminate, the petals and mid-lobe are subelliptic, the ratio of mid-lobe to the labellum is 1/3. Molecular biological analysis based on nuclear markers (ITS) and plastid markers (trnL intron) indicates that D. moniliforme var. hongbinii is a variety of D. moniliforme.
The Coalition of the Willing and Pro-NATOism: Russia–Ukraine Anti-war Discourse
Authors: Altamimi Sadiq
Published: December 2024

NATO ideology, or NATOism, has been part of American and European political agendas for decades. This ideology remapped the unipolar ruling system represented by the United States. However, NATOism not only prompts an identity of self-belonging and appreciation in pro-NATO states for a union of military and security cooperation, but also incites alarming panic and apprehension in anti-NATO countries by triggering an unconscious survival-of-the-fittest race. The world is now experiencing both of these conditions, discursively reformulated by political elites. The current study applies a multidisciplinary approach to the analysis of NATO discourse and its effect on creating unipolar coalitions. The methods of discourse analysis, corpus linguistics and cognitive linguistics were employed in the analysis of Joe Biden’s speeches in order to uncover his role in constructing pro-NATO ideology within international relations, and the ways in which the Russia-Ukraine war has decontextualized the mental structure of NATO identity. The results revealed that certain discourse strategies were inextricably linked in constructing a coalition of the willing against Russian hostility.
On the Language Choices of Ukrainian Refugees in Poland
Authors: Vaseiko Yuliia
Published: December 2024

The article analyses the content, structure and functional purpose of an academic paper which examines the multilingualism of refugees from Ukraine who moved to Poland after the full-scale invasion by Russia. The methodological basis of the study is characterized, and the effectiveness of using the method of language biographies and questionnaires is emphasized.
Economy Wide Impact of Trade Restrictions on Exporters: Evidence Based on a New Measurement Applied to India
Authors: Tiwari Vivek , Sikdar Chandrima
Published: December 2024

Significant trade barriers exist worldwide despite formal efforts at lowering these. Against this backdrop, the present article proposes to examine the economy wide impact of import restrictions on an exporting country based on a new measure for trade restrictiveness. This measure is based on tariffs, non-tariff measures (NTMs) and border trade restrictions imposed by importing countries. Using India as a case, the empirical results of the article suggest that Indian exporters have been grappling with substantial trade restrictions over the years, particularly on account of NTMs. The economy suffered welfare losses due to fall in exports, output, employment, terms of trade and loss in allocative efficiency. Tariffs and NTMs have received much attention from academia and policymakers. But, due to their relatively smaller share in trade restrictions, trading across border restrictions has received much lesser attention. However, as observed in the present article, though border restrictions appear marginal, their welfare and other macroeconomic impacts are substantial. Hence, to ignore these restrictions is likely to result in significant underestimation of trade restrictions faced by exporters.

JEL Codes: F1, F13, F140
Two Traditions in the Conceptualisation of Competition
Authors: Walling S. Limakumba
Published: December 2024

This article adopts a history of economic thought approach in examining the conceptualisation of competition and its development and role in different theoretical systems. Taking the nonlinear view of knowledge, this article identifies two distinct and different theoretical systems—classical political economy and neoclassical economics (brought through the marginalist revolution in the late 19th century). This article argues that classical political economy had a notion of competition as a dynamic process irrespective of the market structure (number of participants in the market). For classical economists, competition was conceptualised as the perfect mobility of resources, establishing a tendency towards uniform prices or rates of return to commodities, assets and labour that are identical in all economically relevant respects. Competition was operationalised through the theoretical idea of natural prices; in fact, competition is the method of abstraction employed by classical economists to study basic economic processes. Meanwhile, in neoclassical economics, the concept of perfect competition depends on the number of participants in the market, with imperfections introduced only as deviations from the idealised benchmark.
Open the UPL Gates and Let the Robot-Lawyers Walk In
Authors: Gupta Suvrajyoti
Published: December 2024

In this work, I argue that almost all categories of legal tasks can be, in principle, performed more efficiently by legal AI (i.e. specialized AI for legal services). However, in legal services, the monopoly of the legal profession is embedded in law and is enforced through the Unauthorized Practice of Law (UPL) regulations. UPL acts as a barrier to large-scale introduction of AI tools in legal services, which ought to be removed. Legal AI can enhance access to justice and consumer welfare and promote disintermediarization of law, thereby empowering the consumer of legal services. I address some key concerns associated with this, namely the problems of accountability for legal AI and problems arising out of AI bias, hallucination and the blackbox effect. These problems are all subject to technological solutions which in turn depend upon a proper regulatory scaffolding involving appropriate assurance, standardization and product and service liability norms. A contour of such regulation might already be emerging. Concerns arising from possible social and human consequences of diluting a lawyer’s role are considered as well. The regulations in the United States and the United Kingdom are considered to share learnings. I conclude that in the near future, the legal services market may be pivoted on legal AI with a far diminished role for lawyers.
Migraine in the multiple sclerosis prodrome: a prospective nationwide cohort study in pregnant women
Authors: Eid Karine , Torkildsen Øivind , Aarseth Jan , Cortese Marianna , Holmøy Trygve , Myhr Kjell-Morten , Riise Trond , Wergeland Stig , Gilhus Nils Erik , Bjørk Marte-Helene
Published: December 2024

Background

People with multiple sclerosis (MS) have an increased risk of migraine. However, little is known about migraine and other headaches during the prodromal phase (before MS symptom onset). Our objective was to study the risk of migraine in women with MS before MS onset.

Methods

A nationwide, prospective cohort study of women participating in the Norwegian Mother, Father, and Child cohort study 1999–2008. The women reported the occurrence of migraine and other headaches prior to or during pregnancy. We identified women who later developed MS through data linkage with national health registries in 2018. We excluded women with an established MS diagnosis (n = 125) and women who had experienced their first clinical symptom of MS, but not yet received an MS diagnosis (n = 91). The reference group comprised all other women in the cohort (n = 85,292). We used logistic regression to estimate adjusted odds ratios (aORs) with 95% confidence intervals (95% CIs).

Results

Two hundred and forty-six women developed MS during follow-up. Of these, 116 women had MS symptom onset after 1–5 years, 92 after 6–10 years, and 38 after 10 years. Migraine was more common among women who developed MS compared to the reference group, 18% vs 11%, aOR 1.6 (1.2–2.3), adjusted for age, smoking, socioeconomic status and overweight. The risk of other headaches was similar for women who developed MS compared to the reference group, 29% vs 27%, aOR 1.1 (0.8–1.4). Migraine was reported by 21 of 116 (18%) women with $$\le$$
≤
5 years until MS symptom onset (aOR 1.7 [1.1–2.8]) and 19 of 92 (21%) women with 6–10 years until MS symptom onset (aOR 1.9 [1.1–2.8]. Only three of 38 (8%) women with > 10 years until MS symptom onset reported migraine, aOR 0.7 (0.2–2.2).

Conclusions

Women with MS have increased risk of migraine, but not other headaches, up to a decade before the onset of classical MS symptoms. This supports that migraine can be a symptom of the MS prodrome. Special attention in people with migraine may lead to earlier recognition of MS.
Molecular dissection of laboratory contamination between two schistosome populations
Authors: Jutzeler Kathrin S. , Platt Roy N. , LI Xue , Morales Madison , Diaz Robbie , Le Clec’h Winka , Chevalier Frédéric D. , Anderson Timothy J. C.
Published: December 2024

Background

Genomic analysis has revealed extensive contamination among laboratory-maintained microbes including malaria parasites, Mycobacterium tuberculosis, and Salmonella spp. Here, we provide direct evidence for recent contamination of a laboratory schistosome parasite population, and we investigate its genomic consequences. The Brazilian Schistosoma mansoni population SmBRE has several distinctive phenotypes, showing poor infectivity, reduced sporocyst number, low levels of cercarial shedding and low virulence in the intermediate snail host, and low worm burden and low fecundity in the vertebrate rodent host. In 2021 we observed a rapid change in SmBRE parasite phenotypes, with a 10-fold increase in cercarial production and fourfold increase in worm burden.

Methods

To determine the underlying genomic cause of these changes, we sequenced pools of SmBRE adults collected during parasite maintenance between 2015 and 2023. We also sequenced another parasite population (SmLE) maintained alongside SmBRE without phenotypic changes.

Results

While SmLE allele frequencies remained stable over the 8-year period, we observed sudden changes in allele frequency across the genome in SmBRE between July 2021 and February 2023, consistent with expectations of laboratory contamination. (i) SmLE-specific alleles increased in the SmBRE population from 0 to 41–46% across the genome between September and October 2021, reflecting the timing and magnitude of the contamination event. (ii) After contamination, strong selection (s ≅0.23) drove the replacement of low-fitness SmBRE with high-fitness SmLE alleles. (iii) Allele frequency changed rapidly across the whole genome, except for a region on chromosome 4, where SmBRE alleles remained at high frequency.

Conclusions

We were able to detect contamination in this case because SmBRE shows distinctive phenotypes. However, this would likely have been missed with phenotypically similar parasites. These results provide a cautionary tale about the importance of tracking the identity of parasite populations, but also showcase a simple approach to monitor changes within populations using molecular profiling of pooled population samples to characterize single-nucleotide polymorphisms. We also show that genetic drift results in continuous change even in the absence of contamination, causing parasites maintained in different labs (or sampled from the same lab at different times) to diverge.

Graphical Abstract
Symbiotic bacteria associated with entomopathogenic nematodes showed molluscicidal activity against Biomphalaria glabrata, an intermediate host of Schistosoma mansoni
Authors: Ardpairin Jiranun , Subkrasae Chanakan , Dumidae Abdulhakam , Pansri Supawan , Homkaew Chanatinat , Meesil Wipanee , Kumchantuek Tewarat , Phoungpetchara Ittipon , Dillman Adler R. , Pavesi Coralie , Bode Helge B. , Tandhavanant Sarunporn , Thanwisai Aunchalee , Vitta Apichat
Published: December 2024

Background

Biomphalaria glabrata acts as the intermediate host of schistosomes that causes human schistosomiasis. Symbiotic bacteria, Xenorhabdus and Photorhabdus associated with Steinernema and Heterorhabditis, produce secondary metabolites with several biological activities. Controlling B. glabrata is a potential strategy to limit the transmission of schistosomiasis. The aims of this study were to identify Xenorhabdus and Photorhabdus bacteria based on recA sequencing and evaluate their molluscicidal activity against B. glabrata snail.

Results

A total of 31 bacterial isolates belonging to Xenorhabdus (n = 19) and Photorhabdus (n = 12) (X. ehlersii, X. stockiae, X. indica, X. griffinae, P. luminescens, P. akhurstii, and P. laumondii subsp. laumondii were molecularly identified based on recA sequencing. Five isolates of bacterial extracts showed potential molluscicide, with 100% snail mortality. P. laumondii subsp. laumondii (bALN19.5_TH) showed the highest effectiveness with lethal concentration (LC) values of 54.52 µg/mL and 89.58 µg/mL for LC50 and LC90, respectively. Histopathological changes of the snail were observed in the head–foot region, which showed ruptures of the epithelium covering the foot and deformation of the muscle fiber. A hemocyte of the treated snails was observed in the digestive tubules of the digestive glands. The hermaphrodite glands of treated snails showed a reduction in the number of spermatozoa, degeneration of oocytes, and deformation and destruction in the hermaphrodite gland. In addition, liquid chromatography–tandem mass spectrometry (LC–MS/MS) of three symbiotic bacteria contained compounds such as GameXPeptide, Xenofuranone, and Rhabdopeptide.

Conclusions

Five bacterial extracts showed good activity against B. glabrata, especially P. laumondii subsp. laumondii and X. stockiae, which produced virulent secondary metabolites resulting in the death of the snails. They also caused histopathological alterations in the foot, digestive glands, and hermaphrodite glands of the snails. This study suggests that extracts from these bacteria show promise as molluscicides for the control of B. glabrata.

Graphical abstract
Microbial diversity of soils under different land use and chemical conditions
Authors: Yoon Jung-Hwan , Adhikari Mahesh , Jeong Seok Soon , Lee Sang Phil , Kim Hyuck Soo , Lee Geon Seung , Park Duck Hwan , Kim Heejung , Yang Jae E.
Published: December 2024

Soil microbial communities are crucial to ecosystem functionality, influencing soil fertility and health. Microbial diversity in soil is impacted by various land-use practices and environmental conditions, but the effects on both prokaryotic and eukaryotic communities remain insufficiently understood. This study investigates the influence of different land-use types and soil chemical properties on the composition and diversity of prokaryotic and eukaryotic microbes using next-generation sequencing (NGS). Soil samples were collected from seven distinct locations in South Korea, representing various land uses, including paddy fields, upland fields, forest areas, hydrocarbon- and heavy-metal-contaminated sites, greenhouse soils, and reclaimed tidal soils. Alpha diversity, assessed using Chao1 and Shannon indices, and beta diversity, evaluated through Bray-Curtis dissimilarity and Principal Coordinates Analysis (PCoA), were used to characterize microbial diversity. Soil chemical properties were analyzed, and their relationships with microbial community structure were examined. Results revealed significant variations in both prokaryotic and eukaryotic diversities across different land uses. Soils under conventional agricultural management (paddy and upland fields) showed higher microbial diversity compared to soils with high salinity, contamination, or low suitability for agriculture. Prokaryotic communities were dominated by Proteobacteria, Chloroflexi, Acidobacteria, and Bacteroidetes, with variations in abundance linked to soil condition and quality. Eukaryotic communities predominantly consisted of Opisthokonta, SAR (Stramenopiles, Alveolates and Rhizaria), and Amoebozoa, with distinct abundance patterns across different soils. In conclusion, land-use practices and soil chemical properties significantly influence microbial diversity and community composition. Soils subjected to less stress, e.g., agricultural soils, exhibited higher microbial diversity, while stressed soils, e.g., contaminated and saline soils, showed reduced diversity. These findings emphasize the importance of understanding the interplay between land management and microbial ecology for optimizing soil fertility and health.
Remote STEM education in the post-pandemic period: challenges from the perspective of students and faculty
Authors: Maisano Domenico A. , Carrera Giovanna , Mastrogiacomo Luca , Franceschini Fiorenzo
Published: December 2024

This study examines the critical aspects of remote STEM education in the post-pandemic period, from the perspectives of students and faculty at four European universities. This research was conducted approximately four years after the COVID-19 pandemic began, aiming to evaluate the effectiveness and challenges of remote learning alongside advancements in educational technology and teaching models. Data were collected via structured questionnaires from over five hundred students and almost two hundred faculty members from four European universities. The study focused on resource availability, technical responsiveness, training adequacy, online assessment feedback, and social dynamics. Results reveal notable discrepancies between student and faculty perceptions regarding both assessment feedback and the significance of student-faculty interactions. While students viewed timely and detailed feedback as essential to their learning, faculty placed less emphasis on it, prioritizing instead the importance of direct interactions with students, a factor that students themselves considered less critical. Despite these differences, both groups acknowledged the broader challenges to educational quality and academic integrity. The study also found a general consensus among the universities involved, with no significant gender bias. These findings provide valuable insights for educational policymakers and institutions, helping refine remote teaching strategies and assessment methods to improve the quality and effectiveness of remote STEM education.
Fedrtid: an efficient shuffle federated learning via random participation and adaptive time constraint
Authors: Yang Qiantao , Du Xuehui , Wu Xiangyu , Wang Wenjuan , Liu Aodi , Wang Shihao
Published: December 2024

Federated learning is a promising new distributed machine learning paradigm, where the client realizes secure and collaborative multi-user training of machine learning models by retaining private data and sharing model parameters with the server. However, with the frequent interaction of model parameters between the client and the server, the client will consume a large amount of network and arithmetic resources, and resource-constrained clients can hardly maintain model security while ensuring the efficiency of collaborative user training. Therefore, we propose FedRtid, a shuffle differential privacy federated learning scheme with random participation and adaptive time constraints, to improve the efficiency of collaborative user training while considering model privacy. First, in model training, the participating clients have the right to decide on random participation in training locally and independently, to alleviate the user’s resource constraints and reduce the time of user interaction to train the model, while adding differential noise to the shared model parameters to ensure model security. In addition, to avoid the global model security decline of server aggregation due to fewer clients participating in training, and the model accuracy decline caused by adding differential noise to all model parameters, we constructed user sparsification and adaptive time-constrained shuffle techniques to reduce the number of model parameters to which the user adds noise, and enhance the model security. Under two types of data distributions, independently and identically distributed and non-independently and identically distributed, we conduct a large number of experiments on three real datasets, and the results show that FedRtid can effectively balance the accuracy and privacy of the model.
Metabolic syndrome prediction based on body composition indices
Authors: Alkhatib Buthaina , Orabi Aliaa , Agraib Lana M. , Al-Shami Islam
Published: December 2024

Background

Metabolic syndrome (MetS) is an important public health issue that has been lately linked as a growing concern worldwide.

The objective

To find out which anthropometric and body composition indices can prognosticate MetS in Jordanian adult females.

Methods

A sample of 656 Jordanian adult females was recruited (January–March 2024) in the middle of Jordan. Weight, height, waist and hip circumference, lipid profile (triglycerides and high-density lipoprotein), fasting plasma glucose, and blood pressure were measured. Fat mass index (FMI), body mass index (BMI), fat-to-muscle ratio, and waist-to-hip ratio (WHR) were calculated. The presence or absence of MetS was the outcome of interest. Receiver operating characteristic (ROC) analyses were used to examine the predictive accuracy of the indices, and the area under the curve (AUC) was measured.

Results

40.6% had MetS, and their mean age was 45.5 years. 90.2% of the participants with MetS were obese based on body fat percentage. The MetS participants had significantly higher means of all the anthropometric indices except the fat-to-muscle ratio. None of the MetS participants were underweight, and 70.8% and 73.8% were obese based on BMI and WHR, respectively (p < 0.001). The highest proportion of the MetS participants (35.5%) was within the Q4 of the FMI compared to those without MetS (p<0.001). The discrimination ability for all indices was almost equal in predicting the existence of MetS (fair prediction power; AUC = 0.66–0.72), except for the fat-to-muscle ratio, which had poor prediction power.

Conclusion

Fat mass %, muscle mass %, FMI, BMI, and WHR could be used as predictors of MetS in Jordanian females, while the fat-to-muscle ratio was not. We suggested that more extensive sample size studies from both genders and different age categories are necessary to develop a superior predictor for MetS in Jordan.
Relationship between sleep disturbances, alexithymia, psychiatric problems, and clinical variables in children with type 1 diabetes mellitus
Authors: Ahmed Gellan K. , Hasaballa Elshaimaa I. , Abdalla Alaa A. , Refaee Abdelrahman , Nofal Marwa , Shaban Mohamed , Abdurrahman Enas AM. , Shehata Salma , Alsaied Rawan
Published: December 2024

Background

Type 1 diabetes (T1DM) in children is frequently associated with sleep disorders. The prevalence of these disorders varies by region attributable to variations in co-sleeping customs, nocturnal rituals, and environmental influences. This study aims to assess sleep problems and their related characteristics among children diagnosed with T1DM in the Upper Egypt region. This cross-sectional study involved 118 children with T1DM, aged 6 to 12 years. The outcomes from the Children’s Sleep Habits Questionnaire-Abbreviated (CSHQ-A) were utilized to categorize the subjects into two groups: the first group comprised individuals with sleep problems (N = 70), and the second group included individuals with non-sleep problems (N = 48). All participants were evaluated using the Socioeconomic Scale, the Clinical Evaluation of Emotional Regulation–9 (CEER-9), the Strengths and Difficulties Questionnaire, the Pediatric Quality of Life Inventory, and the Children’s Alexithymia Measure (CAM).

Results

Sleep problems were prevalent in 59.3% of participants. Children with sleep problems had elevated rates of overweight/obesity, ocular problems increased medication usage, exercise difficulties, diminished quality of life, higher mean scores on the emotional regulation scale, and greater levels of alexithymia as compared to those without sleep disorders. The prevalence of conduct and hyperactivity problems was greater in children with sleep problems compared to those without sleep problems. Regression analysis revealed that increased BMI, presence of ocular issues, higher alexithymia scores, and poor quality of life were associated with a greater likelihood of experiencing sleep problems.

Conclusions

Nearly two-thirds of children with T1DM in Upper Egypt experienced sleep problems. These issues were associated with higher BMI, ocular problems, elevated alexithymia scores, and poorer quality of life.
Progressively complex neurorehabilitation: from a severe coma to a vegetative state, minimal consciousness ... and the Phoenix bird survives and is reborn from its ashes
Authors: Vlădulescu-Trandafir Andreea-Iulia , Anghelescu Aurelian , Cristea Ana-Andreea , Onose Gelu
Published: December 2024

Romania has the highest rate of car accidents and deaths per million inhabitants in the European Union, primarily due to poor driving and substandard road conditions. Victims of car accidents often present with complex medical cases involving multiple injuries across various organs, frequently leading to severe complications. Case report. With the approval of the patient's family and the Ethics Committee (no. 19348/25.04.2024) at the Neuromuscular Rehabilitation Clinic, Emergency Hospital "Bagdasar-Arseni," a case study was conducted. A 22-year-old man without a previous medical history suffered severe polytrauma as a result of a road accident. He was transferred to the Neuromuscular Rehabilitation Clinic in a vegetative state (after multiple fractures of the skull base and visceral cranium, with 3 points GCS), atlantoaxial subluxation (requiring cervical collar immobilization), rib fractures, drained hemothorax, near-complete atelectasis of the left lung, status post-pericardial effusion. The medical intervention involved a multifaceted pharmacological approach. Focal intramuscular injections of botulinum toxin targeted specific muscles significantly alleviated spasticity. Special robotic orthoses were used to correct postural abnormalities and maintain the favorable effects of the botulinum toxin. The patient received a personalized and complex nursing rehabilitation program including tracheal stoma management, nutritional support through IV infusions and percutanous gtrostomy (PEG) feeding, re-education of deglutition, intermittent urinary catheterization, and positioning on a special mattress to prevent pressure ulcers. The kinesiotherapy was progressively adapted to the neurologic and general condition. The patient had slight improvements in motor and cognitive abilities, resulting in simple voluntary gestures and affective response schemes. In time, he progressed to a minimally conscious state; he responded to verbal stimuli through blinking and reactive adequate affective mimic in the context of persistent expressive aphasia. He was safely discharged at home. Our main functional objective is the patient’s reinsertion into his family. Ad vitam prognosis and ad functionem outcomes are guarded. Ad laborem prognosis is nil. The patient was periodically reassessed for further rehabilitative care. This case emphasizes the importance of a complex multidisciplinary and patient-centered approach in managing post-traumatic vegetative status following a road traffic accident. The Phoenix symbolically takes steps toward fulfilling its destiny - to survive and gradually rise from its ashes.