bluuebunny/crossref_metadata_2025 huggingface dataset:
Homelessness and housing problems in admitted psychiatric patients in Flanders, Belgium
Authors: Catthoor Kirsten , Van den Broeck Kris , Hage Mathilde , Van Suetendael Luna , Wuyts Yves , Van Isterdael Geert , De Hert Marc
Published: June 2024

Homelessness in psychiatric patients in Flanders, Belgium, has never been investigated. Advocacy groups from patients with lived experience of psychiatric disorders have sounded the alarm on the scarcity of suitable housing options, the strain on psychiatric institutions, and the challenges faced by social service workers. To investigate the extent of the problem a survey on the topic was initiated. A “homelessness-in-mental-health-questionnaire” was designed by experts in the field. The social services of all Flemish psychiatric hospitals and all psychiatric wards in general hospitals were contacted and invited to complete this survey. 24 of 70 contacted services responded. The total number of homeless patients in the inpatient setting on an annual basis are estimated to an average 19.5%. 18% of homeless patients remain longer in admission due to the lack of housing options. 13.7% of homeless psychiatric patients are referred to a community care facility such as an assisted living facility. Social service respondents reported spending an average of 27.4% of their work time on housing issues. The main focus points according to the respondents are the lack of priority measures for homeless psychiatric patients, psychiatric problems as a barrier to housing options and the shortage of adapted housing capacity. The conclusion of this study is the need for comprehensive policy interventions to ensure an adequate supply of suitable social housing for psychiatric patients, accessible mental health care, alternative housing options and crisis accommodation facilities. We propose a 10-point action plan on housing for psychiatric patients for policymakers and politicians.
Training neural networks with universal adiabatic quantum computing
Authors: Abel Steve , Criado Juan Carlos , Spannowsky Michael
Published: June 2024

The training of neural networks (NNs) is a computationally intensive task requiring significant time and resources. This article presents a novel approach to NN training using adiabatic quantum computing (AQC), a paradigm that leverages the principles of adiabatic evolution to solve optimization problems. We propose a universal AQC method that can be implemented on gate quantum computers, allowing for a broad range of Hamiltonians and thus enabling the training of expressive neural networks. We apply this approach to various neural networks with continuous, discrete, and binary weights. The study results indicate that AQC can very efficiently evaluate the global minimum of the loss function, offering a promising alternative to classical training methods.
Comparing 5G hybrid beamforming in indoor environments—collocated vs distributed mmWave arrays
Authors: Bagheri Alireza , Bencivenni Carlo , Glazunov Andrés Alayón
Published: June 2024

This paper studies the performance of hybrid digital-analog multi-user multiple-input multiple-output (MU-MIMO) downlink communication based on various antenna systems for 5G applications. The analysis is focused on comprehensive numerical simulations comparing hybrid beamforming schemes for collocated vs distributed phased array antennas (PAA) deployments in two indoor office environments. This study uses measured beamforming radiation patterns from two 28 GHz state-of-the-art PAAs. The channel models employed are the standardized statistical 3GPP 38.901 indoor channel models implemented in the QuaDRiGa software. A beam selection algorithm is implemented to maximize the achievable sum rate, assuming either matched-filtering or zero-forcing precoding. The evaluated figures of merit are the gain of the RF power amplifier, the per-user signal-to-interference-plus-noise ratio (SINR), and the resulting achievable sum-rate capacity. Based on the results, the distributed deployment scenario always shows a higher SINR and achievable sum-rate capacity at the user locations while requiring a lesser amplification of the conducted power. Specifically, the largest PAA with slant-polarized 16 × 4 elements producing 16 horizontal analog beams in combination with zero-forcing digital precoding proved to be the best solution in both open and mixed indoor office environments in absolute values. On the other hand, the array based on the same type of elements but in a more compact realization with 4 × 4 elements, but with beams arranged as 8 horizontal times 2 vertical, offered the most significant relative gains.
Cerebrovascular lesion loads and accelerated brain aging: insights into the cognitive spectrum
Authors: Beheshti Iman , Potvin Olivier , Dadar Mahsa , Duchesne Simon
Published: June 2024

Introduction

White matter hyperintensities (WMHs) and cerebral microbleeds are widespread among aging population and linked with cognitive deficits in mild cognitive impairment (MCI), vascular MCI (V-MCI), and Alzheimer's disease without (AD) or with a vascular component (V-AD). In this study, we aimed to investigate the association between brain age, which reflects global brain health, and cerebrovascular lesion load in the context of pathological aging in diverse forms of clinically-defined neurodegenerative conditions.

Methods

We computed brain-predicted age difference (brain-PAD: predicted brain age minus chronological age) in the Comprehensive Assessment of Neurodegeneration and Dementia cohort of the Canadian Consortium on Neurodegeneration in Aging including 70 cognitively intact elderly (CIE), 173 MCI, 88 V-MCI, 50 AD, and 47 V-AD using T1-weighted magnetic resonance imaging (MRI) scans. We used a well-established automated methodology that leveraged fluid attenuated inversion recovery MRIs for precise quantification of WMH burden. Additionally, cerebral microbleeds were detected utilizing a validated segmentation tool based on the ResNet50 network, utilizing routine T1-weighted, T2-weighted, and T2* MRI scans.

Results

The mean brain-PAD in the CIE cohort was around zero, whereas the four categories showed a significantly higher mean brain-PAD compared to CIE, except MCI group. A notable association trend between brain-PAD and WMH loads was observed in aging and across the spectrum of cognitive impairment due to AD, but not between brain-PAD and microbleed loads.

Discussion

WMHs were associated with faster brain aging and should be considered as a risk factor which imperils brain health in aging and exacerbate brain abnormalities in the context of neurodegeneration of presumed AD origin. Our findings underscore the significance of novel research endeavors aimed at elucidating the etiology, prevention, and treatment of WMH in the area of brain aging.
What helps, what hinders?—Focus group findings on barriers and facilitators for mobile service robot use in a psychosocial group therapy for people with dementia
Authors: Wasic Catharina , Erzgräber Robert , Unger-Büttner Manja , Donath Carolin , Böhme Hans-Joachim , Graessel Elmar
Published: June 2024

Introduction

Many countries are facing a shortage of healthcare workers. Furthermore, healthcare workers are experiencing many stressors, resulting in psychological issues, impaired health, and increased intentions to leave the workplace. In recent years, different technologies have been implemented to lighten workload on healthcare workers, such as electronic patient files. Robotic solutions are still rather uncommon. To help with acceptance and actual use of robots their functionalities should correspond to the users’ needs.

Method

In the pilot study Care4All–Initial, we developed and field-tested applications for a mobile service robot in a psychosocial, multimodal group therapy for people with dementia. To guide the process and assess possible facilitators and barriers, we conducted a reoccurring focus group including people with dementia, therapists, professional caregivers as well as researchers from different disciplines with a user-centered design approach. The focus group suggested and reviewed applications and discussed ethical implications. We recorded the focus group discussions in writing and used content analysis.

Results

The focus group discussed 15 different topics regarding ethical concerns that we used as a framework for the research project: Ethical facilitators were respect for the autonomy of the people with dementia and their proxies regarding participating and data sharing. Furthermore, the robot had to be useful for the therapists and attendees. Ethical barriers were the deception and possible harm of the people with dementia or therapists. The focus group suggested 32 different applications. We implemented 13 applications that centered on the robot interacting with the people with dementia and lightening the workload off the therapists. The implemented applications were facilitated through utilizing existing hard- and software and building on applications. Barriers to implementation were due to hardware, software, or applications not fitting the scope of the project.

Discussion

To prevent barriers of robot employment in a group therapy for people with dementia, the robot’s applications have to be developed sufficiently for a flawless and safe use, the use of the robot should not cause irritation or agitation, but rather be meaningful and useful to its users. To facilitate the development sufficient time, money, expertise and planning is essential.
Awareness of Genitourinary Cancers Risk Factors—A 2024 Population-Based Cross-Sectional Study in Poland
Authors: Moczeniat Gabriela , Jankowski Mateusz , Duda-Zalewska Aneta , Gujski Mariusz
Published: June 2024

Objective

This study aimed to assess the awareness of genitourinary cancers risk factors among adults in Poland and to identify factors associated with public awareness of risk factors for genitourinary cancers.

Methods

This cross-sectional survey was carried out between 1 and 4 March 2024 in a nationwide sample of 2,165 adults in Poland. Quota sampling was used. Data were collected using computer-assisted web interview (CAWI) method.

Results

Regardless of the type of cancer (kidney, bladder, or prostate cancer), a family history of cancer was the most recognized risk factor indicated by over half of respondents. Over one-third were aware that chemical exposure increases the risk for bladder cancer (39.4%) or prostate cancer (34.2%). Smoking was recognized as a risk factor for kidney cancer by 40.6% of respondents. Female gender, having higher education, being occupationally active and the presence of chronic diseases were the most important factors (p < 0.05) associated with a higher level of awareness of genitourinary cancers risk factors.

Conclusion

This study revealed gaps in public awareness of genitourinary cancers risk factors among adults in Poland, especially lifestyle-related and workplace-related risk factors.
A Data-Driven Method for Water Quality Analysis and Prediction for Localized Irrigation
Authors: da Silva Roberto Fray , Benso Marcos Roberto , Corrêa Fernando Elias , Messias Tamara Guindo , Mendonça Fernando Campos , Marques Patrícia Angelica Alves , Duarte Sergio Nascimento , Mendiondo Eduardo Mario , Delbem Alexandre Cláudio Botazzo , Saraiva Antonio Mauro
Published: June 2024

Several factors contribute to the increase in irrigation demand: population growth, demand for higher value-added products, and the impacts of climate change, among others. High-quality water is essential for irrigation, so knowledge of water quality is critical. Additionally, water use in agriculture has been increasing in the last decades. Lack of water quality can cause drip clog, a lack of application uniformity, cross-contamination, and direct and indirect impacts on plants and soil. Currently, there is a need for more automated methods for evaluating and monitoring water quality for irrigation purposes, considering different aspects, from impacts on soil to impacts on irrigation systems. This work proposes a data-driven method to address this gap and implemented it in a case study in the PCJ river basin in Brazil. The methodology contains nine components and considers the main steps of the data lifecycle and the traditional machine learning workflow, allowing for automated knowledge extraction and providing important information for improving decision making. The case study illustrates the use of the methodology, highlighting its main advantages and challenges. Clustering different scenarios in three hydrological years (high, average, and lower streamflows) and considering different inputs (soil-related metrics, irrigation system-related metrics, and all metrics) helped generate new insights into the area that would not be easily obtained using traditional methods.
Development of a Cross-Platform Mobile Application for Fruit Yield Estimation
Authors: Duncan Brandon , Bulanon Duke M. , Bulanon Joseph Ichiro , Nelson Josh
Published: June 2024

The Fruit Harvest Helper, a mobile application developed by Northwest Nazarene University’s (NNU) Robotics Vision Lab, aims to assist farmers in estimating fruit yield for apple orchards. Currently, farmers manually estimate the fruit yield for an orchard, which is a laborious task. The Fruit Harvest Helper seeks to simplify their process by detecting apples on images of apple trees. Once the number of apples is detected, a correlation can then be applied to this value to obtain a usable yield estimate for an apple tree. While prior research efforts at NNU concentrated on developing an iOS app for blossom detection, this current research aims to adapt that smart farming application for apple detection across multiple platforms, iOS and Android. Borrowing ideas from the former iOS app, the new application was designed with an intuitive user interface that is easy for farmers to use, allowing for quick image selection and processing. Unlike before, the adapted app uses a color ratio-based image-segmentation algorithm written in C++ to detect apples. This algorithm detects apples within apple tree images that farmers select for processing by using OpenCV functions and C++ code. The results of testing the algorithm on a dataset of images indicate an 8.52% Mean Absolute Percentage Error (MAPE) and a Pearson correlation coefficient of 0.6 between detected and actual apples on the trees. These findings were obtained by evaluating the images from both the east and west sides of the trees, which was the best method to reduce the error of this algorithm. The algorithm’s processing time was tested for Android and iOS, yielding an average performance of 1.16 s on Android and 0.14 s on iOS. Although the Fruit Harvest Helper shows promise, there are many opportunities for improvement. These opportunities include exploring alternative machine-learning approaches for apple detection, conducting real-world testing without any human assistance, and expanding the app to detect various types of fruit. The Fruit Harvest Helper mobile application is among the many mobile applications contributing to precision agriculture. The app is nearing readiness for farmers to use for the purpose of yield monitoring and farm management within Pink Lady apple orchards.
Variable Depth Tillage: Importance, Applicability, and Impact—An Overview
Authors: Šarauskis Egidijus , Sokas Simas , Rukaitė Julija
Published: June 2024

Tillage, as a key agricultural operation, has an important influence on soil properties and crop productivity. However, tillage at the same depth is not always the best choice as differences in soil texture, compacted topsoil, or plow pan at different depths, crop rotation, and root penetration potential signal that the depth of tillage should take greater account of the factors involved. Variable depth tillage (VDT) is an important precision farming operation, linking soil, plants, tillage machinery, smart sensors, measuring devices, computer programs, algorithms, and variability maps. This topic is important from an agronomic, energy, and environmental perspective. However, the application of VDTs in practice is currently still very limited. The aim of this study was to carry out a detailed review of scientific work on variable depth tillage, highlighting the importance of soil compaction and VDT; the measurement methods and equipment used; and the impact on soil, crops, the environment, and the economy. Based on the reviewed studies, there is a lack of studies that use fully automated depth control of tillage systems based on input data obtained with on-the-go (also known as online) proximal soil sensing. In precision agriculture, rapidly developing Internet of Things technologies allow the adaptation of various farming operations—including tillage depth—to site-specific and temporal conditions. In this context, the use of proximal soil sensing technologies coupled with electromagnetic induction, gamma rays, and multi-sensor data fusion to provide input for recommended tillage depth would be beneficial in the future. The application of VTD in specific areas is promising as it helps to reduce the negative effects of soil compaction and avoid unnecessary use of this expensive and environmentally damaging technological operation.
Assessment of Fluxes and Ecological and Health Risks of Toxic Trace Elements in Atmospheric Deposition from the Baicheng-Songyuan Area, Jilin Province, Northeast China
Authors: Liu Yinghong , Gao Wen , Chai Sheli
Published: June 2024

A total of 56 atmospheric deposition samples were collected on a yearly basis from the Baicheng-Songyuan areas, Jilin Province, Northeast China. Each sample was subdivided into wet (soluble) and dry (insoluble) fractions, and the concentrations of toxic trace elements including As, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Se, and Zn in both fractions were analyzed. The bulk fluxes and ecological and human health risk of these toxic trace elements in atmospheric deposition were evaluated. The bulk deposition fluxes of toxic trace elements decreased in the order of Mn > Zn > Cr > Pb > Cu > Ni > As > Co >Mo > Cd > Se > Hg. The fluxes of toxic trace elements in dry fraction accounted for 74.7–99.9% of their bulk deposition fluxes, indicating that the atmospheric deposition of toxic trace elements in the study area is predominantly dry atmospheric deposition. The mean values of the geo-accumulation index (Igeo) and enrichment factor showed moderately heavy contamination with Cd via dry atmospheric deposition, but no contamination from other toxic trace elements; Cd and Hg in dry atmospheric deposition were in the moderate to considerable ecological risk levels, while other trace elements were at low ecological risk levels. The health risk assessment showed that the effects of toxic trace elements in dry atmospheric deposition via three exposure pathways were in the order of ingestion > inhalation > dermal contact for adults and children. The mean values of hazard quotient (HQ) and hazard index (HI) of toxic trace elements via three pathways were less than one, indicating that their non-carcinogenic risks in dry atmospheric deposition may be low or negligible for adults and children. The mean values of carcinogenic risk (CR) and total carcinogenic risk (TCR) of As and Cr via the three pathways for adults and children were between 10−6 and 10−4, indicating that the carcinogenic risk levels of As and Cr were tolerable or acceptable, and the mean TCR value of Cd through the three pathways for adult and children was less than 10−6, implying that the carcinogenic risk level of Cd was negligible. Mn, Ni, Cr, and Co in dry atmospheric deposition were mainly contributed from the crustal sources, while As, Cd, Cu, Hg, Mo, Pb, Se, and Zn in dry atmospheric deposition were derived from both crustal and anthropogenic sources. The results obtained in this study advocate the necessity for monitoring atmospheric deposition in some rural areas, and also provide a scientific basis for controlling contamination posed by toxic trace elements in dry atmospheric deposition.
Influence of Nanoceramic-Plated Waste Carbon Fibers on Alkali-Activated Mortar Performance
Authors: Sambucci Matteo , Al-Noaimat Yazeed A. , Nouri Seyed Mostafa , Chougan Mehdi , Ghaffar Seyed Hamidreza , Valente Marco
Published: June 2024

Waste carbon fibers as reinforcing elements in construction materials have recently gained increasing interest from researchers, providing outstanding strength performance and a lower environmental footprint compared to virgin fibers. Combination with cement-free binders, namely alkali-activated materials, is becoming increasingly important for sustainable development in the construction industry. This paper presents results relating to the potential use of waste carbon fibers in alkali-activated mortars. The waste carbon fiber fraction utilized in this research is difficult to integrate as reinforcement in ceramic–cementitious matrices due to its agglomerated form and chemical inertness. For this reason, a nanoceramic coating pretreatment based on nanoclay has been implemented to attempt improvements in terms of deagglomeration, dispersibility, and compatibility with alkali-activated materials. After chemical–physical and microstructural analysis on the nanoclay-plated fibers (including X-ray diffraction, IR spectroscopy, contact angle measurements, and electron microscopy) mortars were produced with four different dosages of treated and untreated waste fibers (0.25 wt.%, 0.5 wt.%, 0.75 wt.%, and 1 wt.%). Mechanical tests and fractographic investigations were then performed. The nanoclay coating interacts compatibly with the waste carbon fibers and increases their degree of hydrophilicity to improve their deagglomeration and dispersion. Compared to the samples incorporating as-received fillers, the addition of nanoclay-coated fibers improved the strength behavior of the mortars, recording a maximum increase in flexural strength of 19% for a fiber content of 0.25 wt.%. This formulation is the only one providing an improvement in mechanical behavior compared to unreinforced mortar. Indeed, as the fibrous reinforcement content increases, the effect of the nanoclay is attenuated by mitigating the improvement in mechanical performance.
0.98(K0.5Na0.5)NbO3–0.02(Bi0.5Na0.5)(Zr0.85Sn0.15)O3 Single Crystals Grown by the Seed-Free Solid-State Crystal Growth Method and Their Characterization
Authors: Uwiragiye Eugenie , Pham Thuy Linh , Lee Jong-Sook , Lee Byoung-Wan , Ko Jae-Hyeon , Fisher John G.
Published: June 2024

(K0.5Na0.5)NbO3-based single crystals are of interest as high-performance lead-free piezoelectric materials, but conventional crystal growth methods have some disadvantages such as the requirement for expensive Pt crucibles and difficulty in controlling the composition of the crystals. Recently, (K0.5Na0.5)NbO3-based single crystals have been grown by the seed-free solid-state crystal growth method, which can avoid these problems. In the present work, 0.98(K0.5Na0.5)NbO3–0.02(Bi0.5Na0.5)(Zr0.85Sn0.15)O3 single crystals were grown by the seed-free solid-state crystal growth method. Sintering aids of 0.15 mol% Li2CO3 and 0.15 mol% Bi2O3 were added to promote single crystal growth. Pellets were sintered at 1150 °C for 15–50 h. Single crystals started to appear from 20 h. The single crystals grown for 50 h were studied in detail. Single crystal microstructure was studied by scanning electron microscopy of the as-grown surface and cross-section of the sample and revealed porosity in the crystals. Electron probe microanalysis indicated a slight reduction in K and Na content of a single crystal as compared to the nominal composition. X-ray diffraction shows that the single crystals contain mixed orthorhombic and tetragonal phases at room temperature. Raman scattering and impedance spectroscopy at different temperatures observed rhombohedral–orthorhombic, orthorhombic–tetragonal and tetragonal–cubic phase transitions. Polarization–electric field (P–E) hysteresis loops show that the single crystal is a normal ferroelectric material with a remanent polarization (Pr) of 18.5 μC/cm2 and a coercive electrical field (Ec) of 10.7 kV/cm. A single crystal presents d33 = 362 pC/N as measured by a d33 meter. Such a single crystal with a large d33 and high Curie temperature (~370 °C) can be a promising candidate for piezoelectric devices.
Structure and Phylogenetic Relationships of Scolopacidae Mitogenomes (Charadriiformes: Scolopacidae)
Authors: Li Quanheng , Jiang Peiyue , Li Mingxuan , Du Jingjing , Sun Jianxiang , Chen Nuo , Wu Yu , Chang Qing , Hu Chaochao
Published: June 2024

The family Scolopacidae presents a valuable subject for evolutionary research; however, molecular studies of Scolopacidae are still relatively understudied, and the phylogenetic relationships of certain species remain unclear. In this study, we sequenced and obtained complete mitochondrial DNA (mtDNA) from Actitis hypoleucos and partial mtDNA from Numenius arquata, Limosa limosa, and Limnodromus semipalmatus. The complete mtDNA contained 13 protein-coding genes (PCGs), two ribosomal RNA genes, 22 tRNA genes, and a control region. Scolopacidae contained three types of start codons and five types of stop codons (including one incomplete stop codon, T--). In 13 protein-coding genes, average uncorrected pairwise distances (Aupd) revealed that ATP8 was the least conserved while COX3 had the lowest evolutionary rate. The ratio of Ka/Ks suggested that all PCGs were under purifying selection. Using two methods (maximum likelihood and Bayesian inference) to analyze the phylogenetic relationships of the family Scolopacidae, it was found that the genera Xenus and Actitis were clustered into another sister group, while the genus Phalaropus is more closely related to the genus Tringa. The genera Limnodromus, Gallinago, and Scolopax form a monophyletic group. This study improves our understanding of the evolutionary patterns and phylogenetic relationships of the family Scolopacidae.
SRSF3 Knockdown Inhibits Lipopolysaccharide-Induced Inflammatory Response in Macrophages
Authors: Fu Yu , Wang Yanjing , Zhang Luyao , He Tianliu , Shi Weiye , Guo Xueling , Wang Yingze
Published: June 2024

Serine/arginine-rich splicing factor 3 (SRSF3), the smallest member of the SR protein family, serves multiple roles in RNA processing, including splicing, translation, and stability. Recent studies have shown that SRSF3 is implicated in several inflammatory diseases. However, its impact on macrophage inflammation remains unclear. Herein, we determined the expression of SRSF3 in inflammatory macrophages and found that the level of SRSF3 was increased in macrophages within atherosclerotic plaques, as well as in RAW-264.7 macrophages stimulated by lipopolysaccharides. Moreover, the downregulation of SRSF3 suppressed the levels of inflammatory cytokines by deactivating the nuclear factor κB (NFκB) pathway. Furthermore, the alternative splicing of myeloid differentiation protein 2 (MD2), a co-receptor of toll-like receptor 4 (TLR4), is regulated by SRSF3. The depletion of SRSF3 increased the level of the shorter MD2B splicing variants, which contributed to inflammatory inhibition in macrophages. In conclusion, our findings imply that SRSF3 regulates lipopolysaccharide-stimulated inflammation, in part by controlling the alternative splicing of MD2 mRNA in macrophages.
Impact of the Fly Ash/Alkaline Activator Ratio on the Microstructure and Dielectric Properties of Fly Ash KOH-Based Geopolymer
Authors: Yadav Meenakshi , Saini Neha , Kumar Lalit , Singh Vidya Nand , Jagannathan Karthikeyan , Ezhilselvi V.
Published: June 2024

Geopolymer materials, alternatives to cement that are synthesized using industrial byproducts, have emerged as some of the leading champion materials due to their environmentally friendly attributes. They can significantly reduce pollution by utilizing a plethora of waste products and conserving natural resources that would otherwise be used in the production of conventional cement. Much work is being carried out to study geopolymers’ characteristics under different conditions. Here, a geopolymer derived from fly ash (FA) was synthesized using a combination of sodium silicate and potassium hydroxide (KOH) (2.5:1 ratio) as an alkali activator (AA) liquid. The FA/AA ratios were optimized, resulting in distinct geopolymer samples with ratios of 1.00, 1.25, 1.50, and 1.75. By adjusting the contribution of alkaline liquid, we investigated the impacts of subtle changes in the FA/AA ratio on the morphology and microstructure using X-ray diffraction (XRD) and field emission scanning electron microscopy (FESEM) techniques. The FESEM analysis illustrated a mixed matrix and morphology, with the sample with a ratio of 1.00 displaying consistently fused and homogenous morphology. The XRD results revealed the prevalent amorphous nature of geopolymer with a few crystalline phases of quartz, sodalite, hematite, and mullite. An electrical study confirmed the insulating nature of the geopolymer samples. Insulating geopolymers can provide energy-efficient buildings and resistance to fire, hurricanes, and tornadoes. Additionally, using KOH as a part of the alkali activator introduced a less-explored aspect compared to conventional sodium hydroxide-based activators, highlighting the novelty in the synthesis process.
Predicting Transmissibility-Increasing Coronavirus (SARS-CoV-2) Mutations
Authors: Çalışkan Ege , Işık Murat , Kuru Cansu İlke , Chakraborty Somenath
Published: June 2024

Advantageous variants of the SARS-CoV-2 virus have arisen through mutations, particularly on a single amino acid basis. These point mutations can cause changes in the structure of SARS-CoV-2 and affect the efficiency of interaction with the ACE2 protein. N501Y and E484K mutations affecting binding by ACE2 have been widely observed. This study aimed to predict SARS-CoV-2 mutations that could be as effective as N501Y and E484K and pose a danger due to their high contagiousness. Experimental data on SARS-CoV-2 and ACE2 binding and stability were associated with different amino acid properties and integrated into machine learning and computational biology techniques. As a result of the analyses made in algorithms, N501M, Q414A, N354K, Q498H and N460K have been predicted to be likely to have a dangerous effect. The N501W mutations are most likely to have dangerous effects on the spread of the coronavirus. We suggest that attention should be paid to the position 501 mutation since this position is repeated in the lists of mutations that the algorithm detected as dangerous. G446, G447, Y505, T500, Q493, Y473, and G476 were determined as the positions where dangerous variants could be seen as a result of the analyses of the multiple interaction data created with the ACE2 and RBD interaction data. The 13 dangerous positions and mutations have been detected to accurately describe the position of the mutations caused by the Omicron variant and were among the known dangerous mutations similar to those occurring at Q498, G446, Y505 and Q493 positions.
The Emerging Challenges of Wearable Biometric Cryptosystems
Authors: Ajlan Khalid Al , Alsboui Tariq , Alshaikh Omar , Inuwa-Dute Isa , Khan Saad , Parkinson Simon
Published: June 2024

Cryptographic key generation and data encryption and decryption using wearable biometric technologies is an emerging research area with significant potential for authentication and communication security. The research area is rapidly developing, and a comprehensive review of recently published literature is necessary to establish emerging challenges. This research article aims to critically investigate and synthesize current research using biometric cryptosystems that use behavior or medico-chemical characteristics, ranging from gate analysis to gaze tracking. The study will summarize the state of knowledge, identify critical research gaps, and provide insight into promising future implications and applications that can enable the realization of user-specific and resilient solutions for authentication and secure communication demands.
Testing Machine Learning Models to Predict Postoperative Ileus after Colorectal Surgery
Authors: Brydges Garry , Chang George J. , Gan Tong J. , Konishi Tsuyoshi , Gottumukkala Vijaya , Uppal Abhineet
Published: June 2024

Background: Postoperative ileus (POI) is a common complication after colorectal surgery, leading to increased hospital stay and costs. This study aimed to explore patient comorbidities that contribute to the development of POI in the colorectal surgical population and compare machine learning (ML) model accuracy to existing risk instruments. Study Design: In a retrospective study, data were collected on 316 adult patients who underwent colorectal surgery from January 2020 to December 2021. The study excluded patients undergoing multi-visceral resections, re-operations, or combined primary and metastatic resections. Patients lacking follow-up within 90 days after surgery were also excluded. Eight different ML models were trained and cross-validated using 29 patient comorbidities and four comorbidity risk indices (ASA Status, NSQIP, CCI, and ECI). Results: The study found that 6.33% of patients experienced POI. Age, BMI, gender, kidney disease, anemia, arrhythmia, rheumatoid arthritis, and NSQIP score were identified as significant predictors of POI. The ML models with the greatest accuracy were AdaBoost tuned with grid search (94.2%) and XG Boost tuned with grid search (85.2%). Conclusions: This study suggests that ML models can predict the risk of POI with high accuracy and may offer a new frontier in early detection and intervention for postoperative outcome optimization. ML models can greatly improve the prediction and prevention of POI in colorectal surgery patients, which can lead to improved patient outcomes and reduced healthcare costs. Further research is required to validate and assess the replicability of these results.
Real-World Safety of Niraparib for Maintenance Treatment of Ovarian Cancer in Canada
Authors: Guan Qi , Aktar Suriya J. , Pataky Reka E. , Stephen Mariet Mathew , Marques Maud , Gambaro Karen , Rachedi Kahina , Forster Katharina , Strub Samara , Stock David , de Léséleuc Louis , Cheung Winson Y. , Peacock Stuart , Farrer Christie , Gavura Scott , Tadrous Mina , Grant Robert C. , Chan Kelvin K. W.
Published: June 2024

Niraparib was recently funded in Canada for the maintenance treatment of ovarian cancer following platinum-based chemotherapy. However, the drug’s safety profile in the real world remains uncertain. We conducted a cohort study to describe the patient population using niraparib and the proportion that experienced adverse events between June 2019 and December 2022 in four Canadian provinces (Ontario, Alberta, British Columbia [BC], and Quebec). We used administrative data and electronic medical records from Ontario Health, Alberta Health Services, and BC Cancer, and registry data from Exactis Innovation. We summarized baseline characteristics using descriptive statistics and reported safety outcomes using cumulative incidence. We identified 514 patients receiving niraparib. Mean age was 67 years and most were initiated on a daily dose of 100 or 200 mg/day. Grade 3/4 anemia, neutropenia, and thrombocytopenia occurred in 11–16% of the cohort. In Ontario, the three-month cumulative incidence of grade 3/4 thrombocytopenia was 11.6% (95% CI, 8.3–15.4%), neutropenia was 7.1% (95% CI, 4.6–10.4%), and anemia was 11.3% (95% CI, 8.0–15.2%). Cumulative incidences in the remaining provinces were similar. Initial daily dose and proportions of hematological adverse events were low in the real world and may be related to cautious prescribing and close monitoring by clinicians.
Assessment of Mathematical Approaches for the Estimation and Comparison of Efficiency in qPCR Assays for a Prokaryotic Model
Authors: Molina-Mora Jose Arturo , Sibaja-Amador Meriyeins , Rivera-Montero Luis , Chacón-Arguedas Daniel , Guzmán Caterina , García Fernando
Published: June 2024

Quantitative PCR is a molecular technique for DNA quantification that depends on reaction efficiency and the Ct value (“cycle threshold”). However, the results are dependent on laboratory conditions and mathematical approaches. Thus, the data of 16 genes from Pseudomonas aeruginosa strain AG1 were generated using qPCR to assess the effect of DNA concentration and three mathematical methods (a standard curve and two individual-curve-based approaches called exponential and sigmoidal models) on efficiency and DNA quantification. Differences in efficiency were revealed depending on the mathematical method used; the values were 100% in three out of the four standard curves, but estimations of the expected fold change in DNA serial dilutions were not achieved, indicating the possible overestimation of efficiency. Moreover, when efficiency was compared to DNA concentration, a decreasing trend in efficiency as DNA concentration increased in the reaction was observed in most cases, which is probably related to PCR inhibitors. For all 16 genes at a single DNA concentration, the efficiencies for the exponential model were found in the range of 1.5–2.79 (50–79%), and for the sigmoidal approach, the range was 1.52–1.75 (52–75%), with similar impact on normalized expression values, as indicated by the genes for standard curves. Jointly, DNA concentration and mathematical model choice were demonstrated to impact the estimation of reaction efficiency and, subsequently, DNA quantification when using qPCR.
Penetration Strategy for High-Speed Unmanned Aerial Vehicles: A Memory-Based Deep Reinforcement Learning Approach
Authors: Zhang Xiaojie , Guo Hang , Yan Tian , Wang Xiaoming , Sun Wendi , Fu Wenxing , Yan Jie
Published: June 2024

With the development and strengthening of interception measures, the traditional penetration methods of high-speed unmanned aerial vehicles (UAVs) are no longer able to meet the penetration requirements in diversified and complex combat scenarios. Due to the advancement of Artificial Intelligence technology in recent years, intelligent penetration methods have gradually become promising solutions. In this paper, a penetration strategy for high-speed UAVs based on improved Deep Reinforcement Learning (DRL) is proposed, in which Long Short-Term Memory (LSTM) networks are incorporated into a classical Soft Actor–Critic (SAC) algorithm. A three-dimensional (3D) planar engagement scenario of a high-speed UAV facing two interceptors with strong maneuverability is constructed. According to the proposed LSTM-SAC approach, the reward function is designed based on the criteria for successful penetration, taking into account energy and flight range constraints. Then, an intelligent penetration strategy is obtained by extensive training, which utilizes the motion states of both sides to make decisions and generate the penetration overload commands for the high-speed UAV. The simulation results show that compared with the classical SAC algorithm, the proposed algorithm has a training efficiency improvement of 75.56% training episode reduction. Meanwhile, the LSTM-SAC approach achieves a successful penetration rate of more than 90% in hypothetical complex scenarios, with a 40% average increase compared with the conventional programmed penetration methods.
Lightweight and Efficient Tiny-Object Detection Based on Improved YOLOv8n for UAV Aerial Images
Authors: Yue Min , Zhang Liqiang , Huang Juan , Zhang Haifeng
Published: June 2024

The task of multiple-tiny-object detection from diverse perspectives in unmanned aerial vehicles (UAVs) using onboard edge devices is a significant and complex challenge within computer vision. In order to address this challenge, we propose a lightweight and efficient tiny-object-detection algorithm named LE-YOLO, based on the YOLOv8n architecture. To improve the detection performance and optimize the model efficiency, we present the LHGNet backbone, a more extensive feature extraction network, integrating depth-wise separable convolution and channel shuffle modules. This integration facilitates a thorough exploration of the inherent features within the network at deeper layers, promoting the fusion of local detail information and channel characteristics. Furthermore, we introduce the LGS bottleneck and LGSCSP fusion module incorporated into the neck, aiming to decrease the computational complexity while preserving the detector’s accuracy. Additionally, we enhance the detection accuracy by modifying its structure and the size of the feature maps. These improvements significantly enhance the model’s capability to capture tiny objects. The proposed LE-YOLO detector is examined in ablation and comparative experiments on the VisDrone2019 dataset. In contrast to YOLOv8n, the proposed LE-YOLO model achieved a 30.0% reduction in the parameter count, accompanied by a 15.9% increase in the mAP(0.5). These comprehensive experiments indicate that our approach can significantly enhance the detection accuracy and optimize the model efficiency through the organic combination of our suggested enhancements.
A Large Scale Benchmark of Person Re-Identification
Authors: Yin Qingze , Ding Guodong
Published: June 2024

Unmanned aerial vehicles (UAVs)-based Person Re-Identification (ReID) is a novel field. Person ReID is the task of identifying individuals across different frames or views, often in surveillance or security contexts. At the same time, UAVs enhance person ReID through their mobility, real-time monitoring, and ability to access challenging areas despite privacy, legal, and technical challenges.To facilitate the advancement and adaptation of existing person ReID approach to the UAV scenarios, this paper introduces a baseline along with two datasets, i.e., LSMS and LSMS-UAV. Both datasets have the following key features: (1) LSMS: Raw videos captured by a network of 29 cameras deployed across complex outdoor environments. LSMS-UAV: captured by 1 UAV. (2) LSMS: Videos span both winter and spring seasons, encompassing diverse weather conditions and various lighting conditions throughout different times of the day. (3) LSMS: Including the largest number of annotated identities, comprising 7730 identities and 286,695 bounding boxes. LSMS-UAV: comprising 500 identities and 2000 bounding boxes. Comprehensive experiments demonstrate LSMS’s excellent capability in addressing the domain gap issue when facing complex and unknown environments. The LSMS-UAV dataset verifies that UAV data has strong transferability to traditional camera-based data.
Mothers’ Breastfeeding Self-Efficacy after a High-Risk or Normal Pregnancy: A Greek Longitudinal Cohort Study
Authors: Brani Panagiota , Mrvoljak-Theodoropoulou Irina , Pechlivani Fani , Gourounti Kleanthi , Iliadou Maria , Palaska Ermioni , Antsaklis Panagiotis , Drakakis Peter , Dagla Maria
Published: June 2024

Background: the objective of this longitudinal study (from pregnancy to the end of the sixth month postpartum) is to elucidate the association between maternal self-efficacy, defined as a mother’s confidence in her ability to breastfeed, and breastfeeding outcomes. Methods: This prospective cohort study was conducted among high-risk pregnant women (including those with conditions such as gestational diabetes, hypertension, pre-eclampsia, and other pathological medical conditions) and normal-risk pregnant women in Greece. The high-risk group included 164 women, while the normal-risk group comprised 154 women. Data were collected using validated psychometric scales, including the Breastfeeding Self-Efficacy Scale-Short Form, State-Trait Anxiety Inventory, Edinburgh Postnatal Depression Scale, Maternal Antenatal Attachment Scale, and Iowa Infant Feeding Attitude Scale. Results: Higher maternal self-efficacy was significantly associated with a longer duration and greater exclusivity of breastfeeding. A statistically significant relationship between the type of breastfeeding and the degree of breastfeeding self-efficacy was observed at multiple postpartum milestones: in the first and third 24 h postpartum, and at the end of the sixth week, third month, and sixth month postpartum. Conclusion: The findings underscore the critical role of maternal self-efficacy in breastfeeding success, influenced by individual psychological factors and broader socio-cultural contexts. Strengthening maternal self-efficacy is essential for improving breastfeeding outcomes.
Serial Mediation Models of Future Anxiety and Italian Young Adults Psychological Distress: The Role of Intolerance of Uncertainty and Non-Pathological Worry
Authors: Regnoli Giorgio Maria , Tiano Gioia , De Rosa Barbara
Published: June 2024

Previous research has already examined the relationship between Future Anxiety, a construct recently introduced in Italy, and mental health in young adults, although possible mediating variables in this relationship have so far never been investigated. The present study attempts to fill this gap by exploring the incidence of Future Anxiety on psychological distress (i.e., Stress, Anxiety and Depression) in a group of 302 young Italian adults (18–30 years; M = 21.9; SD = 2.6; 49.0% males; 51.0% females), presenting and evaluating the simultaneous mediating effect of Intolerance of Uncertainty and Non-Pathological Worry. Findings highlighted how Future Anxiety had a positive and significant direct effect on Stress and Depression, but not on Anxiety. In the three serial mediation models proposed, Intolerance of Uncertainty and Non-Pathological Worry mediated the relationship between Future Anxiety and mental health outcomes. The results also confirmed the hypothesized serial mediation effect by highlighting how young adults with greater Future Anxiety experienced more Intolerance of Uncertainty, which positively affected Non-Pathological Worry levels and, in turn, exacerbated psychological distress. Finally, results indicated that female participants experienced more Stress, Anxiety, and Depression in relation to Future Anxiety compared to males. Starting from the review of main references on this subject, the results discussed provide new insights for understanding youth psychological distress. Finally, practical implications for the design of supportive interventions for this study’s target group are proposed.
Network Analysis of the Association between Minority Stress and Activism in LGB People from Poland
Authors: Krok Aleksandra , Kardasz Zofia , Rogowska Aleksandra M.
Published: June 2024

(1) Background: This paper presents an analysis of the associations between minority stressors and activism in the population of lesbian, gay, and bisexual individuals in Poland. (2) Methods: The cross-sectional online-based research was conducted among 192 lesbian, gay, and bisexual (LGB) people in two groups: activists (n = 51) and people not engaged in activism (n = 141). Four scales of the Sexual Minority Stress Scale were used: Internalized Homophobia, Expectation of Rejection, Concealment, and Sexual Minority Negative Events Scale. Activism was measured using the Activism Scale and a single item asking about belonging to an activist group. (3) Results: Activists, lesbians, and gays scored significantly higher in the Sexual Minority Negative Events than people not involved in activism and bisexual individuals. Attitude toward LGBT+ activism correlated weakly and positively with Sexual Minority Negative Events and Expectation of Rejection while negatively with Internalized Homophobia. The Network Analysis showed that positive attitudes toward LGBT+ activism, the expectation of rejection, and negative events in minority stress are the most influential variables in the network model, playing a crucial role in the interaction between particular dimensions of minority stress. (4) Conclusions: Prevention and intervention programs should focus on reducing minority stress, especially negative events and rejection, especially among lesbians, gays, and people engaged in LGBT+ activism. The cooperation of politicians, lawyers, social workers, and psychologists is required to decrease homophobia and the stigmatization of people representing sexual and gender minorities.